So you want to be a stock trader? (Part one of many)

I am a professional stock trader. I sit in my home office all day and look at numbers on my computer screen clicking here and there. For some reason, people believe that stock trading is sexy, fun, or a worthwhile hobby. It is not. It is perhaps one of the most difficult pursuits available because it is a zero-sum game. For every winner there is a loser. Whereas if you are a hand surgeon or a statistician or a synthetic organic chemist or an entrepreneur you can make money just by being good at what you do, if you are a stock trader you make money only if you are better than the traders that take the opposite side of your trades. In other words, the second best entrepreneur might become a billionaire. The second best trader will lose money. If you are interested in trading stocks I will give you some pointers, but first I will explain why most of you should not trade stocks.

Types of Games

Negative-Sum Games

In game theory, there are three basic types of games. There are negative-sum games, in which everyone who plays loses (and the outcome determines only the extent of the loss). The classic example of this is global thermonuclear war (if you have not seen the movie War Games, see it). In any conceivable global nuclear war every side incurs massive loss of life and infrastructure. The unrealistic best-case scenario for a country might involve losing 2/3 of its population and having 50% of its land mass rendered uninhabitable. A more realistic ‘victory’ scenario would involve 95% loss of life and 90% of a country’s land mass rendered uninhabitable. Clearly no one would wish to play such a game.

Yet, another example of a negative-sum game is mutual funds (and particularly the largest funds). On the whole, they make up so much of the market that they cannot realistically outperform the market. And after expenses, they are guaranteed to underperform the market. So on average, mutual fund investors would be better off if they all invested in low-cost index funds. This is exactly the point of Warren Buffett’s Gotrocks parable in his letter to shareholders (excerpted here).

Okay, you say, you think other people should use index funds, but you are smart enough to pick good fund managers. Oh really? But what if the probability of any one fund outperforming is not better than random chance (and that is before fees)? Oh, and those “growth fund managers” that outperform their benchmark? That is only because there is such dreck in their benchmark (like American Superconductor or Ener1 at the moment), stuff that is so odious it is easy to avoid. But growth benchmarks on the whole underperform the broad stock market, meaning that even if growth fund managers can reliably beat their benchmark, they will underperform the broad market (as measured by the Dow Jones Wilshire 5000 or a similar index).

I could go on, but this article is not focused on mutual funds. I just wanted to illustrate the concept of negative-sum games in finance.

Positive-Sum Games

The opposite of course is a positive sum game, where each player expects to win, although the degree of winning may differ. Spin the bottle is of course an example of this type of game. Any cooperative game (the type I hate playing, which is why I cannot think of more examples) would qualify as a positive-sum game.

The stock and bond markets are positive-sum games for long-term investors, because the expected return from stocks and bonds is positive. So in picking stocks, on average everyone makes money. Some people will lose money because they invest emotionally or stupidly and are not diversified, but on average stocks and bonds provide a certain positive return. That return on the whole (for stocks) comes from economic growth (ultimately driven by population growth and productivity growth) and dividends.

Zero-Sum Games

Zero-sum games are of course the most common when we think about games. Some common games include chess, checkers, and betting on football games (although that becomes a negative-sum game if you place it with a bookie, because the house takes a cut). Stock trading is also a zero-sum game. When you are literally day-trading stocks (buying them), holding nothing overnight, your expected market return will be about zero, because your transaction costs will nullify the tiny expected daily gain of the stock market. Looked at another way, the market return on such a short timescale is irrelevant; all that matters is whether you can outsmart another trader who takes the opposite side of your trade.

Even if you buy and hold stocks as an investor, you are betting that the stocks you buy will beat the market. In that sense, you are playing a zero-sum game (trying to beat the market) with professionals who are a lot better and smarter than you. Why would you play that game?

A Million Grand Masters

Do you play chess? Would you imagine that by studying chess for a few hours a week you could become a master, let alone a grandmaster? Would you then believe that you could go up against Garry Kasparov? Studies of experts in various fields have generally shown that to reach an elite level of performance (whether playing a musical instrument or chess or catching a football) takes something on the order of 10,000 hours of practice. That is three hours a day for ten years. Worse, those who achieve that elite level of performance start when they are very young, when their capacity to learn is much greater.

