Alex Castaldo writes:

what does dr burry mean by his btc mining tweet today

(20) Cassandra on Twitter: "70% of $BTC is mined in sanctioned countries, China, Iran, Russia. Crypto is in a race - add enough reputable agents of commerce to counter and overcome the inevitable coordinated actions of the ECB/BoJ/Fed/IMF/WorldBank-level powers-that-be to crush it. https://t.co/glYdmeTJ4g." / Twitter

70% of $BTC is mined in sanctioned countries, China, Iran, Russia. Crypto is in a race - add enough reputable agents of commerce to counter and overcome the inevitable coordinated actions of the ECB/BoJ/Fed/IMF/WorldBank-level powers-that-be to crush it. 

Pete Earle writes:

He's saying that BTC needs to grow its network and become a more compelling medium of exchange in order to survive the likely outcome of the governments and monetary authorities in China and Russia (Iran?) putting severe criminal penalties on its mining and usage. Meh. 

Does not follow that if mining were quashed in those countries other miners elsewhere wouldn't fill the gap, but it would be a bumpy ride and the price would almost definitely suffer. (There's also the remote chance that in the tumult that would follow miners going offline there could be a 51% attack.) But it strikes me that when an illicit enterprise becomes big enough (gambling, marijuana, etc.) governments would rather become privileged providers and gatekeepers than suppressors. 

Jayson Pifer writes:

Burry seems to be missing that cheap electricity wins.  It's not a matter of simply adding reputable agents when they will not be able to scale to compete with Chinese miners.  Not to mention that those sanctioned countries are incentivized to mine heavily and therefore keep the security of the network high.  I don't really see this changing.

I'm curious what sort of coordinated actions he supposes will be taken by the power-that-be.  For the foreseeable future it appears to be concurrent debasement of currencies to the benefit of cryptos.

I forget who on the list was running a mining operation, the information shared was valuable.  Perhaps we could get some recent insight.



Alex Castaldo writes: 

Last Friday was the day to roll long positions in ZB futures: to sell

the December futures (which are nearing expiration) and buy the March futures instead. I noticed something a little puzzling. For the last 2 years the far away (new contract) future was cheaper than the nearby one (the old  contract). But last Friday it was the opposite:

> 2PriceDate old contr new contr  oc price     nc price     roll cost

> 05/30/2018   ZBM8      ZBU8     145 14/32    144 19/32    - 27/32

> 08/30/2018   ZBU8      ZBZ8     144 31/32    144  7/32    - 24/32

> 11/29/2018   ZBZ8      ZBH9     140  4/32    139 16/32    - 20/32

> 02/28/2019   ZBH9      ZBM9     145  4/32    144 15/32    - 21/32

> 05/30/2019   ZBM9      ZBU9     153  2/32    152 14/32    - 20/32

> 08/29/2019   ZBU9      ZBZ9     166  2/32    165  8/32    - 26/32

> 11/27/2019   ZBZ9      ZBH0     160  3/32    159 10/32    - 25/32

> 02/27/2020   ZBH0      ZBM0     168 16/32    167 15/32    -1  1/32

> 05/28/2020   ZBM0      ZBU0     178 21/32    177  2/32    -1 19/32

> 08/28/2020   ZBU0      ZBZ0     176 14/32    174 25/32    -1 21/32

> 11/27/2020   ZBZ0      ZBH1     173 28/32    175  1/32    +1  5/32

As long as short term interest rates (repo rates) are positive, it would seem that an object delivered 3 months further away should be cheaper than the same object delivered 3 months sooner. (The good old Time Value of Money). Which makes me think that the Cheapest to Deliver for ZB March 2021 must be different from the CTD for ZB December 2020 if the March is priced higher? But I am not sure if this explanation is correct. And I find it disturbing that even though I traded tbonds for a while I do not fully understand some of the basic mechanics. Do you have any insight? Why did the price difference (technically know as the Roll Cost) flip like this?

George Zachar  writes: 

On Bloomberg, pull up USZ0 and USH1 CMTY DLV.

The cheapest to delivers did change:

Z0 = 4.5% '36

H1 = 5.0% '37



Alex Castaldo writes: 

BUT THIS IS NO LONGER THE CASE. Since Monday October 26, 2020 the CME always uses 4pm as the settlement time. So the 4:15 price and the settlement are now different every day, not just on the last day of the month.

Steve Ellison writes: 

Thanks, Doc. I had done a double take a couple of times when the S&P 500 settlement price was completely outside the range of the last 15-minute bar. For example, on November 12 the range of the 16:00-16:15 period was 3543.75 to 3533.00, but the emini settlement price was 3532.50. Because I do intermarket analysis, I have long kept a separate database of each day's 16:00 price in the S&P 500 and other markets (US Eastern time).

Victor Niederhoffer  writes: 

more important what is the predictive  properties  of the move from 1600 to the real 1615 price



Alex Castaldo  writes: 

Hello fellow Spec Listers -

I'm curious as to anybody's informed take on the likelihood of the implementation of a Financial Transaction Tax, especially at a Federal Level - as this would quite materially harm our craft. Looking to learn more, and to prepare if necessary by pivoting asset classes / markets etc.

Kim Zussman writes: 




American Odds

August 24, 2020 | Leave a Comment

Alex Castaldo writes: 

On this web site https://www.oddsshark.com/politics/2020-usa-presidential-odds-futures the American Odds for Biden to win are quoted as -135 and for Trump +115

For those not familiar with America Odds here is how you convert AO to a probability

if AO < 0:

   prob = |AO| / ( |AO| + 100)


  prob = 100 / (AO + 100)

So the probability for Biden is 135/235 = 0.574

and for Trump 100/215 = 0.465  

Richard Owen writes: 

What is the origin of the AO format? It was deemed more accessible to punters to speak in terms of $100 units? Is the negative sign subconsciously in front of the favourite to dissuade people from betting the favourite? Old school bookrunners tended to be overweight the favourite winning, but now tend to be hedged?

Stefan Jovanovich writes: 

I hate to disagree with Jeff about anything regarding gambling, but he is ignoring what he knows about hedging.  The amount of money actually bet online on  American politics is - at most - a tiny fraction of what is wagered on sports.  The success of DraftKings alone confirms this.


The line on Predict-It and other politics wagering sites can be moved with a bet that will have zero effect on any of the dozen parlays that are offered for the next Liverpool match.  Since the actual wagering does not need to be balanced by the bookies, they can set whatever line will attract the most attention and give them the most publicity and general  traffic.   Political odds are used the way Sam Walton used the stacks of peanut brittle when he started with his J. C. Penney stores.  He put them on the sidewalk or just inside the entrance and the people walking/driving down Main Street have to take a look.  

Biden and his odds are the peanut brittle.  Odds favoring Trump would have the opposite emotional effect.



Alex Castaldo writes: 

Heres the skinny. from math puzzles volume 1, by  presh talwalkar. doc here. from nature walk. originally to stretch aubrey's mind . odds of a comebak victory

Consider 2 teams a and b that are completely evenly matched. given that a team is behind in score at half time, what is the prob that a team will overcome the deficit and win the game. assume the first halve and the second half are taken to be independent events. Presh solves it as follows logically:

Since the two teams are evenly matched, it is equally likely that the team will score enuf points to overcome the deficit or that it will not score enuf points. fo example the event of falling behind 6 pts in a half game happens with the same prob as gaining 6 pts in a half game. He concludes prob is 0.25

Now we posted the empirical resutls from basektaball games and many others have given the empiriclal results for football games … and i gave some results for the markets.. this seems to be of interest to everyone , had the most views of any posts, and it was good for 7 or 8 points today.. lets have your discussion and solution of this problem. presh says the answer is 0.25 both empirically (NFL in 1995) and logically.

Jared Albert writes: 

In a game with two teams where in the first round, the team 1 advantage varies from flat to all the points available in the second round, the probability of  team 0 coming from behind to win are in array with 20 available points in the second round:

[0.49, 0.306, 0.22, 0.129, 0.09, 0.03, 0.018, 0.011, 0.004, 0.002, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

For example, if the teams are even going into the second round with 20 available points, .490 chance that team0 wins; with a one point advantage to team1 at the start of round2, team0 wins .306 of the time;

2 points to team1, team0 wins .220 of the time etc

Here's the montecarlo:

import numpy as np


out_list = []

out_list = []

count = 1000

win = 1

lose = 0

team0_start = 0

team1_start = 0


def runs():

z = np.sum(np.random.choice([win, lose], size=size, replace=True, p=None))

return z

def outcome(team1_start, count = count, team0_start=team0_start):

 l= []

 for _ in range(count):

 team0_end = runs() + team0_start 

 team1_end = runs() + team1_start 

 came_from_behind = team0_end > team1_end 


    #print(f'l: {l}')

    outcome = sum(i > 0 for i in l)


for i in range(size):


print(f'outlist: {out_list}') 

Victor Niederhoffer writes: 

up your alley i  think. we have done something similar for market with real empirical results. the  unconditional prob is much less than20%

Stephen Stigler writes: 

I am sure you know but I repeat anyway:

1) the simple calculations ignore correlation between teams.

2) they also ignore information on the distribution of changes

3) Calculations using the distribution of changes are not hard.

4) But the information about the probability of extreme events is not well determined so they can be inaccurate

5) In any case  markets unlike sports are not zero sum games.



Black was right: Price is within a factor 2 of Value:

J. P. Bouchaud, S. Ciliberti, Y. Lempérière, A. Majewski, P. Seager & K. Sin Ronia Capital Fund Management, 23 rue de l'Université, 75007 Paris, France


We provide further evidence that markets trend on the medium term (months) and mean-revert on the long term (several years). Our results bolster Black's intuition that prices tend to be off roughly by a factor of 2, and take years to equilibrate. The story behind these results fits well with the existence of two types of behaviour in financial markets: "chartists", who act as trend followers, and "fundamentalists", who set in when the price is clearly out of line. Mean-reversion is a self-correcting mechanism, tempering (albeit only weakly) the exuberance of financial markets.

See also: "the holy hand grenade"

Doc Castaldo writes: 

"Black was right: Price is within a factor 2 of Value"

This goes back to a famous difference of opinion between Robert C. Merton and Fischer Black.

In trying to explain Efficient Markets to a student audience, Merton said that to him an Efficient market was one where prices are within 5% of true value 95% of the time. This was his subjective estimate of how efficient he thought the stock market was, and a way of communicating the idea of high but not perfect efficiency to the audience.

Fischer Black had a looser concept and said that to him, efficiency only meant that prices are within a factor of 2 of true value at least half the time. The rest was what he called "noise", i.e. random divergences from true value.

The problem of course is that these are only analogies and no one knows what the "true value" is and therefore how far away from it the market is.




Andy Aiken writes: 

And in response, bitcoin says that Dimon is a fraud…

Alex Castaldo writes: 

Not very courageous either:

JPM's Jamie Dimon on #Bitcoin: "Don't ask me to short it, it could be at $20,000 before this happens but it will eventually blow up."

anonymous writes: 

Mine, it's better :-)

"JPMorgan patents Bitcoin-like payment system"  



Are these turkeys here to challenge Victor to a tennis game?



 Why does Scholes say that option pricing is like crowd sourcing as opposed to the market itself? Also are option prices calling for a decline or rise? And are option prices generally right? If it were worth studying what Dr. Scholes wrote in detail, I would have many more questions but since he was well known I think as the weakest link in our program, I will not delve into it, possibly at my cost.

Alex Castaldo writes: 

The article in question is

"Return to 'Old Normal' Hasn't Begun Yet: Scholes and Alankar After Trump's election, option prices signaled greater inflation risk. That no longer seems to be true."
By Myron Scholes and Ash Alankar

I don't quite grasp what option markets are telling us, and how it relates to real and nominal rates.



Today I attended a lunch presentation with pension funds as the target audience. They defined risk as volatility and wanted reduce risk while maintaining much of the return. It was said that buying puts reduced return too much for most fund managers. The strategy presented was to reduce equity exposure from 100% to 50% and invest 50% in a low risk asset (short term bonds), at the same time sell both OTM calls and puts. They presented a back test of 10 years where the strategy outperformed index slightly while having a lower volatility (they outperformed during the 2008 crash and vol looked to be lower all along). I'd think they expose themselves tail risk by selling OTM puts, so was surprised they outperformed during the GFC and that they came out ahead. I still think they make it 'look' good during 'normal' markets but will get killed performance wise during sufficiently high upside and downside volatility–so I really think it is somewhat of an intellectual fraud to call this a 'low risk equity exposure' for pension funds.

Alex Castaldo responds: 

I did not attend the presentation mentioned, but I am familiar with this kind of option selling strategy. One of the simplest is the PUTW (or PUTSM) strategy whose results are updated daily on the CBOE web site.

In the attached chart I compare it's total return since 1/2007 to the SPY total return (the S&P 500). Starting both strategies at an arbitrary level of 923, we see that PUTW falls to 690 in early 2009 (a 25% drop), while SPY falls much further to 503 (a 45% drop). What I find particularly interesting is how well PUTW holds up in the first half of 2008: while the stock market is going down PUTW manages to be steady or slightly up because it is selling puts at a high implied vol; only when the stock market begins to sell off very sharply after 9/30/2008 does PUTW also drop.

But even more interesting is what happens in March 2009 and after: the SPY begins to climb faster than the PUTW, slightly faster at first but markedly so after September 2012 and soon thereafter SPY passes PUTW. At the end of December 2016 SPY is at 1798 while PUTW is at 1668.

My conclusions are:

(1) The PUTW strategy has a lower volatility than SPY, both in terms of a lower drawdown in 2008 and a generally smoother path throughout the period (std dev of 11.5% per year versus 15.2% for SPY). The claims made during the presentation are believable. There is no intellectual fraud here.

(2) Everything has drawbacks as well as benefits. The drawback of PUTW is not that it will lose heavily in the future during periods of enormous volatility, but the opposite: that it will underperform during prolonged bullish periods for the market and probably over any sufficiently long period (long enough for the implied vol to adjust to whatever the situation might be and for the law of large numbers to take effect). So there is nothing magical here, as Dr. Zussman would say just the familiar tradeoff between volatility and return.

(3) The correlation between SPY and PUTW monthly returns is 0.85 (beta is 0.65) so PUTW is not all that different from SPY in terms of sources of risk (it is not a very good diversifier for stock market risk).

(4) The performance of PUTW is not theoretical or proprietary or reserved only for pension funds; it is explained on the CBOE web site (roughly speaking: sell 1 month ATM puts fully collateralized with cash) and since February 2016 there has been an ETF (also called PUTW) that implements it. So far it is small (30 million in assets). It has a 0.38% expense ratio, and so far has been tracking the CBOE version with accuracy that is quite respectable, and in line with expectations.

(5) Yes, you can reduce risk by selling Vega. Or increase risk by selling Vega, it is all in the proportions and how you do it.



 Up to now the trade balance of the United States has been determined as a residual item, in other words it is not a target that policy makers and citizens look at but is the result of other variables like GNP growth, inflation, productivity, the value of the dollar, opportunities, preferences, etc. in the US and abroad.

If the Trump administration decides to target a reduction in the trade deficit (as seemed implicit in the campaign) that is a MAJOR change in how the economy operates and I wonder what consequences it would have. Is it even possible? Of course it is possible and many countries (such as Japan in the 60-70s, germany, china) have operated with such policies), but it is a big change in thinking for the US. My economics professors decades ago derided such policies with the epithet 'mercantilist' and refused to even discuss them ("it's not Pareto optimal, so forget it"). But at least some countries (esp. developing countries) have had some success with it as long as the ROW did not follow their example. We now understand such policies have some *benefits* as well as costs.

What form would the trade policy of the Trump administration take?

The most attractive would be an export promotion strategy, like the Asian Tiger countries. I have a friend who believes that the US should set up Special Economic Zones like the Shenzhen area in China, where special rules are applied to favor the establishment of new enterprises that are oriented to export. Low taxes, light regulation, waiver of union rules and so on would be targeted to a small geographic area (near an international transportation hub) with a view to boosting its productivity and export ability. It is a very Asian top-down approach and  have some doubts whether this could be done in the context of Western decentralized, democratic and rule of law traditions. But there is an even bigger problem: where would the exports go to, when there are no longer any large developed countries available as export destinations.

The other strategy would be an import compression or 'onshoring production' strategy. This can be accomplished via Tariffs such as Trump has proposed but would have the side effect of raising the cost of products to US consumers. In addition because one country's imports are another country's exports it would result in a decrease in international trade. Since the mid-2000s international trade has been stagnant (after a period of high growth) so it would not be surprising that after Trump, Brexit, etc, it would turn down. What would be the investment implications of a fall in international trade and investment? Historical periods of decreased international trade are not exactly bullish for the economy.

As you can see, although I do believe the Trump administration will be the greatest and best, I have trouble seeing how the policies can be applied without causing major changes and some kind of negative disruptions in the world economy. I would like to hear from you any ideas how we can analyze the situation in a clear and practical way. What asset prices will be affected and how.

Anonymous replies:

My sense is that the Trump trade policy would work through tax incentives (like the tax incentives now offered by states to businesses who locate or stay there) rather than through tariffs.

West coast dude suggests:

Future increases in prices of consumer products as a result of switching to domestic production are already being reflected in the price of bonds, which are falling in response to higher expected inflation.



 Based on the timing indicated, he must be significantly underwater at this time. That assumes he has not thrown in the towel by now: "Soros Doubles His Bet Against S&P 500 Index"

John Bollinger writes: 

The interesting question for me is: Why is he advertising this now?

Peter Tep writes: 

Good point, John.

Sounds like he is releasing the hounds, so to speak.

Did the same for his short Aussie dollar trade some years back and also his long gold position–get long, get loud.

