Asset Pricing.

• Author: Cochrane, John H.

John H. Cochrane [*]

As the name suggests, a large part of our effort in this program involves understanding the prices of financial assets -- stocks, bonds, options, currencies, and derivatives. Why do prices move? Why do some assets give consistently higher returns than others? What are the underlying macroeconomic risks that balance low prices and attractive returns? Asset pricing researchers also study the economics of financial markets more generally, including the formation and design of securities and security markets, the nature of financial contracting and banking, the trading mechanisms in securities markets, and the regulation of markets and related issues. Asset pricing research is characterized by a lively interplay between theory, empirical work that evaluates theories, and empirical work that focuses on interesting facts.

New Factors, Time-Variation, and Macroeconomics

Average returns are higher and prices are lower for securities that pay off poorly in macroeconomic "bad times." Our central task is to find the correct measure of "bad times." A body of empirical and theoretical work now suggests a quite radical change in traditional views of this measure. First, something like a "recession factor" is important in addition to swings in the market as a whole. In fact, the bulk of cross-sectional variation in stock prices and average returns may be attributable to this additional risk factor rather than to a stock's tendency to move with the market as a whole. Second, mean returns, covariances, and risk premiums all vary through time; the variation is as large as typical values, and also has a suggestive business cycle pattern. Theory, application, and empirical work can be profoundly affected by this fact. For example, almost all variation in the cost of capital is caused by varying risk premiums, not interest rates. Much research on asset pricing fieshes out these points, a nd John Y. Campbell [1] and I [2] review this literature in depth.

Martin Lettau and Sydney Ludvigson [3] find that a conditional capital asset pricing model (CAPM) and a conditional consumption-based model can explain the cross-section of stock returns just as well as the Fama-French model which is based on size and book-to-market portfolios. In "bad times," measured by the consumption-to-wealth ratio, value stocks covary strongly with market return and with consumption growth. This covariance is absent in good times, which is why an unconditional CAPM or a consumption-based model does not work. This striking paper merges both the importance of a conditional approach and the search for additional "recession factors" that drive risk premiums.

Tano Santos and Pietro Veronesi [4] create an asset pricing model that prices a claim to dividends that is distinct from consumption. The dividend/consumption ratio (also interpreted as the ratio of human capital to market value) becomes a state variable that forecasts returns and drives variation in expected returns and covariances.

George M. Constantinides and Darrell Duffie [5] show that an increase in cross-sectional risk to labor income during market downturns can, in priniciple, explain all asset pricing puzzles. Alon Bray, Constantinides, and Christopher C. Geczy [6] take up the empirical challenge: does cross-sectional risk increase enough in market downturns to be the "recession factor"? They find some preliminary support, but document the troublesome noise in individual income and consumption data.

Campbell and I [7] focus on the subject of conditioning information. We examine a model economy in which a conditional consumption-based model holds perfectly, but risk aversion varies over time. This model generates predictable returns. We find that the static CAPM is a better approximate model than the static consumption-based model, and that multifactor models beat the static CAPM in their artificial data. This calculation suggests the importance of including conditioning information in evaluating asset-pricing models.

Wayne E. Ferson and Campbell R. Harvey [8] test conditional asset pricing models. They check Merton's classic idea that bad news also indicates bad times, and thus that returns that are correlated with news variables should carry risk premiums. They find that such information variables are in fact important risk factors, This paper links the new factor question with the time-variation question nicely.

Owen Lamont [9] provides an intriguing new description of the relation between asset prices and macroeconomic events by constructing "economic tracking portfolios." While it is well known that the market as a whole is correlated with GDP and macroeconomic events, Lamont goes further and constructs portfolios that are correlated maximally with specific macroeconomic events and forecasts. He finds that using tracking portfolio returns as instruments for future economic variables substantially raises the estimated sensitivity of asset prices to news about those economic variables. Furthermore, the tracking portfolios can be used to partially hedge economic risks directly, without creating new securities as Steven J. Davis, Jeremy Nalewaik, and Paul Willen advocate. [10]

The high-tech econometric end of empirical asset pricing is similarly engaged with modeling time-varying conditioning information. [11]

Predictable Returns and the Value Effect

The fact, character, and interpretation of return predictability are still hotly debated. It appears that over the long run, stock returns "mean revert" -- high prices relative to dividends, book value, and so on signal that subsequent returns will be low. In fact, this pattern may be thought of in reverse: high prospective returns mean that cash flows are discounted at a higher rate, which in turn lowers current prices. Thus, prices reveal changes in expected returns. This pattern holds across individual stocks -- the "value effect" -- as well as over time for the market as a whole. That is a sobering thought to holders of recently hot dot-com growth stocks (with very high price-to-anything ratios). In principle, this phenomenon can be explained by slow, business-cycle related variation in risks or risk aversion, but asset pricing researchers are actively exploring the nature of that risk or risk aversion.

Randolph Cohen, Polk, and Tuomo Vuolteenaho [12] examine whether firms that have high market prices relative to book value (Tobin's q) have higher expected cash flows, or lower expected returns. In contrast to the market as a whole, in which such variation is almost entirely driven by variation in risk premiums, Cohen, Polk, and Vuolteenaho find that half or more of the cross-sectional variation in market and book ratios is...