Dec

6

Some of us are kicking ourselves for not having slavishly followed the Almanac, which shows that November has historically been a bullish month.To what extent are the Almanac observations predictive, and how would we have done over the years if we had used Almanac-like data as it was available at the time, to guide our market bets? The following study takes a quick look at that question.

I looked at monthly returns of the Dow Jones Industrial Average (ignoring dividends) going back to January, 1959. Each month, I paired that month’s percent return (”Y”) with the average percent return (”X”) of the previous 10 instances of that same month. For example, I paired the October, 2003 return with the average of the returns from October 2002, October 2001, October 2000…October 1993. An Almanacian approach would be to say that if the prior 10 Octobers have been good, then this one is likely to by good as well.

It turns out that there seems to be some value in this approach. The regression is of the form:

Y = m*X + b

or

(This month’s percent return) = m*(average of prior 10 instances of the month) + b, and the result is:

Y = 0.184*X + 0.495

There were 573 months under observation (That’s about 46 years times 12 months/year). The adjusted R-squared was about 0.2%, meaning that the prediction “explains” only about 0.2% of the observed variation. The observed slope has a t-score of about 1.5, which indicates that there’s about a 14% chance that a slope this large or large would come about through randomness alone. That doesn’t meet most thresholds for “statistical significance”, but it’s not too far from it.

Below are the few most recent rows of data.

Date Month Dow close Dow % change Avg Dow % change over prior 10 instances of that month
08/31/2006 8 11381.150 1.748 -1.809
09/30/2006 9 11679.070 2.618 -2.162
10/31/2006 10 12080.730 3.439 2.965
11/30/2006 11 12221.930 1.169 3.750
12/31/2006 12 NA NA 1.489

For the month of December, the predicted return is 0.495+0.184*(1.489), or about 0.77%. This approach was correctly bullish in October and November but missed the rallies in August and September.

I’d conclude that it looks like there might be a little bit of value in this particular Almanac-like approach, but not much.

Stephan Kraus Responds:

Thanks for that interesting article. Since I find it hard to evaluate such effects on their own, I calculated the sum of absolute deviations between the seasonality-based forecast and the actual performance, using the same data set and 10-year period as you did. In addition, I calculated a non-seasonal forecast based on all monthly returns during the previous ten years, i.e. 120 observations. Surprisingly, the sum of absolute deviations for the non-seasonal forecast is smaller than that of the seasonal one, even though the difference is small and probably not statistically significant (though I didn’t test that yet). The seasonal forecast is the better one in only 210 out of 456 months, or 5.5 months per year. Using a 5-year look back period, that spread widens, and the seasonal forecast is only better in 211 out of 516 months, or less than 5 months per year.

Nov

9

Victor and Laurel note: A heated debate regarding Joel Greenblatt’s “The Little Book That Beats the Market” recently cropped up among our colleagues. Below is some detailed follow-up work from one of our eminent researchers who is as adroit at analysis of single crystal NMR of high temperature superconductors as he is at uncorking the seemingly suggestive system work of hedge fund managers with putative 40% returns. Please note our response, which follows, as well as earlier intriguing commentary which began in early November and is found further down on the site.I’ll report here the results of a study that I did that addresses the results in Joel Greenblatt’s book. This study focuses on the large cap stocks that make up the S&P 500 index. Just as in Greenblatt’s work, I used the Compustat Point-in-Time database, in which the fundamental data are listed as they were at the time, and not restated.

Greenblatt’s ranking method involved both “earnings to price” ratio and “return on capital”. For “earnings to price”, he actually uses “EBIT” (earnings before interest and taxes) divided by “enterprise value” (market cap + debt + preferred stock), and for “return on capital” (”ROC”) he uses EBIT/(working capital + property, plant, and equipment). All these items can be specified using Compustat Point-in-Time.

After ranking stocks separately by E/P and ROC, he then takes these two ranking numbers and literally adds them together, and then finally ranks again based on that sum. He finds that the stocks that have both high E/P and high ROC tend to do well.

Here are the ground rules for my study. Stocks are ranked and then purchased at the end of each quarter, and held in that decile until the next quarter, when stocks are re-ranked. The most recent trailing four quarters of EBIT are summed to find the trailing yearly EBIT. In order to be purchased, stocks must have been components of the S&P 500 as of the start of the calendar year under consideration. As of the purchase dates, their share price must be greater than $2.

I checked and found that yes, the study did include Enron and WorldCom. Enron was bought on 9/28/2001 at $27.23 and sold at $0.60 for a loss of 97%. It was not re-purchased the next quarter because its share price had fallen below $2. At the time of purchase, Enron was near the middle of the rankings in terms of both E/P and ROC.

For each stock the “total return” was calculated, including dividends, using data from what we believe to be a reputable commercial vendor. However, I confess that I need to check on what the exact algorithm is for computing total return when there is something complicated, such as a merger or a spinoff.

At the start of each quarter the stocks were sorted into deciles according to Greenblatt’s ranking method. For each decile, the average of the forward 1-quarter fractional total returns for the approximately 50 stocks was calculated. Calling that number “R”, we then calculated 100*ln(1+R) for that decile and that quarter, and I’ll let Dr. Phil McDonnell (a frequent site contributor, trader and academic) explain why we did that. (As long as that number is not too big or small, it’s going to be pretty close to the percentage change in the portfolio.)

Our study covers 1992 to present, 59 quarters of data. The reason that we went back to 1992 was simply that we happen to already have had a convenient file listing the S&P components year-by-year back to 1992.

For each decile there are 59 quarterly returns. Below we give the results of our study, the average and standard deviation of those 59 numbers for each decile.

Decile 1 is the one with high E/P and high ROC; decile 10 is the one with low E/P and low ROC. The last column is the average divided by the standard deviation. Multiply that number by two and you have the annualized “Sharpe ratio” for that decile, if I understand the definitions correctly.

1    3.84    7.89     49%
2    3.33    8.57     39%
3    3.07    8.23     37%
4    3.69    7.46     49%
5    3.34    6.79     49%
6    3.04    7.40     41%
7    2.44    7.32     33%
8    2.47    7.46     33%
9    2.35    9.98     24%
10  2.51   13.27     19%

The Greenblatt “favorites” portfolio averages 3.84% per quarter with a standard deviation of 7.89%, with an average/standard deviation of 49%. The Greenblatt “bad guys” decile, decile 10, averages 2.51% with a standard deviation of 13.27%. So this confirms that the Greenblatt strategy has worked reasonably well since 1992 on the kinds of large-cap stocks that make up the S&P 500.

An investment of $1 in decile 1 stocks grew to $9.63; $1 invested in decile 10 stocks grew $4.39, and it was more volatile along the way.

Greenblatt’s data end at the end of year 2004, so below I will show you how this S&P 500 version of Greenblatt has performed since then. However, first, I will show you how some other strategies fared during the same 59 quarter period since 1992.

First, here are the results for a ranking based solely on E/P:

Avg      SD     Avg/SD
3.54    8.91     40%
3.88    8.20     47%
3.50    8.55     41%
3.01    7.00     43%
3.04    6.62     46%
2.77    6.78     41%
2.65    6.97     38%
2.40    7.76     31%
2.88   10.05     29%
2.36   13.78     17%

(First row: Highest E/P, Last row: Lowest E/P)

The results are similar to Greenblatt’s, though perhaps not quite as good. All that’s not surprising (if you believe Greenblatt’s thesis), since E/P is one of Greenblatt’s two ranking factors.

ROC is Greenblatt’s other ranking factor, and below is the performance of deciles sorted based on ROC alone:

Avg      SD    Avg/SD
3.72    8.03       46%
3.65    7.92       46%
3.08    6.69       46%
2.97    7.68       39%
2.68    7.81       34%
2.77    7.72       36%
2.72    8.25       33%
3.09    8.16       38%
2.85    8.76       33%
2.62   13.02       20%

(First row: Highest ROC; Last row: Lowest ROC)

Again, the highest ranked ROC deciles performed better than the lowest ROC deciles.

So it seems that both E/P and ROC each have some independent value as ranking criteria (though we haven’t examined the extent to which E/P are correlated or anti-correlated).

Finally, here are a few other ranking methods.

First, here’s another “value” ranking method. Many value investors claim that it’s bullish if a company has a high ratio of cash-and-equivalent on hand to market-value-plus-debt. Below is the performance according to that ranking:

Avg      SD   Avg/SD
3.96   10.40      38%
3.42   11.43      30%
3.14    9.68      32%
2.73    8.95      31%
3.44    7.72      45%
2.81    7.47      38%
2.91    6.73      43%
2.81    6.79      41%
2.49    6.26      40%
2.57    6.05      42%

First row: Highest cash/(market value plus debt); Last row: Lowest..

Here the firms with the highest cash had the highest average return, but they also had a relatively high standard deviation, and there is no clear trend in the Sharpe ratio vs. decile number. I would argue therefore that this “cash” ranking did not have much value.

Others have suggested that the Greenblatt effect might be some artifact of share price and/or market capitalization. So here are studies of those factors.

First, share price:

3.04   14.94    20%
3.14   10.12    31%
3.39    9.11    37%
2.62    7.43    35%
3.35    7.58    44%
3.19    7.14    45%
2.76    7.75    36%
2.75    6.37    43%
2.71    6.74    40%
3.03    6.76    45%

First row: Lowest share price; Last row: Highest share price

This table shows no trend in return vs. share price. The lower share prices, however, do have higher standard deviations in their returns, so arguably one should focus on higher share priced stocks for a smoother ride.

Next here are the results for a decile ranking based on market capitalization:

3.33   12.14    27%
3.52    9.94    35%
2.89    9.36    31%
3.35    8.21    41%
3.46    7.48    46%
3.36    6.49    52%
2.62    7.71    34%
2.45    7.19    34%
2.57    7.22    36%
2.66    7.67    35%

First row: Lowest market cap; Last row: Highest market cap

The lowest market caps did outperform the highest market caps by a small amount. However, their volatility was much higher, and their Sharpe ratios were about the same or lower. So it is not plausible to think that the Greenblatt effect, as observed in this study, is an artifact of small market capitalization.