Jeff Watson writes: 

The more important thing is, who cares what the Palindrome says he does. Whenever anyone who's purportedly a big player discloses his supposed position, I look at his motives with a big grain of salt.. People bluff in the markets as much as they bluff at final table of the WSOP. It's all a mind game, and while one should take in what the opponent says, keep in mind that their disclosure is not for your benefit and it could be a bluff. A good lesson is to look at announcements like this and try to find tells….they exist. Nobody ever discloses their position(real or fake) to the media to be altruistic and benevolent. The sad thing is that many people(retail investors, CNBC watchers etc) believe in the good will of the Palindrome and the Oracle to the small investor. Those same unknowing investors are the pilchards that are eaten by the sharks.

anonymous writes: 

"keep in mind that their disclosure is not for your benefit"

That is a key. Even if it is true it is still not for our benefit. For example "they" cover while "we are riding a growing loss waiting for the idea to play out. Our entry was their exit. The flexions/greats/insiders see angles we can't, if we listen regularly our account balances will be eaten. 

Petr Pinkasov writes: 

I struggle to see how in 2016 it's even intellectually sound to present Q as another 'dagger on the steering wheel'. It's hard to quantify the intellectual capital that investors are willing to pay 50x earnings. 

Alex Castaldo writes: 

Exactly. What is the Q ratio for AAPL, how many factories do they own and how much are those factories worth in the marketplace? (Rhetorical question). The Q Ratio is a statistic from another era, when John D. Rockefeller built oil refineries bigger than anybody else's or when Mr. Ford bragged about his new River Rouge plant. It has limited value in many businesses today.

Another smaller point: the proposed tail hedging strategy is designed to break even if the S&P declines by 20% in a calendar month. In the last 30+ years (367 months) this has happened on only one occasion (October 1987). It is quite a rare event. Would you do this tail hedging all the time? I am not convinced that the numbers work when you consider that every month you are paying for put options.

 Alston Mabry writes: 

Doing some searching, I ran across this on FRED:

Nonfinancial Corporate Business; Corporate Equities; Liability, Level/Gross Domestic Product

Cheap-seat question: I know what GDP is, but I'm not sure about "Nonfinancial Corporate Business; Corporate Equities; Liability". Is that simply adding up the liabilities side of the ledger for public companies? Actually, it peaks Q1 2000, so it must involve market capitalization.

But it does peak Q1 2000 and Q3 2007. Of course, ex ante how do you know it has "peaked"?

Ralph Vince writes: 

All measures from an era when there was an ALTERNATIVE to assuming risk — that alternative now is to assume a certain loss, or, at best, a large rate markets exposure for the (slightly) positive rates at the longer durations.

This is an ocean of money that is coming through the breaking dam. It likely will go much farther and for much longer than anyone ever dreamed. Imagine the unwinding of the government-required-soviet-style Ponzi schemes like Social Security, which, at some point must start affording for self-direction to provide an orderly unwinding. Not only from the natural bookends of life expectancy, and pushing out the book ends to where too few could expect to ever collect from it, but the pressure from below in a runaway market for self-direction. This too will fuel the hell out of this run and make it last much longer than anyone dreams of.

Every equity that yields a dime has greater value than the certain loss; every wigwam that provides shelter too, from investing in the ingredients of pizza in Pulaski to Poontang in Pyongyang, all the wealth of the world must come out of the shadows and find a risk — and this creates a self-perpetuating feedback that is something we've never seen.

This is the move that comes along once in a century at best, and we're already starting into it. The measures of the world of positive rates (which may not be seen for a long time) I do not believe are germane to the world today.



Can you please outline the color coding rationale for the daily performance chart. I am confused on why some down days are red and the others are yellow..etc - A Reader

We track daily movements in U.S. stocks and bonds (specifically S&P Index futures and Long Bond futures).

The colors are based on the performance of both markets:

Red days: both stocks and bonds down.

Green days: both stocks and bonds up.

Yellow: stocks up, bonds down.

Blue (technically azure): stocks down, bonds up. 

The bond futures movements are expressed in points and thirty-seconds of a point. For example 1.25 actually means 1' 25" i.e. 1+25/32. The S&P futures changes are in points.The abbreviation USB stands for US Bonds (the actual futures symbol is US on Bloomberg, ZB on some other systems).

(Original post dated June 25, 2011. Edited January 14, 2016)



There are arguments for and against the log returns in data analysis. Any preferences and why?

Alex Castaldo writes: 

The nice thing about log returns is they are additive across time. The log return for the year is the sum of the log returns for Jan, Feb, Mar… Also the log return is to a first approximation normally distributed.

The nice thing about the percentage return is that the % portfolio return is a weighted average of the individual stock percentage returns, weighted by portfolio weights. So for example if you have 1/2 your money in MSFT and 1/2 in GOOG, the portfolio percent return will be the average of the percent returns on these two stocks.

So when I work with portfolios I always prefer the percentage returns. When I work with indexes I work either with percents or with logs (especially when concerned with options, since option theory is all in terms of log returns).



 I was looking at Greece's unemployment rates historically last night and found something interesting. The Greek economy seemed to hit a pothole in 1981 from which it never extricated itself. Between 1980 and 1982, unemployment tripled, and has stayed that way as a base since then. (I say 1981 because the rate didn't return to where it was, it increased.) Now, there were recessions in the US in 1980 and 1982, and Greece is a tourism-based economy. So a short-term increase in the rate can be explained in that way. However, that doesn't explain that the rate didn't go down in the 1980s. Why? Any suggestions as to the reason? It seems to me that that reason may provide more insights to the current situation than simply that the Greeks lived beyond their means. Something changed in their means.

Alex Castaldo writes:

According to Greek analyst Nick Tsafos, one reason for the low growth rate that started in 1981 was monetary mismanagement.

From 1953 to 1973 the 'third drachma' like most currencies was tied to the dollar; the exchange rate was 30 GRD per USD. This was the period that Greece experienced its best economic performance.

After the mid 70's the currency floated. It was (in round numbers) 58 in June 1981, 148 at the end of 1985, 157 at the end of 1989, 240 at the end of 1994, 328 at the end of 1999 and 325 in 2002. (In 2002 the Euro was introduced).

In other words from 1981 to 2001 the GRD was a 'soft currency' that allowed the Greek government to finance itself easily at the cost of higher inflation and currency depreciation. It could create government jobs, pay generous retirement benefits and get away with it by issuing more drachma. And the Greek politicians were masters at this kind of thing, buying support with monetarily financed expenditures.

The inflation ended in 2002 with the introduction of an externally managed currency, the Euro. For a time everything seemed wonderful. But old habits die hard and the politicians kept up their old ways of solving problems. Government debt increased but interest rates were very low, so it did not seem to matter. But the debt this time was hard debt, that inflation and devaluation would not erase…

Now for a rhetorical question: if Greece abandons the Euro and introduces the new drachma, how do you think the new currency will be managed? The past history is not encouraging.



See picture enclosed.
























 Humor from Yellen:

"The economic and financial news has been grim," she told colleagues at a mid-March2009 policy meeting, according to the transcripts. "Things are now so bad that I actually open the Greenbook with greater trepidation than my 401(k)." The Fed's so-called Greenbook is its official summary of economic and financial conditions.



The Fundamental Dilemma of all market manipulation, whether legal (as in this case) or illegal: when you stop pushing up the value of an asset, it falls back, giving you mark to market losses on your inventory. Today the SNB is taking tremendous losses on the Euro assets it has accumulated in its Euro buying binge. I would not want to be a private shareholder of the SNB (or a swiss taxpayer) on a day like this.



I first met Ed sometime in 1997 or 1998. I was in graduate school and trying to earn a few bucks by working as a data cleaner and spreadsheet builder at a hedge fund not far from the university. I and the rest of the staff worked in cubicles in the  middle of a big trading room, while the important people had glass enclosed offices around the perimeter. One of these people, a well dressed, tall and large gentleman soon introduced himself as Ed Dunne. In the following few days he would often stop by and chat about what was going on in markets all over the world. Most often I could see him in the office of the head of the firm, the billionaire Mr. Why, talking and gesturing while Mr. Why nodded politely from time to time. I never talked to Mr. Why, but I felt I had a indirect link to him via my conversations with Ed. And he knew or spoke with so many other important people in the investment world.

Ed was to me was the quintessential expert on international financial markets. He would explain how hedge funds and re-insurance companies work together, how oil can be stored in idle ships for later delivery, what factors affect the prices of grains, and a hundred other things. All things that I was very curious to learn about. To become as savvy and as wealthy as Ed seemed within easy reach if I could have a few more discussions with him. One time he invited me to attend one of his meetings at Princeton University with several economics and statistics professors (though I did not see Ben Bernanke there), and afterwards to a sumptuous lunch in a nearby restaurant. That was just one example of his generosity.

Later on Ed claimed that he introduced me to Victor and got me the dream job that I now have. I think it was more complicated than that and that there were several people involved but I don't remember the details. But certainly Ed played a role and I am grateful to him for that.



Here's a pretty kettle of fish. Suppose you have two forecasts that are disparate. One is bullish and the other is bearish. For example it's up 100 over 4 days. That's bearish. But it's up 4 days in a row, that's bullish. How to combine? There's a bayesian approach, a regression approach, and an inverse variance weighted approach, and a practical approach that Zarnowitz found. Just add up the number of bullish and bearish and that's your forecast. But what's your best way of solving same? The answer might provide a meal for a lifetime. I asked Stigler this question 15 years ago, and he thought it was a very good question, and I've not seen a good answer yet.

Alex Castaldo writes:

I would start with Diebold and Pauly: The use of prior information in forecast combination.

Gary Rogan writes: 

There has got to be some way of incorporating the rare nature of one of the set of circumstances. Clearly 100 points is more unique than 4 days. Does this carry any special weight? Also there is a very large number of other possible "circumstances", like time of day, month, year, what the future portends if prior history was similar during this time of day, month, year. where are we in the economic cycle? With respect to various moving averages? What's the money supply and it's history? What has the price of oil and any number of other thing doing and where is it? And what matters more: all these other things or the one unique thing?

anonymous writes: 

You're mixing apples and oranges. The premise for regression and related approaches is that there is a fixed law that can be discerned, or at least modeled, in such a way that it does not vary in any dimension. Whatever the model/rule was 50 years ago is still what it is today—unless of course, additional information either disproves the model or allows for its refinement. Either way, it's time invariant. Bayesian analyses are different by definition. Unless the prior is the same, the result will be different. Since priors will change with the passage of time, the analysis is time-dependent. You might try to specify the Bayesian model as fixed at any one point in time and try some form of combination, but since the moment you do that, the prior will shift and the exercise becomes worthless.



 I have been thinking about what could be a good set of criteria to measure trading (strategy) performance for individual traders.

The criterion of average return divided by the variance of the returns seems to have its shortcomings. One reason is that some large positive returns can cause the variance to go up resulting in an indication by the criterion that the performance deteriorates. But some large positive returns are good to have.

Other criteria like Sharpe ratio seem more suitable for institutions.

I think using properties of the linear regression line of the cumulative return curve might be a better choice.

Two useful properties are the slope and the "width" of the linear regression line. By "width" I mean the deviation of the cumulative return curve around the linear regression line.

A good performance should have high slope on the one hand. And if we do not consider reinvesting profits, it should have narrow "width" around the linear line.

So then the value of slope/width seems meaningful.

If we take the linear regression line as a risk free benchmark, then this value may be very similar to the definition of Sharpe ratio, but practical for individuals.

Would anyone please comment on the pros and cons of this, or any other better ways to measure performance.

Alexander Good writes: 

Great post!

I think it makes sense to measure linearity of PNL and convexity separately so I agree with you that R sq is a good one to employ. I am curious how width differs from the strategy's std though…

One thing that you can do as a cheap proxy is median return * sqrt(252)/std return and then for skew then have a (rolling max peak to trough draw down)/(rolling max peak to trough draw up).

You can benchmark your strategy vs. bonds, the S&P and a traditional 60-40 mix or your other strategies. It's very hard to beat a vol weighted portfolio of stocks and bonds so it's a good benchmark in my humble opinion assuming you're trading your PA and you don't have large retirement holdings. I assign different weights to skew and median return depending on my portfolio construction.

In portfolio construction you'll often find things with strongly positive skew have good inverse correlation to market PNL series and are typically 'long vol' (idea ripped off AQR's value and momentum everywhere).

Trending strategies frequently have very positive skew (momentum) whereas mean reversion tend to have skew that looks like the S&P (value). So if I'm net long beta my marginal utility of doing trending models is higher whereas if I'm net short I tend to size up mean reversion strategies.

Would be curious to know what other people are using/ how other people think about this/ if they have good papers on the subject. 

Leo Jia writes: 

Aren't they different?

std of returns has this term: (Ri - mu)^2, where mu is the same for all i's.

The width has this term instead: (CRi - Vi)^2 where Vi is the value on the linear regression line at time i and is all different across all i's.

Alex Castaldo writes: 

Personally I just like to look at the equity curve visually, and it is not difficult to store large numbers of graphic files in a folder and quickly "flip" through them by hitting a key on the computer.

But for automated evaluation Leo's two criteria (slope of regression, and "width around the regression" (which is also called the SEE or standard error of estimate.in regression textbooks) make sense to me.

However I know there are many other criteria that have been proposed. There is one with a foreign name that I think starts with "v" but that I can't remember. I am sure some people here know what I am talking about, it was much blogged about 2 or 3 years ago.

In looking for it I accidentally googled another measure of equity quality, the k-ratio , that believe it or not has 3 different versions.

Any other ways to measure equity curve "quality"?

anonymous writes: 

As with many things involving non linear information, my experience suggests that one must mix, blend or combine different 'quantities' to form a unique and proprietary time series.

For example, some form of 3D 'curve' that combined the three quantities return, AUM & volatility that gets thicker as AUM in the strategy grows and changes colour as volatility of returns increases perhaps… 

Ralph Vince writes: 

percent of 6 month periods underwater
percent of 1 year periods underwater
percent of 2 year periods underwater

percent of time at equity highs
percent of time within 1% of equity highs
percent of time within 5% of equity highs
percent of time within 10% of equity highs
percent of time within 20% of equity highs

I have all of these programmed up in javascript which you can peruse at lspindexes.com and click the "compare" tab. 



I believe there are 4 cases, conventionally called:

h(t) > h(t-1) and l(t) > l(t-1) an uptrend day

h(t) < h(t-1) and l(t) > l(t-1) an inside day

h(t) > h(t-1) and l(t) < l(t-1) an outside day

h(t) < h(t-1) and l(t) < l(t-1) a downtrend day

Victor Niederhoffer writes: 

Excuse me? 

Alex Castaldo replies: 

OK, OK, let's call then upshift day and downshift day then.


Kim Zussman adds: 

What about outie and innie?

Using SPY (2000-present), checked for outside days (today's high > yesterday's high, today's low < yesterday's low). Then checked the return for the next day.

Days after outies were also checked if the market was up-trending (20DMA > 100DMA), and, in addition to up-trending, if the intra-day return was up (C>O). Here are the c-c returns after (vs zero), for all days (c-c ret), all days after outside days outie+1), days after outside days and up-trending (and 20>100), days after outside days and up-trending, and the outside day was up intra-day (and C>O):

One-Sample T: c-c ret, outie+1, and 20>100, and C>O

Test of mu = 0 vs not = 0

Variable        N       Mean     StDev   SE Mean       95% CI             T
c-c ret         3550   0.00022  0.0131  0.0002  (-0.0002, 0.0006)   1.01
outie+1         353   0.00049  0.0118  0.0006  (-0.0007, 0.0017)   0.77
and 20>100  234   0.00061  0.0090  0.0005  (-0.0005, 0.0017)   1.03
and C>O       89    -0.00047  0.0090  0.0009  (-0.0023, 0.0014)  -0.49

Zeroish, with none significant, and days after outside days and up-trending winning the race at 0.06% per day avg.



 There was a lot of interest a few years ago in Joel Greenblatt's book The Little Book That Beats the Market . He set up a web site www.magicformulainvesting.com which selects stocks based on this method (using a combination of 2 criteria: earnings yield and return on capital).

Two years ago I saved a list of 30 stocks it recommended and just recently I calculated the performance up to now. The results are below:

MagicFormula Investing Stock total return from 2012/01/29 to 2014/01/29

Source: Bloomberg

atvi       43.04
apol      -40.51
amat       44.77
hrb        87.55
cub         9.80
dell      -14.40   acquired 10/30/2013
dlx       102.63
dlb        27.57
xls       110.55
expe      111.76
gtat       10.06
gme        54.34
esi       -44.16
icon       95.89
idcc      -18.98
klac       24.79
lps       129.00   acquired 1/3/2014
lo         51.17
mant      -10.67
mrx        37.67   acquired 12/11/2012
msft       32.90
nsu       -38.53
noc       103.22
prx        37.99   acquired 10/01/2012
rtn        94.84
ldos       48.21   ticker change from sai to ldos
sndk       48.91
save      183.80
stra      -65.67
vphm       67.37

average    44.16

spy        40.30
rsp        44.02

As you can see the return over 2 years was good, at 44.16%, but indistinguishable from the equal weighted S&P at 44.02% and close to the (ordinary) S&P at 40.30%.

[Strictly speaking you are supposed to hold the portfolio for a year and then rebalance it, which I forgot to do, so it is not a completely accurate replication of the MagicFormula method.].

Bill Rafter writes:

Before Greenblatt there was Haugen who ranked stocks by 60+ variables. He published them in his book, the Inefficient Stock Market. BTW all of his books are interesting. Haugen's material looked really good, but seemed to fail to deliver outstanding returns.

Then Greenblatt's book came out and showed good results from only using two variables: ROE and ROA. One of those was a Haugen variable (I forget which). Then Haugen produced a second edition in which he bumped up the number of variables to ~70, and included both ROE and ROA.

