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.

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