You surely would not attempt to take on Garry Kasparov if you were but a lowly chess master, let alone a dilettante who cannot beat a simple computer chess game. Yet many people, knowing little about analyzing companies, think that a few hours and a couple books prepares them to beat professional investors who spend 60 hours a week on investing or trading.

It is not necessary for the stock market to be efficient for amateur investors or traders to have very low odds of succeeding. It is only necessary that there be professionals who can profit off of the idiocy of the amateurs.

The Impossibility of Mastering Multiple Skills

If you spend 40 to 50 hours a week at your day job and a significant amount of time with your family and friends, then you do not have enough time to master another skill. I am a great example of this–when I first started to learn about investing and trading while in graduate school I devoted more and more time to learning investing, such that by the time I decided to leave school my advisor was unsurprised, because I had not been a very productive or good graduate student. Mastering one subject left me little time to master another. Even a man much smarter than myself, the famous cognitive psychologist Richard Shiffrin, experienced the same problem. In graduate school he started playing the game Go and eventually reached the level of 6 dan, perhaps roughly equivalent to a chess master; yet he realized that he had to quit either Go or psychology if he wanted to master one. Thankfully, he chose psychology. You are probably not smarter and more dedicated than Rich or myself, so don’t believe you can master a skill without devoting absurd amounts of time to it.

While stock trading is not as difficult as Go, it requires a lot of time to study patterns and learn what does and does not work. Most dilettants do not have the time or focus required to do it right.

Most Traders Lose Money

Most stock traders lose money. For day traders in Taiwan, the figure is 80% over any 6-month period. The figures are probably similar in the USA (one study finds that 20% of US daytraders make decent amounts of money). Many of those are just lucky or trade using a strategy that ceases to work; they do not continue to make profits over a period of months and years. Keep in mind that studies like these greatly overestimate the probability of a trader making money because they focus on those who trade the most; by using that there is a selection bias: traders who make money will trade more; those that lose money will tend to quit. If you could survey all the people who have tried daytrading, even just briefly, I am sure that fewer than 1% make any money while many lose large sums.

Okay, I Warned You

If you still wish to learn about trading, read on. Hopefully I have scared you enough so that you will not try it. In the followup article to this I will address different trading methodologies and strategies.

Okay, I’m Warning You Again

If you want to stand a chance at making money trading, here are a few suggestions of things not to trade: Apple (AAPL), GE (GE), (AMZN), and any other well-followed large-cap stock (or index or ETF). The big hedge funds have programs and experienced traders that trade these stocks based on thousands of statistical factors, news, and many other things I cannot even imagine. There is no point in trying to trade these. You have no edge whatsoever.

Another bad idea is to trade based on news or rumors. The hedge funds and iBanks hear the rumors first, so you won’t stand a chance (I’ve ignored that one a few times). The same thing goes for news as for rumor–the big traders hear it first. And with news they have programs to read the press releases and SEC filings and instantly make trades (that is what happened when a years-old report of United Airlines’ bankruptcy mistakenly hit the wires back in September: program trading sent the stock down 50% in minutes). If you trade solely based on what you do know (price momentum), you can make money, but you will be trading blind. This is what some traders I know did with UAUA and what I did with Constellation Energy (CEG) when there were rumors that it would go bankrupt. If you do this you are at a huge disadvantage.

If you do wish to trade stocks, the key is to have an edge. Whenever you want to do something, whether investing in a random stock or trying a quick day-trade, ask yourself: “What are the risks?” and also ask yourself, “Why aren’t other smart investors / traders / hedge funds doing this trade and taking this opportunity away from me?” You will quickly realize that most “trading ideas” have no value and that you almost never have an edge.

I recommend reading the next post in this series on emotion and developing a trading system.

Disclosure: No positions in any stock mentioned. This article was originally posted on my investing blog on 2/2/2009. I have a disclosure policy.