We did a lot^2 of work with both H and G and found that neither are particularly good at picking winners, but both are very good at finding clunkers. And of course if you eliminate the clunkers, the remaining portfolio will certainly outperform the market indices. But of course that only really works if you essentially invest in ALL of the non-clunkers. Thus they are good strategies for large mutual funds.

FWIW we found that you can cull out most of Haugen's variables (but include ROA and ROE) and do fairly well with 16 variables. But again, they are best used as ranking tools to eliminate the underperformers. Our opinion is that both H and G methods are the financial markets equivalent to comfort food. If comfort food makes you feel better, then use it.

anonymous writes:

I agree that Haugen (1942-2013) was an interesting character who did some innovative work. I have read many of his books/papers; how well it holds up remains to be seen. He used the kitchen sink approach which Rocky so detests, but he found things (such as the low vol phenomenon) that were new and interesting (now low vol has maybe become too popular). BTW the two year test period I used is far too short, it could be argued. You need more like 10 years.

Gordon Haave writes:

"Our opinion is that both H and G methods are the financial markets equivalent to comfort food."

This by the way is very important for non-professionals. Most not-professionals lose money because of over-trading, moving in and out on a whim, chasing winners, etc. (actually, most professionals do as well).

A system like Greenblatt's that tells a good story, tells you to only re-balance one per year, and does "as good" as the market will tend to outperform what the individual otherwise would have done.

Richard Owen writes:

Bill, when you say clunkers, do you mean it was great at selecting clunkers by the lowest ranking? Or it was great at selecting clunkers by false positives - i.e, many of the best stocks were very bad despite their attractive ROE etc. and this is what hurt performance? And so they are good for 'cleanup'? Perhaps it's a bit unfair to pick '12-14 data, but very interesting nonetheless. I suspect the best outperformance of such strategies would come in the early bull market and then converge with the market as it matures, then lagging at the end / collapse of the bull?

Bill Rafter writes:


Sorry, I did not mean to be cryptic. By clunkers I meant stocks that you should not own, because they are and will be poor performers.

We used both Greenblatt and Haugen variables to rank the Russell 3000 for 20 years and put the stocks into 10 bins (i.e. deciles) and observed the performance of those bins over the following 6 months (with no overlapping periods). Considering the deceased stocks the total number of securities exceeded 8,000. You have to include the dead stocks or you will have survivor bias. The lowest ranked bin performed really bad, such that they could have been shorted profitably (we are long-only and had no interest). Importantly the performance of each higher decile was monotone increasing. That last bit means the strategy has merit. However, the top three deciles did not have the performance that would encourage us to pursue the strategy. That is, they outperformed the market, but they were not an improvement over other strategies.

It makes perfect sense to find those clunkers and eliminate them from your subsequent ranking routines. But there are many ways to find them. It turns out that the underperforming stocks have the mark of the beast written all over them, and you don't need to look at 10+ variables. If data mining for fundamentals is too onerous for the researcher, ranking by volatility will find clunkers. Deselecting for volatility will also keep you out of some high-flyers, but that's a good price to pay. High short interest coupled with declining prices is another.

We did the research 2-3 years ago and I apologize that I don't have the time to dig it out and clean it up for public consumption, as it was only done for our shop. Realize that we are statistical traders who typically hold either ~20 or ~50 positions because we cannot forecast individual security performance, but only bin performance.



Every business has its peculiarities. In the article
"The Tell All Diary of a Sundance Producer" by Galt Niederhoffer we get a window into the world of independent film production from this week's New York magazine. 



I am only now taking a look at "credit suisse global investment returns sourcebook 2013" which was published in February 2013.

Chart 2

Cumulative returns on US asset classes in nominal terms 1900-2012

Equities 1 to 25507 (9.4% per year)
Bonds 1 to 253 (5.0% per year)
Bills 1 to 74 (3.9% per year)
Inflation 1 to 27 (3.0% per year)

Chart 3

Cumulative returns on US asset classes in real terms 1900-2012

Equities 1 to 952 (6.3% per year)
Bonds 1 to 9.4 (2.0% per year)
Bills 1 to 2.7 (0.9% per year)

Table 1 shows real equity returns for various countries 1900-2012:

USA 6.3% per year (see above)
UK 5.2% per year
World 5.0% per year

The authors are exceedingly cautious about the future, making various downward adjustments to these figures for prediction purposes.

N.B. The Sourcebook is not easily found online, an abbreviated free version called the Yearbook is available here: Credit Suisse Yearbook 2013 [69 page PDF] .  The range of data is less and it is not organized in the same manner, however.



In the last 4 days we have seen every colour on our calendar (see above).

Tueday June 4 Red - Stocks and Bonds down

June 5 Blue - Stocks down and Bonds up

June 6 Green day - Both Stocks and Bonds up

June 7 (today) Yellow - Stocks up and Bonds down

Interesting, but what does it portend?



I learned today there is an apparently relatively simple and practical option pricing formula for the case when the stock follows a (slightly extended) GARCH(1,1) process.

"Fast Analytic Option Valuation with GARCH" by Thomas Mazzoni, September 2008

I thought it might interest the option theorists on this site.



I attended a presentation yesterday on "Optimal Order Placement in Limit Order Markets" given by Arseniy Kukanov.

It was an interesting presentation but was perhaps best summarized by a well dressed white haired gentleman in the audience who asked at the end: "given all the simplifications and assumptions you had to make, of what use is this 'solution' other than to enable you to get a PhD".

In choosing between limit orders and market orders you have to trade off cost savings provided by limit orders against "non-execution risk" that accompanies limit orders (risk that the limit order will not execute).

The problem is: you must buy S shares of stock within a time horizon T.

There are N markets (exchanges) available, for each market you know: the bid ask spread the bid queue lengths at time zero the maker/taker fees that each market charges.

In each market you can at time zero place a market order (which has a 100% probability immediate of execution) and/or or a limit order at the best bid price (other price choices are not included in this version of the model).

There are two parameters lo and lu which are the penalties (in dollars per share) that you charge yourself for buying fewer than S or more that S shares in the available time.

The model minimizes the sum of the trading costs plus the penalty.

The simplest solution is in the case of only one exchange (ex: the minis are only traded on the CME).

In this case there is an algebraic expression for the optimal strategy, and what to do depends on the value of lo:

If lo is below a certain value lbar1, it is optimal to enter a limit order.

If lo is above a certain value lbar2, meaning there is a high urgency to buying the stock, it is optimal to enter a market order.

For lo intermediate between these two values it is optimal to enter both a limit order and a market order at time zero that add up to the desired quantity.

Certain theorems about the solution can be derived, for example as the amount of stock S to be bought increases there is an increased reliance on market orders.

From my point of view one of the most unrealistic assumptions is that the probability distribution of future order flows and cancellations does not depend on the size of the order you enter. In my experience on the contrary if you enter a big limit order it can 'scare' the market and move it away from you.

All in all it was very good for an academic presentation.

The full paper can be found here.

Thanks to Anatoly for the invitation.



 Written in honour of all splitters, rookies and lookbackers:

A common pursuit in decision making is to compare data of many kinds and find the best way of separating the observations into different classes.

For example you mite be considering what are the factors that contribute to long life. Weight, height, mid range of parents age , % of meat consumed , exercise mite be the variables. You mite find that when parents mid range was above 140, and meat eaten less than 2 times week, and exercise more than 5 hours a day, the longevity is 10 years higher than the average.

Trees are usually used to separate the groups. An example picture of such a tree is here: [pic ] Lots of good pictures of such trees and a discussion of a typical tree sort is in An Introduction to Classification and Regression Tree (cart) by Roger Lewis. And programs like CART and AID and many modern variants are widely used in medicine and markets. In fact all market people systematically or implicitly consider such problems. When does the market go up? When gold is down more than 10, when bonds are up more than 100, and the previous 5 day sp move is between -1% and 0 %? The problem is that there are many variables to consider, and many levels at which one would reasonably split the data.

A good starting point in considering such problems is to note that when there are two mutually exclusive groups X and Y the variance of the difference between means is the sum of the variances of the means. The variance of a mean is the variance of an individual observation divided by the number of observations. For example, consider the average move in sp during a day has a sd of 10 and variance of 100. If you take two independent samples of 10 from such a distribution , the variance of one mean will be 100/10= 10 and the variance of the difference between means is 20. The standard dev of the difference is 4.4 What does this mean in practice? One will round. A 50% confidence interval in which 50% of the observation range is 0.65 x 4.4 = 3 points on either side of the difference between means which we'll assume to be 0. That means that by chance half of all the observations will show a difference in means ranging from -3 to +3. To find a region where 95% of the observations lie, ie. A 95% confidence interval we'd multiply 2 x 4.4 =8.8. In other words to find a difference that has less than a 5% chance of occurring thru randomness we would have to find a difference between means of 8.8 on any binary split.

But of course that's not all. Normally we look at 2, or 3 or 4 different splits to find the one that gives the greatest difference. It turns out that if we look at the highest of two differences, the average difference is 1.5 times as high as for one difference. In other words by randomness , the standard deviation for the highest of 2 difference between means is 1.5 x 4.4 = 6 a 50% confidence interval assuming randomness for the highest of 2 differences is 0.65 x 6 or from -4 to + 4. A 95% confidence interval would be between 2×6 and -2 x 6 i.e. between -12 and +12.

Now there were several assumptions made in this analysis. We assumed normality. And we didn't take account that there is sampling without replacement for one. And there are two cases: you look at the algebraic difference (2 tailed test, as above) or you try to maximize the *magnitude* of the difference (the 1 tailed approach). To get a good handle on it, Doc and I simulated what would happen if we divided up 20 random observations from a distribution that had a mean of 0 and a standard deviation of 10. This corresponds to the most frequent and typical thing one does when dividing up stock market changes in points (S&P rises or falls by about 10 full points a day). We then took two random subsamples of 10 each and calculated the mean of each of the two groups of 10. We then looked at the absolute value of the average difference between means when we repeated the process 1000 times. It turns out that the average absolute difference between means is 3.5 for a single split (this 3.5 absolute difference is in line with the std dev of 4.4 previously estimated)), 5 for the highest of two splits, and about 6 for the highest of 3 splits. This leads to incredibly high differences that you must be aware of when you split data to have anything more than a 50% chance that the difference occurred thru luck alone. To be 95% sure that you have something departing from luck for the highest of 3 splits you'd need a difference between the groups of 10. As the numbers in the group get less than 10 ,i.e. for a second or third split, the numbers would get increasingly large. This is a warning to all who search for regularities in data using methods that are implicitly or explicitly like tree sorts. By vic and doc.

Here is a more extensive table of simulation results

Table 1
Col A: number of observations being split
Col B: number of splits considered, best one is chosen
Col C: mean absolute difference generated by best split (in S&P points)
Col D: standard deviation of absolute difference
Col E: lower 5% confidence interval
Col F: upper 95% confidence interval

20 1 | 3.56 2.66 0.27 8.65
20 2 | 4.97 2.67 1.24 9.81
20 3 | 5.84 2.64 2.07 10.55
20 4 | 6.45 2.55 2.84 11.05
48 1 | 2.26 1.72 0.18 5.63
48 2 | 3.25 1.71 0.90 6.40
48 3 | 3.75 1.66 1.36 6.78
48 4 | 4.20 1.68 1.79 7.24

Table 2 same as above but
Col C: average (algebraic) difference between means (in S&P points)
Col D: standard deviation of difference between means

20 1 | -0.06 4.61
20 2 | -0.13 5.62
20 3 | 0.01 6.42
20 4 | -0.07 6.88

Let's try to apply this to a typical example.

The 30 year bonds each day have a move of about 80 points.i.e a move from 142 exactly to 142 and 26 /31. Call that the stand dev . Let's say you have 100 observations of bonds in your sample. And you split it into two equal halves based on whether gold is up the previous day or down. The variance of any mean difference would be 6400 x2 /50 =256 . That gives a stand dev of 16.

Turn to the prob that a standard normal deviate will be between -z and + z. the prob is 50% that a normal deviate will be etween -0.65 and + 0.65, the prob is 90% that a stand norm dev will be betweeen -1.6 andd +1.6. Thus if you find a difference that's less than 0.6 x 16 = 10 its 50% chance, and its 90% chance to be 1.6 x 16



 It's now clear to me that everything Rogan said recently is right. There is a force, probably without memorialization wishing to keep the world state going. That force will do everything it can to keep the market up before the election. Shortly after or in conjunction with the wish fulfillment of the incumbent win, a revulsion will occur.

Please help me with the timeline. It starts with the Roberts decision to allow medicare. Then the German decision to allow redistribution from Germany to Europe, then the GE 4 the cooling of Arab Spring protests so as not to embarrass their fellow traveler in the "circular" office, then the 7.8 % from a temporary head who lunches as agrarian party bashes with her kid.

Let us hope that the adversary does very well in cardinal events such as debates and polls in the interim so that massive European and central bank activities unlimited to support the stock market can be implemented in the remaining few weeks.

Alex Castaldo writes:

The next logical step would be a Nobel prize for Bernanke or one of his ilk.



The VIX futures never made much sense to me (among other things there is no real deliverable — it is just a bet on what a highly volatile number will be on a specific day in the future) , but maybe the Stock Index Variance Futures to be introduced by the CBOE later this year will have more applications (and certainly could make more economic sense):

CBOE Futures Exchange And DRW Trading Group Complete Agreement To Create Stock Index Variance Futures



Some interesting stats from Easley, D., M. Lopez de Prado and M. O. Hara (2012b): Bulk Volume Classification, Working paper about E-mini trade sizes:

Most of the[..] trades are small, averaging 4.50 contracts per reported fill. Figure 1 plots the frequency of trades per trade size. […not shown…] The frequency line quickly decays as a function of the trade size, with the exception of round trade sizes (5, 10, 20, 25, 50, 100, 200, etc.).

That round trade sizes are much more common than their neighbors may be attributed to so-called ‘mouse’ or ‘GUI’ traders, i.e. human traders that send orders by clicking buttons on a GUI (Graphical User Interface). As an interesting aside, this footprint of ‘GUI traders’ could be used by machines to learn the patterns of their human competitors, and eventually anticipate them to the advantage of the ‘silicon traders’. For example, size 10 is 2.9 times more frequent than size 9. Size 50 is 10.86 times more likely than size 49. Size 100 is 16.78 times more frequent than size 99. Size 200 is 27.18 times more likely than size 199. Size 250 is 32.5 times more frequent than size 249, and size 500 is 57.06 times more frequent than size 499. Such patterns are not typical of ‘silicon traders’ who usually randomize trades to disguise their footprint in markets.



 Do markets learn from each other? For example, is the S&P market this year, following a similar path to bonds last year, with every trepidatious move down being requited with a rise? Are such "learnings" graduated to the point of regularities. And is it a domino effect or a path of least resistance or consilience or convergent evolution or what have you? What do you think? Can it be quantified? Should it be quantified?

Gary Phillips writes: 

Price discovery has become as anachronistic a term as capital formation. The Fed is supposed to be listening to the market to give it guidance in it's policy decisions, not dictating to the market, what the market should do. If investors feel there are no surprises left, as the Fed is concerned, they will once again lever up, and inflate asset prices…QE1 - QE whatever. Rinse and repeat.

Bill Rafter adds: 

Here’s a related (perhaps derivative) question: Do stocks learn from each other?

Let’s say you take a list of ~6,000 stocks and look at them over a 10+ year period encompassing both up and down markets. And you come up with a trading plan that buys a stock if it exhibits pattern ‘A’ and sell it if it exhibits pattern ‘B’. It is not unreasonable to then have a universe of perhaps 150,000 transactions.

On the first pattern you pick you will find positive average results for certain stocks, while other stocks on average will be negative. Some of these winning stocks will knock the cover off the ball by having say 35 of 50 trades positive, and vice-versa.

Now let’s say you pick different patterns, and again you find a collection of stocks that outperform. You think this is going to be somewhat of a random process where some of the winning stocks from pattern A/B become losers when you try patterns C/D or Y/Z. And that does occur. But you also find that some stocks are consistent winners throughout the various patterns. And of course, some are repeat losers (perhaps hoo-do stocks).

That leads to further inquiry to find what constitutes winning qualities or hoo-do qualities. Note that this is not a study of profitable patterns since the behavior is exhibited across different patterns, some mutually exclusive like trend-following and mean-reversion. Nor is it a study of good management styles, as the same behavior is exhibited with ETFs, which typically have no management.

You then try to identify specific characteristics (or group membership) of the winners. You might think sectors, because the behavior also occurs in ETFs, but not all of the constituents of a winning ETF are consistent winners, and ETFs behave differently than their constituents. You trot through the various possibilities: volume, volatility, beta, name recognition, size, sales, earnings, debt, short interest, institutional holdings, etc.

Continuously you come back to the possibility that some stocks are simply winners and others hoo-dos, until you can prove otherwise. It turns out (tongue firmly in cheek) that this is a good thing to know.

Alex Castaldo writes: 

Probably I misunderstand or oversimplify the issue, but I think what is happening is like this.

Among stocks in your database there are some that have considerable price runups and declines, and others that have fewer such features. It is not exactly a question of volatility as conventionally defined, but it is somewhat related to it.

When you examine trading rules, selecting ones that are more successful with some stocks, you are necessarily picking up more stocks in the first category and fewer in the second.

There is a kind of oversampling at work that concentrates the successful population with stocks from the first category.

To take an absurd extreme to make things clear, if there is a stock that spent the entire data period at 10.00 (the ultimate quiet stock), then no method will make money from this stock, not trend-following, not mean-reversion, not seasonality, etc. This stock has zero chance of being among the 'winner' stocks, not because of its industry, or who is the CEO but simply because of the time-pattern of prices.




Some hypotheses regarding the reaction to information? Case very similar to the flexionic move down when the bailout was passed. Since it was known to all flexes, they had to liquidate when they realized that what they positioned for happened? Of course there was the certainty that the new man of change would be elected also? Or was it a case of going up whenever it looked likely that the vote in Greece would be positive, and then when it happened, …as Dooley would say "how will they make it happen? What you think?".

