1584 The Journal of finance Fama and French(1989 )suggest a different way to judge the implications of return predictability for market efficiency. They argue that if variation in expected returns is common to different securities, then it is probably a rational result of variation in tastes for current versus future consumption or in the investment opportunities of firms. They show that the dividend yield the NYSE value-weighted portfolio indeed forecasts the returns on corpo rate bonds as well as common stocks. Moreover two term-structure variables (1)the default spread (the difference between the yields on lower-grade and Aaa long-term corporate bonds)and (2) the term spread(the difference between the long-term Aaa yield and the yield on 1-month Treasury bills), forecast returns on the value- and equally weighted portfolios of NYSE stocks as well as on portfolios of bonds in different(Moodys)rating groups Keim and Stambaugh(1986)and Campbell(1987)also find that stock and bond returns are predictable from a common set of stock market and term structure variables. Harvey(1991)finds that the dividend yield on the S&P 500 portfolio and U. S. term-structure variables forecast the returns on portfo lios of foreign common stocks, as well as the S&P return. Thus the variation in expected returns tracked by the U.S. dividend yield and term-structure variables is apparently international Ferson and Harvey (1991) formally test the common expected returns hypothesis Using the asset pricing models of Merton(1973) and Ross(1976). they try to link the time-series variation in expected returns, captured by dividend yields and term-structure variables, to the common factors in re turns that determine the cross-section of expected returns. They estimate that the common variation in expected returns is about 80% of the pre dictable time-series variation in the returns on government bonds, corporat bonds, and common-stock portfolios formed on industry and size. They can't reject the hypothesis that all the time-series variation in expected returns is commo Fama and French(1989)push the common expected returns argument for market efficiency one step further. They argue that there are systematic patterns in the variation of expected returns through time that suggest that it is rational. They find that the variation in expected returns tracked by D/P and the default spread(the slopes in the regressions of returns on D/P or the default spread) increase from high-grade bonds to lowgrade bonds, from bonds to stocks, and from large stocks to small stocks. This ordering corre sponds to intuition about the risks of the securities. On the other hand, the variation in expected returns tracked by the term spread is similar for all long-term securities(bonds and stocks), which suggests that it reflects varia tion in a common premium for maturity risks returns on bonds and stocks captured by their forecasting variables s pected Finally, Fama and French(1989)argue that the variation in the tent with modern intertemporal asset-pricing models(e.g, Lucas(1978), Breeden(1979), as well as with the original consumption-smoothing stories of Friedman(1957)and Modigliani and Brumberg(1955). The general mes sage of the Fama-French tests(confirmed in detail by Chen(1991)) is that
1585 D/P and the default spread are high (expected returns on stocks and bonds are high) when times have been poor (growth rates of output have been persistently low). On the other hand, the term spread and expected returns are high when economic conditions are weak but anticipated to improve (future growth rates of output are high). Persistent poor times may signal low wealth and higher risks in security returns, both of which can increase expected returns. In addition, if poor times(and low incomes)are anticipated to be partly temporary, expected returns can be high because consumers attempt to smooth consumption from the future to the present For the diehard bubbles fan, these arguments that return predictability is C variation in expected mean that irrational bubbles are correlated across assets and markets (domestic and international). The correlation between the common variation in expected returns and business conditions may just mean that the common bubbles in different markets are related to business conditions. On the other hand, if there were evidence of security-specific variation in expected returns, an efficient-markets type could argue that it is consistent with uncorrelated variation through time in the risks of individual securities. All of which shows that deciding whether return predictability is the result of rational variation in expected returns or irrational bubbles is never clearcut My view is that we should deepen the search for links between time- varying expected returns and business