Stock to watch: Cannabis Science (OTC BB: CBIS)

CBIS has surged from $0.08 to $1.14 on millions of shares of volume over 5 days. It is a top watch for shorting on green to red action tomorrow. Unfortunately it is hard to borrow at Interactive Brokers and as an OTC stock is unavailable to short at SogoTrade.


Here is some of the “news” that has sent the stock skyrocketing:

July 27: Cannabis Science to Apply to the FDA to Utilize Their Fast Track Procedures to Help Speed the Approval of Its Cannabinoid Medicines in Treatment of H1N1 Swine Flu as AP Report Predicts Swine Flu Could Hit up to 40 Percent in the USA

July 24: Today’s CNN Pandemic Report on H1N1 Pushes Cannabis Science to Move Faster to Release Its Anti-Inflammatory Cannabis Drug as Similarities Between 2009 and Deadly 1918 Influenza Pandemic Cause Concern; FDA Approved Drugs Needed Now to Meet This Urgent Situation

July 23: Today’s Wall Street Journal Reports on Cannabis Science Inc. in Article on Booming Medical Cannabis Industry; Indicates Great Demand for Products From a Patient Oriented Company

The common factor in all those press releases is that none of them are material to the company. The WSJ article only mentioned the company because the company’s CFO is involved in medical marijuana in California (the company is not). The other two press releases simply state that the company is going to try hard to get its cannabis drug approved quickly. Any self-respecting biotech would not even have put out press releases to say they are trying to get fast-track approval. Everyone tries it. Only a few of those who apply for fast-track approval get to use it. The press releases above therefore offer no real information and serve only to hype the stock. Once the hype dies down the company’s stock will head back to the gutter in which it belongs.

Note: as of the company’s most recent 10Q, it had a net worth of negative $870,000 and tangible assets of $2,467. It is hard to develop and market drugs with no money.

Disclosure: No position. I have a disclosure policy.

The Greatest stock chart of the year: Aspyra (APY)

This is a classic trading post from my investment blog I have added a few more details to the end of this post.

This brought tears to my eyes … never before have I put a limit price on a big order so far below the market price, but alas there were no shares.

Here is how it played out … I caught this on the Scottrade MarketMovers list around $1.50 on the way up, was ready to short as the bid backed down to $1.30, could not find borrows, but hoping that IB could find borrows I placed a limit short for 5,000 shares at 90 cents (yes, 40 cents below market) … no shares became available.

This is the one-hour chart from 3pm EST to 4pm EST today.

This is a perfect example of what I call “fat-fingered shorts” … stocks with little volume where the price and volume shoot straight up on no news. They are likely caused by traders accidentally placing market orders instead of limit orders. This is one of the easiest charts to short sell. I usually run across one decent one a week. Of course, you need to get in on these quick; because of these constraints this is only a viable trading strategy for people who are already full-time daytraders and the potential profit is limited. As of this re-posting on 7/27/2009, my net profit in 2009 from similar trades is $2,505.99, with an average profit margin of 0.79%.

Disclosure: No position.

Proper weighting of evidence in making investment decisions

This is a classic trading post from my investment blog

This article should not surprise you, but I think it important to emphasize points I have made earlier about the predictable irrationality of investors. I recently came across a paper written by Dale Griffin and Amos Tversky, entitled The Weighing Of Evidence and the Determinants of Confidence (no full-text version available online). This article gives evidence as to why investors ignore regression to the mean and why they do not pay attention to the reliability of certain kinds of financial information.

The research behind the article is not brilliant nor even very interesting. What is exciting is the theory that Griffin and Tversky put forth. They also review relevant prior research. It is best to start with their theory.

Their theory is that people pay too much attention to the strength or extremity of evidence and not enough attention to its weight or reliability. They do not put forth a theory as to why people do this, but such a theory does not matter to us. What is important is what this theory predicts and how we can use it to predict how other investors will behave so that we may profit from it. One of the most important predictions of this theory is that people will be overconfident when information strength is high and information weight is low but they will be underconfident when weight is high and strength is low.

What do I mean by information weight and strength? The strength of information would be its extremity. For example, when hiring from among a pool of job applicants, a job interview that goes very poorly is strongly negative information. Information weight is the reliability of the information. A 20 minute job interview is not a reliable indicator of a potential employee’s demeanor and character and thus can be characterized as low-weight information.