Alex Castaldo writes: 

Most were positioned in expectation of a favorable vote: long stocks and short bonds/bunds.  For a while it seemed the markets cared about nothing else.

At 6:41 am ET Papadimitriou (from the opposition party) announced that he would vote for. The S&P vaulted past 1300 and the Bunds future fell below 126.  The size of the market reaction perhaps incongruent with an announcement from a single politician. 

Then the voting started.

At 8:45 Kouroublis voted against, and there was a noticeable sudden decline in stocks and rise in bonds, bunds. This did not last long.

At 8:56 Athanasiadis, who had been expected to vote against, voted in favor, with the opposite (though smaller) effect.

After the 9:04 announcement that the votes are available to guarantee passage, there was a period of hesitancy and then an attempt by the majority to make a dignified exit now that the expected had happened, only to find the exits rather more crowded and disorderly than hoped…  The stock investors, especially, experiencing some downdrafts. 

Alston Mabry writes: 

Given the news about previous governments in Athens cooking the books to get into the €mark in the first place, and then all the reports of tax evasion and government benefit exploitation and so forth, it's easy to be skeptical, and even cynical, and take the view that things like "negotiated agreements" and "midnight parliamentary votes" and "austerity budgets" are things that politicians use as their own currency, and that they keep trying to spend this currency in an alternate imaginary world in which they believe they can hold off hard consequences with soft ideas. (See: "Munich agreement".)



Can you please outline the color coding rationale for the daily performance chart. I am confused on why some down days are red and the others are yellow..etc - A Reader

We track daily movements in U.S. stocks and bonds (specifically S&P Index futures and Long Bond futures).

The colors are based on the performance of both markets:

Red days: both stocks and bonds down.

Green days: both stocks and bonds up.

Yellow: stocks up, bonds down.

Blue (technically azure): stocks down, bonds up.

If you are interested in days when the Bonds are down, those would be Red or Yellow (depending on Stocks' performance). Personally, if I want to look at a single market I find it easier to ignore the color and just look at the sign of the price change shown. 



 I think this essay is worth reading:

"primitive agricultural communities are `dynamic'. They are subject to continuing change in agricultural technology, induced by population pressure…"

And also this article by Grantham: "We're Heading Toward a Disaster of Biblical Proportions".

Victor Niederhoffer asks Alex Castaldo to explain to him what this is all about.  Alex Castaldo writes: 

The first link is a 108 page essay written in the early 1960s by Ester Boserup , a European agricultural economist I have heard about before but don't really know. At this time many people were concerned that overpopulation was a big problem for the world. In this essay she argues that actually in some cases a surge in population forced people in an area of the world to improve their agricultural technology and make other changes that were beneficial. So (local) population increase was actually a spur to innovation and economic progress.

Jeremy Grantham on the other hand is a contemporary money manager from Boston (born in England) who is always somewhat bearish (except in March 2009 when he briefly and correctly turned bullish). He is very environmentally concerned and always worries that humankind is using too many resources or using them unwisely. Quote: "Grantham [believes] that the world has undergone a permanent "paradigm shift" in which the number of people on planet Earth has finally and permanently outstripped the planet's ability to support us."

So the Boserup thesis and the Grantham thesis contradict each other, and Mr. Depew is quoting Boserup to counter Grantham.

Victor Niederhoffer writes:

Julian Simon would turn in his grave, as would the author of The Improving State of Humanity.

Vincent Andres writes: 

If on the other hand, the most valuable resource is the human brain, a larger population is better.

Steve Ellison writes: 

I would rephrase, "if on the other hand, the most valuable resource is the human independent brain,"(Because globally, what's the use of similar brains ?).

The ratio in the brain distribution between the tails and the body probably matters. And the bigger the body, the bigger its reinforcement, and maybe (?) the bigger the crushing of independant brains.

… hopefully, this line of reasoning is wrong.



Chair suggested this 2007 article of his (with Mr. Downing) on Dailyspeculations on the Fed Model (not the FRB/US econometric model) may be of interest to some.

Steve Ellison writes: 

12 month forward earnings estimate now: 87.63
Index close today: 1320.88
Forward earnings yield: 6.63%

Alex Castaldo adds:

Ten year Treasury yield: 3.662%



The more I learn and the more I experience the vicissitudes of life, (and the more aware of my ignorance I become), I think more and more that the wisdom of musicals, especially those of Hammerstein and Ira Gershwin, is especially poignant and sagacious.

Whenever I give advice, I find myself going back to things that I learned from my favorite musicals. I recently told a damsel in distress (one of my card's imperatives besides creating value, destroying ballyhoo, and fomenting revolutions) to listen to "September Song" by Kurt Weil and "Diamonds are a Girl's Best Friend".

I have to expand this thread to market wisdom but don't have the knack that the collab or my brother has in this regard. I believe because the musical word is so much slower than reading, and the scenes have to be so focused and riveting, and they have to appeal to the common American thread that the successful musicals are forced to get to the common nitty gritty that keeps one's feet on ground.

Alex Castaldo writes:

To be in Chair's trading room in August with the temperature 97 F and the musical Carousel playing for the 100th time is indeed a learning experience. 

George Coyle writes:

 Well put, Doc. I had to walk outside into the cold for a second just reading that.

I wasn't a big fan of musicals until I heard them all several hundred times last summer, then they kinda stuck. I find the interesting thing to be that human psychology never seems to change as the messages inherent in the various musicals of the 40s-70s are the same things you hear today adjusted for the times.

The market takeaways to me are:

1. Patterns exist in musicals and markets, find them and potentially reap the benefits

2. Human psychology hasn't changed as evidenced by many of the themes in musicals still being applicable today. In as much as collective human psychology governs markets, markets probably haven't changed much either.

3. Sometimes a 3 minutes song can outdo a thousand page novel at describing some phenomenon…at times keeping it simple beats a lengthy analysis. 

Ralph Di Fiore writes:

 I totally agree with you Victor. My all time favorite musical is the Fantasticks having watched it in Manhattan. Several of the songs from the Fantasticks have wisdom in them that would have saved immeasurable grief in the lives of some families I know (not my own thankfully) had they heeded the wisdom of this musical. One song from the Fantasticks screams out at me when I think of an older gentleman who botched up his family life and has an estranged son but this individual loves spending time in his garden. The closing line from the song Plant a Radish:

Plant a cabbage.
Get a cabbage.
Not a sauerkraut!
That’s why I love vegetables.
You know what you’re about!

Life is merry
If it’s very
A man who plants a garden
Is a very happy man!

A vegitari-
Very merry

Another family had a daughter that brought home a young man and asked her pa what he thought. He hated him so guess who got married… Here is the line from the song entitled Never Say No. Again from the Fantasticks:

Your daughter brings a young man in,

Says ‘Do you like him, Pa?’
Just say that he’s a fool and then:
You’ve got a son-in-law!
You’ve got a son-in-law!

Keep up the great posts, Victor.



At the time of his death on March 31, 1913 J.P. Morgan had an estate worth $80 million. Compared to his peers of the era, especially Rockefeller, it was not such a large estate. In fact, Rockefeller's comment at the time [after reading the pages and pages of obituaries, was rather sniffy:], "And to think he wasn't even a rich man."

Source: Ahamed: Lords of Finance



 A recent study shows that Asians need about 50 points higher on the SAT to get into college than White students and 100 more than Black students. It is well known that Asians have a higher IQ by 5 points than Westerners. This is tested in numerous academic papers. Also, well known is that the more intelligent the CEO, the better the performance of his company or hedge fund. One hypothesizes therefore that the companies whose CEO's are Asian will show superior performance to those headed by your average non-Asian Harvard Business School Graduate, (although if they haven't taken the mandatory ethics course there, they are more likely to be caught in flexionic pursuits that the elite schools are so good at whitewashing). What is the support for all these statements? and: How could they be tested?

One knows that this is the most rancorous subject under the sun, and I have lost many friends when I was foolish enough to discuss this in the past, and point them to the incontrovertible evidence about individual differences from a Galtonesque perspective, but let us please try to keep it civil, and stick to the scientific literature (none of this armchair stuff about this or that study being culturally biased as the more culture free the tests, the greater the differences) and no anecdotes, but predictions and tests and references.

Alex Castaldo adds:

Since the Chair does not give footnotes, it is not easy to find the sources for his information.

I believe the "50 point study" may be the one mentioned by Steve Sailer's blog.

An IQ figure of "6 points higher" is given in the Rushton Jensen review paper.

Russ Sears comments:

I have doubts to the usefulness of CEO's IQ test as over-performance of a companies stock. It is not that CEO's do not need to be smart, it is simply that there are enough smart people around and business is tough enough that those that made it have been culled out and vetted pretty completely. Show me the numbers, I am a skeptic.

Further, while creativity to overcoming obstacles in ones life may suggest carry-over into a CEO's performance. I doubt that overcoming one political rigid standard of race by offsetting another no doubt equally rigid political standard for entry into elite colleges would translate into the creativity needed for a successful business. Rather it signals the willingness to conform for acceptance. You mention Asian, but how many of the Asian's admitted for instance are women, versus men? Any stock study of CEO IQ education and minority must consider that education by minority race in the USA is widely distorted by the politically preferred sex of a student. May I suggest that one takes the list of Jewish Noble winners find how many come from the ivy league schools and compare this percentage to the non-Jews. Yes, the ivy league alumnus have a smaller world than most. But may I suggest those with the creativity to overcome this lack of sheepskin, are those that would out perform.

Here are some reasons why I believe IQ can be a handicap to a CEO…

1. Those great at answering questions that others already have the answers, a test situation, often find it uncomfortable and difficult to switch to asking questions that the answers are not known. The ability to ask questions that others did not is a key to make a difference.
2. Necessity is the mother of invention and desperation is the mother of risks taking. A well paying job, is often the road-block to starting a business. Probable failure is hard to choose when you have almost certain path to mild success. Yet it is the probable failures that succeed that skyrockets company. And vise-a-visa its the probable successful businesses that are blind-sided by innovation.
3. Like Reagan, often the most brilliant performers as a team are those that want to work with the smartest minds, they do not have to be the smartest guy in the room. The successful CEO does not have to ask the right question, he simply has to ask the right person to ask the right question.
4. High IQ people perform best with less stress, they can choke more in stress, does the Peter Principle apply to them under stress?

There are a couple thoughts that come to mind that could be tested.

1. New Research has shown that the brain does develop new cells, These baby brain cells are produced by cardiovascular exercise. Further, test after test suggest that cardio improves your creativity. Do CEO's that exercises out perform? There was a study of CEO gulf handicaps, is there a similar study of say 5k times? Are there other test of creativity, say CEO's that are talented pianist, CEO's that are writers do they out perform? Do CEO rising from the operational side ( engineers, IT etc) outperform those that come from the marketing side?

2. A few years ago it was suggested that many CEO's are dyslexic, Could a twice exceptional CEO (high IQ but learning disability) out or under perform?

There are a couple thoughts that come to mind that could be tested.

Victor Niederhoffer adds:

For those interested in a factual, scientific discussion of environment versus innate influences, rather than armchair speculations so grievously present in our environment, and so dysfunctional to proper thinking about markets if similarly believed or proposed, I would recommend this article and the references cited thereto.

Also note the same kind of commentary there relating to making all individual differences consistent with the idea that has the world in its grip, and the purpose of life being self sacrifice.



The next meeting of the NYC Junto, on Thursday September 2, will feature Gene Epstein, Economics Editor of Barron's. He will be talking about "what is wrong with economics".

The meetings are at the Mechanics Institute, 20 West 44 th Street, starting at 7:00pm.



Richard RollThe annual meeting of the IAFE in New York on 2010/06/18 featured UCLA's Prof. Richard Roll , who was a colleague of Chair at the University of Chicago many years ago. He started out by warning the audience that his explanation is different from everybody else's, cannot really be considered proven, and may be hard to accept. Nevertheless he urged the audience to keep an open mind, if only because if this explanation is correct then the current remedies may actually be harmful.

First he dismissed the popular idea that inappropriately low interest rates caused a bubble in real estate prices, which then crashed. Although nominal interest rates were low, the more relevant real interest rates (as shown by the yield on TIPS) were actually rising during the period 2004 to 2007.

Also, defaults in the debt or derivatives markets cannot have been at the root of the crisis, contrary to common opinion. The net amount of debt in the economy is zero (someone owns each debt and someone else owes it) as is the net amount of derivatives (for every long there is a short). Default on debt simply involves a redistribution of wealth, not a destruction of wealth. For example if a borrower defaults on a $300,000 mortgage, and the house is now worth 200,000, the result is essentially as if the bank had given a $100,000 "gift" to the borrower. One is better off and the other is worse off, but the net national wealth is unchanged. These are just redistributions with no (or little) system wide effect.

So what happened? Roll believes that the root of the crisis was a reduction in wealth, and specifically a drop in the value of human capital. Recall that human capital is the present value of all future labor income streams for all persons. It is very difficult to measure because it requires knowledge (or estimates) of the future; but it must be a very large number, perhaps the largest component of national wealth. Roll believes that the value of human capital is correlated with the value of real estate and, to a lesser extent to the value of the stock market. The correlation can be seen, for example, in the fact that people who expect to have a high income in the future live in expensive houses; the value of someone's house is to some extent an estimate of that person's future income.

According to Roll a sharp drop in the value of human capital took place in 2007-2008. This immediately, or perhaps with a short lag, caused a drop in the value of real estate and (to a lesser extent) a drop in stocks (because if the people's future income is expected to be lower, the revenues of corporations will also be lower). We cannot measure the drop in human capital directly, but the drop in real estate and stocks is a clue that (according to Roll) the value of human capital dropped.

The only remaining question is why the value of human capital fell. Roll's controversial explanation is that the market correctly anticipated that government intervention would greatly increase in the years ahead, and that his would cause a permanent lowering of the rate of growth of labor income. Economists have found that past a certain point, a decrease in the share of GDP generated in the private sector leads to lower growth; conversely "liberalization" or an increase in the private share typically leads to higher growth.

This explanation was contested by a member of the audience, who said he had worked in the mortgage securities field and who felt that enormous problems developed in the mortgage market which the Professor was leaving out and which were essential to understaning the crisis.

Another member of the audience pointed out that the professor's explanation is similar to the theory of Amity Shlaes as to why the Great Depression lasted a long time.

Another critique was made by derivatives textbook author John Hull, who felt that the human capital explanation was on the right track, but disagreed about the cause. He felt that the markets began to realize that the US was increasingly unable to compete with China and could not easily restructure itself because of weaknesses in education and skills of the US population. Roll replied that this explanation was too specific to the Us, and did not account for the fact that other countries, for ex. Great Britain, also experienced a severe financial crisis.

Steve Ellison writes:

One possible reason for a decline in human capital is the aging of the population. As the average age of the population increases, the value of future income decreases.

Rocky Humbert writes:

Roll says, "Default on debt simply involves a redistribution of wealth, not a destruction of wealth. And that wide-spread defaults have no system-wide effects."

His argument is like saying "Muggers and bank robbers simply involve a redistribution of wealth, not a destruction of wealth." He ignores the costs and consequences that wide-spread mugging and bank robbing would have on behavior and economic activity. He also ignores that bankruptcy and reorganization imposes significant costs on all of the stakeholders (and by extension, society as a whole.) That's one reason why the value of an enterprise declines as it enters bankruptcy protection. Defaults is not a zero sum game with the value moving from shareholders to creditors. It's a negative sum game.

Professor Roll's argument falls down when one considers that "human capital" is a balance sheet item, but "human income" is on the cashflow statement. A country, company and individual with a negative net worth (negative human capital) can function without any problems — but it's when the cash flow cannot support the expenses that the problem causes a crisis. Hence, human capital is like goodwill on a corporate balance sheet. It's an accounting fiction. It's the human cash flow that matters.

Gibbons Burke writes:

Abortion and contraception have taken a heavy toll on human capital. Since Roe v. Wade was decided, over 50 million potentially productive human beings have been murdered in the womb in the U.S. alone.

Kim Zussman adds:

You wrote "the drop in real estate and stocks is a clue that (according to Roll) the value of human capital dropped."

Then it must have been true that human capital (anomalously) increased, causing the housing bubble in the first place.

In the attached chart (Case-Shiller real house-price data, 1890-2010), the bubble peaking in 2006 DWARFS all other housing price peaks over 120 years.

Perhaps a surreptitious rally in human capital occurred, manifested by the unprecedented housing bubble?

Rudolf  Hauser comments:

It is not quite fair to criticize Prof. Richard Roll without having heard his presentation, but based on Alex's summary thereof, I will do so nonetheless. I agree with some of what he has to say but differ in many respects. The main failure is to make any reference to the discount rate. Wealth may be the present value of future income but that is both a function of those future income streams and the rate by which they are discounted. I also find the focus on the value of human capital a strange form of analysis which ignores some market realities as to what actually happened.

Let me start by a very simplified explanation. Real wealth in the capital stock is created when someone labors to produce it. That includes amounts spend in developing human capital via education and training. That labor means that consumption will be less than the amount of production by the amount of that capital investment. But that tells us nothing about how existing wealth is valued. Assume A is moving from NY to LA and B is moving from LA to NY. Both purchased their homes for $100,000. They now decide that their homes are worth $500,000. They trade homes. All they have is capital transfer. It hardly matters what the actual value is in that it is an even exchange. Now in reality A will sell his home for that amount to someone, just a B will, and they will both buy their respective homes from others. The ignoring the intermediate transactions, that is in effect what you have. Now in the past people and lenders would base their decisions largely on their expected future incomes. But in the sort of bubble situation we had in housing, people were expecting home price appreciation to bail them out. In essence, expected future appreciation was part of their anticipated income stream. Now when it was finally realized that this assumption was a chimera that it was decided that the two homes were only worth $300,000. Now wealth has declined in value. The only question is who bears the loss. Given the mortgage amounts the lenders might well find that they must bear some of that loss. There is no reason to refer to "gifts" to the borrower. It is the decline in the value of the property, of wealth that causes the loss. It makes no sense to call that loss a gift.