Victor and Laurel compliment and caution:

We would just add that the “Minister’s” study leaves out the performance since the retrospective data ran out and it ain’t pretty. The Minister is complimented on the perfect study for DailySpec: totally good methodology suggesting fruitful lines of inquiry, but nothing that violates his mandate as “Minister of Non-Predictive Studies”.

Professor Pennington returns with updated figures:

Here is an update of the recent performance of the Greenblatt ranking system applied to S&P stocks. Greenblatt’s book gives data through the end of 2004. Shown below is data since 2004.

10      9        8        7        6        5        4       3        2       1
12/31/2000  -7.6   -3.4    -0.9    -5.3    -2.0     0.8    -0.7    -0.1    -2.2   -1.5
03/31/2005   5.6    0.9     4.7     1.1     3.0     2.6     3.1     0.9    -0.2    5.3
06/30/2005   7.1    7.8     3.9     6.3     3.7     0.8     4.8     4.0     4.7    3.5
09/30/2005  -2.9    2.1     1.6     2.4     1.6     3.6     3.2     4.8     1.9    5.9
12/31/2005  10.2    6.6     7.8     4.4     7.3     6.7     5.9     4.9     5.9    1.8
03/31/2006  -7.9   -4.5     1.4    -1.8    -0.1    -0.9    -1.2    -1.8     0.4   -1.4
06/30/2006   0.9    3.3     2.7     4.8     3.9     7.9     1.6     5.7     3.2    4.9
09/30/2006   3.5    3.8     3.6     4.5     2.6     4.3     4.4     3.6     3.6    1.4
Avg                1.1    2.1     3.1     2.0     2.5     3.2     2.7     2.7     2.2    2.5
SD                 6.7    4.3     2.6     3.9     2.8     3.0     2.5     2.7     2.7    2.9
Avg/SD           17%  48%   120%  52%   89%   106%  104%  101%  80%  85%

Short story is that the high ranked decile, decile 1 (high E/P, high ROC), gained an average 2.5% per quarter since 2005 with standard deviation 2.9%, and the least favored decile, decile 10 (low E/P, low ROC) returned an average 1.1% per quarter with standard deviation 6.7%.

In such a short time frame, this one’s probably a coin toss, but it looks like it did go in Greenblatt’s favor.

Dr. Phil McDonnell lauds and extends:

Kudos to Prof. Pennington for his thorough review of the Greenblatt study. His use of the log of the price relative is exactly the right way to go to take into account compounding.

In my opinion the best time period to study is the out of sample post publication time frame from 12/2004 to the present. Using this period eliminates most of the concerns and biases which I feared including the post publication bias.

Based upon that period I looked at the Spearman rank correlation coefficient for the mean and the Sharpe Ratio(*). The basic idea is to see if there is an overall correlation beyond just a differential between the top decile and the bottom. In this case we would expect a negative correlation simply because of the arbitrary ordering of the deciles by Dr. Pennington. The following R code gives us our answer:

# Test the Pennington-Greenblatt data using robust Spearman rank correlation
av<-c(1.1,2.1,3.1,2,2.5,3.2,2.7,2.7,2.2,2.5)
n<-c(10,9,8,7,6,5,4,3,2,1)
sr<-c(17,48,120,52,89,106,104,101,80,85)
cor.test( av,n,method="spearman" )
cor.test( sr,n,method="spearman" )

With respect to the average we get:

Spearman's rank correlation rho

data: avg and n S = 226.3731, p-value = 0.2899 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.3719581

Here the rho is -37% and has an insignificant p value of 29%

With respect to the Sharpe Ratio(*) we get:

Spearman's rank correlation rho

data: sr and n S = 218, p-value = 0.3677 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.3212121

Here rho is 32% and the p value is 37% also non-significant.

(*) Minor quibble on the Sharpe Ratio: The usual formula for the Sharpe Ratio is:

SR = (average - tBillRate) / stdev

The idea is that it purports to measure excess return over and above the riskless tbill rate. It is thus the excess return one received for taking on risk. However in the present case making this adjustment would not change the ranking of the deciles at all since each average is being adjusted by the same thing. Thus the Spearman rank correlation test is robust even to this factor.

Victor and Laurel rejoin:

We suspect, as does Russell Sears, who ran a four minute mile and is always on target, that Greenblatt isn’t as careful with his data as he would lead us to believe, and that a student did it for him, and that there are millions of multiple comparisons involved in his original work. it doesn’t make sense that you could make a profit without a forward earnings estimate, and that you would be paid just for assuming things so close to cost, with little risk.

Robert Pinchuk adds:

I concur with the essence of your doubts (What!, no expectations!?) even with Prof. Pennington’s detailed validation. Haugen also re-did Greenblatt’s work verbatim on his (cleaner? better?) database (written up in Barron’s some time ago) and derived some different numbers — but not wildly different. But then Haugen is touting advice-for-profit of nearly the same kind, so there are caveats. But, Haugen is not dishonest, and the advice he sells also does carry expectational measures that help him squeeze more alpha with less variance (so he says), as we would both expect.

I am hesitant to disagree with you that the market rarely offers “freebies” for naively assuming risk, but I cannot help but ruminate upon the question: “Do the results make sense?” Bogus data, future information, dredging and questionable strategy heuristics aside, “loss-aversion” and “disposition effects” are powerful anomaly creators, especially in combination with feedback trading. I will grant you that “The Price Is (rather often) Right”, especially when conflicted with sparse non-price time-series data. Maybe elevated short-interest levels will soon make these disappear too, or at least delay gratification for a sufficiently demoralizing period of time.

One nagging thought: Is there really, as you suggest, such “little risk” in the undertaking? I think one might be surprised by the qualitative “risk”, when anecdotally assessed over time. Someone like Lakonishok might answer: “How can they be riskier if they produce more return?” But this seems insufficient. Risk, like HIV, can hide or remain dormant for extended periods (e.g. inflation in the 90s). I posit that there is risk being shouldered, but perhaps it’s different (i.e., a different array of factor risks) in each epoch, so it’s hard if not impossible to systematically isolate, let alone forecast. How can one measure the risk of buying a Chapter 11 candidate concurrent to potential deflation? It’s binary. Perhaps it’s just this embedded tail risk for which, like a reinsurance company, is good business to write if properly priced (The Reversion Trader?). And perhaps one day, the inherent risk will manifest itself and thereafter, disabuse anyone from naively pursuing The Magic Formula. Then again, maybe there are just a preponderance of traders with differing forms of myopia.

By the way, Prof. Pennington’s high/low return spread numbers for RoC seem elevated. The E/P spreads look about right, but it remains the inferior value proxy. “Quality” in general seems more efficiently priced.

Nov

8

                                                                 Open to
Election Day   Close  Day after     Open    Close  Open Move  Close Move
11/4/1986      246.3  11/5/1986    245.7    247.2       -0.6        +1.5
11/8/1988      275.1  11/9/1988    274.0    274.1       -1.1        +0.1
11/6/1990      311.8  11/7/1990    312.1    307.3       +0.3        -4.9
11/3/1992      419.9  11/4/1992    418.5    416.2       -1.4        -2.3
11/8/1994      467.0  11/9/1994    471.6    465.8       +4.7        -5.8
11/5/1996      715.4  11/6/1996    715.8    729.4       +0.4       +13.6
11/3/1998     1114.0  11/4/1998   1125.5   1124.6      +11.5        -0.9
11/7/2000     1444.0  11/8/2000   1445.0   1413.3       +1.0       -31.7
11/5/2002      914.0  11/6/2002    920.5    925.7       +6.5        +5.2
11/2/2004     1130.6  11/3/2004   1145.8   1145.1      +15.2        -0.7
11/7/2006     1389.0  11/8/2006   1382.5   1391.6       -6.5        +9.1 

Nov

2

I am a 27-yr old professional equity derivatives trader with several questions and comments for Dr. Niederhoffer and Ms. Kenner. I just read Practical Speculation. I had previously read Joel Greenblatt’s The Little Book That Beats the Market. Needless to say, the two works propound extremely different views on the relative merits of growth versus value stocks and on the ideas of Benjamin Graham. I’m sure this is a debate that has been beaten to death before I was born, and I’m sure you are entirely sick of the whole thing, but please bear with me. I am interested in reconciling the ideas of the two authors. I would like your opinion on Mr. Greenblatt’s work and his “system” for investing.

I wondered specifically what Dr. Niederhoffer and Ms. Kenner’s response would be to the data cited in Greenblatt’s book. Is this evidence entirely worthless due to statistical and sampling errors? Is it only since 1965 (the Value Line data in the book was for 1965-2002) that growth has overtaken value? What do Dr. Niederhoffer and Ms. Kenner think is the correct way to value a stock? Since it’s difficult to precisely ascertain current or even past “real” earnings for a single stock, let alone the mkt, how can one hope to accurately predict the level of future earnings (as you must do for growth stocks). What valuation model should be used? What valuation model can be used that works for both “growth” and “value” stocks (it seems fairly silly to categorize all stocks into one of these two fairly arbitrary columns, but that’s what seems to happen).

Anyone can go to Mr. Greenblatt’s website and get a list of “value” stocks. He argues that his system (buy 20 or 30 of these value stocks and then sell them after a year and get new ones from an updated list on his website) will beat market returns over time. I am suspicious, but where is the logical flaw or statistical error in Mr. Greenblatt’s book. Will his method really work, and if not, why ? Mr. Greenblatt posted excellent returns over many years (I believe 10 years of returns are necessary to eliminate luck as the explanation of a trader’s returns) at his hedge fund. I’m sure he wasn’t simply applying the method from his book, but he is clearly a “value” investor.

To me, the strength of “value” investing, especially as described by Mr. Greenblatt, is its seeming logic. Even though you can’t buy a stock portfolio for 50% of its liquidation value as Graham suggested, the market and especially individual stocks can fluctuate fairly wildly even over short time frames, so clearly it is possible at times to buy good stocks or the whole market “cheaply.” As I write this, AMD has a 52 week range of 16.90 - 42.70… with roughly 485 million shares outstanding, that means in terms of market value AMD was (according to the market) “worth” almost $21 billion in late January, and only $8 billion or so in late July. Maybe some of this move was due to new (bad) information, but in all probability (since the stock subsequently recovered- then dropped again) it was due to the overtrading and ridiculous focus on short-term results that Dr. Niederhoffer and Ms. Kenner lambaste in their book. Take a look at the way retail stocks move around on monthly same-store sales numbers or oil and gas move on weekly reserves numbers for further examples of ridiculous overtrading and short-term focus.