The simplest test of this theory involves having people guess about a spinning coin. Unbeknownst to most, coins that are spun will tend to land on either one side or the other, due to imperfections in the manufacture of said coins. Griffin and Tversky told their subjects about a series of coins. They gave their subjects results of coin spinning experiments that varied in both the strength of evidence (the percentage of spins that landed on either side) and the weight of that evidence (the number of times the coin had been spun). Subjects had to guess whether the coin was weighted towards heads or tails. Subjects also had to give their confidence for each decision they made. Keep in mind that even if a coin lands on head 60% of the time, it would not be implausible for it to wind up on heads four out of 10 times or 10 out of 20 times, due to random error.

If people were perfectly logical, their confidence would increase as a function of both the strength of the evidence (the percentage of heads) and the weight of the evidence (the number of spins). There is a way to calculate the statistically correct inference and the confidence one can have in that inference given a certain weight and strength. This can be done by using Bayes’ theorem, which I will mercifully avoid describing here. While it would not be reasonable to expect people to always draw the statistically correct conclusion, it would be reasonable for them to be consistent. That was not the case.

In fact, the researchers’ theory was confirmed: given strong evidence with little weight (e.g., a coin that lands on heads 80% of the time, after five spins), people tend to be overconfident. Given weak evidence with a high weight (e.g., a coin that lands on heads 60% of the time, after 17 spins), people tend to be less confident than they should be. Overall, the researchers found that people relied over twice as much on the strength of the information as they did on its weight.

What does this mean for us as investors? First, if we pick our stocks using our intuitive judgment, we are at risk of becoming overconfident in our stock picking when our decision is heavily influenced by strong information of low weight (low reliability). A good example of this would be the novice investor’s choice to invest in a company primarily because he likes their product. Unless that investor is an expert in that type of product, such information is rarely useful. Conversely, we are at risk of being underconfident in our stock picking when the evidence is rather weak but highly reliable. I think Wal-Mart WMT is a great example of an investment with weak but reliable information in its favor: its PE ratio is average, its recent growth has been steady but unremarkable, and its brand name is well-known. These pieces of information are all highly reliable, but they indicate that Wal-Mart is a pretty good investment, not a great investment.

Oftentimes, conflicting information about a company will be of different strengths and weights. Whether by using a quantitative investment method or by just being aware of how much weight you should give to each piece of information, you must always consider the reliability or weight you should put on a piece of information when you are making investment decisions.

Besides the implications for costs and our personal investment decisions, this research has important implications for us because other investors will make these mistakes. They will not pay enough attention to the reliability of information (its weight). This will lead to consistent mispricing of stocks. This explains why value stocks outperform growth stocks: investors put too much weight in unreliable estimates of future growth, which leads them to bid up the price of growth stocks too high.

Other consistent inefficiencies in the market are also likely caused by investors not paying enough attention to the reliability of information. Companies in exciting sectors or industries are valued more highly (have higher PE ratios) than companies in less exciting industries, despite evidence that hot sectors do not outperform the market and may under-perform the market as a whole. While some might say that investors buying into the latest hot sector are just being stupid, I would argue that they are just over-weighting the importance of the industry’s future and under-weighting the importance of value (as measured by the PE ratio).

There are probably other stock market inefficiencies that this can explain. One anomaly that this explains is the success of stock promoters. The rubes who buy promoted stocks (Spongetech comes to mind) pay attention to the extreme positivity of press releases and newsletter while ignoring the low reliability of such sources.

Disclosure: I have no position in WMT. I have a disclosure policy. This article was originally written three years ago and published elsewhere. I have a disclosure policy.

What every trader needs to know about regression to the mean

This is a classic trading post from my investment blog

Perhaps one of the most widely disseminated and most widely misunderstood statistical concepts is that of regression to the mean. It is also one of the most important concept for investors to understand.