Then we get to what brings about the additional losses of real wealth. That happens when capital, either physical or human, becomes useless in producing future income. That can happen when lenders refuse to renew loans and/or revenue declines cause bankruptcies. Long periods of unemployment destroy human capital. Physical capital decays from neglect. And that can happen because firms are driven into bankruptcy because of debt defaults and their ability to refinance themselves.

As to the argument that this was a drop in the value of human capital, it is first of all something that cannot be measured. The wealth we can measure has cyclical tendencies. While the recent recession was one that might logically influence future expectations, for the most part recessions are and should be expected as they have always happened from time to time. There is no reason why they should change long-term expectations other than emotions. What does happen is that there is a need in the time of crisis to have more liquidity. That increases the risk premium on longer-term and less liquid assets. Part of that increase in returns is an expectation of capital gains when the liquidity crisis/recession ends. That is why we often see an inverted yield curve leading into such declines. Logically, the yield curve should become more positive, not turn negative, because risk premiums should rise more on the longer term assets. But what matters is total return and that included anticipated capital gains. When those are not great enough the yield curve does not invert, as was the case in the 1930s. Well if the declines in wealth were due to changes in future expectations that is not what you would see. Rather it is because of what I would call risk liquidity premiums rising. If Roll's argument were valid with regard to government intervention, how does he explain the increase in stock values of the past two years-a period when by all logic the changes in government should be increasing fears of greater intervention?

What happened was that the markets finally realized that with all the complicated debt and derivate structures that depended on counterparties many transactions away to deliver was in doubt and that no one's balance sheets could be trusted anymore. With that lending dried up and all values where put into question. That cause a large increase in risk liquidity premiums that was only mitigated by the Fed belatedly pouring in large amounts of liquidity and the government offering guarantees for parts of the financial system.

Another point, although a bit more trivial. When Alex writes that Roll said that "The net amount of debt in the economy is zero." he ignores the fact that some of that debt is owed to foreigners. That is the statement is only true in an international sense.

What you had in this period was an increase in the foreign inflow of savings. Net fixed non-residential investment by business relative to GDP was significantly lower than it had been in the 1990s. In essence there was too much savings wanting to earn higher returns relative to the business investment opportunities leading investors to finance a housing boom instead.



On June 5 , 1933 the United States went off the gold standard.  Would we be better off today if it had not happened? - A Reader.

The classical gold standard, which brought price stability and prosperity to the major countries of the world, ended with the outbreak of World War I in 1914. Price stability was thrown overboard as a goal; fighting the war was deemed more important by the leaders at that time. The gold standard was dead.

What happened next is quite instructive.

In 1924 England (the most important country in the world) decided to restore the gold standard using the same parity for the British pound as before the war. But the price level was a lot higher as a result of the inflation during World War I. Have you ever tried to put on an old pair of blue jeans after you have gained 10 pounds? It is quite painful, especially around the waist and crotch. And so it was: the newly restored gold standard began to put heavy downward pressure on prices and on economic activity. Ultimately this was a major contributor to the severity of the Great Depression.

The 1924 decision is one of the greatest economic policy blunders in history, probably second only to the decision by emperor Diocletian in 301 AD to put price controls on food. It was highly contractionary and backward looking, attempting to restore a status quo that was realistically no longer achievable. They had not carefully thought it through and were doing mostly for reasons of prestige and tradition. Starting in 1932 and 1933 the error had become obvious and countries began to get off the gold standard and they immediately began to experience economic improvement; the worst of the depression was over. Relief at last, the blunder had been corrected.

So June 5, 1933 is not really the end of the gold standard but the end of the ill-advised 1924-1933 attempt to re-establish the gold standard in a mistaken and poorly coordinated manner. And we should all be glad that episode is over.

Stefan Jovanovich opines:

Alex repeats several common misconceptions that are thoroughly embedded in the received wisdom of the current academic age. The post-Civil War advocates of the resumption of the classical gold standard - President Grant being the most notable - were quite clear about what they wanted - the resumption of the absolute right of holders of U.S. Treasury notes to convert their paper dollars to gold at the Constitutional standard. They did not promise or expect the classical gold standard to bring "price stability"; they did not even expect it to bring "prosperity". They expected it to bring a fundamental honesty to the Federal government's accounts by making it impossible for Congress to indulge in serial deficits. (It is no accident that Grant's political opponents challenged his proposals by accusing him of personal dishonesty; if you are going to attack a straightforward plan for keeping straight books, argue that the proponent has been stealing from petty cash.) What seems almost impossible for the well-trained mind of the present to understand (whether educated in New Haven or Cambridge East or West) is that it was the discontinuities of the classical gold standard that were its great strength. The earnest reformers who brought us the Federal Reserve Act and those who are now eager to bring us a world currency and unified central bank share the Marxist illusions that the marketplace fluctuations in prices can be tamed if only the government gained absolute control over money and credit. It is an appealing and enduring fantasy, even if it is also a folly. What the supporters of the classical gold standard understood is that free exchange between people can create wonderments of credit and commerce if there is open competition and the price terms for the ultimate clearing of transactions are not subject to government manipulation. They also understood that governments cannot avoid being monopolies where the question of legal tender is concerned; indeed, the U.S. Constitution itself required that Congress have monopoly power over the United States' money. The only solution was to limit the government authority by requiring its paper to be backed by specie. Hence, the classical gold standard.

One of the complications of dealing with the history classical gold standard is that, while the United States was on the classical gold standard from 1791 onwards, our government, unlike Britain's, never resolved in the 19th century the issue of whether a central bank to have the right to issue notes that were to be accepted by the Treasury (in Britain the Exchequer) as legal tender. The United States had the further complication of bimetallism - dealing with what Gresham had explained to be a logical impossibility, having two legal tenders whose exchange ratios would be fixed. What both Britain and the United States did have in common was the presumption that the fluctuations in relative prices between different countries would be adjusted through discounting of trade bills, not through adjustment of their respective conversion ratios of paper into gold. In that sense both Alan and Alex are right. Britain (along with France, Germany and Austria-Hungary) abandoned the classical gold standard at the onset of WW I; but the United States did remain on the classical gold standard until June 5, 1933. Until that date a person could tender $20 in paper U.S. currency to the Treasury or a national bank and receive an ounce of gold stamped by the U.S. Mint as legal tender.

Alex is also correct in stating that Britain's attempt to do what the United States had done after the Civil War - resume convertibility - was a failure. But the failure came not from the choice of the pre-war ratio but from the assumption that a gold exchange standard could replace private party discounting as a mechanism for adjusting relative prices between countries. The classical gold standard was not restored after World War I. During the war and after its end every country in Europe had exchange controls and limits on specie redemption; even exchanges of specie for paper currency between countries were limited by international agreement. What was "restored" was something that had not existed before the Great War - a gold exchange standard. The gold exchange standard presumed that the terms of international trade would be controlled by coordination between the central banks, not by the marketplace results of private credit transactions. The gold exchange standard allowed central banks to accept each other's paper based on the assurance that the inter-government swaps would be backed by gold, but that guarantee was a fiction. The U.S. had substantially all of the world's gold reserves, just as it did after WW II; Britain's ability to pay its war debts in gold was based on the assumption that Germany would pay its reparations in gold which it would borrow from the United States.

Britain's valuing the pound at the pre-war exchange standard would not have had any ill effects if private credit markets had revived because Britain's trade bills would have been freely discounted. The best way to understand the post-WW I world economy is to see it as comparable to the present situation in the U.S. real estate market; the massive expansion of government debt and guarantees had left the world with an enormous mound of crap paper that could not be written down to its actual value because the pricing mechanisms of the pre-war world had literally been destroyed. It was very much extend and pretend. The abandonment of the gold exchange standard did not, as Alan suggests, revive world economies; Europe's output of consumer goods rose only slightly from 1932 and did not recover to 1929 levels until the 1950s; and the United States' record was not much better. The only production that did increase substantially in the 1930s and 1940s was spending for war.



The DailySpeculations.Com calendar has been advanced to May 2010.  A curious but possibly insignificant fact is that there were no red days in April 2010.  A red day is a day when both S&P futures and Tbond futures are down. The other possible colors are: blue (S&P down, bonds up), orange (S&P up, bonds down) and green (both up).

You can look at past months' calendars here:

January 2010

February 2010

March 2010

April 2010



Can readers help me understand the meaning of backwardation vs contango in the past in the ES? Why is it negative now? Does it mean people think it's going to go down, or are the rates that low? I've studied, but don't really understood the formulas for computing the values of the future contracts or why there is a negative spread now when is was +4 points or higher spread in 2007 as the market topped on the rolls.

Nick White lends a hand:

Garden variety futures valuation is just a simple cost of carry model: the price of the underlying today adjusted for the cashflows you expect to pay/receive until expiry. The whole bundle is then appropriately adjusted via interest rates for time — effectively, the exact same as any other asset.

Intuitively, this is easy to understand if you think of how NPV — or a DCF model — works and then team it up with the laws of arbitrage. What is your asset worth today given what you will spend and receive for it over a given period, adjusted for interest rates? If your asset can be exactly replicated, is the price of that replication worth more or less than the original? If so — ceteris parabus — you can arb it.

The proviso to the above is that not everybody has the same interest rate in their model… your cost of funding may be very different to mine, which will be very different to GS's. I would argue that — care factor on Index Arb notwithstanding — one's ability and inclination to practice any form of index arb depends vitally on this cost of funding rather than some point spread in the rolls… and that in turn "depends" on whether the arb is long stock / short future or vice versa. Risk free rates are just a proxy.

So, if you cannot perform index arb… what is this info useful for? Knowing the fair value spread might give you a few ticks edge when placing an order because the future may already be a bit over/under extended vs the cash market. So, to provide an example, if you're buying, you may be better to place your order — per the Chair's admonition — a couple of ticks behind the BBO if the fut is over-extended vs the cash. Otherwise, you might want to lift the fut if the cash has moved and the future is lagging.

Very simplistic — but backwardation and contango are just natural progressions of these pricing models, adjusted for the vagaries of short-term supply and demand.

Steve Ellison comments:

 Philip L. Carret, in his 1931 book The Art of Speculation, considered it very bullish when stock dividend yields exceeded the margin interest rate. In such circumstances, he said, stocks "carried themselves", i.e., one could buy stocks on margin and pay the interest on the loan using dividends. Backwardation indicates that S&P 500 futures now carry themselves.

The S&P 500 futures began trading in 1982 and almost never traded in backwardation for the next 20 years. They went into backwardation in mid-2002 and stayed in backwardation for most of the next two years, advancing 53% during this time. They have been in backwardation continuously since October 17, 2008, advancing 25% during this time.

Russ Sears interjects:

 I have seen some option quotes on Enron, that had calls, same strike, different maturities (I believe it was Oct and Dec maturities) that apparently had some time arbitrage. Not sure they were actionable though. In 2000 I bought some deep deep out of the money long term custom interest rate options, that later became in the money, for my old company. The selling counterparty called in 2003 and begged us to sell them back, because they were very difficult to hedge. He told me they were so far out of the money that they sold them thinking they would never have to actually hedge them. I suspect in both cases the option seller, simply booked the premiums as 100% profit, so the theory really went out the door.

Quant Chicken writes in:

The personal impression I formed when I reviewed the empirical academic literature 4-5 years ago was that the forward is not an unbiased predictor (contrary to the theories of FX I had learned in school). The "forward premium puzzle" has been confirmed using so many different statistical tests (some quite esoteric) that I came to believe there is something to it.

I was getting interested in investing real money in this anomaly when I was dissuaded by wiser colleagues, who pointed out that this "carry trade" idea (borrow in low yielding currencies, invest in high yielding ones) was getting crowded, everyone was getting into it, DB started an ETF to allow public to participate (this was in 2006), etc. And statistically the evidence was not very strong. In retrospect I am glad my friends advised me to stay out of it.



With one week of March now over, the Dailyspeculations.Com calendar has been updated to no longer show the month of February. Those wanting more context can look at  the old February page.



Now that the first week of February has elapsed, we will tear off the January page from the calendar and show only February.  We have saved a copy of January for future reference.



Just as Daily Spec is starting to present financial data in sports-statistic-like terms, Bloomberg has announced that it will use its analysis tools on sports.

Robert Smythe asks:

Can someone explain briefly the chart at the top of the page? What do these numbers mean? USB?

Alex Castaldo says:

These are the price moves in S&P futures and in Bond futures on the given day. (Sorry about the ugly abbreviation USB, the full word US Bonds did not quite fit the allotted space). In each case the nearby futures contract (currently March) is the one we use.

The price change for bonds is quoted in points and thirty-seconds of a point, as is traditional in Bonds. So for example on January 12 bonds rose by 1 point and 20/32, and this is shown as +1.20.

As Paul Marino notes, the whole thing is inspired by sport scorecards that show the recent wins/losses for a team.

The four colors are based on who wins and loses on any given day. A Red ink day is when both Equity and Bond investors lose, while Green is when bond and equity prices both go up. The mixed cases are: a warm orange color when the environment was favorable to those who take equity risk but unfavorable to those who avoid equity risk (i.e. bond investors), a cool blue when stock prices went down and bond prices went up (sometimes called a flight to safety or flight from risk day).

We hope you find our calendar interesting.



I have two investments A and B. If I regress B's return on A the intercept (alpha) will show whether B is preferable to A. But whether I regress A against B, or B against A, I get a positive intercept. It's as if A has alpha over B, and also B has alpha over A, which makes no sense. — A Reader

In Markowitz's theory, given two different investments A and B it is not in general possible to say "it has to be the case that either A outperforms B or B outperforms A, tell me which it is." There is no way to compare two investments and rank them in this way in general (as mathematicians would say there is not a "total order"). So it is not entirely surprising that the method of regressing A on B and B on A does not give a consistent answer as to which of A or B is better. No method will give such an answer in general. We have to live with that.

What Markowitz does say is that if you have $1, you can allocate it across A and B in various proportions (which could include shorting one of the assets to buy more of the other) and thus generate a "portfolio frontier" of points with various risk/return. Whether, in the particular case the letter writer brings up, this exercise would yield worthwhile insights I do not know. For example, if the writer revealed to us the variance-covariance matrix of returns (i.e. three numbers c11, c12 and c22) we could compute the Global Minimum Variance Portfolio weights [which are w1= (c22-c12)/(c11-2*c12+c22) and w2=(c11-c12)/(c11-2*c12+c22)], and if we knew the returns as well we could trace out the frontier. We could plot it and look at it. Useful? I don't know.

The theoreticians who came after Markowitz (Sharpe, Jensen, et. al.) believed that there is a very special portfolio in the universe called the Market Portfolio and that everyone would want to hold that plus perhaps small amounts of "other stuff." Under some fairly restrictive assumptions the desirability of the "other stuff" could be gauged by regressing its return on the Market Portfolio, always assuming that you could identify what this Market Portfolio is. Only by convention, or approximation, is this portfolio identified with the Standard & Poor 500. In any case the situation is not symmetric and the Market Portfolio plays a very special role in the theory.

What I am trying to point out is that we are so used to regressing things against the S&P 500 or other indexes every day that we sometimes lose track of the fact that this procedure does not in general allow us to rank two arbitrary investments. To compute an alpha you have to do the regression against "the market factor(s)" — not just any investment.



In pure spirit of "contrarianism", I like this article about global cooling:

Statisticians Reject Global Cooling

In a blind test, the AP gave temperature data to four independent statisticians and asked them to look for trends, without telling them what the numbers represented.

….Global warming skeptics base their claims on an unusually hot year in 1998. Since then, they say, temperatures have dropped — thus, a cooling trend.

….if you analyze the trend during that 10 years, the trend is actually positive

….to find the cooling trend, the 30 years of satellite temperatures must be used.

….It's what happens within the past 10 years or so, not the overall average, that counts

….the 10-year average for the past 10 years is higher than the previous 10 years

….You're going to get a different line depending on which year you choose

….The trend disappears if the analysis starts in 1997. And it trends upward if you begin in 1999

….it's important to look at moving averages of about 10 years

….looking back 31 years, temperatures have gone up

Oceans, which take longer to heat up and longer to cool, greatly influence short-term weather…..the current El Nino is forecast to get stronger, probably pushing global temperatures even higher next year

The quote I liked when I studied statistics at the University (although it could be perceived as politically incorrect nowadays) looks appropriate to me in this case:

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital. Aaron Levenstein

These discussions recall those about the stock market. Is it better a 50-days moving average or 13-days? Is the secular up drift over because of the recent downturn or what we are living is just another historical buy opportunity?

It looks like a discussion at a sports bar in Italy about the winner of the next soccer season.

In the meantime, let's take a look at El Niño and its effects on commodities and stock markets. In fact, the pattern of winds and ocean temperatures during an El Niño changes the jet streams steering storms over North and South America. It affects also monsoons carrying away moist air that would produce monsoon rains.

Provided that the forecast is statistically significant.

Paolo Pezzutti writes:

From Bloomberg Beijing's Heaviest Snow in 54 Years Strands Thousands

….the heaviest snowfall in the Chinese capital in at least 54 years blanketed the city for the third day this month. ….The government induced snowfall in the capital on Nov. 10 by seeding clouds with silver iodide, the China Daily newspaper reported yesterday, citing an unidentified official at the Beijing Weather Modification Office. Zhang Qiang, head of the office, said the agency induced snow on Nov. 1 by seeding clouds with 186 doses of silver iodide, according to a separate Xinhua report. The seeding brought an additional 16 million tons of snow, according to the report. Beijing takes every opportunity to induce precipitation as the city is suffering from drought, Xinhua cited Zhang as saying.