Nevertheless, to ignore volatility (which is how I make my living) and keep your eyes firmly on the long-term potential of a stock leads to two pitfalls. First, you miss out on opportunities when the stock swings around in the short run (for example, you could have sold some medium-dated calls in AMD in Jan, then used the proceeds to buy additional stock in July). Second, you are ignoring risk; in the short-run, you could see such severe swings that you go broke instead of getting your 1.5million % a century return. Volatility might be much higher than it “should” be, it might be due to overtrading, and it certainly is the result of a focus on meaningless short-term information, but it is a fact of life. In my opinion, it’s better to take advantage of this fact than to ignore it.

One solution is to actually buy volatility itself. There are several studies showing that a portfolio containing a volatility component of 10% or so will outperform a similar portfolio with no volatility component (an example of a volatility component would be VIX futures or a similar instrument, essentially just a long option position). The general basis for this is that implied volatility in the options market usually increases when the market drops. You are diversifying your portfolio with a negatively correlated asset. Since the VIX hovers at a very cheap 10 or so these days, it seems like a great hedge.

Any reply or even a suggestion of further reading on the value/growth debate would be greatly appreciated. I have also emailed Mr. Greenblatt’s website with similar questions (you can find that email below).

Doc Castaldo illuminates:

He has so many inter-related questions it is hard to know where to begin. The Tim Loughran article “Do Investors Capture the Value Premium?” which some Spec (Dr. Zussman perhaps?) sent to Steve Wisdom recently seems relevant, and I sent it to him (the answer Loughran gives is no). I believe Prof. Pennington and Mr. Dude reviewed the Greenblatt book and found it well done; though some of us have doubts as to how well the results will hold up going forward.

Steve Leslie adds:

I have studied this deeply and although impossible to adequately reconcile this argument, my reply is that there is enough room in the world for value investors and growth investors. One is more of a science and the other is more of an art. And that which works for one will not work for another. And they tend to be complementary, whereas when value investing is in favor growth is out of favor and vice versa.

Case in point late ’90s. Nobody and I mean nobody wanted to be a value investor. At the time I was with a regional brokerage firm and we had one of the best value fund managers around, and he was never asked to speak anywhere. Everybody wanted growth and hard chargers. He told me directly that the worm would turn and that which one is hated will once again be loved. In 2001 and onward his style came back into vogue. His numbers became very good when the implosion of growth occurred and value turned to the good.

I feel that value investing is more of a quantitative approach to investing. It requires arcane methods and such as roe, price to sales, price to book. You can have value investors, deep value, vulture investors etc. And it is very important that with value investing that one be a patient investor with longer term time frames. I have referenced the Hennessy Funds as excellent quant funds. They have a very rigid stock selection process and rebalance their portfolio annually which they bought the rights to from James O’Shaughnessey who brought this methodology out in his book How to Retire Rich. Their long term track record is very good and they did very will since 2000 but this year for the most part the results have been flat. Martin Whitman is a deep value investor and his Third Avenue Fund has done very well over time. As has the Davis Funds. The First Eagle funds does excellent work with their global funds.

Growth investing is more of an art. It requires timing. Growth investing such that William O’Neil supports can be very successful yet very volatile. Small cap growth investors many times requires a longer term time horizon as the swings in price can be quite hard to take. I have always liked Ralph Wanger (A Zebra in Lion Country) and Tom Marsico in this area.

It is very important that the style of investing one uses incorporates their financial education, character and personality among others. They most definitely require knowledge and different wiring.

As to the trading of that the chair employs, I will let him speak for himself but I am confident that he will say the methods that one uses for value investing and growth investing would never work for his methods of day trading or swing trading.

To use a poker analogy (alas it always comes down to poker) I liken value investors to people like Dan Harrington, Howard Lederer and Phil Hellmuth. They are percentage players very methodical. They wait for premium hands and play those. These are the tight players.

On the other side of the ledger are the growth investors such as Phil Ivey and Gus Hansen, aggressive sometimes to a fault and they play many hands and many times on feel.

Both styles and much more in between are effective and can bring one to the promised land, they just take different routes.

Dr. Phil McDonnell reminisces:

Many years ago I was engaged in fundamental research on stocks for a finance class at Berkeley. Upon showing my results to one of the rising young finance Professors in the Business School I had a rude awakening. He promptly but kindly pointed out to me the myriad of biases which enter into such a study.

It prompts one to paraphrase the poem poem by Elizabeth Barrett Browning:

“How Do I Confound Thee?” Let me count the ways in which fundamental stock data can confound:

  1. Stale Data. Data are not always reported on time. Some is late, but most studies do not account for this adequately.
  2. Retrospective Bias. Most fundamental databases use the current ‘best’ information believing that is what you want now. But for historical studies that means the data may have been retrospectively edited as much as several years after the fact. This is a form of knowledge of the future. If you analyzed Enron before its collapse the fundamentals looked good and the stock was too cheap. If you analyzed today with a retrospective database you know that the company had catastrophic losses. But the truth about the losses was not known at the time and the adjusted numbers only came out years later.
  3. Sample or Survivor Bias. Use of a current database often results in a sample bias due to the fact that only companies which continue to exist in the present will be included in the sample. In order to avoid this issue one must go to an historical source in existence at the time in order to manually select the sample for each month by hand. Many companies are delisted or otherwise stop trading. For these the data must be manually reconstructed from historically extant sources. Otherwise this bias translates into a strong bias in favor of value investing strategies. A strategy which buys out of favor, or high risk or near bankrupt companies will always do well with this bias. The bias guarantees that they will still be around years later because they are still in the database.
  4. Data Mining. There are many variables to choose from with fundamental data. There are countless more transformed ratios or composite variables which can be constructed. This leads to the ability to try many things. Thus the researcher may have inadvertently tried many hypotheses before coming to the one presented as the best. Because fundamental data are low frequency (quarterly at best) there are only 40 observations in a 10 year period. True statistical significance can quickly vanish in a study of many hypotheses.
  5. Data Mining by Proxy. Everyone reads the paper and keeps up with current trends in investments. Thus our thoughts are always influenced by findings of other researchers. Thus even if a researcher did a study which avoided the usual data mining bias it may be simply because he took someone else’s results as a starting point. In effect he used their results as a form of data mining by proxy to rule out blind alleys.
  6. Fortuitous Events. In the 1990’s F*** & Fr**** published papers about factor models to augment the Sharpe beta model. Their significant new factor was Price to Book ratio. In James O’Shaugnessy’s book What Works on Wall Street one can see a sudden upward surge in value strategies in the early 1990’s coincident with the publication of the F & F model. However the event was a single one time upward valuation of value models in the 1990’s. Before and after that, the effect vanishes.
  7. Post Publication Blues. After publication of any academic paper or book the money making method usually stops working. Sometimes it is due to data mining or some flaw in the study and the putative phenomenon was never really there. The market is efficient. If everyone knows something it will usually stop working even if the original study was valid.

Prof. Greenblatt’s book is a fun read and remarkably brief. In fact if someone wanted to just get the gist of it, each chapter ends with a very clear summary of the key points in that chapter. It would be possible to get all the main points in about 10 minutes simply by reading the summaries. Let me say that if one were to use a fundamentally oriented strategy then the profit margin and Book to Price are probably the first two on the list. To be fair to the author, reciting one’s efforts to avoid sample biases in a book intended for a popular audience probably would not help sales. Such discussion is usually reserved for academic papers but nevertheless its absence does not give reassurance that all possible bias was eliminated.

The best way to test this strategy is not to go to the library and do all the work yourself. Rather one could simply go to the web site and copy down all the stocks recommended. Then in 6 months and 12 months revisit them to see how they have done and to see if the performance was statistically significant.

Ever since those Berkeley days more than 30 years ago I have always been distrustful of fundamental studies. That lesson from then Prof. Niederhoffer has helped shape my market studies in many ways. The bias of fundamental data is yet another way the market can confound the research oriented trader.

Jaim Klein replies:

Let’s simplify. The market universe is large and diverse enough to accommodate different successful strategies. One catches fish with net, another with bait. Regarding the value of anything, no such. The value of a thing is the price it can fetch in a certain moment and place. At 27 I was also confused. Experience is the best (probably the only) teacher. He has to do his own work and reach his own conclusions. It is time consuming, but I know no other way. He can also observe what successful people is doing and try to copy them till he can do it too.

Prof. Charles Pennington rebuts:

Dr. Phil lists 7 things that can go wrong in research on stock performance and its relation to fundamentals. Oddly enough, the Greenblatt book itself also lists exactly 7 such reasons on page 146! They’re not exactly the same ones, but there is plenty of overlap. I’ll list Greenblatt’s 7 with my own paraphrasing:

  1. Data weren’t available at the time (look-ahead bias)
  2. Data “cleaned up”, bankruptcies, etc., removed (survivorship bias)
  3. Study included stocks too small to buy
  4. Study neglected transaction costs, which would have been significant
  5. Stocks outperformed because they were riskier than the market
  6. Data mining
  7. Data mining by proxy

Greenblatt: “Luckily the magic formula study doesn’t appear to have had any of these problems. A newly released database from Standard and Poor’s Compustat, called ‘Point in Time’, was used. This database contains the exact information that was available to Compustat customers on each date tested during the study period. The database goes back 17 years, the time period selected for the magic formula study. By using only this special database, it was possible to ensure that no look-ahead or survivorship bias took place.”

To all the biases that we consider, I’ll add the “not invented here” bias. It’s too easy to assume that no one else out there can do rigorous research. I think Greenblatt’s is fine.

(He didn’t however do any original results on jokes. His jokes are all out of the Buffett/value-school jokebook. Fondly recall “There are two rules of investing. 1. Don’t lose money. 2. Don’t forget rule number 1.” That one’s there along with all your other favorites.)