The simple definition of regression to the mean is that with two related measurements, an extreme score on one will tend to be followed by a less extreme score on the other measurement. This definition will not suffice for us as it is incomplete. Regression to the mean only happens to the extent that there is a less than perfect correlation between two measures. Thus, as a technical definition, let us use that of Jacob Cohen: whenever two variables correlate less than perfectly, cases that are extreme on one of the variables will tend to be less extreme (closer to the average or mean) on the other variable.

For those of you who have been away from math for too long, a correlation is simply a measure of how well one thing can predict another. A correlation of 0 indicates that two things are unrelated, while a correlation of 1 or -1 indicates that they are perfectly related. See this website for a nice graphical presentation of what different correlation coefficients mean. For example, the price of a restaurant is correlated with its quality at about .60 (this is just my rough guess)—more expensive restaurants tend to be higher quality than less expensive restaurants, but there are plenty of exceptions.

On the other hand, I would estimate that price correlates more strongly with the quality of chocolate—probably around .80. Except for exceptions such as Candinas and Sees, most really good chocolates are horribly expensive, while cheap chocolates (such as Russell Stover) are invariably bad. An example of a near-perfect correlation would be the correlation between altitude and temperature at any given time in any given place—as the altitude increases, the temperature drops.

Some people refer to regression to the mean as a statistical artifact. It is not. It is a mathematical necessity. Let us start with a very simple example. Suppose that people who have more money tend to be happier than those with less. This is actually true, but the correlation is weak—money really matters to happiness only to the extent that people can afford the basic necessities. If we were to predict the happiness of both 100 billionaires and 100 people who live on welfare, we might expect that the billionaires would be significantly happier. In fact, billionaires are only slightly happier than those on welfare. Because the correlation is so weak, we would be better off ignoring the correlation of wealth and happiness and just guessing that everyone was of average happiness.

Let’s try another example. Suppose that you work as an admissions officer for Harvard. You have two main sources of information in order to decide whether or not to admit prospective students. You have the candidates’ SAT scores and you have the results of their admissions interview. Suppose that one student has an SAT score of 1550 (out of 1600 possible points) and a very bad interview—the interviewer considered the student to be uninteresting and not very bright. Another student had an SAT score of 1500 and an outstanding interview. Assuming there is only one spot left, which student should you admit and which should you reject?

Take a moment to think and make your decision. You most likely chose the student with the lower SAT score and better interview, because the SAT score was only slightly lower, while the interview was much better than that of the first student. However, this is the wrong decision. Repeated studies have shown that admissions interviews have no correlation whatsoever with college student performance (as measured by graduation rate or college grades). SAT scores, on the other hand, do correlate (albeit less strongly than most believe) with college grades. Thus, you should completely ignore the interview and make a decision purely based upon SAT scores.

I admit that this example is unfair—truth be told, SAT scores are only correlated moderately well with college grades: about .60. That means that there is little difference between a score of 1550 and a score of 1500. However, a small, meaningful difference is still more informative than a large, meaningless difference.

To make this a little more clear, we can do this without an interview, since the interview is useless. Rather, we throw a die (as it is equally useless). For the student with the 1500 SAT, we roll a 6. For the student with the 1550 SAT, we roll a 3. Would you decide to admit the student with the 6 because of his higher die roll? Obviously not, because the die roll is pure chance and does not predict anything. The same reasoning applies to the interview, since its relation to school performance is just chance.

Suppose we selected students based on a roll of the die—how would they fare? The students with the best scores would tend to do average, while those with the worst scores would also do average. This is perfect regression to the mean. Simply put, the die roll adds nothing.

Regression to the mean only happens to the extent that the correlation of two things is less than perfect (less than 1). If the correlation is 0, then there will be perfect regression to the mean (as with the die). If the correlation is between 0 and 1, then there will be partial regression to the mean. Let us now look at example of this.

There is a correlation between income and education level. I cannot find the actual data, so I will make it up—I will say that it is around .60. Therefore, level of education (as measured numerically by highest grade level or degree completed) is a fairly good predictor of a person’s income. More educated people tend to make more money. Let’s look at a sample of the top 10% of money-earners. If education perfectly predicted income, then those top money earners would be the top 10% most educated. Whereas education imperfectly predicts income, we will find regression to the mean. Those earning the highest incomes will tend to be well educated, but they will be closer to the average education level than they are to the average income level.