Maybe a global cooling could be induced artificially.

Anton Johnson comments:

Though theoretically possible, is induced global cooling really what we want? Looking at the Chinese snow example, what is happening? When seeding clouds, atmospheric super-cooled water completes nucleation and freezes, and then precipitates as snow. At liquid-solid phase change, latent heat is released. The converse occurs at the solid-liquid phase change, thus environmental heat energy nets out. That is, assuming the snow melts.

However, the higher albedo of snow compared to most of the earth’s surfaces causes increased solar energy to be reflected into space. So what happens when this cycle is taken to the extreme, that is, if we get the temperature balance wrong? A net increase in global snow cover will cause a net decrease in total solar energy absorbed globally, causing more precipitation to fall as snow, etc, etc; thus plunging the earth into an ice-age. Good-bye New York, Chicago and London. That is until the oceans cool sufficiently, reducing evaporation and precipitation to an inflection point that reverses the cycle – maybe after 10K-20K years.



 Friedrich Hayek always said that he was a "Burkean Whig". Since England was always the place where he felt most at home, it is not surprising that he would have looked to late 18th century English politics for his model of liberty. But, he might have had greater success in finding successful advocates for his ideas about money and commerce if he had looked across the pond.

When the Constitutional Convention took up the question of legal tender, the initial draft carried over the language from the Articles of Confederation: "the legislature of the United States shall have the power to borrow money and emit bills of credit." Hayek's intellectual forefathers would have none of it. They gave the Congress the Power to "borrow Money on the Credit of the United States" and "To coin Money, regulate the Value thereof, and of foreign Coin" and "To Provide for the Punishment of counterfeiting the Securities and current coin of the United States".

This was a political argument that Hamilton had no trouble winning. The delegates took it as self-evident that "to emit an unfunded paper as the sign of value ought not to continue a formal part of the Constitution, nor ever hereafter to be employed; being, in its nature, repugnant with abuses and liable to be made the engine of imposition and fraud." (Alexander Hamilton)

Washington, as always, had the final word on the subject: "We may become a great commercial and flourishing nation. But, if in the pursuit of the means we should unfortunately stumble again on unfunded paper money or any similar species of fraud, we shall assuredly give a fatal stab to our national credit in its infancy.

Easan Katir writes:

This past month I studied "Capital & Finance in the Age of the Renaissance" [translated] in 1928, to understand how the end game might work when governments spend beyond their means. In the 1500s kings and princes overspent to fight wars, and borrowed from the wealthy to pay the soldiers, who otherwise survived through loot and pillage. In the 2000s the governments overspent to fight wars (including war on terror, war on drugs, war on poverty) and borrowed from wealthy nations. During the Renaissance, the wiser creditors insisted on collateral for their loans governments, and thus eventually foreclosed on pledged Spanish copper and Tyrolean silver mines. Even with collateral, they had trouble collecting, and would frequently advance new loans in return for receiving partial payment on previous notes.Executive Summary: back then, in the short-term, the creditors did well (for about 50 years ), in the long term (200 yrs), it ended badly for the creditors when they slipped up and lent money to governments without collateral, which of course, defaulted.

Alex Castaldo comments:

Ehrenberg's book Capital and Finance in the Age of the Renaissance: A Study of the Fuggers and their Connections  was a favorite reading assignment from the late economic historian Ch. Kindleberger.  He even provided a capsule summary:

"This classic study of the Fugger bank in Augsburg in the 16th century focuses on war and money. Money is needed to buy soldiers, but with soldiers one can acquire money. The Fuggers were involved with Venice, Austria, the Holy Roman Empire, the financial markets of Lyons, Bruges and Antwerp, and through them, with the Spanish crown, which proved their undoing. The financial center of Europe was moving northward from Lucca, Florence and Venice (Genoa, a rival of the Fuggers for the Spanish treasure that was overdue in payment for loans when it reached Seville, hung on longer). The period was one of struggling capital markets. The mistake that brought down the Fuggers, and many a bank before and since, was lending too much to kings on inadequate security".

Stephen Jovanovich adds:

As much as I appreciate the patron saint of those of us in the finance bleachers for having the wit to say he studied economic history because he could not "hack the math" of plain economics, Professor Kindleberger did have his blind spots. He was, inevitably, a devoted member of the "where have all the flowers gone" Pete Seeger school of military history. The Fuggers were "involved in Venice" because they, like everyone else in Germany, went there to learn the new Italian science of double-entry bookkeeping. The soldiery that needed money - and ultimately bankrupted Philip II - were not the ones who discovered, collected and shipped the treasure of the Indies and the Americas to Spain; they paid for themselves a hundred times over. The military extravagances that doomed Philip II were the ones made not for profit but for faith - against the Dutch for their stubborn Protestantism and against the bastard Queen Elizabeth for her persistence in the Anglican heresy. The bit about "inadequate security" is actually funny; as Hayek kept reminding the members of the German history school and their Keynesian and Marxist successors, the state NEVER keeps its promises. It is the monopoly that is above all law and, as Philip II devoutly reminded all his petitioners, the sovereign's pledges can only be made good by God. 



 A key to today's action was Senator Reid's comment, "I hate to think of what's going to happen to Wall Street tomorrow" (Friday) overnight at S&P 830.

Jim Sogi comments:

Yen and Bonds shot to new highs, concurrent with the drop in equities, temporarily in the night market when everyone's defenses were down. Yen this morning, with no jiggles or pauses, mechanically marched down from 1.11 to 1.10, the proscribed PC number. It had the signs of a heavy hand. All seemed geared in lockstep in the various directions, like a clock. The equities are barely spare change compared to these markets. Imagine the economic effects of the marginal trades on a currency or on the the bond holdings. It made the equity moves almost seem… sedate? As for the comment, what a hubristic and ultimately wrong thing for a politician to say, almost like the mayor of a windy town. And now these are the people controlling the markets?

Thomas Miller writes: 

He has a bright future on Wall Street after the political thing is over.

Alex Castaldo goes over the facts and figures:

S&P March futures closed at 885.40 on December 12, up 10.90 on day.

From Bloomberg December 11:

“I dread looking at Wall Street tomorrow,” Majority Leader Harry Reid said before the vote in Washington. “It’s not going to be a pleasant sight.”

Asian stocks and U.S. index futures immediately began falling after Reid’s comments. The MSCI Asia Pacific Index slumped 2.2 percent to 86.13 as of 12:33 p.m. Tokyo time, while March futures on the Standard & Poor’s 500 Index slipped 3.4 percent [i.e 844.75 at 22:33 EST, although they fell further to 830.0 in the next 10 minutes].



On November 21 we published a table from the Avunc in a way that made it difficult to adduce the point being made. After comment from aforesaid I have redone the table in a way that should be more self explanatory.  Sorry and I hope to still be invited to the party.



SPX = 1000
Estimated Earnings = 97.74
=> earn yield 9.77%

10-yr Treasury Yield = 3.65%

=> spread 6.12%

=> SPX fair value = 2676 = earnings/10-yr

=> undervaluation -62.64%

=> expected return = 33.74% = 0.082 + 4.172 * spread

The model is forecasting a one year return of approximately 34%.  



V NI wonder naively, with the news of the bailout: will there not be clamors with the $1 trillion of assets that are being bought by the government at above market values, to extract some bits of flesh from those who are bailed out? Peter Public is being robbed to pay Paul Financial Firm, so to speak. But will Peter not complain and get his ounces of flesh? And will that not tarnish the luster of the gains in financial institutions in due course?

This is a speculation about which I have no expertise and no recommendation over and above saying, as I have for 30 years, that when you get out of the market because it's a "bear market," you have to get back in some time to reap the drift, and I don't know anyone astute enough to overcome that drift while he's out.

Alex Forshaw adds:

It reminds me of the October 15, 2007 announcement, except that this time the "Super Siv" (or MLEC) is $1 trillion-plus in size (instead of $75-100bn), the regulations are all the more drastic, the government has thrown $1 trillion away to save Wall Street's richest socialists, and… yeah, that's pretty much it.

If one had actually stuck to one's capitalist convictions throughout all this, one might actually not even be very surprised at the enormity of Bernanke's and Paulson's failure.

Alan Millhone worries:

I wonder if that 'long spoon' cradles castor oil? You hear the term hard to swallow. To me this applies to the bailout as the Bureau of the Treasury is running the presses 24/7 with someone holding the oiling can to keep down the sparks from the printing presses and all that paper may over time become nothing more than shin plasters!

Nigel Davies writes:

GM NigelOn the long term drift: Can someone please show me the data for all these centuries in which stocks went up 1 million percent, or are we talking about just one, the 20th? The last 24 hours have admittedly seen some of the most desperate short covering from a heavily leaning market, but I don't think one should extrapolate too much from this.

About the bailout: Maybe these measures will "save the system," but there's a huge cost involved for Mr Taxpayer. And as Mr Taxpayer is also Mr Voter I wouldn't want to bet against his supporting some heavy handed regulation by those seeking office. Not to mention the fact that he's being hit real hard in the wallet region by this mess.

James Sogi comments:

J SogiThe problem with the rescue plan and the upcoming regulation is that the creators of the plan are filled with hubris. Why should these few men with limited experience and knowledge compared to the smartest people of the entire financial world be able to solve the problems that the entire financial world was unable to? Like central planners around the world, they will just create new problems and backlogs and inefficiencies that were so prevalent in the authoritarian and socialist countries.

The country is sliding into socialism, which is the extension of the moral hazard. Where there is no more risk, there will be little reward. On the television, the prevailing meme seems to be the bailout is for the benefit of the greedy Wall street moguls and is paid for by Joe Sixpack. In any case, it will create new opportunities as cycles change yet again. Today's S&P high from yesterday's low was the greatest up move. This is a signal of new cycles, just as much as February 28, 2007 was a signal to move into a high vol cycle. The definition of cycles resists quantitative testing, so the qualitative will have to suffice.

Alex Castaldo takes a turn to the left:

Why should these few men with limited experience and knowledge compared to the smartest people of the entire financial world be able to solve the problems that the entire financial world was unable to? — James Sogi.

Yes, but don't we also need to revise downward our estimate of how smart the so-called smartest people were? When the Warren Spector's, the Dick Fuld's, etc. etc. issue so much mortgage debt to people who now can't pay, that the entire financial system is put at risk, can we really continue to call them the smartest people?

Irrespective of that (…maybe I would have made the same error…), doesn't it make sense at this point to have the "smartest people" take a time out while the second-rate people in government (and I fully agree that they are second rate) try to patch up the problem so the game can resume again? Or do we just let the system blow up because the mistakes were made in good faith by the smartest people available at the time?

Don't tell me that markets are better than Soviet style central planning, Mr. Sogi, I already know that. Tell me what is to be done under these circumstances.

Someone told me today that the nationalisation of AIG is just like what happens in France and Argentina. I am sorry but again I have to disagree. The French government ran Air France for 40 years. The AIG measure is temporary; rather than a nationalisation in the Argentinian sense I would call it a controlled liquidation of AIG. Rather than be liquidated immediately (as was about to happen) they will do so gradually over two years; rather than receive subsidies from the Argentinian government they will have to pay LIBOR plus 8%, a punitive rate, etc. The differences are major. Let's not put all government interventions on the same plane.

Back to the "smartest people" issue. The analogy I see is the following: you have been operated on by the best available surgeon; unfortunately he made a mistake and left a clamp in your abdomen before sewing you up. It is midnight on a Saturday and the only available surgeon is a semi-retired practitioner of average skills. Would you agree to have him operate on you to save your life? It may well be that you would have not agreed to be operated on by this guy in the first place. But what do you do now?

[Disclosure: Alex is a depositor of Washington Mutual and owns Morgan Stanley stock].

James Sogi replies:

J SogiIt is the spoiled child syndrome. Each time the spoiled child is saved from his mistakes, errors, rudeness, tantrums – he is inadvertently being trained to make these mistakes again. Better to mete out a measured negative punishment, time out, a reprimand, or suffering the consequences of bad behavior. Soon the child learns. There are behavioral cycles, adaptive mechanisms inherent in nature and free markets. By tampering with these, we end up with worse and worse swings as the adjusters over-adjust. Better to let Dick Fuld, and the overborrowers, take the hit. The entire financial system will not fail. It will start up again the next day no matter what happens. It may look different. There may be different players, but it will be there.

Remember the bitter pills Volcker dealt out in the 1980s with 24% mortgage rates, 14- 17% bonds. I saw many people take the hit. But inflation was crushed, and we enjoyed 20 years of moderation and prosperity. That was worth the price. Those who make bad choices should not be bailed out. It will encourage wild swings. It's the Greenspan Put all over again. If people know there's no second chance, they won't take the risks. If they do, they should be entitled to their profit or the pain of failure. When you do it your way, there is no cleansing cycles, and the toxin remains. Like Japan. It's just hiding the problems and they'll resurface somewhere else. Better to kill it now.

Let the big banks, big brokerages go down. New ones will take their place, smaller, faster moving. The market will find a way.



 Ron "Suki" King of Barbados retained his title as World Champion in GAYP (Go-As-You-Please) checkers today.  He defeated the challenger Lubabalo Kondlo of South Africa on the 24th and last game in the series, held in Medina, OH.  All previous games had ended in draws.  The referee was Alan Millhone. Our congratulations to the winner.

[In Go-As-You-Please Checkers, the players begin in the starting position shown, and can make any opening move. In Restricted Opening Checkers, the first three moves are drawn at random to generate the starting position, and the players continue from there].



Ayn RandFinally I could invest the time to start reading Atlas Shrugged. I have chosen the word invest advisedly here; I have finished reading Part I and decided to take a pause at the end of page 312.

Bearing fully in mind the introduction by Leonard Peikoff that begins by stating that Ayn Rand held that art is a “re-creation of reality according to an artist’s metaphysical value judgments”, it strikes me very hard to seek your opinion if really in the America of the last century there indeed were characters such as Jim Taggart, Orren Boyle and the sort of hoi polloi that has been described continuously in these 312 pages. I have no doubt that there were a lot of Dagny Taggarts, Hank Reardens, Ellis Wyatts who helped (re)build modern America further, but it beats me if really there was a time when the over-riding thought and action of the day was being shaped by Jim Taggart and Orren Boyle types as well. What do you think? Has the author erred in stretching the shadows far longer to produce the effect or was there really an America like that also?

Alex Castaldo attempts a reply:

You are not the first non-US reader of Ayn Rand to be puzzled by this question. As a foreign-born American I was surprised that her books were set in the US when you could easily come up with better examples of government/business connivance from other countries. Americans can consider themselves lucky that they are better off in this respect than some others. Indeed I have often asked myself where is the Italian Ayn Rand who would speak up about how some of Italy's wealthiest people have made their fortune largely through political connections and improper operations, and explain the difference between this and true entrepreneurship. Sadly he/she does not seem to exist (possibly for lack of readers).

Part of the answer may be that Ayn Rand was most familiar with Russia and the US, so of course she chose to write about these countries. Also, she was concerned about trends and developments rather than the immediate situation; the US in her books is perhaps the model of what could happen to any country if the disturbing developments she saw around her were to continue. Her books are, among other things, a plea for the US to retain (and improve) its traditional values and not adopt those of the then ruling class in Soviet Russia.



 We have learned that Ken Smith, a frequent contributor to this web site, passed away on June 2, 2008 at age 78 (of an apparent heart attack).

Ken travelled to many places over his life, was involved in all kinds of interesting situations and shared many of them with us through his writing. Several of us had a chance to meet him in person as well.

Our condolences to his wife Ina.

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Andrew Moe remembers: 

Here is my favorite post from a good friend who often gleaned remarkable insight from the wonderful life he led and was generous enough to share it with us. A true Spec.

####### Original Post by Ken Smith #######

At 6 am I looked out to the front lawn and observed a lone American Robin listening for worms and insects in the dew-moist grassy ground. The bird shuffled from point to point in a random-appearing change of direction. This is breeding season; the birds are now carnivores. After breeding they will switch to a vegetarian diet. The Robin was patient at each stop, giving his senses time to pick up the signals nature has programmed him to use in his search for food, food which furnishes him with reproductive energy. He has arisen early and discovered this niche for himself, my front lawn, recently watered.

A signal from the ground is perceived and Sir Robin quickly has his prey, no hesitation. This guy is an active hunter, as in active trader. His little computer brain and sense organs are crawling the field in search of prey, a morsel to fatten his resources. Sir Robin will switch to another lawn or playground or marsh when his present search produces less energy than the energy required to do the search.

He will fly away, perhaps randomly choosing the next site for exploration. When breeding season is over, the eggs hatched and nurtured, Sir Robin will change his diet preference. And the turning of the earth, the sun, and the moon will influence him to change his territory, his environment, his location in relation to these planetary orbits. A trader seeks a niche, as the good Doctor Niederhoffer has suggested. Sir Robin, as an epitome of nature's example, has a bird brain yet survives, breeds, and flourishes. How complicated do we need to be to survive as traders?

"Once breeding season is over, the sweet-singing and familiar robin of our backyards becomes more furtive and shy. Large nomadic flocks form and range over the countryside in search of berries such as mulberry, sumac, grape, viburnum, and cedar, as they shift from their breeding season diet of insects and earthworms to become wholly vegetarian. By September, many are moving south from the northern parts of the eastern half of the country to winter with southern residents in the Middle Atlantic and Gulf states. In the West, Robins wander broadly in search of food and move generally to areas of lower altitude. But some linger as far north as Canada when food supplies are adequate, so the first robin you see in spring may not have come from too far away." (Cornell U. ).



Is the Subprime mess one more indicator of the devil may care, not my fault, no care no responsibility, times we are living in? Are the Fed bailouts the ultimate back up to keep this poor social situation alive, leading to a poor thought process for the next rogue trader, head fund manager, and for that matter retail trader sitting down at his platform.