Dr. Phil McDonnell replies:

The way we all remember the late 1990s is the dot com bubble. It was the front page mega meme. The stealth meme was the value stock idea.

Rather than think of it as a single paper consider the paper as the seminal idea of a meme. From the original paper there were follow on papers by various academics as well as FF. From there the meme spread to the index publishers who always want a new ‘product’ to generate marketing excitement. Naturally the index guys sold it to the funds and money mangers who promptly started new funds and rejiggered old funds along the lines of the new meme. The money management industry always wants new products but also each firm needs to act defensively as well. For example Vanguard cannot eschew the new fad and leave the playing field open for Fidelity. As with all memes it grows slowly and diffuses through society.

In all fairness one can never ‘prove’ cause but only correlation using statistics. But it is clear to me that something happened which caused the value part (really just Magic Formula) of the market to triple during those years albeit with only negligible public awareness early on.

For the sake of argument assume that the cause was not the FF paper and its impact on the value meme. Then what was Dr. Zussman’s ‘unseen factor(s)’ which caused a triple in value? Which factor or factors are more plausible?

My prediction for the end of the next meme is the collapse of the Adventurer’s bubble. To play it one needs to sell. But I would guess that it is only a one to three year collapse.

Oct

30

2004: St Louis Cardinals
Regular season: 105-57
Best record in baseball.
Playoff record: 7-8
World Series: swept by Boston

2006: St Louis Cardinals
Regular season: 83-78
Worst record of all playoff teams, requiring a last-game loss by Houston to Atlanta to get into the playoffs at all, and Houston lost that game by out-hitting Atlanta 9-3 but leaving 11 men on base. Playoff record: 11-5
World Series: beat Detroit in five games

There must be some market lessons in there somewhere. Probably about randomness.

Steve Leslie replies:

I am not sure as to the market lessons here. However I do know something about playoffs in baseball.

Baseball is unique from the other two sports. In baseball the regular season record is completely meaningless. Due to one major factor. In the other sports, home field advantage is critical to getting to the championship series. In baseball, it is all about qualifying for the playoffs. After that, anything can and does happen.

Basketball is the most critical for regular season success. Without home field advantage you are swimming upstream the whole way. There is perhaps no greater factor in predicting a winner than looking at who has the home field advantage.

In football, if you have the best record, you are rewarded in two ways. First you get a bye week to get well and rested (and after 20 games this goes a long way to making your team well) and secondly, you don’t have to travel at all. When the regular season concludes, you can be at home for 3 weeks and only have to play 2 games. Plus your team is usually designed with the type of home field you play on.

Winning baseball games in the postseason is all about two things: Pitching and momentum. If you pitching comes out strong, like Boston two years ago or the Tigers this year, you can get on a roll and continue on a roll. Anecdotally, the Tigers lost their momentum by having to wait a week for the Cardinals to conclude their long 7 game series with the Mets.

Furthermore in baseball you can win a series by having your #1 and #2 pitcher carry the series. Who can forget Schilling bleeding in his ankle and giving the pitching performance of a lifetime. Or Kenny Rogers coming out of nowhere and pitching an amazing number of scoreless innings.

So if there are corollaries to be made to stocks, I will submit these two suggestions:

Pitching = earnings. great stocks have great earnings. They get their earnings from a great product with great margins. Microsoft in the 1980’s. Xerox in the 1960’s and Resorts International in the late 1970’s. I find it interesting that GE wanted to be #1 or #2 in the fields that they chose to compete. They were not interested in filling out the roster for the sake of filling out the team. The moral is if you have a great franchise coupled with a great product line, this will translate to success in the stock.

Momentum = trends. Stocks once they get on a roll, stay on a roll for some time. Look on Taser a few years ago. Oil stocks for the last year. Stocks tend to take on a character all its own when they become in favor.

There a many more examples and I hope I have stimulated some thought for additional corollaries.

Allen Gillespie responds:

Having the pain of being a Braves fan, I can tell you what it is. The regular season is long, so a deep pitching rotation is more important than a lot of good bats as the weaker teams you will beat with either and the stronger teams may or may not be focused on a particular night. So, if you have a strong 3 or 4 pitcher, then you will likely win one of those two games giving you a solid record. In the play-offs, however, pitching rotations are shortened so the best guys get on the mound more. In fact, it has been demonstrated that two really good pitchers are about all you need in the play-offs. You need, however, bats that go at least 5 deep with some moderate production 6-8. The one year the braves had 6 decent bats, they won, the other years, check the record. Painful.

The lesson I think is that for long pull trading, statistics and time work for you, while in short term trading and series being able to score quickly is critical.

Larry Williams responds:

Baseball has more stats than stocks; some are just obvious: for example, teams that reach the playoffs can be quite different later in the year due to injuries and trades — good to great pitchers are added to the roster of teams headed for the playoffs so the team then has more “mound power”. Case in point this year was David Wells going to San Diego.

It’s not just all numbers…

Steve Leslie replies:

My points are not assertions not supported by anything. I am not sure what you want to have counted. However if you want to go into greater detail about sports betting, I can tell you that it is an interesting exercise and in all likelihood futile because I have never met anyone who had a successful career as a sports handicapper. There are countless books on the market that one can research on the subject. I can not reference any since I learned years ago that sports bettors are losers.

I can tell you that the Yankee offensive lineup was so lethal this year that everyone went in thinking that they would overpower their opponents. They were overwhelming favorites to win the series. Until the pitching took over. In 2004 Boston was down 3-0 and won the series against the Yankees and went on to win the World Series. Thus momentum took over.

It is a fact, that good pitching trumps good hitting. This has been proven I don’t know how many times. Look back to Arizona Diamondbacks beating the Yankees and The Florida Marlins last World Series championship. Their team was loaded with young and great “arms”

As far as stocks are concerned. William O’Neill proved overwhelmingly that stocks that are in the highest quintile in earnings growth and relative strength outperform all other stocks. so when you combine these two facets your chance of success goes way up. especially in the long run which as far as I am concerned is a minimum of 9 months and longer. Read his books

Read William O’Shaughnessy book How to retire rich. He has some great strategies for success in selecting stocks. Look at an extremely successful no load mutual fund the Cornerstone Growth Fund offered by Hennessy Funds. This is a quant fund. or Bernstein’s book Against the Gods. The remarkable story of risk.

Other than that I am not going to type endlessly in an exercise to convince one of anything. If one does not agree with my points so be it.

As they say “That’s what makes markets.”

Professor Charles Pennington replies:

It is always tempting to say that some particular field, in which one thinks he has a special understanding, can not be approached through counting, but it’s usually not true, and especially not here.

For the examples here:

In baseball the regular season record is completely meaningless.

A rudimentary, better-than-nothing way to test this would be to look at the playoff series for the past N seasons and count the fraction of them that was one by the time with the superior preseason record. If it’s not substantially bigger than 50%, then that would support the claim.

[In basketball] there is perhaps no greater factor in predicting a winner than looking at who has the home field advantage.

Here you could take all NBA games played over the past N seasons and count the fraction that were won by the home team. If it’s greater than 50%, that would show that playing at home is an advantage. But is there “no greater factor”? Hard to prove, but you could try to DIS-prove it by looking at some other factor that might be important. For example, it’s possible that knowing which team has the best record over the past 100 games is more important. That could be tested as well.

Winning baseball games in the postseason is all about two things: Pitching and momentum.

For pitching: The question, I guess is whether pitching is more important than hitting in the post-season. You could take the past N series and count the fraction that was won by the team that had the better ERA during the regular season. Then you could count the fraction that was won by the team that had the highest number of runs scored per game during the regular season.

For momentum: For each series, calculate the fraction of games won by a team for the full series, call that Y, then calculate the fraction of games that they won when they also won the previous game, and call that fraction X. Now calculate X/Y for each series over the past N years. If X/Y, averaged over the past N years, is much bigger than one, then that would support the momentum idea.

Chris Cooper replies:

In contradiction, I have a close friend who has made a nice living for 15 years exclusively from betting football in Las Vegas. He is not a “handicapper”, though. He applies a computerized, brute-force strategy to tournament-style contests.

Oct

27

One wonders, too, if there are mechanical/behavioural dynamics. Are buy orders predominantly limit, and sell predominantly market? Or, if one had the data, would one find that “take-profit” sell orders on an up day tend to be limit, whereas “get-out” sell orders on a down day tend to be market? And what about stop-loss cascades?

STOP-LOSS ORDERS AND PRICE CASCADES IN CURRENCY MARKETS C. L. Osler

This paper provides empirical evidence that currency stop-loss orders contribute to rapid, self-reinforcing price movements, or “price cascades.” Stop-loss orders, which instruct a dealer to buy (sell) a certain amount of currency at the market price when its price rises (falls) to a prespecified level, are a natural source of positive-feedback trading. Theoretical research on the 1987 stock market crash suggests that stop-loss orders can cause price discontinuities, which would manifest themselves as price cascades. Empirical analysis of high-frequency exchange rate movements suggests the following: (i) Exchange rate trends are unusually rapid when rates reach stop-loss order cluster points; (ii) The response to stop-loss orders is larger than the response to take-profit orders, which generate negative-feedback trading and are therefore not likely to contribute to price cascades; (iii) The response to stop-loss orders lasts longer than the response to take-profit orders. Most results are statistically significant for hours. Together, these results indicate that stop-loss orders propagate trends and are sometimes triggered in waves, contributing to price cascades. The paper also provides evidence that exchange rates respond to non-informative order flow.

**********

Are Transactions and Market Orders More Important Than Limit Orders in the Quote Updating Process? Ron Kaniel Finance Department The Wharton School

Hong Liu The Olin School of Business Washington University

This paper details that transactions, market orders and limit orders are three major factors which affect a specialist’s information set and her inventory position. In modeling a specialist’s quote updating process, before any exclusion of any of these factors, one should first address the fundamental question of their relative importance in this process. This question, however, has received little attention both in the theoretical and empirical microstruc-ture literature. Using a simple nonparametric test we investigate the relative importance of these three factors. We demonstrate that both transactions and market orders affect the quote updating process signifcantly more than limit orders, and that transactions affect it more than market orders. Furthermore, we nd that market orders convey more information than limit orders about the value of the underlying security. These results hold even after controlling for transaction and order size.

