Morningstar Advisor - October/November 2012 - (Page 38)

Spotlight The excess return of an asset is its return minus the return on a risk-free asset, denoted R ƒ. Beta represents the “exposure” of an asset to the market factor, and the expected excess return of the market represents the factor risk premium. Let us think about the intuition behind this equation. An asset with a higher beta is expected to yield a higher return. The higher return is a compensation for the asset’s exposure to the factor. Whenever the market declines, everyone is poorer and unhappier, but the high-beta assets are the ones that do the most damage. How can we test this equation, given that we do not observe expectations? Let us rewrite the equation as follows: Ri  R ƒ  forecasting error M ƒ i (R  R )  i Alpha is the intercept term in this regression. An empirical test of any factor model (including the CAPM) is whether the alphas for all assets are plausibly equal to zero. Alternative Factor Models to a considerable extent, the shortcomings of the CAPM disappeared. Again, the main test is whether alphas are plausibly different from zero. Additional factors The size and value factors are now widely accepted and form the basis of most factor models. Both practitioners and academics continue to look for systematic return patterns that cannot be explained by the FamaFrench model alone. John Cochrane (2011) lists “momentum, accruals, equity issues and other accounting-related sorts, beta arbitrage, credit risk, bond and equity market-timing strategies, foreign-exchange carry trade, put option writing, and various forms of ‘liquidity provision’.”3 Momentum4 is the most widely recognized additional factor and helps explain why stocks that have done well in the recent past often continue to outperform stocks that have recently done poorly. The reason for this premium is not well understood. It has been argued that momentum is difficult to exploit due to the fact that it requires frequent trading, which increases transaction costs5. Factor Investing in Practice The CAPM was a great success and remains to this day a useful benchmark for understanding asset returns, in particular for stocks. Not surprisingly, however, researchers have sought and found alternative factor models that are now widely accepted as superior to the CAPM. Departures from the CAPM are sometimes called anomalies. This is a bit of a misnomer, because it should not be surprising at all that the CAPM fails to account for certain return patterns. Recall that the assumptions underlying the CAPM are pretty strong, so it would be surprising if CAPM were in fact found to be “true.” The most famous alternative factor model is the Fama-French model. Fama-French Model In 1993, Eugene Fama and Kenneth French introduced a model with three factors. The first factor is a measure of the market portfolio, as in the CAPM. The second factor is a portfolio that is long on companies with a high book-tomarket ratio and short on companies with a low book-to-market ratio. This factor is called high-minus-low (HML). Companies with high book-to-market ratios—companies with high debt-to-equity ratios—are called value companies, while those with a low book-tomarket are called growth. The third factor is a portfolio long on companies with relatively small market capitalization and short on those with a big market capitalization. This factor is called small-minus-big (SMB). When running regression analyses using these three factors, Fama and French found that, Note the difference: We now have returns, whereas before we had expected returns. You may recognize this as a linear regression model.2 If we wish to uncover (historical) betas from returns data, we need to assume that 1) forecasting errors are zero on average and 2) that they are not related to excess market returns. Put differently, people’s expectations of returns are correct on average, and their incorrectness is not related to excess market returns. Alpha Let us add one final ingredient to the equation, a parameter called alpha: Ri  R ƒ  alpha  0 i We turn now to the question: How might an investor use any of the insights from asset pricing research? But first we need to discuss an important caveat. Asset pricing theory does not really offer any practical guidance on how to value an asset. In other words, it does not say which information to examine if one wishes to forecast future payments or (equally important) the relationship between payments of different assets. In short, it does not offer a method for examining investment opportunities and evaluating whether they are worthwhile.  i (R M  R ƒ)  i 2 The forecasting error is given by i  i (E(R M )  R M)  Ri  E(Ri ) 3 Cochrane, John (2011), “Discount Rates,” Journal of Finance, vol. 66, no. 4. 4 Jegadeesh, Narasimhan, and Sheridan Titman (1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, Journal of Finance, vol 48, no. 1. 5 Carhart, Mark (1997), “On Persistence in Mutual Fund Performance,” Journal of Finance, vol. 52, no. 1. 38 Morningstar Advisor October/November 2012

Table of Contents for the Digital Edition of Morningstar Advisor - October/November 2012

Morningstar Advisor - October/November 2012
Contents
Contributors
Letter From the Editor
Ill Communication
Do You Use Factor-Investing Strategies?
Practicing What She Preaches
How to Determine the True Price of ETFs
The Quant Factor
Managers Dispute the Death of Equities
Investments á la Carte
Investment Briefs
Five Inconvenient Truths of Manager Research
Health Care’s Outlook Clarifies
Exploring the World of Factors
Uncloaking the Alpha Machine
Factor Strategies Gain Footholds in Practices
Big Mo
Fitting Factors Into the Formula
Clients Have a Friend in Luther King
Less-Liquid Holdings Mean More-Solid Results
Retirement-Withdrawal Strategies Quantified
Amid Turmoil, Don’t Discount Foreign Equities
Our Favorite Mutual Funds
50 Most-Popular Equity ETFs
Undervalued Stocks With Wide Moats
Should I Stay or Should I Go?

Morningstar Advisor - October/November 2012