Figure 1.1: Analysis of funds’ top stock picks
Based on Graph 3 in Cohen, Polk, and Silli (2010). The graph shows, over the subsequent quarter, the average six factor adjusted annual alpha for the largest relative overweighted stock in a mutual fund portfolio, the next most overweighted, and so forth. Based on all active US equity mutual funds from 1991 to 2005.
Figure 1.1 reveals that a fund’s best idea, as measured by the largest relative portfolio weight, generates an average six factor, annualized after-the-fact alpha of 6%. What is more, the next best idea stocks also generate positive alphas. This is evidence that it is possible to build a superior stock portfolio. CPS did not explore the source of these returns, but it is reasonable to conjecture that much of the return is the result of fund BDIs (buy-side analysts and portfolio managers) harnessing behavioral factors. Probably of less importance is the investment team’s ability to build a superior information mosaic for the stocks in which they invest. [10]
Reconciling two stock picking skill research streams
A better known conclusion from this line of research is that the average active equity mutual fund earns a return that is less than, or at best equal to, the index return. [11] That is, the average fund earns a zero or negative alpha. This leads to the oft-stated conclusion that equity fund managers lack stock picking skill, which is in fact the opposite of what was just presented.
One would think that professional investors, such as mutual funds, hedge funds, and institutional managers, would be BDIs. Indeed, the analysts within such organizations are most often BDIs, but the further up one goes in the organization and the larger the fund, the more like the Crowd it becomes.
In order to grow AUM, funds must attract and retain emotional investors, which means the fund often caters to client emotions and thus ends up taking on the features of the Crowd. As the fund grows in size, it increasingly invests in those stocks favored by the Crowd, since it is easier to attract and retain clients by investing in those stocks to which clients are emotionally attached. [12] What often starts out as BDIs harnessing behavioral factors ends up with a fund morphing into something that is acceptable to the Crowd, a process I refer to as “bubble wrapping” the portfolio. Such behavior is rational on the part of the fund, as revenues are based on AUM. [13] Consistent with this argument, others have found that returns decline as the fund grows large. [14]
The combination of the many documented price distortions and the excess returns earned by active equity mutual funds on their best idea stocks provides empirical support for basic principle II. But many investors will find it more difficult to assimilate principle II than principle I, since the emotional barrier of social validation must be overcome in order to build a successful BDI portfolio.
Basic Principle III: Investment risk is the chance of underperformance
There is no more confusing issue regarding the role of investor emotions than how to measure investment risk. Those measures currently used to capture investment risk, once carefully examined, are mostly measures of emotion. As an example take volatility, as measured by return standard deviation. Earlier I reviewed the evidence regarding stock market volatility which concludes that most volatility is generated by Crowds overreacting to information flowing into the market. Indeed, almost none of the current volatility can be explained by changes in underlying economic fundamentals at both the market and individual stock level. So volatility is mostly a measure of emotions and not necessarily investment risk. This is also true of other measures, such as downside standard deviation, maximum drawdown and downside capture.
Investment risk is the chance of underperformance. Measuring underperformance depends on the time horizon of the investment and the specific goal of the investor. For example, if the goal is to have a fixed amount at a fixed time in the future (e.g. $100,000 in two years), risk is measured as the chance of ending up with less than $100,000 in two years. In this case, short-term volatility is an important contributor to risk.
In cases where there is a specific long-term need (e.g. $1,000,000 in 30 years), risk is measured as the chance of not meeting this goal. In the cases where there is no specific time horizon, however, the appropriate benchmark is the highest expected return investment being considered, since over long time periods the actual return should approximate the expected return due to the law of large numbers. Most long-term investment situations fall into the latter.
Another important consideration is that short-term volatility plays an ever smaller role as the time horizon lengthens. This is because the short-term emotionally and economically-driven price changes tend to offset one another over the long run, to the tune of reducing long-term volatility by a factor of three to four relative to short-term volatility.
Risk and volatility are not synonymous
The widely used mean-variance optimization methodology for constructing portfolios was first introduced in 1952 by 1990 Nobel Prize laureate Harry Markowitz of the Rand Corporation:
“We first consider the rule that the investor does (or should) maximize discounted expected, or anticipated, returns. This rule is rejected both as a hypothesis to explain, and as a maxim to guide investment behavior. We next consider the rule that the investor does (or should) consider expected return a desirable thing and variance of return an undesirable thing. This rule has many sound points, both as a maxim for, and hypothesis about, investment behavior.” [15]
Unfortunately, the industry took this approach and mistakenly began building portfolios by minimizing short-term volatility relative to long-term returns. This places emotion at the very heart of the long horizon portfolio construction process. In the context of BPM, the reason this approach became so popular is that it legitimizes the emotional reaction of investors to short-term volatility. I refer to this as the unholy alliance between mean-variance optimization, on the one hand, and emotional investors on the other. This is a classic case of catering to client emotions.
Currently, risk and volatility are frequently thought of as interchangeable. One of the ironies is that, by focusing on short-term volatility when building long-horizon portfolios, it is almost certain that investment risk increases. Since risk is the chance of underperformance, focusing on short-term volatility will often lead to investing in lower expected return markets (e.g. low expected return bonds versus higher expected return stocks) with little impact on long-term volatility. [16] Lowering expected portfolio return in an effort to reduce short-term volatility actually increases the chance of underperformance, which means increasing risk.
A clear example of this is the comparison of long-term stock and bond returns. Stocks dramatically outperform bonds over the long run, so by investing in bonds rather than stocks, short-term volatility is reduced at the expense of decreasing the long-term return and, in turn, increasing long-term investment risk. Equating short-term volatility with risk leads to inferior long-horizon portfolios.
The cost of equating risk and emotional volatility can be seen in other areas as well. It is known that many investors pull out of the stock market when faced with heightened volatility, but research shows that this is exactly when they should remain in the market and even increase their stock holdings, as subsequent returns are higher on average while volatility declines. [17]
It is also the case that many investors exit after the market declines only to miss the subsequent