One of the beautiful things about regression to the mean is that if we know the correlation between two things, we can exactly predict how much regression to the mean will occur. This will come in handy later.

If all we had to worry about when two things are not perfectly correlated was regression to the mean, we would be fine. It is fairly simple to calculate a correlation coefficient and then figure out how much of some effect is caused by regression. Unfortunately, there is one more complicating factor: measurement error.

Imagine you have a bathroom scale that has 100% error. In other words, the weight it shows is completely random. One morning you weigh yourself at 12 pounds, while the next morning you weigh 382 pounds. Whereas height is normally correlated strongly with weight, your weight as measured by your scale will not correlate with your height, since your measured weight will be random. If we make the bathroom scale just a little more realistic and say that its measurement has 2% error (quite normal for bathroom scales), the same problem applies—the measurement error reduces the apparent correlation between height and weight and increases regression to the mean.

This is exactly the problem that we see in the stock market, although the errors are much larger than with your bathroom scale. The value of a company is a function of only one thing: the net present value of its future cash flows. That, in turn, is determined by two things: the company’s current price (as measured most typically by P/E or P/CF) and its future earnings growth. The measurement of P/E has very little error. The estimation of future growth has much error, though.

For the moment let’s assume that P/E and future growth each account for half of the current value of a company. (This is actually wildly inaccurate—as the growth of a company increases the growth will become much more important than the current P/E in determining the net present value of the company. Conversely, if growth is zero, then P/E will completely determine the net present value of a company.)

Since P/E accounts for half of present value, it is correlated at r=.71. (R2 is the proportion of variance explained, which is .50 in this case, so the square root of this is the correlation coefficient r). This is a fairly strong correlation. Nevertheless, it is far from perfect. Regression to the mean will ensure that companies with the most extreme P/E ratios will be less good values than is purely indicated by their P/E ratios. When you think about it, this makes perfect sense—some companies deserve low P/E ratios because their prospects are poor.

Now for the other half of the equation: growth. Growth is correlated at r=.71 with the net present value of the company. However, that is assuming that we can accurately predict future growth. This is simply not true. Analyst predictions of company earnings less than one year ahead are on average off by 17% of reported earnings (meaning that near-term estimates have a .83 correlation with actual earnings*). Their estimates of growth years in the future are of course much worse. So while the correlation between future growth and present value of a company is fairly strong, .71, the correlation between predicted growth and present value is very much less than that (about .28).

Due to this reduced correlation, there will be much greater regression to the mean for growth as a predictor of value than there is for P/E. The one problem is that investors do not take this into account. Investors and analysts put faith in projections of high growth for years in the future. However, the chances are only 1 in 1,250 that a company will go for 5 consecutive years without at least one quarter of earnings over 10% less than analysts’ estimates. This even understates the problem, because in the above calculation, the estimates can be updated until just before a company actually announces earnings. Estimating earnings five years in the future is impossible.

Remember how I earlier mentioned that as a company’s growth rate increases, its current P/E has less and less relation to its true value? The true value of these companies (such as Google GOOG is determined primarily by their growth rate. So in effect, when the growth investors say that P/E does not matter if the growth is fast enough, they are correct.

There is one problem with this: because of regression to the mean, those companies that grow the fastest are also most likely to under-perform analyst and investor expectations. So the predictions of growth will be least accurate for those companies whose value most depends on their growth rate!

Investors do not realize this and they thus bid up the prices of growth stocks in proportion to the anticipated future growth of a company. Because of regression to the mean caused primarily by the lack of reliability of analyst estimates of earnings, earnings for the best growth companies (as measured by anticipated future growth rates) will tend to disappoint more often than other stocks. The converse will actually happen with the most out of favor stocks: analysts and investors are too pessimistic and thus they will underestimate future earnings and cash flow growth. See “Investor Expectations and the Performance of Value Stocks vs. Growth Stocks” (pdf) by Bauman & Miller (1997) for the data.