**********

The Limit Order Effect Juhani Linnainmaa The Anderson School at UCLA November 2005

The limit order effect is the appearance that limit order traders react quickly and incorrectly to new information. This paper combines investor trading records with limit order data to examine the importance of this effect. We show that institutions earn large trading profits by triggering households’ stale limit orders and that individuals’ passivity significantly affects inferences about their behavior. Individuals are net buyers on days when prices fall because institutions unload shares to households with market orders-and vice versa on days when prices rise. An analysis of earnings announcements shows that institutions react to announcements, triggering individuals’ limit orders: the orders executed during the first two minutes lose an average of -2.5% on the same day. Investors’ use of limit orders may help to understand many findings in the extant literature, such as individuals’ seemingly coordinated tendency to trade against short-term returns.

James Sogi observes:

Interesting that the sells tops are hidden, but the buys are shown to 5 ticks. Why is that? The finger revealed the various orders at a cheap price in the middle of the night.

Recently been missing fills on limits and the market takes off. Almost feel the need to just order at market to get a toe hold in. Not sure if this is just me or a changing market cycle?

Professor Pennington comments:

The paper by Linnainmaa is important.

The paper, from rigorous analysis of data on stock trading in the 30 biggest names of the Helsinki Stock Exchange, concludes that individual investors lose a lot of money by putting in limit orders that are far from the market. These orders become “stale”, in the author’s words, if and when news is announced, and the authors demonstrate that when such limit orders get executed, it’s bad news, on average, for whoever entered them.

Conversely, they show that market orders entered within the first 5 minutes after intra-day earnings announcements tend to make a lot of money. It’s easy to understand. An alert trader is monitoring the earnings news in real time. If a favorable report emerges, he quickly snaps up shares offered at a stale price by an individual trader who could be out playing tennis.

My bias is that limit orders are bad news, specifically limit orders entered by a trader who is not going to actively monitor the news on a minute-by-minute basis.

Oct

26

I’ve always enjoyed Rod Stewart, including especially his remakes of the songs of others, such as “Handbags and Gladrags” (Cat Stevens), “I’m Losing You” (Rare Earth), “So Far Away” (Carol King), etc. My aunt was always partial to his disco era “Do Ya Think I’m S-xy”. In his early career he had much street cred as a rocker, singing great songs, accompanied by the very best instrumentalists, such as guitarist Jeff Beck. He’s long since become the accommodating butt of jokes, and there’s a mutual understanding between him and his audience that though he long ago sold out, the sell-outs are quite enjoyable, and they keep selling out. After a lucrative foray into the world of non-rock standards, he just released a new album of rock song remakes, including versions of:

…and more!

Yes, yes, the album is a little bit by-the-numbers, but it’s still enjoyable. He’s picked out good songs. He does them in his distinct voice, and you end up liking them as companions to the versions of the original artists.

“Father and Son” is another Cat Stevens tune. Stewart-Stevens is a nice collaboration. The original Stevens versions can become a little bit too dark if you listen to them for ten minutes or so, and Rod takes some of the edge off.

“It’s a Heartache” is a nice choice. Bonnie Tyler, who did the original version, has raspy voice that’s similar to Stewart’s. Compare/contrast/enjoy.

“Have you ever seen rain?” Who wouldn’t like this song? And the funny thing is, when you hear a Rod Stewart remake, you don’t tend to resent that it’s supplanting the original. They can live side-by-side.

“Love Hurts” Originally by the Everly Brothers. Then remade by metal group Nazareth. Finally, now remade by Rod Stewart. Who’s next?

Finally, for the ladies, here’s what this s-x symbol looks like these days, lounging on his yacht.

And for the guys, here’s a candid of Barbra Streisand.

Oct

20

As a wonderful illustration of the harmonious accord and pedagogical perfection that prevails on Daily Speculations, I cite the example of Hermogenes, student of Socrates, as he appears in Plato’s dialogue “Cratylus”. Listed below is Hermogenes’ side of the dialogue, with Socrates’ parts removed. You will see that Hermogenes always finds a way to agree, though with a variation each time that keeps the conversation lively.

He starts with an elegant “That is my notion”, followed by spare “Yes”, and then a “He would, according to my view.”. Note that even apparently negative words such as “No” can actually be part of the process of agreement, as in “No indeed”. It is acceptable to display disagreement if it is a disagreement with a third party, not present, as in “There have been times, Socrates, when I have been driven in my perplexity to take refuge with Protagoras; not that I agree with him at all.” And don’t forget one-worders like “Precisely” and “Certainly”.

[Her.] That is my notion.
[Her.] Yes.
[Her.] He would, according to my view.
[Her.] Certainly.
[Her.] To be sure.
[Her.] Yes; what other answer is possible.
[Her.] Certainly.
[Her.] No; the parts are true as well as the whole.
[Her.] I should say that every part is true.
[Her.] No; that is the smallest.
[Her.] Yes.
[Her.] Yes.
[Her.] Yes.
[Her.] So we must infer.
[Her.] Yes.
[Her.] Yes, Socrates, I can conceive no correctness of names other than this.
[Her.] There have been times, Socrates, when I have been driven in my perplexity to take refuge with Protagoras; not that I agree with him at all.
[Her.] No, indeed; but I have often had reason to think that there are very bad men, and a good many of them.
[Her.] Not many.
[Her.] Yes.
[Her.] It would.
[Her.] Impossible.
[Her.] He cannot.
[Her.] There cannot.
[Her.] I think, Socrates, that you have said the truth.
[Her.] Yes, the actions are real as well as the things.
[Her.] I should say that the natural way is the right way.
[Her.] True.
[Her.] Yes.
[Her.] True.
[Her.] I quite agree with you.
[Her.] That is true.
[Her.] True.
[Her.] Precisely.
[Her.] I agree.
[Her.] Yes.
[Her.] Certainly.
[Her.] True.
[Her.] An awl.
[Her.] A shuttle.
[Her.] A name.
[Her.] Certainly.
[Her.] Well.
[Her.] Very true.
[Her.] To be sure.
[Her.] I cannot say.
[Her.] Certainly we do.
[Her.] Yes.
[Her.] Assuredly.
[Her.] Yes.
[Her.] That of the carpenter.
[Her.] Only the skilled.
[Her.] That of the smith.
[Her.] The skilled only.
[Her.] There again I am puzzled.
[Her.] Indeed I cannot.
[Her.] Yes, I suppose so.
[Her.] I agree.
[Her.] The skilled only.
[Her.] True.
[Her.] Certainly.
[Her.] To the latter, I should imagine.
[Her.] I think so.
[Her.] Yes.

Oct

20

Flying is a miserable experience. I’ll leave it at that, but trust me, I could rant, if anyone would like to hear it.

Here, though, is a tip. Fly early in the day. Here are the statistics on four American Airline flights from LaGuardia (LGA) to Atlanta (ATL), for the time period Jan 1, 2006-Aug 1, 2006:

Flight#  SchdDeprTime  #LateFlights/#AllFlights
2377       640AM               30/211 
4864      1215PM               43/182  
2393       355PM               68/200
1297       740PM               117/196

So the 640AM flight was “late” only 30/211 times, or 14% of the time. The 7:40PM flight was “late” 117/196 times, or 60% of the time.

On average, the 640AM flight departed 27 minutes late. This is the average lateness of all flights, not just the “late” ones. (It actually arrived 5 minutes early, on average, because of schedule-padding.) The 740PM flight departed 70 minutes late, on average, and arrived 42 minutes late.

This material can be looked up on the BTS site.

Oct

20

I recently reviewed a paper which drew my attention to the long term rise of the US Treasury long bond future (continuously adjusted with all contract shifts), showing a price rise from 78-20 to 114-06 from 1977 to present. The question we are batting around the office is whether there is any economic reason for there to be a long term upward drift in prices. Such a drift would be related to the normally rising structure of the yield curve, with long term yields higher than short term. The upward shape is supposed to occur because of increased price variability of the long term bond vs. short term and liquidity preference; the desire to have your money sooner rather than later because your risk on holding the investment until it expires is greater. Liquidity would also seem to relate to the ability to trade the issue at tight spreads. Any educated comments on the subject would be welcomed.

Prof. Charles Pennington replies:

I assert that Treasury futures will have a long term upward drift if and only if long term bonds outperform short term in total return, over the long term.

Suppose that a bond maturing in 30 years is trading at price 100, and let’s assume that long term yield are 10% and short term yields are 2%.

Consider a futures contract on 30-year bonds that settles in one year, and suppose that this contract is trading at price P.

We could construct a risk-free portfolio consisting of a long position in treasury bonds and a short position in the treasury bond futures contract. This should earn the short term risk-free rate (and let me assume that 1 year is close enough to being “short term”).

Let’s also suppose that after one year, the price of the 30-year Treasury, which will also be the settlement price of our futures contract, has risen by $1 to a value of 101.

The final value of our portfolio, which cost 100 initially, is:

101 + 10 + (Pi-101)

(The “10″ is the dividend, and “Pi-101″ is the gain or loss on the short sale.)

This final value should be equal to 102, since we should earn the risk-free return. From that, we can solve Pi and get 92.

So in this example, the total return of a Treasury bond was 11% (10% dividend and 1% capital gain). The total return that we would have had by going long the futures contract would have been (101-92)=9, or 9% of the notional value. That’s equal to the total return that we would have had from holding the bond minus 2%, the short term rate.

In other words, the return from the futures contract is “as if” we had borrowed at the short term rate and bought the long term bond.

If that strategy makes money over the long term, then the continuous futures contract will show a long term upward drift. The Siegel book indicates that that strategy was about breakeven from 1802 through about 1980 and then did quite well since then.