Is this ultimately leading to a internal breakdown of our own risk mismanagement on our own accounts even though at the end of the day it's us on our private accounts and we will be ultimately responsible.

How can we change the situation - When is someone going to stand up and say, "I'll take the hit , I'll wear the pain , It was me!"

Janice Dorn explains:

Maslow's hierarchy of needs is often shown as a pyramid where people go from the base to the apex in terms of what they focus on. The lowest level (base of the pyramid) reflects the most basic human needs– food, health, sleep, physiological needs). The next level is shelter and safety from danger. The next three have to do with belonging (love, affection, socialization), esteem (self and from others) and-finally-the highest state is self-actualization (evolution to a higher consciousness, authenticity, achievement of individual potential, transcendence, creativity).

Humans are unable to progress to the higher levels when they are preoccupied with the needs of the lower levels. In order to distract people from higher levels, one need do nothing more than threaten their basic needs. When people are focused on their basic needs, they do not have the capacity to deal with powerful issues such as personal responsibility. They are too busy focusing on either feeding themselves, dealing with illness or worrying if they will have a roof over their heads tomorrow.

Throughout history, the best way to strip power from a person is to divert their progress up the pyramid by doing something that forces them to stay "stuck" near the base of the pyramid.

We are not evolving. Rather, it appears that there is a not insignificant amount of devolution occurring. As long as we look to "the powers that be to get us out of this mess, the less chance we have to move through the bottom two stages and get on with creative evolution. We will, as a nation, remain, worried, frightened, and sick. The way that power was taken from kings was to poison them. They did not die, but they stayed sick and thus fell down the pyramid to the lowest level.

The person that must stand up and take the hit and wear the pain is the person who looks back at each of us in the mirror. If we cannot do this, we will turn to everyone else to rescue us, to fix the mess, to take care of us, to save us from ourselves. The war against personal responsibility and individual empowerment is in full force. We are unraveling.

Alex Castaldo notes:

I was surprised by the headline in the Financial Times on April 10 "Banks take blame for crisis".  Maybe there is hope after all. 

Russ Humbert offers a deeper perspective:

While nobody wants to take responsibility for the sub-prime mess, the media has certainly laid blame at the feet of the capitalist. "Capitalists acting too aggressive", "Capitalists only out for their own self interest", are a couple of the "causes" I have heard from the media. However, if the origins and the incentives in the sub-prime markets are studied or in other words the true "cause" is explored, it clearly was due to the markets letting socialism creep into their midst.

The timing of the GSEs entry into subprime seems highly suspect.

The deterioration of underwriting standards can be understood, if you understand the rating agency or risk management was graded almost solely on industry average and industry statistics. Such "pooling" of risk management might as well been pooling of agricultural production. What happens is nobody works. It was a mad rush to capitalize on others' efforts.

Thankfully, the capitalist inspired puts in the contract led to most of those irresponsible enough to think they could get a free ride on everybody else's risk management efforts paying the price. The capitalist insisted they had a least enough skin long enough that they couldn't ignore it without getting caught. Thankfully, some of those capitalists caught this problem early enough and are driving a hard bargain to make sure this mess gets cleaned up fast and making sure it won't happen again. While this may be a high price to pay, just imagine if those aggressive capitalists hadn't all dived in at once. The march to socialism might have been slower, but like a boiling a frog, this would have slowly allowed the GSE's to eat a cancerous toxin, driving them to a slow painful unavoidable death. Or if the short sellers had not been allowed to price the actual risk, those executives responsible would have crippled the banking system and the economy for perhaps a decade, bleeding but not admitting wrong doing to stay in power (as happened in Japan).

Capitalist was the cure, socialism the cause.



Three peaks in VIX (all of them above 30) on 11/12, 01/22 and 03/17. It never went below 15 after end of July 2007.VIX



How often do the S&P futures change in price (up or down) by more that 10 points?

Date         Num Days Num Days
Year Mon       Up >10   Dn >10          Tot    Diff
2000   1            5       8            13      -3
2000   2            6       6            12       0
2000   3            9       6            15       3
2000   4            4       7            11      -3
2000   5            8      10            18      -2
2000   6            6       4            10       2
2000   7            6       6            12       0
2000   8            5       2             7       3
2000   9            3       6             9      -3
2000  10            5       8            13      -3
2000  11            3      10            13      -7
2000  12            6       8            14      -2
2001   1            5       3             8       2
2001   2            3       9            12      -6
2001   3            7       8            15      -1
2001   4            9       5            14       4
2001   5            4       6            10      -2
2001   6            4       5             9      -1
2001   7            3       6             9      -3
2001   8            2       8            10      -6
2001   9            4       6            10      -2
2001  10            6       3             9       3
2001  11            7       1             8       6
2001  12            3       4             7      -1
2002   1            4       3             7       1
2002   2            6       4            10       2
2002   3            4       3             7       1
2002   4            3       4             7      -1
2002   5            5       6            11      -1
2002   6            2       7             9      -5
2002   7            5      11            16      -6
2002   8            7       7            14       0
2002   9            5       7            12      -2
2002  10            8       8            16       0
2002  11            6       4            10       2
2002  12            3       6             9      -3
2003   1            4       7            11      -3
2003   2            3       4             7      -1
2003   3            3       4             7      -1
2003   4            6       3             9       3
2003   5            5       1             6       4
2003   6            4       4             8       0
2003   7            3       4             7      -1
2003   8            0       1             1      -1
2003   9            3       3             6       0
2003  10            2       2             4       0
2003  11            2       1             3       1
2003  12            4       0             4       4
2004   1            3       1             4       2
2004   2            3       0             3       3
2004   3            5       6            11      -1
2004   4            1       3             4      -2
2004   5            2       2             4       0
2004   6            1       3             4      -2
2004   7            1       3             4      -2
2004   8            3       3             6       0
2004   9            1       1             2       0
2004  10            3       3             6       0
2004  11            2       1             3       1
2004  12            2       1             3       1
2005   1            1       2             3      -1
2005   2            3       1             4       2
2005   3            2       3             5      -1
2005   4            2       7             9      -5
2005   5            2       2             4       0
2005   6            1       1             2       0
2005   7            1       1             2       0
2005   8            1       2             3      -1
2005   9            2       2             4       0
2005  10            4       6            10      -2
2005  11            3       0             3       3
2005  12            1       1             2       0
2006   1            3       1             4       2
2006   2            2       3             5      -1
2006   3            3       0             3       3
2006   4            1       2             3      -1
2006   5            3       5             8      -2
2006   6            3       5             8      -2
2006   7            3       2             5       1
2006   8            2       0             2       2
2006   9            3       1             4       2
2006  10            2       0             2       2
2006  11            2       2             4       0
2006  12            2       0             2       2
2007   1            1       2             3      -1
2007   2            2       2             4       0
2007   3            4       4             8       0
2007   4            4       1             5       3
2007   5            3       2             5       1
2007   6            3       6             9      -3
2007   7            4       6            10      -2
2007   8           10       6            16       4
2007   9            3       3             6       0
2007  10            6       4            10       2
2007  11            6       9            15      -3
2007  12            6       7            13      -1
2008   1            7      11            18      -4
2008 2*             4       4             8       0

.                            Autocorr   0.658   0.197

The highest volatility (by this measure) occurred in January 2008, with 18 double-digit moves, although we have a tie with May 2000.

Generally, there were a lot of such days (>10) in 2000-2002, few in 2003-2006, and many again starting in July of 2007.



It is interesting to look at a chart of the number of large S&P moves per month.



 The Fed Model postulates that if the forward earnings yield of the S&P Index is higher than the 10-year treasury yield, stocks are “undervalued“, and vice versa. As of December 31, the S&P was at 1468.36 and expected forward S&P 500 earnings for the next 12 months were 101.86, making the forward earnings yield 6.94 percent (101.86/1468.36). The yield on the 10-year T-note was 4.02 percent.

Historically, subsequent market returns have been correlated with the differential between the S&P forward earnings yield (estimated 12 months earnings divided by the S&P 500 level) and the 10-year treasury yield. On the 10 occasions when this differential has been greater than 1 percent, the S&P 500 has risen ten out of ten times for an average of 13.5 percent in the subsequent 12 months. (This differential currently stands at 2.92 percent, which the highest it has ever been at year end).

We have found that the best way to specify the Fed model relationship for forecasting purposes is with a linear regression in the form:

S&P Return[t+1] = a + b * ( Forward Earnings Yield[t+1] - 10 Year Yield[t] )

Estimating this regression using yearly data since 1980, we obtained the following equation:

S&P Return[t+1] = 0.082 + 4.172 * ( Forward Earnings Yield[t+1]  -   10 Year Yield[t] )

t-stat     2.66      1.85

p-values   1.32%     7.53%

The R-Squared of 0.12 is quite high for a predictive regression in the financial markets and indicates that 12 percent of variation in subsequent returns was explained by the independent variable over the time period studied.

To determine current Fed Model forecast:
Current S&P (as of 12/31/07) stands at 1468.36
Forward Earnings = 12 months consensus forward earnings for the S&P 500 = 101.86
Forward Earnings Yield = Forward Earnings / S&P = 101.86/1468.36 = 6.94 percent
10.Year.Yield = The Current Yield on 10-Year government note is 4.02 percent
The Differential (Earnings Yield - 10.Year) = 2.92 percent
Substituting these numbers into the regression formula :
0.082 + 4.172 * (0.0694 – 0.0402 ) = 0.203
Therefore, Fed Model yields a forecast of 20.3 percent for next 12 months.

Jason Humbert asks:

How does Dr. Castaldo counter the failings of the Fed model in other G10 countries? Japan has been horrible under that model. Germany has been OK, barely statistically significant. UK has been good, like the US.

Alex Castaldo replies:

I believe Mr.Humbert is referring to the paper "The Fed Model: A Note" FRL (2006) by Javier Estrada who tested the Fed model in a number of foreign markets.   I have exchanged Emails with the professor but neither one of us was convinced by the other's arguments; we will just have to disagree.



Two insights from How to Get Rich by Felix Dennis, a British publishing entrepreneur:

1. If you are too concerned with fitting in, with being well regarded by others, with avoiding public faux pas or highly visible mistakes, then you will have difficulty becoming very wealthy

2. The entrepreneur should try to retain 100% ownership (which, however, raises the question of how to motivate employees) but not be involved in the company 100% of the time (in fact he should disappear from time to time and let others run the company)

A fuller review will be coming soon. For the impatient, this old article on the web summarizes some of the main points of the book.



The Fed's upcoming liquidity swaps will be keyed off the OIS rate for one month funds, which I mistakenly assumed was roughly the rate for a term loan of the duration.

I have since learned that the OIS is effectively the same as the rate on the second Fed Funds future, which is currently ~4.20%, a modest discount from the spot target rate of 4.25% and substantially lower than the 4.75% discount rate.

This makes the upcoming set of auctions a one-off discount rate cut, albeit in relatively small size. The two day window between bid submission and auction award announcements further muddies the program's value.

Alex Castaldo remarks:

I have the impression that the Fed is experimenting with a view to a permanent auction mechanism for supplying liquidity, based on this paragraph in the news release:

Experience gained under this temporary program will be helpful in assessing the potential usefulness of augmenting the Federal Reserve’s current monetary policy tools–open market operations and the primary credit facility–with a permanent facility for auctioning term discount window credit.

This mechanism would be similar to the weekly auctions that the ECB holds (the so called main refinancing operations). From ECB web site:

Main refinancing operations are regular liquidity-providing reverse transactions with a frequency and maturity of one week. They are executed by the [National Central Banks of the Eurosystem] on the basis of standard tenders and according to a pre-specified [weekly] calendar. The main refinancing operations play a pivotal role in fulfilling the aims of the Eurosystem's open market operations and provide the bulk of refinancing to the financial sector.

European fashions in Central Banking coming to the U.S. ? We shall see.



Here are the largest upward point moves in the S&P (cash) index from 1999 up to (and not including) November 13, 2007.

Largest one day point moves since 1999

(up to and not including 11/13/2007)
Rnk       Date Px Last     Chg

1   03/16/2000 1458.47   66.33

2   01/03/2001 1347.56   64.29

3   12/05/2000 1376.54   51.57

4   04/05/2001 1151.44   48.19

5   04/25/2000 1477.44   47.58

6   10/19/2000 1388.76   46.63

7   04/18/2001 1238.16   46.35

8   07/29/2002  898.96   46.12

9   10/28/1999 1342.44   45.73

10  07/24/2002  843.42   45.72

11  04/17/2000 1401.44   44.88

12  05/30/2000 1422.45   44.43

13  10/13/2000 1374.17   44.39

14  09/18/2007 1519.78   43.13

15  04/18/2000 1441.61   40.17

16  10/15/2002  881.27   39.83

17  11/13/2007  1478.6   39.42

18  05/08/2002 1088.85   39.36

19  09/03/1999 1357.24   38.13

20  01/07/2000 1441.47   38.02

Today's rise of 41.87 would be the 15th entry.

Jim Sogi adds:

All of them are from the 1999-2002 period or from 2007.  There are none from the low volatility years 2003, 2004, 2005, 2006.  The list of big down moves is similar.



ShoeYears ago a friend of mine applied for a clandestine job at a major intelligence agency and was invited to McLean VA for a series of interviews. His story may be of interest if you are looking for a similar job.

The first interview was quite uninteresting, even boring, according to my friend. He was ushered into a small nondescript office by an average looking guy who seemed to want to do most of the talking. My friend perhaps expected the agent to "sell" the agency to him with recitals of interesting adventures during his career, but it was nothing like that. The man talked mostly about himself, but in a dull, matter of fact way, full of details. Just as an example of how boring and pointless the conversation was my friend said that on two occasions the man pulled out a battered wallet and showed him pictures of his children; the second time my friend's eyes glazed over and he looked away. My friend was not impressed by the caliber of people working at the agency, to say the least.

After 45 tedious minutes the meeting was over and my friend went to his next appointment with a man who was obviously a top official, sitting in a nice big office. Now the real interview began, as the official fired question after question at my friend: Tell me about the man you just met: what did he look like, what would you estimate his height and weight? Did he wear brown shoes or black? Please summarize what he said. Did he mention anything about American policy in the Philippines? How many children does he have? Are they boys or girls? Unfortunately my friend had not paid enough attention during the first meeting; he thought of himself as detailed oriented and having a good memory, but was surprised at how difficult it was to come up the information requested.

Well, he did not get the job, but instead joined a big accounting firm, became a partner and lived happily ever after.




I've been studying complex variables lately because I find the imaginary very important these days, and I had to brush up on them for one of my daughters.

It led me to consider the imaginary part of the moves during a day or week, and the real part. Consider last week. O/H/L/C:

9/28 1538.20 1545.20 1519.00 1538.10
9/21 1491.80 1552.00 1485.20 1534.40

The real part of the move, from 1534.40 to 1538.10 was 3.70. The low of the week 1519 so there was a -15.40 point imaginary negative part, and the high was 1545.20 so the imaginary positive part was 10.80.

A similar calculation could be done for the day, looking at the amount below the previous close, the amount above the close, and the final move.

We can look at the two points on an Argand like diagram. I claim that the length and the angle between the two lines connecting the negative and positive imaginary could be useful as a predictor. Better yet, the two angles themselves and the real part. Similarities might be useful. Such angles should be quantified , classified, and subjected to prediction and falsification.

Another example. The week of August 17 showed a real move of -1.10 and a negative imaginary of -76.00 and a positive imaginary of 21.50.  A small real move but non-negligible imaginary moves.

Laurence Glazier adds:

I'd also be interested in trying volatility as the orthogonal parameter (it is to do with the imagination after all.)

Michael Cook follows up:

I love complex variables - it is one of the most beautiful subjects in mathematics. Everything comes together and illuminates and integrates everything that's gone before in the traditional mathematics curriculum.

I don't understand how you are defining the imaginary part of price moves - can you clarify? I am intrigued!

Alex Castaldo explains:

If I understand Vic correctly, he defines two complex numbers, the AboveMove and the BelowMove:

AboveMove = (c[t]-c[t-1]) + i (h[t]-c[t-1])
BelowMove = (c[t]-c[t-1]) + i (l[t]-c[t-1])

And plot these as two vectors on the Argand diagram. The real parts are the same, but the imaginary parts are different (and always of opposite sign). Next you can get the angles and the lengths.

Adi Schnytzer queries:

Are these the complex components of the change simply because they exceed the bounds of the price at the start and end of the week? If so, why a week and not a day or a month? And perhaps more to the point, can the maths of complex numbers then be used to predict? Analyze the moves?



WheatWith the amazing moves in wheat lately, I'd like to recommend The Plunger by Edward Jerome Dies. Published in 1929, The Plunger focuses on Benjamin Hutchinson, a legendary Chicago trader. 'Old Hutch' was King of the Wheat Pit in the late 19th Century and I read in awe about how he dominated trading at the CBOT. There are reprint editions made in the mid 1970s at a reasonable price.

Alex Castaldo adds:

As a reminder of how difficult it is to hedge a generalized deflation, let us look at a chart of wheat prices from Kindleberger's book The World in Depression, on page 88. If wheat prices in 1929 are set at 100, they subsequently plunged to under 50 in 1931, 1932 and 1933 before gradually recovering and reaching 100 again in 1938.  In the prosperous year of 1925 they had reached a maximum of 120. A terrible time for wheat producers indeed…

J. T. Holley remarks:

I have on the back of an envelope somewhere a study I did on softs/grains. I did this study to learn scale trading. The counting that stuck in my head is that when corn, soybeans, and wheat reached the top five percentile of their historical price distribution they were significantly lower two years from the date of entry. My staring point was that 1974 high of 650, which was probably breaking massive statistic rules!