Some converging evidence for my regression to the mean hypothesis would be useful. According to my hypothesis, earnings growth for the lowest P/E or P/BV (Price/Book Value) stocks should increase over time relative to the market, while earnings growth for the highest P/E or P/BV stocks should decrease relative to the market. The value stocks in the following data are those with the lowest 20% of P/BV ratios, while the growth stocks are those with the highest P/BV ratios. Ideally, I would look not at P/BV, but at projected earnings growth, but these data will do.

The value stocks have earnings growth of 6.4% at the point in time when they are selected for their low P/BV ratio. After 5 years, their earnings growth increases to 11.6%. Their increase in earnings growth rate was thus 5.2 percentage points. The growth stocks, on the other hand, see their earnings growth rate fall from 24.6% to 12.1% (decrease of 12.5 percentage points), while the market’s rate decreases from 14.2% to 10.6% (decrease of 3.6% percentage points). The figures for cash flow growth are similar: value stocks increase their growth rate by 2.3 percentage points, while the market decreases its growth rate by 3.3 percentage points and the growth stocks see a decrease in growth rate of 10.3%. Changes in sales growth rates are not as convincing, but do not contradict my hypothesis: value stocks do as well as the market (seeing a 3.6 percentage point decrease in sales growth), while growth stocks see a whopping 6.5 percentage point decrease in sales growth rate.

The icing on the cake is in return on equity (ROE) and profit margin. In both cases there is no such benefit for value stocks over growth stocks. Why? Both ROE and profit margin are primarily determined by the industry a company is in: commodity industries will see lower ROE and lower profit margins, while industries with a possibility of long-lasting competitive advantage will see higher ROE and profit margins. ROE and profit margins tend to remain relatively stable (but generally decreasing over time for every company), meaning that they are reliable measurements. More reliable measurements means less regression to the mean.

So what does this all mean? Investors do not overreact to good or bad news. Or at the very least, it is not some sort of emotional overreaction—rather, they predict that current (either negative or positive) trends will continue. They do not take the unreliability of their estimates into account. Thus, they do not anticipate nor do they understand regression to the mean.

While this article is geared towards investors, traders need to know how regression to the mean works. I will address specific regression issues in trading in a future article.

*This is not true. I am not sure how to calculate the correct number, though, so I will use this as an approximation.

How the NYSE and SEC abet stock fraud by limiting short selling of penny stocks

This is a classic trading post from my investing blog,

I’ve been going over Regulation T (Reg T; you can see it in its full glory here), which is the SEC rule that governs margin loans, as well as the NYSE margin rules for margin accounts. And if I were designing regulations to increase stock fraud, I could think of no better way to do it.

Why is this? The margin requirements for short selling stocks are greater than for buying stocks, at least for cheap stocks (below $2.50 in value). Here is how it works for stocks above $5. You will note the nice symmetry between short and long margin requirements. While the margin requirement for buying stocks is 50%, the requirement for short-selling stocks is 150%. Here’s an example: if I buy a stock for $10 per share (let’s say 100 shares), I only need to put up $500, or half the total value of the stock. If I want to sell the same stock short, I need to put up $500 (plus the $1000 in proceeds from the sale of the borrowed stock). So there is symmetry between short and long margin requirements. (Investopedia has an in-depth explanation of this). If the price of a stock is below $5, there is no margin allowed on either long or short sales. So if I want to buy 100 shares of a stock at $3, I must have $300 in cash (or margin from a higher-priced stock). If I want to short sell the same stock I would likewise need the same amount of cash or margin available.

The symmetry between long and short breaks down, however, with stocks under $2.50 per share. The NYSE has a rule (rule 431 (c) 2) that requires $2.50 in cash or margin for every stock below $2.50 per share sold short. A comparable rule does not exist for long positions. So if I want to buy 1000 shares of a penny stock trading at $0.40, I need $400 in cash or margin ability from marginable stocks. But if I want to short 1000 shares of a $0.40 stock I need $2,500 in cash or margin. So any time someone shorts a stock under $2.50, they have negative leverage: the position value ($400) is but a fraction of the money needed to hold the position ($2,500). For this reason, very few short sellers sell short cheap stocks. Fraudulent companies or worthless shell companies trade at absurd valuations because their share prices are too low to attract short sellers.