Paul DeRosa Responds:

It is true that the bond future trades at a discount to its delivery value equal to the positive carry on the cash bond. If that carry is negative, the future will trade at a premium. There are hundreds if not thousands of traders who spend their days bent over desks enforcing that condition. It doesn’t necessarily imply the price of the futures contract will drift upward over time. They drift upward only within each quarter. So in the example you give, the contract will start each quarter at a discount of 2% to its maturity value. If the level of market interest rates were to stay at 10%, the next contract also would start the quarter at a 2% discount, but it would have the same maturity value as its predecessor. I would add one caveat, which sounds like a technicality but who overlooking as been the cause of tens if not hundreds of millions of dollars in trading losses during the past 30 years. The 2% discount I alluded to can be reliably captured only by owning the contract and being short the so called “deliverable” bond. At any point in time, several different bonds can satisfy the delivery conditions against the contract, only one of which is cheapest to deliver. Being long the contract and short the wrong bond can lead to any one of several outcomes.

George Zachar replies:

Yes. The accretion of the forward months should be identical to the positive carry one would receive by owning the underlying bond outright and financing it at the overnight/repo rate. You can make the money by carrying the “cash” or buying the forward, but the dollar amounts should be the same. This is carry and not drift/true price appreciation.

There is very slight positive carry on bond futures now:

USZ6 Dec06 110-18
USH7 Mar07 110-17
USM7 Jun07 110-16

The two-year future shows the impact of negative financing/curve inversion, where holding the instrument costs money (as your asset yields less than its cost to carry).

TUZ6 Dec06 101-28 3/4
TUH7 Mar07 102-02 s

The carry/deliverables/basis on these contracts is perhaps the most “crowded” trade on the planet.

“In the day”, one could make money in the forward mortgage market, when lenders would sell their production forward at a discount to carry. Those glorious days are long gone.

Carry hogs, er, traders have been known to “ride the Japanese curve” with enough leverage to make your eyes tear.

JBZ6 Dec06 133.56
JBH7 Mar07 132.83

They’d buy the forward Japanese bond, and pocket the carry, enduring the interest rate, yield curve and currency risks along the way.

The takeaway here is that one must be aware of all this when looking at fixed income debt futures prices. To evaluate long term interest rates cleanly, it is best to look at yields of relevant “constant maturity” indices.

Earlier posters observed that very long-term secular trends of dampened reported inflation and declining risk premia since the financial shocks of the Volcker era account for the observed trend toward lower yields and higher bond prices. I wholeheartedly second that analysis.

Stocks, famously, have unlimited long-term upside. Fixed income has the “zero bound” on rates, and central banks who have shown themselves willing to ensure that Deflation is rarely seen. Therefore, with an assurance that there’ll always be a little inflation, debt instruments are effectively capped out when their yields reach the low single digits.

Michael Cohn responds:

I would recommend everyone find a way to get “Rolling Down the Yield Curve” by Martin Leibowitz, circa early 1980s. Few articles as clear about how bonds work. I stopped trading basis myself in 1989 when four JGB basis-traders for what was then Mitsubishi Bank took me out to lunch one day in Japan.

Very little can go wrong when you are long the cheapest to deliver and short the future. Depending upon the set-up it was also a way to play changes in yield curve shape but now there are so many instrument, such as swaps, to play it an explicitly.

Jon Corzine made his name at Goldman by trading the delivery options and the dead period after the US bond contract stopped trading each delivery cycle. The legendary trader Mark Winkleman at Goldman made his name buy buying the bond basis and funding it cheaper. He had it to himself and our friends at Salomon. The old days were relatively easy.

You need to have a firm grasp of reverse repo rates for the deliverable bonds, and yield curve volatilities, to play from short side where you are short the bond and long the future. I recall the programs at my firm for modeling the change in deliverables to be extensive, as I use to play the bund basis, but with no apparent skill as I did not control the collateral, as could a German insurance company.

Dr. Alex Castaldo responds:

The question that started this thread was: is there an upward drift in fixed income markets like there is in equities?

The article by Vesilind claimed that this is so, and this drift arises from the fact that the yield curve is (on average) upward sloping due to the “liquidity preference hypothesis” and/or the “preferred habitat hypothesis”. In other words the “expectation hypothesis” of interest rates does not hold and there is a non-zero “term premium” embedded in interest rates.

Certainly there have been plenty of academic articles in recent years saying the expectation hypothesis does not hold. But I am more interested in the practical money-making potential here.

A simple strategy to capture the drift, that works well according to Vesilind, is to be long the “fourth nearmost eurodollar future”. I decided to test an even simpler strategy: each September buy the eurodollar future with one year to expiration and hold it until expiration.

You can think of it as a test of the good old “Keynesian normal backwardation hypothesis”: is the price of the future one year before expiration biased low compared to the expectation of what the settlement price will be.

Here is the data:

Contract       Date      Price      ExpDate  Price    Chg

EDU6 06 9/19/2005 95.690 9/18/2006 94.610 -1.080
EDU5 05 9/13/2004 97.045 9/19/2005 96.080 -0.965
EDU4 04 9/15/2003 98.165 9/13/2004 98.120 -0.045
EDU3 03 9/16/2002 97.535 9/15/2003 98.860 1.325
EDU2 02 9/17/2001 96.425 9/16/2002 98.180 1.755
EDU1 01 9/18/2000 93.470 9/17/2001 96.890 3.420
EDU0 00 9/13/1999 93.755 9/18/2000 93.340 -0.415
EDU9 99 9/14/1998 95.040 9/13/1999 94.490 -0.55
EDU8 98 9/15/1997 93.825 9/14/1998 94.500 0.675
EDU7 97 9/16/1996 93.700 9/15/1997 94.281 0.5812
EDU6 96 9/18/1995 94.260 9/16/1996 94.440 0.18
EDU5 95 9/19/1994 93.180 9/18/1995 94.190 1.01
EDU4 94 9/13/1993 96.070 9/19/1994 94.940 -1.13
EDU3 93 9/14/1992 96.140 9/13/1993 96.810 0.67
EDU2 92 9/16/1991 93.550 9/14/1992 96.870 3.32
EDU1 91 9/17/1990 91.670 9/16/1991 94.500 2.83

Avg 0.724

T Stat 1.940

At first the results look impressive: there is a 72.4bp per year gain, with a t-statistic near 2. However, much of the result is driven by the first two years (1991 and 1992) when interest rates were dropping rapidly. Without these two years the gain is only 39 basis points with a t-statistic of 1.15.

As mentioned by others, it is difficult to distinguish the term premium from the general interest rate decline after 1990.

George Zachar adds:

The Vesilind paper is an excellent and reasonably accessible overview of mechanistic currency trading systems that execute carry trades based on yield differentials, and volatility (implied riskiness).

The authors find, retrospectively, that during a period of irregularly declining rates and risk premia (1993-2006), rotating capital between currency pairs offering high yield spreads at times of high perceived risk earned worthwhile alpha.

The carry/risk aversion trade is a standard formula for speculating in currencies, and the authors “kept it simple”, making evaluation of their strategy relatively easy.

The study’s charts neatly show the performance of different strategies as the cycles change.

I must, however, disagree with the chair that currency pairs trading per se can be said to showcase “drift”. The time period involved was particularly favorable to yield chasers, and the regular shifting of positions from one set of underlyings to another strikes me as antithetical to the notion of passive drift.

That said, I believe there is a different speculative lesson to be drawn from this study, and that is the seeking of relative value within the confines of a large, complex set of related instruments.

Relative value among currency pairs based on yield/return vs. vol/risk strikes me as analagous to relative value within the stock market between sectors and individual stocks. There’s no shortage of relaive value measures with which to “count” fundamental and performance dispersion in stocks. Ditto risk/vol.

The paper at hand provides a nice introductory framework for setting up relative value/risk matrices.

Oct

12

Academic resumes — itemizations of where you went to school, your grades and (even) your test scores — became common only after World War II. Before that time the questions on any job interview were about what jobs had you worked who you knew. My Dad attributed the change to the GI Bill, and he wisely anticipated that the change would lead to a dependence on standardized testing not only for schools, colleges and universities, but also for all licensed occupations. However, the rule that he followed as an extremely smart student and a future textbook publisher was not "parrot the textbook." He was shrewd enough to know that every teacher prided himself on being smarter than the textbook. The key to straight As was to attend every lecture, take copious notes and "parrot" exactly what the teacher said. Following that rule and working at it industriously each day earned him a Phi Beta Kappa key just as it has earned his granddaughter one. "Thinking for yourself" is fun and has its own rewards, but it is guaranteed to get you a 2.7 GPA. That has been the state of American education for over half a century. It is the reason the "rednecks", among others, have lost their faith in "education". They see it as an extended exercise in obsequiousness. They are right, of course. They are also right to be skeptical about the benefits of academic certifications. Getting a Class 4 license has proven a much more profitable investment over the past decade than completing a graduate degree. In my limited travels here in Northern California I see any number of signs for "Truck Drivers Wanted". I have yet to see a single advertisement reading "PhD in (Gender Studies, etc.) wanted; Steady Pay; Great Benefits". Instead, I see the poor grad students trudging to their classes at the local universities like so many helots marching to the silver mines.

Prof. Charles Pennington responds:

I guess Stefan is using hyperbole, but just in case: PhDs do not do all that badly out there, and better than truck drivers. I had a high school friend who became a truck driver for a while, and he confirms that it's quite stressful and boring and not really all that well paid.

The students who got PhDs in my group have jobs at Pfizer, Intel, University of Hawaii (Associate Professor, not post-doc!), Varian, the Mayo Clinic, Keithley Corp., and Intermagnetics. I don't know exact salaries, but I think they're probably centered around $100K. They do interesting work, too. Most of them are doing things with magnetic resonance imaging, including "functional" magnetic resonance imaging where you literally watch what's happening in someone's brain while he's thinking some designated thought.

I agree though, sort of, on the topic of how to get As from professors. The most efficient way to get As is to attend all the lectures, and, as a first priority, write down everything that's said out loud and written on the chalkboard. If you can understand it in real time, fine, but if you can't, review your notes as soon as possible after the lecture, and try to figure out what was being said. You might figure out 90% of it then and there. Later you'll still have to cram for the test, but you'll be miles ahead.

Professors always feel like they're trying to give everything away, leading the horses to water and begging them to drink. I always tried to design my tests so that a student could get 80% of the answers through diligence alone, though it did require above-average diligence. 15% required some thinking, some creativity, and some aptitude, and maybe 5% could be answered by only the top one or two students in the class.

