Beating Mr. Market

Mr. Market beats some 70% of active fund managers^[1]. Considering that his investment strategy, which is formulated as simply buying the largest names and weighting them according to their market capitalization, this is a conundrum; Many empirical studies have shown that the small capitalization cohort of stocks outperforms her larger brethren, cheap stocks outperform rich, and historical winners outperform losers. In spite of such “anomalies” that have been well known for more than 50 years, more than two thirds of active fund managers are still losing to Mr. Market. On the other hand, 96% of Malkiel’s monkeys beat Mr. Market^[2]. If the fund managers were picking stocks at random, we would expect about 50% to do better and 50% to do worse, thus the managers are doing something wrong in a systematic fashion.

Mr. Market’s Achilles Heel is his algorithm of weighting portfolio positions based on market capitalization. Because price is the key determinant of market capitalization, and because price is the key determinant of value (stock prices fluctuates more than intrinsic value), such weighting scheme will always leave him overweight rich stocks and underweight cheap stocks. This is true even without knowing which stock is cheap and which stock is rich. This is the secret of the monkey’s success. Contrary to the majority of active fund managers, the primates have discovered that they can do about anything [and beat Mr. Market], as long as they sever the link between price and portfolio position size. The monkeys are constructing their portfolio through the simple algorithm of selecting the stocks randomly and sizing the positions equally. The majority of active fund managers, on the other hand, are constructing their portfolios by applying over– and under– weights relative to the country’s, sector’s or security’s weight in the benchmark. Thus they never really break the link between price and position size.

Thus the active fund managers could beat Mr. Market simply by severing the link between price and position weight. For example by copying the monkeys and equal weight the positions. Why do they not? The most likely explanation is an obsession with tracking error. The anomalies mentioned above are working over the long term, but there are numerous episodes in which they underperform Mr. Market. Such episodes could be drawn out affairs and the relative underperformance severe. Thus in a world of managing other people’s money, relative performance has become more important than absolute returns.

Another explanation why Mr. Market beats 70% of active fund managers is lack of discipline. Here the stock market is not very different from other speculative markets. Take for example horse betting. Most people who bet on horse races not only lose money, they lose much more money than they should lose based on chance alone^[3]. What this means is that someone making purely random bets on horses, or a monkey betting on horses, will over time lose the track's 'take'. In other words, if the track's take is, say 15%, then someone who selects horses based on random guesses alone would be expected to lose an average of 15% of the amount of money they bet each race day. However, the average member of the betting public actually loses 33%-100% of the money they outlay over the course of each racing day.

While the vast majority of people lose money at the races, some betting professionals consistently win. These professional bettors generally do not have inside information or any resources that are not readily available to members of the public. Nor is it usual for them to be highly educated. So, how do they win? Since the public is usually so wrong that it manages to lose far more money than it should, it stands to reason that those who are able to consistently win do the opposite of what the public does. A key factor contributing to the public's losses in the game of horse racing, and in all speculative endeavors, is something called "switches". That is, it is not the races that beat the amateurs, it's the switches. Whereas the professionals develop a plan and stick to the plan, the amateurs are continually changing (switching) such things as the types of bets they make, the amount they bet on each race, and the way they select horses.

A key difference between the 30% winners and the 70% losers is that the winners have figured out a way to avoid the switches.

Stock markets are no different and most fund managers will have experienced the frustration wrought by switches. That is, they will at some point have been coaxed by Mr. Market to switch strategies at exactly the wrong time. A key difference between the 30% winners and the 70% losers is that the winners have figured out a way to avoid the switches. In other words, the formula for success is not so much about the depth of the analysis or the exact selection criteria used, but discipline to avoid the switches.

Many compare the stock market to casinos. Whereas the comparison is moot in an absolute sense, it is quite relevant when the subject is excess return. We can think of the whole apparatus comprising the stock exchanges, banks, brokers, research services, banks, etc as the “house”. Transaction and research costs would be the “house take” and active fund managers should, on average lose the take. One fund manager’s excess return is another fund manager’s relative loss. Thus the endeavor to beat the market is all about betting against other traders; The fund manager bets that one particular stock pick will do better than average. If only one such bet was made, he would have to be 100% right in order to beat the market. However, by making several bets his break-even success rate could be a lot lower, and the more bets made, he would converge towards whatever success rate inherent in his betting strategy. In addition to the batting average, the outcome is dependent on how much he makes every time he wins compared to how much he loses when he misses. This combination of success rate and profit per winning trade versus the miss rate and the average loss on the miss, is his “edge”. Note that whenever a trader is making more money on the winning trades than he loses on the misses, he could have an edge even though he is right less than 50% of the time. Realistically any edge is small and it is therefore imperative to make numerous bets using the exact same strategy without switching. Because every time the fund manager changes his betting strategy he needs to start all over again in order to converge towards his true batting average. Such inconsistency in investment approach is a widespread phenomenon among active fund managers. For example, among US large cap mutual funds, 45 per cent shifted from an initial investment style (such as large cap value) five years ago to a different category today^[4]. Discipline is perhaps the most important aspect of active fund management, without discipline any information advantage the manager might have is lost.

There you have it! Two steps on the road to beat Mr. Market; Break the link between price and position size and invest with discipline, discipline and discipline. Because breaking the link between price and position size means that the manager holds a portfolio quite different from his benchmark, he must dare to be different. The difference between an active portfolio and the benchmark is called active share. A study undertaken by Martijn Cremers and Antti Petajisto concludes that it is only the cohort of managers with a high active share that beats the market after transaction costs^[5]. Nothing ventured, nothing gained.

Footnotes

• 1 Aye M. Soe, «S&P Indices Versus Active Funds Scorecard», Mid Year 2013, McGraw Hill Financial, New York.
• 2 Robert D. Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall, «The Surprising Alpha From Malkiel’s Monkey and Upside-Down Strategies”, The Journal of Portfolio Management, Vol 39, Number 4, Summer 2013.
• 3 Robert L. Bacon, «Secrets of Professional Turf Betting», Amerpub Company, 1952, New York
• 4 Ibid (1)
• 5 Martijn Cremers and Antti Petajisto, “How Active is Your Fund Manager? A New Measure That Predicts Performance”, Yale School of Management, August 2006