Most of the financial fraud in public companies nowadays is with penny stocks. The reason is because short sellers cannot afford to sell short cheap stocks. If the NYSE $2.50 rule were eliminated, more short sellers would be willing to take short positions in overvalued penny stocks. Pump and dump scams would not be as effective because short sellers like myself would easily be able to short sell the pumped-up stocks earlier, at cheaper prices, reducing the harm to the poor rubes who fall for such scams.

Removing the $2.50 rule would increase the amount of information available about penny stocks as short sellers like myself would write critically about the overvalued stocks they sold short. This would give the poor rubes a chance to learn the truth about the worthless stock they were considering buying and this would further reduce the success of pump and dump scams.

Please, contact the NYSE and urge them to stop supporting scammers and fraudsters. Urge them to remove the $2.50 requirement.

Disclosure: I love short-selling penny stocks.

Use the Kelly Criterion to determine position size

This is a classic trading post from my non-trading blog,

The Kelly Criterion is a formula for choosing how large a bet to make on each trade/investment/gamble. It works for the stock market, though it was originally developed for gambling. The formula is simple: bet the proportion of your investment as defined by the ratio of expected return divided by maximum return. Expected return is what you expect in the long run.

So, the formula is: P_invest = E(r) / M(r)
Proportion of portfolio to invest = P_invest
Expected return= E(r)
Maximum return = M(r)

Now, a couple of examples:

1. If you flip fair coin and win $1 if heads and lose $1 if tails, the expected return is $0 (.5 x $1 + .5 x -1). The maximum return is $1 (if heads). Therefore, the Kelly criterion suggests you bet no money ($0/$1). This makes sense, because you should not invest money where you expect to only break even.

2. You want to short Apple (AAPL) because you think there is an 80% chance the stock will go down in the next month. You think if that happens, the stock will go down 10%. You figure that there is a 20% chance that the stock will go up 5%. The expected return is 7% (.8 x 10% + .2 x -5%). The maximum gain is 10%. The Kelly formula suggests that you invest at most 70% (7/10) of your portfolio.

3. Same thing, shorting AAPL. You like the odds, so you increase your leverage by buying put options. You buy just out of the money options. Now, there is a 70% chance that your options expire worthless (-100% return) and a 30% chance that you make 300%. The expected return is +20% (.7 x -100 + .3 x 300). The maximum gain is 300%. The Kelly formula says that you should bet less than 1/15 (about 6.5%) of your portfolio (20/300).

One thing to consider is that the Kelly formula seeks only to maximize gains. If you wish to minimize portfolio variability as well, you should invest significantly less than the maximum allowed by the Kelly formula. Also, keep in mind that the formula is only as good as your guesses of probability. In order to minimize portfolio volatility and because it is very difficult to accurately estimate the expected return on a trade a priori, many traders stick to using a very small fixed percentage of their portfolio on each trade.

I recommend a Legg Mason article on the Kelly Criterion, or this paper by Edward Thorp (who used it to great effect).

Visit Cisiova’s website for their advanced online Kelly Criterion calculator, which allows you to enter a large number of possible outcomes.

If you liked this post you may want to check out William Poundstone’s book Fortune’s Formula.

Disclosure: I own no Apple stock, long or short. Unfortunately, I did once lose money shorting AAPL. My disclosure policy never loses me money.

What to expect on this blog

What can you expect on my new blog? I will blog about trading strategy, trading psychology, and my own trades. For the time being I do not forsee offering any pay services even though some readers have encouraged me to start a Reaper Alerts service much like Tim Sykes’ TimAlerts (of which I am a lifetime member). I do not rule out offering such a service in the future, but for the time being protecting my best trading strategy is more important to me than earning a few thousand dollars from selling my knowledge.

In addition to this blog I have a new Twitter account. See my @ReaperTrades twitter account for all future tweets.

First up: reposts of my best trading articles from my other blog. Expect new posts here by the end of the week.

Disclosure: I am an affiliate of Tim Sykes and am a happy customer of both. See my disclosures & disclaimers page for more details on what products I have purchased from Sykes.