Competitive Advantage in Investing. Steven Abrahams
at the University of Chicago and Kenneth French at Dartmouth again ran the numbers on portfolios of stocks built to be either high or low beta (Fama and French, 2004). Assembling these portfolios at the beginning of each year, they looked at performance over the year that followed. CAPM would predict that the safest portfolio of stocks would return only slightly more than the riskless rate for the year. As portfolios became riskier, their return simply would be the excess return of the market portfolio that year multiplied by each portfolio beta. Testing from 1928 through 2003, they compared actual returns on these portfolios to returns predicted by CAPM. They found that low beta stocks had returns higher than predicted, and high beta stocks had returns lower than predicted (figure 3.1). Again, the line was too flat.
Higher beta investments still showed higher returns, but the flatness of the observed capital markets line suggested that CAPM alone might not explain it. The theory had a mysterious flaw.
Figure 3.1 On stock portfolios that vary in their beta, realized returns are too flat compared to the predictions of CAPM.
Note: Average annualized monthly return versus beta for value-weight portfolios formed on prior beta, 1928–2003.
Source: From Fama and French (2004).
Performance Turns on More Than Beta
CAPM also predicted that investment returns depend only on an asset's expected return premium over the riskless rate, or
Starting in the late 1970s, studies of investment returns started making the case that factors other than expected return premium explained asset returns. A study by Sanjay Basu showed that earnings-to-price ratios added to the ability of CAPM to predict stock returns, with high earnings-to-price equities outperforming low (Basu, 1977). A study by Rolf Banz a few years later showed that market capitalization helped, too, with smaller market capitalizations performing better than predicted by CAPM (Banz, 1981). Laxmi Bhandari followed this lead and by the end of the 1980s found that debt-to-equity ratios helped CAPM explain returns, with high debt-to-equity performing better than predicted by beta alone (Bhandari, 1988). And other researchers found that high book-to-market equities also performed better than predicted by beta (Stattman, 1980; Rosenberg, Reid, and Lanstein, 1985).
Eugene Fama and Kenneth French later incorporated influences beyond beta into a proposed model of asset returns (Fama and French, 1993, 1996). In their view of the investment world, the expected excess return on any specific asset at a point in time,
Fama and French fit the elements of their approach together in a single statement:
Fama and French point out that the average annual excess return of the market portfolio over the riskless rate from 1927 to 2003 was 8.3%. The average difference in return between small and large stocks, however, was 3.6%, and the average difference between high and low book-to-market stocks was 5.0%. They also put their approach to the same test that Jensen applied to CAPM:
In this test, if Fama and French's model described the world well, the value of αi also should be zero. Fama and French have broadly found that this approach does leave a value of αi about zero. Work by other researchers has found similar results (see, for example, Loughran and Ritter, 1995; Mitchell and Stafford, 2000). Studies of performance in mutual funds have also found that Fama and French's factors explain performance better than a simple CAPM approach (Carhart, 1997). Beyond the academic, investment firms such as Dimensional Advisors and AQR have built substantial businesses on Fama and French's observation, running portfolios that use these factors to try to outperform broad market benchmarks.
In addition to Fama and French, other work has found more factors that seem to sway asset returns. Jegadeesh and Titman have found that returns seem to depend on a sort of momentum (Jegadeesh and Titman, 1993). Stocks that do better than the market over the most recent three or 12 months tend to continue doing well over the next few months, and stocks that do poorly continue doing poorly. Performance seems to persist and shape returns beyond even the factors that Fama and French describe. The list of influences on asset return grows.
Significant elements of investment performance seem to rely on things beyond CAPM's simple multiple of excess market performance. CAPM again falls short.
Seeds of New Approaches
At the same time that both theorists and practitioners embraced CAPM and began to see its shortcomings, intriguing explanations and steps in new directions began to emerge.
Both the flatness of returns relative to Sharpe's CAPM predictions and the influence on returns of factors beyond beta have led other analysts to argue that structural aspects of markets—not addressed by CAPM—lead to these results. In fact, Fischer Black, who spent years immersed in theory at the University of Chicago and years immersed in markets at Goldman Sachs, notes this possibility (Black, 1993):
Another reason for a flatter line is restricted borrowing. Margin requirements, borrowing rates that are higher than lending rates, and limited deductibility of interest costs all tend to make the line flatter. Those who can't borrow at good rates bid up the prices of high-beta stocks instead.
Yet another reason for a flatter line, I believe, is investor psychology, in particular “reluctance to borrow,” even when the rules allow it and the rates are good. Many people seem to dislike the idea of borrowing or the trading needed to adjust borrowing amounts to the values of their securities portfolios. (p. 38)
Andrea Frazzini and Lasse Pedersen also argue that limits to investors' ability to leverage or deleverage positions in the market basket explain some of the flatness in realized returns (Frazzini and Pedersen, 2012):
For instance, individual investors and pension funds may not be able to use any leverage, banks face regulatory capital constraints, and hedge funds must satisfy their margin requirements. However, an investor can gain substantial market exposure without using outright leverage by buying options, leveraged ETFs, or other securities that embed the leverage. In fact, many of these securities are designed precisely to provide embedded leverage. (p. 1)
Because of these limits to leverage, Frazzini and Pedersen echo Black's assumption that investors pay a premium to buy assets with embedded leverage. That leverage can come from a high beta to an underlying index or assets. They show that a series of assets with high