Sure, there's a lot of alcohol and 420 consumption at universities, but if you looked around at the students at Ohio State, you would find much to admire (insert Professor/coed joke here). Many, many students there had jobs working 20 or more hours per week along with their full course loads, and the curricula, at least in science and engineering, were not designed with that in mind. Plenty of students really do develop their minds and abilities more than they ever thought possible going in, and they experience much satisfaction from that. It is much better to go to college than to become a truck driver.

Stefan Jovanovich responds:

For those lucky and skilled enough to be in your rather select group, the rewards of serious scientific academic study are unquestionable. The point I thought I was making was that for "ordinary" people the traditional academic game is proving to be an increasingly bad bet. For those for whom the choice is "education" in the generic sense of survey courses, breadth requirements and a non-rigorous major vs. a Class 4 license, the Class 4 license is currently looking to be a better deal. That is clearly the conclusion of the masses who are voting with their feet in favor of junior colleges and trade schools. I apologize if I am stepping on someone's rice bowl, but I doubt very much that the current relative values of a certification to operate heavy machinery vs. a B.A. in Anthropology can be questioned, given their respective acquisition costs and likely future incomes.

The utility of formal education must be measured in more than monetary terms, but for the people taking out student loans (whose repayment, under the new bankruptcy laws, cannot be so easily ignored) the question of what a degree is worth and what it costs is hardly academic. As you acknowledge, "the curricula, at least in science and engineering, (are) not designed" for students who have to work to pay their way through school. That is a change for the worse and testimony that the land grant public universities no longer offer genuine opportunities to the poor, bright student. My Dad bussed dishes at the dining hall to make it through the University of Colorado, and my father-in-law slung hash in the kitchen of a fraternity to earn his undergraduate and masters degrees in geology at Texas and Oklahoma. I have never been either as poor or as smart as Dad or Buster; but, if I had not worked a job serving process, I would not have been able to afford to make it through law school at Cal (and the world would have been spared one more tax attorney).

One of my father's very few serious regrets at the end of his life was that in the early 1980s he could not persuade the directors of the public company that he ran to go into the for-profit education business and offer "trade school" educations. The directors feared that the public school teachers would rise up in anger and boycott their textbooks. Dad thought the risk was worth taking since he saw the profit margins in the textbook business evaporating before the advance of high-speed copiers, readers and used book resellers. The capital markets have proven him to be right. The upstart University of Phoenix and its cohort of U.S. for-profit publicly-traded educational companies - which were just being started 3 decades ago - now have a greater market capitalization than all of the world's textbook publishers.

J. T. Holley replies:

My father was a truck driver for 25 years. Boring it's not (regular change of scenery), and the pay is great compared to other jobs in rural areas. But I'll grant it's stressful due to the other drivers on the road. For a young man in the rural U.S. to leave and go to the "U", he must ask a deep deep question: "do I leave my family?". It's easy for those raised in the city or town that the "U" is in, but those living in towns like Grundy, Damascus or Martinsville in Virginia don't make that decision as fast. Being a truck driver allows them the opportunity to "get out" and come back weekly and be around the family. This is usually one of the highest paying jobs in the area if it exists. Now, I'd rather be the "dumb human than the smart pig" as Plato proposed, but in rural redneck America the smart pig might be the better option. In a small town, having a PhD might get you a loan from the bank to start a business, but that's about it. I had a kid from Kansas on my ship in the Navy. He couldn't swim. He asked me why I joined the Navy and I gave him the usual "college money" reply. Feeling a sense of obligation I then asked him why he joined. He said "to see the ocean". Seeing the seriousness on his face I asked him if he'd ever heard of a vacation. He said "It just doesn't work that way up in the Smoky Hills".

Russell Sears responds:

Perhaps I misread the post. But as the good teacher, Adam Robinson says in his book, the smart student learns to parrot the textbook. After all its generally the teacher ego that picks one book over another. Perhaps it has not quite sunk into this "quant" that in the real world you need to think for yourself. That this was an exercise in peer review, not textbook writer worship.

If I am right what is sad, is the state of education at MU. Perhaps, a word of "real world experience" could change this youths direction.

GM Nigel Davies adds:

Consider the incentives in education. What is taught in the classrooms is not necessarily what is required by the world at large, but rather the interpretation thereof by people who are elected and/or appointed to decide such matters. The students have an incentive to toe the line and will be unified in saying that the system/their qualifications are good because it gives them an edge in the jobs market. And the establishment has an incentive in making the system look good in order to maintain funding. Who's going to question its value? Looks like it's only Ken Smith, some truck drivers and maybe a Grandmaster or two.

Greg Rehmke responds:

Whether driving a truck or pursuing a PhD, I suspect results turn on what people read, discuss, and write. Audio tapes from Books-on-Tape, The Teaching Company, and Knowledge Products can provide both truck drivers and commuters a wide-ranging education. I especially recommended is the "Giants of Political Thought" series available from Knowledge Products.

As truck-drivers relax after meals or before sleep, what they read shapes what they understand and how they think about the world. If they read, for example, William Easterly's recent book White Man's Burden, or Rodney Stark's The Victory of Reason, they will better understand why the western world prospered while Africa and Latin America are still stuck with poverty. If, instead, they read the New York Times and watch the evening news, and if they listen to talk radio and NPR instead of thoughtful audiotapes, their minds will be full of some combination of things that aren't true and things that don't matter. (I listen to NPR sometimes because I enjoy it, so I end up with a fair number of not true/doesn't matter items bouncing around my head.)

Graduate work can expand understanding and insight, as well as research and writing skills. But most Masters programs in the social "sciences" waste time, money, and minds. I remember a stand-up comic telling of a professor encouraging him to pursue a Political Science Ph.D. after finishing his undergraduate degree. "What do Political Science PhDs do?" he asked. "They teach other students about political science" was the answer. "What do they do with their political science degrees?" he continued. "They teach still others" was the reply. The comic concluded that political science was a giant Ponzi scheme. And so, unfortunately, are many of the social "sciences."

I am enrolled in the Masters program in Economics at San Jose State University. Classes are in the evenings and most masters students work full-time. At least a dozen SJSU economics profs are accomplish market-oriented scholars (Jeffrey Hummel, Mark Brady, Ed Stringham, Ben Powell, Lydia Ortega, Edward Lopez, and others, many with Austrian/Public Choice educations from George Mason University). Each semester hundreds of undergraduates learn economics, but also learn Austrian and public choice insights that usually remain hidden from students at higher-ranked universities.

In the end we each choose whether we pursue our own course to wisdom and understanding, or just float along in the media mainstream. We could spend a decade in college, as thousands do, but learn little either true or useful. Or we could spend a decade crossing the country by truck accompanied by the greatest thinkers in world history.

Oct

9

My dentist, angling to enhance the growth of her already quite successful practice, finds the secret from Jack Welch:

I started to listen to Jack Welch's book-on-cassette of Winning. Oh man, oh man. I'm barely into the first chapter, and I know the reason for his success — his annoying voice! People probably just want to get away from him; and therefore, get things done quickly.

Sep

25

A note from Vic — the Minister first wrote and posted his 'predicted' column on September 17th, whilst the 'actual' column came out on September 23rd.

Predicted. A one Mr. G. Reaper just can't leave alone a certain Ms. Anna Nicole Smith, last seen in these pages on her betrothal to a somewhat more elderly gentleman who then accommodatingly perished, leaving her a not entirely parsimonious sum for her efforts. We return to her story now, having forgone, ahem, ample opportunities in the past, on the occasion of the near simultaneous birth of her daughter and passing of her son David, of causes that, at the time we grudgingly go to press, remain altogether undisclosed.

Actual. Unless the portents are all wet, there will soon be a number of vacancies on the board of Hewlett-Packard, a company, to its profound regret, much in the news these days. We'd like to suggest some possible candidates to fill those vacant directorships. We hasten to add that no one in authority at the company has asked us for our suggestions, but we're sure that's only because they're so preoccupied preparing their forthcoming testimony before a congressional committee on Thursday…Our proposed additions to Hewlett-Packard's board are: Mahmoud Ahmadinejad and Hugo Chavez, the presidents, respectively, of Iran and Venezuela.

Predicted. One can hardly breathe a word regarding Ms. Smith (and indeed she leaves us breathless) without pausing to think of the not altogether totally dissimilar predicament in which the up-until-very-recent Prime Minister of Thailand finds himself. Thai one on indeed! As Mr. PM sat in traffic here in our fair city, a subset of his countrymen, who one would have expected to be cooing over the tabloid travails of Ms. Smith, were instead coup-ing him right out of office. As he rested his constitution in the back seat of his darkened limo, they wrested the country's heretofore constitutional system and installed martial law!

Actual. The notion of weirdo and whacko as HP directors may strike you as absurd, although one could argue that recent disclosures about the company's approach to corporate governance strongly indicate they might fit right in. We're well aware, too, both suffer from some evident drawbacks. To wit: To judge by his history, Mr.Chavez, if irritated enough by bickering among the board, might easily be tempted to stage a coup, which we've not the slightest doubt would rub the present directors the wrong way. Even more serious, Mr. Ahmadinejad doesn't own a tie, a clear violation of the company's dress code, which mandates zero tolerance for such a major infraction, and, to make matters worse, he doesn't shave anywhere near often enough, either.

Predicted. Fitting for such an eventful week, we found ourselves privileged to meet with one Mr. Julian Smith, a name with which loyal readers over the past one score and ten years may not find themselves totally unfamiliar. He haunts the august chambers of Morgan Goldman, plying the trade of economist-in-chief, which, we aver, we do not hold against him. Mr. Smith's duties include not only the standard bottle-washing fare, but also the task of making predictions, moreover with regard to the future.

We found Mr. Smith in a mood that we would necessarily describe as not so completely altogether sanguine, if not to say perhaps a wee bit bearish, or even just a tad cranky, as this bold prognosticator directed us to the display below, showing the tortuous path followed over the past 160 seasons by his firm's Super-Sentiment-Indicator (SSI). Effusive as always, our perspicacious soothsayer observes that the reading today is higher, yes higher, than it was in December of 1974, reflecting the current effervescence seen both in the current markets and in the bosom of his namesake, the aforementioned Ms. Smith. He hastens to point out the silver lining of this billowing grey mass, that those far off levels of thirty and two years ago, just south of 600 in our old friend the Dow, represent what he avers is true "pound the table" opportunity for our bullish brethren, offering a dividend yield (remember those?) of 132%. Keep the powder dry.

