Step 3: Stock Pickers

Accept that stock pickers do not beat the market.

3.3.6

Stock Pickers are Focused on Short Term

Charles Ellis “The average long-term experience in investing is never surprising, but the short-term experience is always surprising,” - Charles D. Ellis, Winning the Loser’s Game: Timeless Strategies for Successful Investing

The confusion of most investors is derived from their inability to look at large sets of data about stocks, times, managers or styles. Here is the reason for the confusion. With a small set of data, such as the 50 rolls of three dice shown in Figure 3-8, the assumption is that the chances of getting a six on the next roll was the best of all combinations. This poor representation of the long-term characteristics of the three dice is known as random drift, (in the casino they call it luck). This is similar to saying that an investor feels confident about a certain stock, time period, manager or style based on a recent short-term experience. In statistics, this is also known as a sampling error.

However, if one looks at the long term or a thousand rolls of three dice in Figure 3-9 or 5 dice in Figure 3-9a, a far better representation of the risk and return characteristics is demonstrated, which reduces the confusion caused by sampling error, random drift, or luck. In Figure 3-9, it is evident that rolling a six is just as likely as rolling a 15 and a lot less likely than rolling a 10 or 11. The population characteristics for any large data set are best described by the average and the standard deviation, which represents the variance around the average. Investors who think they see a pattern or trend in monthly or quarterly returns are experiencing random drift, just like 50 or 60 rolls of the dice. They are being fooled by randomness.


Figure 3-8	Figure 3-9

Figure 3-9a

Listen to Nobel Laureate Merton Miller explain why there is actually long-term order in the random chaos of short term data.

Merton Miller

Source: The Trillion Dollar Bet

Here is a playlist of videos from IFAtube that demonstrate and explain statistics and probability theory.

The distributions of stock returns in Figures 3-10 to 3-14 look strikingly similar to the roll of the dice in Figures 3-9 and 3-9a above.

Figure 3-10

Distribution of Annual Occurences of the Stocks within the S&P 500

Figure 3-11

Figure 3-12

Figure 3-13

Figure 3-14

Distribution of Stocks* in the S&P 500 Annual Return

In the language of statistics, the distributions seen above are the result of the Central Limit Theorem. The central limit theorem is one of the most remarkable results of the theory of probability. In its simplest form, the theorem states that the sum of a large number of independent observations from the same distribution has, under certain general conditions, an approximate normal distribution. The approximation steadily improves as the number of observations increases. The theorem is considered the heart of probability theory, although a better name would be normal convergence theorem.

Suppose an ordinary coin is tossed 100 times and the number of heads is counted. This is equivalent to scoring one for a head and zero for a tail and computing the total score. The total number of heads is the sum of 100 independent, identically distributed random variables. The central limit theorem states the distribution of the total number of heads will be, to a very high degree of approximation, normal. This is illustrated graphically by repeating this experiment many times. The results of this experiment are displayed in a diagram. The percentage computed over the number of experiments is arranged along the vertical axis, and the total score or the number of heads is arranged along the horizontal axis. After a large number of repetitions, a curve appears that looks like the normal curve.

It has been empirically observed that various natural phenomena, such as the heights of individuals, daily returns of the S&P 500, the managers who fall in the top fifty percent of all managers, and the students who correctly guess the outcome of a coin flip, follow approximately a normal distribution, as seen below.

For more examples of randomness in the market, see below
The Normal Distribution

A suggested explanation is that these phenomena are sums of a large number of independent random effects, like the daily news that moves the market, and hence are approximately normally distributed by the central limit theorem.

From the transcript of the PBS Nova Special, The Trillion Dollar Bet, Boston University Professor of Economics, Zvi Bodie (Bodie research) put it this way: "In flipping a coin, if you flip it long enough, there may be a long run of heads, which doesn't at all imply that the person flipping it had the ability to make it come up heads. It could just be the luck of the toss."

Video Source: The Trillion Dollar Bet

Click the play button in the top left corner of the painting to the left.

Narrator: This strange view arose from an unexpected discovery. After the stock market crash of 1929, economists decided to find out whether traders really could predict how prices moved by looking at past patterns. They decided to run a series of experiments. In one of them they simply picked stocks at random. They threw darts at the Wall Street Journal while blindfolded. At the end of the year, this random choice outperformed the predictions of top traders. This was a revelation: prices must be moving totally at random, and although patterns came and went, they were there by chance alone and had no predictive value. "The economists arrived at a devastating conclusion: it seemed just as plausible to attribute the success of top traders to sheer luck rather than skill."

Zvi Bodie

Zvi Bodie: "When some individual made a fortune in the stock market, we have a tendency to assume that that was because he knew something, and of course the individual himself is happy to reinforce that belief - yes, I was a genius, or I was very clever, or I always said Microsoft was going to make me rich. But what you don't see are the thousands, hundreds of thousands, perhaps millions of people who are going, I always said that ABC company was going to make me rich, and ABC company went bust."

Theory of Speculation

WHAT IS GOING ON HERE?
The answer was first given over 100 years ago, on March 29, 1900, by Louis Bachelier, in his landmark study on the Theory of Speculation. This has since been documented by hundreds of other researchers. Investors are either too lazy, uninterested in learning, or else they rely on some "stock market expert." They operate like gamblers in Vegas, hoping that their skill, which is really just luck, will lead them to market beating returns.

Instead, studies like the one below, have shown that the average investor only captures a small percentage of the market's return.

3.3.7

Stock Pickers are Style Drifters

One of the most difficult problems in confirming stock pickers’ skill is that they are constantly changing the criteria, ownership rules or style of their investments. Since their style is constantly changing, it is very difficult to track and compare them to the proper index. In fact, one study found that 40% of mutual funds are invested outside of their stated styles. This will alter their performance and result in different risk and return characteristics, which is sort of like changing the number of dice in the dice roll example. In fact, every portfolio that differs from the stated benchmark or style will result in a different return. Since these portfolios that have drifted from a benchmark have no long-term characteristics, investors have no idea what to expect from the manager’s newly created style. In the absence of expectations, an investor becomes a speculator, and the expected return of speculation is zero. Style drifters are further discussed in Step 6.

3.3.8

Stock Pickers are Looking for a Needle in a Haystack

John Bogle accurately described stock picking as looking for a needle in a haystack. The top 10 stocks perform 20 times better in their first three years than they do in the following three years, according to a study by Ibbotson and Associates. Stock pickers are often surprised when they purchase what they think have been winners, only to be grossly disappointed in the period after purchase.

Many investors invest in blue chip companies, believing they are reliable and true blue. See Table 3-3 for less than favorable outcomes of 10 of these blue chip companies.

Table 3-3

The solution is to buy the haystack rather than pick and choose certain stocks. This will guarantee market returns at a much lower cost. The only valid question is: Which haystack or index, and in what proportions?

3.3.9

Stock Pickers Play a Zero Sum Game

All financial markets are zero sum games. This is a mathematical fact. In any financial market it is mathematically impossible for the average investor in that market to outperform the average of the market. This is because in any market, the pre-cost returns earned by good, bad, and average stock pickers combined together must be the same as the total market return. The after-cost returns will be less than the total market return. All investors as a group are mathematically obligated to underperform the market by the amount of their costs of investing.

There are occasional active investors who outperform a given market, even after costs and taxes. The market-beating returns they generate must then counterbalance the inferior returns of those who underperform the market. That is, the amount of the outperformance must be offset to the same degree as the amount of the underperformance for reasons none other than simple arithmetic!

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