conditions, as well as for tests of whether the links conform to common sense and the predictions of asset pricing models. Ideally, we would like to know he returns relates to productivity shocks that affect the demand for capital goods, and to shocks to tastes for current versus future consumption that affect the supply of savings. At a minimum, we can surely expand the work in Chen(1991)on the relations between the financial market variables that track expected returns(D/P and the term-structure variables)and the behav or of output, investment, and saving. We can also extend the preliminary attempts of Balvers, Cosimano and McDonald (1990), Cechetti, Lam, and Mark (1990) and Kandel and Stambaugh (1990) to explain the variation through time in expected returns in the confines of standard asset-pricing The fact that variation in expected returns is common across securities and markets, and is related in plausible ways to business conditions, leans me toward the conclusion that, if it is real it is rational. But how much of it real? The standard errors of the slopes for the forecasting variables in the return regressions are typically large and so leave much uncertainty about forecast power(Hodrick(1990), Nelson and Kim(1990)). Inference is also clouded by an industry-level data-dredging problem. With many clever re searchers, on both sides of the efficiency fence, rummaging for forecasting variables, we are sure to find instances of"reliable"return predictability that are in fact spurious
The Journal of finance Moreover, the evidence that measured variation in expected returns is common across securities and related to business conditions, does not neces arily mean that it is real. Suppose there is common randomness in stock and bond returns due to randomness in business conditions. Then measured variation in expected returns that is the spurious result of sample-specific conditions is likely to be common across securities and related to business conditions. In short, variation in expected returns with business conditions is plausible and consistent with asset-pricing theory. But evidence of pre dictability should always be met with a healthy dose of skepticism, and a diligent search for out-of-sample confirmation C. Volatility Tests and Seasonals in Returns C. 1. Volatility Tests Volatility tests of market efficiency, pioneered by LeRoy and Porter(1981) and Shiller(1979, 1981), have mushroomed into a large literature. Excellent reviews(West(1988), LeRoy(1989), Cochrane(1991)) are available, so here I briefly comment on why I concur with Merton (1987), Kleidon(1988), and Cochrane(1991) that the tests are not informative about market efficiency A central assumption in the early volatility tests is that expected returns are constant and the variation in stock prices is driven entirely by shocks to expected dividends. By the end of the 1970s, however, evidence that expected stock and bond returns vary with expected inflation rates, interest rates ther term-structure variables was becoming commonplace(Bodie(19 Jaffe and Mandelker (1976), Nelson(1976), Fama (1976a, b), Fama and Schwert(1977)). With all the more recent evidence on return predictability it now seems clear that volatility tests are another useful way to show that expected returns vary through time The volatility tests, however, give no help on the central issue of whether the variation in expected returns is rational. For example, is it related in sensible ways to business conditions? Grossman and Shiller (1981)and Campbell and Shiller(1988a)attempt to move the volatility tests in this direction. Predictably, however, they run head-on into the joint hypothesis problem. They test market efficiency jointly with the hypothesis that their versions of the consumption-based asset-pricing model capture all rational variation in expected returns C.2. Return Seasonality The recent literature includes a spate of" anomalies"papers that document seasonals"in stock returns. Monday returns are on average lower than returns on other days(Cross(1973), French(1980), Gibbons and Hess(1981)) Returns are on average higher the day before a holiday(Ariel 1990), and the last day of the month(Ariel (1987)). There also seems to be a seasonal in ntraday returns, with most of the average daily return coming at the beginning and end of the day(Harris(1986)). The most mystifying seasonal