Actual. The most obvious explanation for the market's recent favorable action just might be the favorable action itself, which invariably has a tonic effect on investors, as witness the steadily increasing chortling over the prospect of new highs in the averages. While, as we've noted, sentiment hasn't reached ridiculous levels of bullishness, it has been growing increasingly buoyant. At the very least, that's cause for caution.

As a shrewd market-watcher we know points out, for several months now this market has been prone, even while it was edging higher, to sudden reversals. No sooner do the bulls gear up for a sustained march higher than stocks do an about face, and no sooner do the bears start to enjoy life than the decline comes to a sudden end and share prices bounce.

He suspects, though, that such short-term swings of direction as they become the norm lull investors into anticipating an enduring pattern and set them up for a more pronounced and extended move. He further suspects such a move will be down, although he feels we need more febrile and widespread optimism before that happens.

We heartily second that forecast…this time, we fear, what we're in for is … a taste of the inevitable.

Sep

21

Cab Calloway's singing, dancing, and his fantastic band are just hypnotic to see with video. I have seen some of them once or twice as random fillers on TV, but now here they are for the viewing on YouTube! [clip 1, clip2, clip 3]

Sep

18

 Easy, good, and cheap:

– Getting a reasonably good haircut, usually from a recent immigrant (mine Israeli, many Eastern European), less than $20, fast, open on Sunday. In Norwalk, CT I was paying over $40. The haircut was not better, and I think the "stylist" prolonged it all unnecessarily to make it seem more like I was getting my money's worth.

– Laundry wash/dry/fold — less than $20 for a big load, done very, very quickly, without paperwork … they can just remember which bag is yours without even putting your name on it … all cash … and yes, stereotypically, they are Chinese.

– Chipotle — Despite being on very pricey real estate, the prices are still cheap..the premises are very clean, including restrooms … on off-hours you can even get a big table and read a newspaper … unlimited refills on soft drinks … all just like suburbia.

– Of course the subway is a good deal. Taxis are not at all that bad either, and not outrageously expensive, considering all that is involved.

– Jogging — There is the problem of getting stopped at red lights, but once you get in Central Park you are home free. It is easy to jog for an hour without noticing the passage of time. There is a jogging path along the East River, but I often see rats there, which is no fun.

– Food from street vendors … Had a totally excellent chicken gyro from a vendor around Park and 52 or so, and a Coke, and if I remember correctly the total was $4.

Expensive but good:

– Tennis — If you want to play without too much extra time overhead, you really just have to pay…on the order of $100 per hour (plus or minus a factor of 2). There are many choices of surface though. If courts seem unavailable everywhere, you can probably still get one on Roosevelt Island, where the tennis club spans the full width of the land.

– Parking in a garage — Of course this is outrageously expensive. If you do shell out for it, though, they do treat you very well, making you feel at home.

– Obviously, restaurants, Broadway shows, operas, ballet … expensive but very, very good and in endless variety.

Expensive and a Pain:

– Groceries at D'Agostino's … no self-checkout … no express lane … cashiers are dumb (sorry) and get snagged on anything. An Orwellian "D'Ag Tag" required to buy anything at a reasonable price … lines very slow even though everyone is buying just a few things. The groceries are expensive.

– Blinds from Home Depot … I already wrote about this topic. In the end I had to ditch Home Depot and went with a higher end, custom guy. Only had a few windows. The blinds ended up being outstanding, both esthetically and mechanically. But do not deal with Home Depot on this one.

– I have not experienced this personally yet, but I am sure my first experience with the BMV will be difficult. It is hard to imagine though, that it could be worse than Connecticut.

Reasonably Good but not as good as elsewhere:

– McDonalds. I wrote about the great McDonalds on Main Avenue in Norwalk. The McDonald's on 53rd and 1st Avenue is ok, but it can't approach the Norwalk location. Multiple days with the credit card reader broken … no strawberry jelly, only grape, and sometimes out of stock … slower service and, of course, no drive-through, inadequate climate control, effectively no air conditioning…relatively leisurely employees. (See Chipotle above, which is owned at least partially by McDonald's. Somehow they do a very good job with Chipotle here.)

Special category:

– Boston Market. I have not gone to a Boston Market in Manhattan, so this is a comment on Boston Market in general. It is also owned by McDonalds. Boston Market is a disappointment — the concept is good, but the experience is always a letdown. The wait in line is longer than it should be (unlike Chipotle), and for some reason it always seems a little too chaotic, with lots of kids milling around, people who cannot make up their minds, and, especially, slowness at the register. Then when you actually sit down … again it feels a little chaotic … there might be a fly landing on your food occasionally … the soft-drink fountain might be broken … the "Thank You" trash can might be overflowing. None of these things seem to happen at Chipotle.

Aug

14

Here is a review of The Cosmic Landscape: String Theory and the Illusion of Intelligent Design, by Leonard Susskind. Susskind is the “Felix Bloch Professor in Theoretical Physics at Stanford University since 1978, and is a member of the National Academy of Sciences.” So this is a high-powered author.His area of physics is string theory, an intensely mathematical area that has tried to bridge the gap between quantum mechanics and general relativity. My impression is that in order to really understand what is going on in string theory one would need to undertake a multi-year apprenticeship, plowing through some very difficult math. I have not done that, and I doubt I would be able to, so my feel for the subject is limited. I can try to read books like this one for the layman, and try to understand as much as I can through the simplified analogies that the author presents.

The author proposes that string theory provides perhaps the only way to understand the apparently coincidental facts about our universe that make life possible. It has long been observed that the universe appears finely “tuned” to enable the possibility of life. One can make a long list of fundamental physical parameters (e.g. the ratio of the electric to the gravitational force, the charge to mass ratio of the electron, the energy of a certain excited energy state of the carbon nucleus, etc.) which, were they to take on slightly different values, would make life, or sometimes even stars, planets, and galaxies, impossible.

The most astonishing case of apparent “tuning” of the universe has been clarified over the past decade or two, the value of the “cosmological constant”. Einstein proposed the idea of a cosmological constant in his first papers on general relativity. It was an ad hoc device that he put in the theory so that the universe would be static, with unchanging distances between galaxies. Soon afterward, in 1929, Edwin Hubble demonstrated with his observations that the universe is expanding and not static, so Einstein retracted the idea of the cosmological constant, calling it his “biggest blunder”.

Though Einstein did not propose any mechanism for his cosmological constant, there is a clear candidate mechanism. In quantum mechanics, the “vacuum”, or the volume of space that has been evacuated of all matter, is a very lively place, with “virtual” particle-antiparticle pairs forming and annihilating. There is energy and even an effective mass associated with these quantum effects, and that energy can be a factor that can cause the universe to either contract or expand, depending on its sign. “Fermion” particles (such as the electron) would cause a contraction, but “boson” particles (such as the photon) would cause expansion.

The problem is that if one calculates the cosmological constant from, say, electrons, its magnitude is absurdly large. If the total cosmological constant were anywhere near this magnitude the universe would either collapse immediately or expand at such a rate that matter could not form. Other particles (e.g. photons) make contributions of opposite sign and similar but not the same magnitude, so there is a possibility that the contributions from all the particles could cancel each other exactly. They would, however, have to cancel to about 1 part in 10 to the 120’th power, in order to be consistent with current observations! Nobel Prizewinner Steve Weinberg has also shown that even with an only slightly less perfect cancellation, say a one part in 10 to the 119’th, the stars and galaxies would never have formed from the early universe.

This incredibly precise cancellation of the cosmological constant is the most outrageous example of what looks like a “tuning” of the universe to galaxies, stars, planets, and life to form.

String theorists, in their initial efforts, hoped that string theory would provide a unique explanation of the values of the several dozen particle masses and coupling constants of the “Standard Model” of elementary particle physics. Susskind describes, however, the process by which string theorist were forced to conclude that their general idea could be consistent with about 10 to the 500 different possibilities, each of which would be kind of like its own little Standard Model, with its own value of the cosmological constant and just about everything else. The 10 to the 500 possibilities are the “Landscape”.

Initially this finding was thought to be a disaster for string theory. Susskind proposes, however, that it is actually a great blessing. He proposes a “multiverse”, that there are a very large number (presumably much greater than 10 to the 500) universes out there. Within one universe, all the other universes are beyond the “event horizon”, and therefore can not be observed. A key point (which I haven’t fully grasped) is that random factors cause each of the universes to be different, and to “populate” a different part of the “Landscape”. Therefore each universe would have its own little Standard Model.

From this perspective, it is not surprising that our universe appears remarkably tuned to life. The explanation is that there are many, many, many other universes where no life exists. Since there are so many, each with very different properties, it is not so surprising that you could find one (or more) that is remarkably tuned for the possibility of life. And once you believe that, you realize that there is nowhere else that we could possibly be living.

Susskind presents his work as a refutation of the idea of Intelligent Design. What a newcomer might take away, though, is how respectable the idea of Intelligent Design really is, that the best physicists must postulate a near infinity of unobserved universes out there existing in parallel with ours, in order that ours can have its exceptional properties as the result of random chance rather than design. The concern about the “fine tuning” problem is widespread among the physics/cosmology elite (see for example Steven Weinberg’s discussion.)

I have not emphasized here the experimental observations that have been emerging over the past couple of decades that have had great impact. The most important are observations and mapping of the “cosmic microwave background”, the faint afterglow of the Big Bang, which is sort of like observing the red glow of burning coals, except that instead of red we are seeing microwaves, and the coals are the early universe, observed now at a temperature of just a few Kelvin above absolute zero. These experiments, the “COBE” and “ WMAP” map out fluctuations of the night sky of 1 part in 10 to the 5 to predict the temperature of the early universe. This experimental subject is probably a little more accessible to us mortals than is string theory. I think astronomy buffs and others will find it fascinating.

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