I have two time series A and B with 120 monthly observations each. I want to test whether A's yearly changes predict B's yearly changes. But there are only 10 non-overlapping years. What is the least horrible method that would use overlapping 12-months changes? I am thinking of a bootstrap but looking around, I found mention of the Generalized Method of Moments (aka Generalized Estimating Equations) which looks complicated. Do readers have other suggestions?

Alex Castaldo replies:

The traditional approach used in the literature (by Shiller among others) is to do a rolling (i.e. overlapping) predictive regression and then correct for the overlap by using Newey-West standard errors (rather than the usual standard errors that regression software normally uses).

Victor and Laurel do not like the Newey-West approach, and the literature has been coming around to their point of view. The problem is that Newey-West is correct asymptotically (that is, as the number of data points goes to infinity) but in these problems we do not have a large amount of data (that is why we are resorting to using overlap). Simulation studies show that in small samples the Newey_West method can be biased.

What is the solution? I don't know; it is an open research problem. There is something called the Hodrick (1992) method which is said to be free from small sample bias. (It is different from the Hansen-Hodrick method). Also you might try to read recent papers on the subject, such as Ang and Bekaert "Stock Return Predictability" (2006) and the references therein.

Adi Schnytzer writes:

This is what Stata throws up: package lomackinlay from RePEc

      'LOMACKINLAY': module to perform Lo-MacKinlay variance ratio test


      lomackinlay computes a overlapping variance-ratio test on a
      timeseries. The timeseries should be in level form; e.g., to
      test that stock returns vary randomly around a constant    mean,
      you consider the null hypothesis that the log price series is a
      random walk with    drift. The log price series would then be
      given in the varlist. If the assumption of homoskedastic
      errors in the process generating the differenced series is not
      reasonable,  the robust option may be used to calculate a
      variance ratio test statistic robust to    arbitrary
      heteroskedasticity. This is version 1.0.5, corrected for errors
      in logic    identified by Allin Cottrell.

      KW: variance ratio test
      KW: random walk
      KW: heteroskedasticity
      KW: time series

      Requires: Stata version 9.2

      Distribution-Date: 20060804



 There is always much debate whether to equal weight or cap weight indices. If there are 30 securities (country ETFs or stocks) in a portfolio, given that they have similar though different return distributions, what is a good way to estimate how frequently one would expect a cap weighted portfolio to outperform an equal weighted portfolio?

Scott Brooks writes:

It really comes down to what do you see doing better, the larger companies or the smaller companies (large or small in reference to that index/ETF that you are looking at).

If you expect the larger stocks in an index to do better, then go with the cap weighted. If you expect the smaller stocks to do better, then go with the equal weighted. For instance, RSP the SPEWI ETF has nicely outperformed the SPY SP cap weighted ETF for quite a few years now.

Alex Castaldo adds:

I would suggest a bootstrapping approach. Imagine the actual data arranged in a four column table:

Period Ticker  CapWgt  Return
1         GE        0.4     1.05%
1         IBM       0.2    -0.85%
1         …
1         XYZ       0.01   0.97%
2         …

From this table the cap weighted and equal weighted returns can be easily computed. Now generate artificial data by scrambling (i.e permuting) the entries in the return column while leaving the other columns unchanged; compute the cap weighted and equal weighted returns for the artificial table.

Repeat the process 10,000 times and see how the real-life returns stack up compared to the 10,000 artificially generated cases. Some details need to be filled in, but you get the general idea.

Charles Pennington adds:

Alex is sending you on a snipe hunt. It is obvious by symmetry that the required probability is 50%. 



 I have been researching on the web how to teach children to dream. What is left out is how to develop a passion for life when dreams fail to develop. I suspect their father's example is the best teacher.

John Floyd writes: 

I am looking for recommendations for children’s books. I would like to include the right mix of education, capitalism, logic, reason, imagination, and individuality among other things. A few books and stories that I have found, and the kids enjoy: Jonathan Livingston Seagull, Thidwick the Big Hearted Moose, An Airplane is Born, and The Little Prince.  

Scott Brooks adds: 

As much as we push education in our home, we've had a dickens of time getting our children to read outside of school. Finally last year, my oldest daughter got into reading the Goose Bumps series. She loves them and needs no prodding to read up on them.

My youngest son somehow got into reading the Star Wars books. He doesn't read them religiously, but will read outside of class if given a little reminder. Interestingly, I bought him a book on bullets at the Quality Deer Management Association national convention in Chattanooga last week and he's been perusing it almost everyday. He's 8 years old and it's way above his level, but he seems fascinated by it. He had his home school teacher read it with him and explain the more difficult parts to him.

For my 12-year-old, we've had to use a different tactic. He doesn't read unless we push him to do it. However, he's really into the markets and learning about investing. So he reads stuff on the net about companies he's thinking of buying and watches and reads investing information.

I guess the key is to immerse your kids in reading and let them find what they like. When I was kid, I'd read one or two Hardy Boys book's a week. I tried to get my kids into them, but to no avail. Keep searching to help your kids find something that they like. There have been a lot of good books recommended here (and I'm saving this thread for future reference for my kids and their home school).

Many of these books are important and are one's that I'll have the kids read as part of their school work assignments (whether they want to or not). But the biggest thing that I've searched for is, how do I instill in them a love for reading a thirst for knowledge? I can't do that by forcing books on them. Sure, I can help them to learn important lessons by requiring that they read certain books. But what I really want to see is them sitting down curled up with a book reading it because they want to. I believe that should be goal! 

From Bill Humbert: 

One of my children was a reading-avoider. My goal was to get the kid reading and I happened to see the movie League of Their Own in which the Madonna character teaches the non-literate character to read by using trashy novels. I believe the quote was something like, Who cares? She’s reading isn’t she? It’s a scene we always laugh at.

Well, I didn’t use trashy novels, but I did use comic books. We started with the superhero genre and then I gradually slipped in the newer version of the old Classic Comics. For certain works I also acquired Books on Tape, which is more useful than listening to the radio in the car and it gave the child a general understanding of the work.

Since the brain stores different types of input in different locations, this child had an advantage over the children who only had read say Homer’s Odyssey. The child had the pictures from the Classic Comics, the audio from Books on Tape and the printed word itself. After a while the child started to excel in those classes. And only then did the overall desire to read take over. I think it was like a pump that needed to be primed.

Get the child reading. "What" does not matter. If the child finds that useful and desired knowledge comes from reading, eventually that child will take to the books. But you have to prime the pump by starting with something that they want to read, which is not always what we want them to read. 

Larry Williams adds:

When I wanted my kids to read a book I was reading I told them they probably should not read it — that it was too adult for them. A cheap trick, I know, but they pick up those books like a brown trout seeing a grasshopper in August.

Nat Stewart writes:

My parents did much to foster my love of reading. In early grade school I would go with my mother to the local library, where I was allowed to pick any books I wanted for that week. I quickly fell in love with the selection of children's books that focused on biographies of America's great heroes. My particular favorites where books on:

1. Thomas Jefferson
2. Thomas Edison
3. George Washington
4. Paul Revere
5. John Paul Jones
6. George Washington
7. Davey Crockett
8. Henry Ford
9. Daniel Boone
10. the Wright brothers

I loved these books! The children's books focus on a narrative of struggle, adventure, and heroism, ingenuity, and are often historically accurate enough to prove very educational. I remember reading them late into the night, hoping no one notice that I had my light on long past the official bed time.

My parents also spent a good deal of time reading to me. My favorites included books about King Author and Nights of the Round Table, "Little House on the Prairie" books, and The Chronicles of Narnia.

Let a kid explore the library and pick favorites. Provide enough options so that reading can become an adventure rather than a chore. Spend some time reading to them over summer vacation. 

From  Bill Rafter:

 We all remember our trips to the library. However that cannot be replicated today. The libraries simply cannot compete with television and the Internet either with content or "wow" factor. The answer to the problem will be in using the new technology not avoiding it. Television, even the good stuff like National Geographic or Ken Burn's "Civil War", is still second-rate because it's passive. The Internet is active, and thus has more potential as a learning tool.

Games can be very helpful. One that had particularly helped me (both myself and subsequently my children) was Scrabble. After a street game of "boxball" we would dig out the Scrabble board while we cooled down. Those games got very competitive to the extent that several of us kids started doing research on words by randomly reading the dictionary. Scrabble also required you use arithmetic to keep score.

My favorite Scrabble word was "ennui," as it cleaned out your collection of accumulated poor-value tiles. It also led to challenges, which led to another turn and more points. While researching through the dictionary I stumbled upon the word "eunuch", which also had good Scrabble possibilities. Being in 6th grade, I didn't care what it meant, but kept a mental file for future use.

Well somehow I got into a name-calling event in the schoolyard with a girl and called her a eunuch. She had no idea what it meant, but the teacher Sister Mary Hatchetface was in earshot and she most certainly knew. The next thing that happened was that I was in the principal's office (Sister Jane Battleaxe). My father was summoned. He was a Philadelphia policeman, and he happened to be in uniform.

So there I was in the Holy of Holies with the two nuns in their penguin uniforms and Dad in his, trying to learn what trashy literature I was reading. The revelation that it was the dictionary left them with no solution.

Ahhh, the ability to stick it to authority…priceless. 



 I attended a presentation by Andrew Lo on 'The Psychology of Trading,' on May 21 2007. Here is a brief summary of his remarks:

On the one hand the Efficient Markets Hypothesis is at the foundation of Finance (for example all the work by Black-Scholes assumes that the market for options is efficient) on the other hand many people nowadays find it hard to believe that EMH is literally true. This has led to the development of Behavioral Finance, which studies biases that may hinder financial decision making. BF has acceptance problems of its own: it brings up so many possible biases that it is hard to believe (if all these biases are true) that anyone is ever able to make a correct decision. Many economists ask if the behavioral biases even exist.

To try to advance beyond the EMH/BF debate, Andrew Lo has been working on his own framework, which he calls the Adaptive Market Hypothesis, and has been investigating the role of emotion in trading by reading the neuropsycholgy literature and conducting experiments, some of which will be described below.

Do perceptual biases really exist

The first experiment involved the audience. They were invited to watch a video showing college students, some in white T shirts and some in black T-shirts throwing basketballs to each other. Lo told the audience to concentrate on the white T-shirt players and count the number of times they passed the ball to each other. The exercise was made more difficult by the fact that the black-T shirt players intermingle with the white shirt players and that Lo kept talking throughout the video to try to confuse the audience.
After the video was over Lo asked "how many people saw the gorilla?". More than half the people in the audience had not seen any gorilla. [I personally did not see the gorilla, even though I knew that Andy Lo is famous at MIT for showing a video in which a gorilla appears !]. Lo replayed the tape, and sure enough a man dressed as a black gorilla walks through the scene halfway into the tape. Lo explained that people who are concentrating on white figures will often miss black objects; in some sense the human perceptual system is filtering out the black objects.

In conclusion, said Lo, in any debate between economists and psychologists as to whether perceptual biases really exist is going to be won by the psychologists, who have demonstrated these phenomena beyond doubt through careful experiments.

The neuropsychology literature

The book "Descartes Error" by A. Damasio has changed how we view rationality. The classical philosophers believed emotion and rationality were polar opposites. Damasio investigated people who have suffered serious brain injuries and found that people who do not perceive emotions correctly will act irrationally. Emotion is necessary for rational behavior, Damasio says. Emotions allow you to choose quickly and easily among the many choices constantly available to you, saving you time and allowing you to zero in on correct solutions to problems.

The Triune Model of the Brain was proposed by Paul McLean. The human brain is made up of three parts: -the brain stem, which controls basic functions such as breathing and wakefulness is the oldest part of the brain, philogenically speaking. It exists in reptiles as well as in higher life forms. -the midbrain is involved in emotions (such as fear and greed, sexual preference and so on). It exists in mammals. -the neocortex controls higher functions, is the seat of thinking, language, etc. and exists only in hominids. There is a definite order of priority among these three subsystems; a painful stimulus for example will disrupt the processing functions of the neocortex for several hours according to experiments in which blood flow to the brain is measured via MRI scans. When a lower level is activated it disrupts (or takes priority over) the higher level mental functions.

From a financial point of view it is clear that risk-preferences and decisions under risk arise from interactions between the midbrain and the neocortex. Rational decision involves a balance and/or cooperation between the emotional and calculating parts of the brain.

Experiments in neuropsychology and finance

(1) Studying professional traders as they go about their job. Lo attached sensors to traders to measure emotional responses. (2) Lo also interviewed 80 neophyte traders who were learning to trade in a class given by LBR and reviewed their trading


a. emotion is definitely involved in trading decision making, even in the case of experienced decision makers (i.e. it is not solely the beginners who experience these emotions). However, the emotions are somewhat more controlled among the more experienced or more able decision makers. b. traders who experience little emotion during trading have a lower P&L, however traders who experience a great deal of emotion during trading also have a lower P&L. It appears that there is an optimum level of emotion somewhere in the middle. c. people who excessively internalize the outcomes (i.e. attribute everything that happens to their own doing) have a lower P&L, however people who attribute everything to luck also have a lower P&L. Again there appears to be a proper balance, i.e an attitude that events are partly due to ability and partly to luck.

The Adaptive Markets Hypothesis

The AMH takes a biological/evolutionary view of markets, whereas the EMH took a physical/engineering view.

The AMH postulates that financial decision makers:
1. act in their own self-interest
2. make mistakes
3. learn and adapt (through heuristics, not through optimization)
4. competition drives adaptation and innovation
5. natural selection drives the ecology of the markets
6. evolution drives market dynamics

With regard to point 3. Lo has a high regard for Herbert Simon and his idea of "safisficing" (not optimizing), and of making decisions through simplified (and non-optimal) heuristics (since an optimal decision is computationally infeasible). A question that Simon could never answer is "where do heuristics come from", but Lo thinks the answer is that "evolution determines heuristics" (point 5). He did have not elaborate on this. Lo expressed the view that Simon's work is even more important than the Theory of Rational Expectations, even though it has received less attention in economics.

The AMH implies that anomalies can appear, disappear and then reappear again as the market ecology changes. For example the profits to Statarb have waxed and waned over the last 15 years. It is true that the profits have dropped sharply after the Summer of 2002, but this does not mean that hey have been permanently arbitraged away. Statarb profits may not be gone forever, they may come back at some time under different market conditions than what we have now.

So far the AMH is incomplete. Lo is working on extending it and convincing others.


We need emotions to be rational. We need both, it is not either/or. In trading there is a right level of emotion. There is a "right zone". The Zen of Trading.

Jean Paul Schmetz writes:

It is almost impossible to see the gorilla [see video] if you concentrate on the players. I have tried this video with 100+ people in the room and very rarely did more than 10% see it. It usually helps to mention beforehand that males or females are better than the other at keeping score (you do not tell which and so people concentrate even more). 

Chris Hammond adds: 

There is an article in Scientific American this month that discusses a game called the "Trader's Dilemma," which is a variant of an older game called "Prisoner's Dilemma." Experiments involving this game address some of the issues mentioned earlier. There is, of course, some extraneous background story, but essentially, the game works as follows: two people pick a dollar amount between 2 and 100. The smaller of these two amounts will be awarded to both players, except the person who chooses the smaller amount will receive an additional $2 and the person who chooses the larger amount will receive $2 less than the lower of the two amounts.

If the players pick the same amount, then there is no penalty or reward. If you assume that both players are working solely for their own self-interest and make rational decisions, then both will pick $2. However, in an experiment where the range was between 80 and 200 cents, and the penalty/reward varied between 5 and 80 cents, the player's average choice was never the Nash equilibrium of 80 cents. For the 5-cent penalty, it was 180, and when the penalty was 20 cents, it was 120. This particular study used economics students. A similar study used game theorists, and the results were similar.

More interesting is what happens when people play the game repeatedly. Apparently, for "large" rewards, the amount that is picked as people play many times tends towards the low number, 80. For "small" rewards, the amount moved towards the high, 200. The article is not more specific than that.

Here, a system evolves as participants learn. It's also interesting that there is a bifurcation at some particular reward amount, and that the system evolves completely differently on either side of that value. Also worth noting is that not even game theorists think like game theorists.

Yishen Kuik writes:

I have been reading E.O. Wilson's Consilience, which has a nice chapter on what we know about the brain.

He also has a good chapter on what it takes to be a productive scientist engaged in the business of discovery, some of which seemed to me to be uncannily applicable to describing the business of uncovering statistical edges to trading. 



We previously looked at the performance of five Bloomberg indexes of companies located in low tax states up until February 10, 2006.

We have now updated the study:

Bloomberg company indexes for low tax states (performance update)

State     PrevDate   PrevPrice  CurDate   CurPrice    PrcChg    PrcChg
——— ———- ———  ——–  ——–    ——    ——
Arizona    2/10/2006  529.92    5/7/2006  566.90       6.98%    19.14%
Nevada        N/A
North Caro 2/10/2006  127.56    5/7/2006  147.73      15.81%    19.14%
South Caro 2/10/2006  148.30    5/7/2006  162.23       9.39%    19.14%
Florida    2/10/2006  135.16    5/7/2006  144.51       6.92%    19.14%
Texas      2/10/2006  395.19    5/7/2006  465.81      17.87%    19.14%

The out performance we noted earlier has not continued in this most recent period. A result probably not inconsistent with natural variation. 

Steve Ellison adds:

Might I suggest the following ten stocks to represent Nevada?

        Market    Price     Price
       cap ($B) 2/10/2006 5/7/2007
MGM         18.3    38.58     63.71
HET         15.9    72.41     85.21
IGT         13.4    36.84     40.08
WYNN        10.1    59.50    101.85
STN          5.0    67.94     87.57
SRP          4.1    13.56     18.77
BYD          4.0    44.72     45.76
MDG          2.6    23.74     26.01
PNK          1.8    27.98     27.82
CDL          1.0    11.61      9.05

average             39.69     50.58

Change                        27.5%

keep looking »


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