3.3.6. Stock Pickers are Focused on Short Term

“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.

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

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. Central Limit Theorem For more examples of randomness in the market, see below 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. Source: stattucino.com The Trillion Dollar Bet   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." 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. As shown below, studies show the average investor only gets eighteen percent of the market returns. Success of the average investor vs. various indexes 2001 Dalbar Study - 2003 Dalbar - 2004 Dalbar 2006 Dalbar

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.

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!

3.3.10 Stock Pickers in International Markets

Many investors agree that the U.S. financial markets are highly efficient. But are other markets outside the United States efficient? Are there profitable investments that can be made that might outperform their respective index? Many investors believe that these “underdeveloped” markets are inferior to our own, and that analysts are better at choosing stocks in international markets that outperform the appropriate index. Evidence shows that this is not the case.

Several studies have proven that the indexes of these smaller markets, on average, will perform better than an active fund. If one investment manager has an idea about an international country or company, it would only be logical to have numerous other firms investigating the profitable possibilities, with only one conclusion available—that none of the firms will outperform the index average over any lengthy period of time.

In fact, there have been studies that show higher costs associated with international investing make it even harder for active investors to beat their benchmarks. In a research paper by Garret Quigley and Rex Sinquefield titled “Performance of UK Equity Unit Trusts,” the authors concluded that UK money managers were unable to outperform markets in any meaningful sense.

Meanwhile, a study by Cambridge Associates looked at U.S. Small-Cap manager performance form 1995 to 2004. Specifically, the survey looked at the persistence of U.S. small-cap manager performance across two five-year periods: 1995 to 1999 and 2000 to 2004. Of the managers in the top quintile of performance in the first period, 59% landed in the bottom quintile in the following period. A full 97% ended up in the bottom two quintiles. In addition, more than half of the managers in the second best quintile in the 1995 to 1999 period dropped to the bottom two quintiles in 2000 to 2004. The point: there is no evidence to suggest consistency in manager performance. A couple of managers post great performance over an extended period of time. But, the reason is most certainly luck.

3.3.11 Stock Pickers in Small-Cap Markets

Many people are led to believe that active managers can provide a greater advantage and higher value to investors in the small-cap versus large-cap market, thus resulting in a larger alpha. A large alpha infers that the stock or mutual fund has performed better than would be expected based on its volatility or risk, suggesting that active management is the reason for the better than expected performance.

Richard M. Ennis and Michael D. Sebastian of Ennis Knupp + Associates, one of the 10 largest pension consulting firms, published a paper titled “The Small-Cap-Alpha Myth,” in September 2001. In the study, the firm constructed a sample of 128 small-cap managers from the Mobius Group M-Search database, a small-cap database of institutional commingled funds and composites of separate accounts. The researchers concluded that this so-called small-cap-alpha advantage is actually the “small-cap-alpha myth.” At first blush, it appears that a small-cap- alpha advantage does exist. But when looking at the 10-year period ending June 30, 2001, their research showed that the median portfolio in their sample outperformed the Russell 2000 Index by 4.04%. A more accurate picture formed when they delved deeper.

When three important performance evaluation methods were considered, the alpha diminished to virtually zero. These performance evaluation errors include (1) neglecting to account for management fees, (2) comparing the portfolio to an inappropriate benchmark, and (3) overlooking survivorship bias.

Error #1: Ninety percent of the products in the sample reported performance before fees. When fees were included in the equation, the stock picker’s advantage dropped from 4.04% to 3.09%.

Error #2: To derive an accurate net return, appropriate benchmarks must be used for comparison. A single index, such as the Russell 2000, cannot be used for proper comparison if the portfolios being compared are not exactly the same in style and make-up as that index. Ennis and Sebastian created effective style mixes (ESMs) for the products being studied. Based on a type of multiple regression, ESMs are a more precise way to benchmark. Now accounting for errors #1 and #2, the adjusted alpha dropped from 4.04% to 1.2%.

Error #3: Many databases do not include the records of stock pickers that went out of business, which hyper-inflates the performance reports of active managers and funds. This survivorship bias does not accurately reflect the true performance of all managers that started at the beginning of the period.

When considering all three performance evaluation errors, Ennis and Sebastian concluded that the true median alpha in their sample is “likely to be zero or negative, not 4%.” In conclusion they found “no support for the claim that active management of small-cap portfolios is any more fruitful than it is for large-cap portfolios.” In other words, forget about it! Focus on the only important question of investing: What allocation of index funds is most appropriate for you?