Efficient Capital Markets: IT 1587 is the January effect. Stock returns, especially returns on small stocks, are on average higher in January than in other months. Moreover, much of the higher January return on small stocks comes on the last trading day in December and the first 5 trading days in January(Keim( 1983), Roll(1983)) Keim(1988)reviews this literature. He argues that seasonals in returns are anomalies in the sense that asset-pricing models do not predict them,but they are not necessarily embarassment for market efficiency. For example Monday, holiday, and end-of-month returns deviate from normal average daily returns by less than the bid-ask spread of the average stock(Lakonishok nd Smidt (1988)). Turn-of-the-year abnormal returns for small stocks are arger, but they are not large relative to the bid-ask spreads of small stocks (Roll (1983). There is thus some hope that these seasonals can be explained in terms of market microstructure, that is, seasonals in investor trading patterns that imply innocuous seasonals in the probabilities that measured prices are at ask or bid. The evidence in Lakonishok and Maberly(1990)on Monday trading patterns, and in Reinganum(1983), Ritter( 1988), and Keim (1989)on turn-of-the-year trading are steps in that direction We should also keep in mind that the CrSP data, the common source evidence on stock returns, are mined on a regular basis by many researchers Spurious regularities are a sure consequence. Apparent anomalies in returns hus warrant out-of-sample tests before being accepted as regularities that are likely to be present in future returns. Lakonishok and Smidt(1988)find that the January, Monday, holiday, and end-of-month seasonals stand up to replication on data preceding the periods used in the original tests. The intramonth seasonal (most of the average return of any month comes in the first half) of Ariel ( 1987), however, seems to be specific to his sample period Connolly (1989)finds that the Monday seasonal in NYSE returns is weaker after 1974 Recent data on the premier seasonal, the January effect, tell an interesting story. Table I shows that for the 1941-1981 period, the average monthly January return on a value-weighted portfolio of the smallest quintile of CRSP stocks is 8.06%(), versus 1. 34% for the S&P 500. During the 1941-1981 period, there is only 1 year(1952)when the S&P January return is above the CRSP bottom-quintile return. Moreover, for 1941-1981, all of the advantage of the CRSP small-stock portfolio over the S&P comes in January; the February-to-December average monthly returns on the two portfolios differ by only 4 basis points(0.88% for CRSP Small versus 0.92% for the S&P) For 1982-1991, however, the average January return on the CRSP small stock portfolio, 5. 32%, is closer to the January S&P return, 3.20%.More striking, the average January return on the DFA U.S. Small Company Portfolio, a passive mutual fund meant to roughly mimic the CRSP bottom tile, is 3.58%, quite close to the January S&P return(3.20%)and much less than the January return for the CRSP small-stock portfolio(5.32%).The CRSP small-stock portfolio has a higher return than the DFA portfolio in very January of 1982-1991. But January is the exception; overall, the DFA portfolio earns about 3% per year more than the CRSP bottom quintile
1588 The Journal of finance CRSP Stocks, and the DFA U.S. Small Company Portfolio.op Comparison of Returns on the s&P 500, the Smallest Quintile 1941-81and198291 The value-weighted CRSP small-stock portfolio(CRSP Small) contains the bottom qunitile of NYSE stocks, and the AMEX and NaSdAQ stocks that fall below the size (price times shares) breakpoint for the bottom qunitile of NYSE stocks. The portfolio is formed at the end of each quintile of NYSE stocks. AMEX stocks are added in July 1962 and NASDAQ stocks in Januar 1973. The DFA U.S. Small Company Portfolio(DFA Small) is a passive mutual fund meant roughly mimic CRSP Small. DFA Small returns are only available for the 1982-1991 period Average Monthly Returns for January, February to December, and All Months 1982-1990(91 for January) Portfolio Jan Feb-Dec All Feb-Det All 0.92 CRSP Small DFA Small 0.90 Year-by-Year Comparison of January Returns for 1982-1991 CRSP Small DFA Small CRSP-S&P DFA-S&P 1.53 1.96 1091 -400 7.58 5.56 1991 10 8.41 Why these differences between the returns on the CrsP small-stock portfo io and a mimicking passive mutual fund? dFa does not try to mimic exactly the crsp bottom quintile. Concern with trading costs causes dfa to deviate from strict value weights and to avoid the very smallest stocks(that are however, a small fraction of a value-weighted portfolio). Moreover, DFA does not sell stocks that do well until they hit the top of the third (smallestdecile This means that their stocks are on average larger than the stocks in the CRSP bottom quintile (a strategy that paid off during the 1982 of an inverted size effect. The important point, however, is that small-stock returns, and the very existence of a January bias in favor of small stocks, are sensitive to small changes (imposed by rational trading) in the way small-stock portfolios are defined. This suggests that, until we know more about the pricing(and