Knowledge that Transforms

A new empirical model for intertemporal capital asset pricing is presented that allows both time-varying risk premia and betas where the latter are identified from the dynamics of the conditional covariance of returns. The model is more successful in explaining the predictable variations in excess returns when the returns on the stock market and corporate bonds are included as risk factors than when the stock market is the single factor. Although changes in the covariance of returns induce variations in the betas, most of the predictable movements in returns are attributed to changes in the risk premia.

Charges that geographical redlining is widely practiced by mortgage lenders and is associated with racial discrimination have received much attention. However, empirical research in this area has yet to document a convincing answer to the question of whether redlining even exists. Much of the previous research in this area has suffered from failure to account for variations in risk and/or failure to adequately control for geographical differences in demand. This study addresses these problems in an effort to determine whether the disparity in the flow of mortgage credit can be explained by differences in risk and demand.

There is an exact linear relation between expected returns and true 'betas' when the market portfolio is on the ex ante mean-variance efficient frontier but empirical research has found little relation between sample mean returns and estimated betas. A possible explanation is that market portfolio proxies are mean-variance inefficient. The authors categorize proxies that produce particular relations between expected returns and true betas. For the special case of a zero relation, a market portfolio proxy must lie inside the efficient frontier but it may be close to the frontier.

In this article, the authors measure and interpret the common 'factors' that describe money market returns. Results are presented for both three- and four-factor models. The authors find that the three-factor model explains, on average, 86 percent of the total variation in most money market returns while the four-factor model explains, on average, 90 percent of this variation. Using mimicking portfolios, they provide an interpretation of the systematic risks represented by these factors.

The authors investigate the informational role of volume and its applicability for technical analysis. They develop a new equilibrium model in which aggregate supply is fixed and traders receive signals with differing quality. The authors show that volume provides information on information quality that cannot be deduced from the price statistic. They show how volume, information precision, and price movements relate, and demonstrate how sequences of volume and prices can be informative. The authors also show that traders who use information contained in market statistics do better than traders who do not. Technical analysis, thus, arises as a natural component of the agents' learning process.

Many financial markets researchers have sought an explanation for the role of January in stock returns. Any explanation of this phenomenon that is consistent with rational pricing must specify a source of seasonality in expected returns. The pervasive seasonality in the macroeconomy is an appealing possibility. A multifactor model that links macroeconomic risk to expected return is found to show substantial seasonality in expected returns. This model accounts for the seasonality in average returns, while the capital asset pricing model cannot.

Using a database that is free of survivorship bias, this article finds that book-to-market equity, earnings yield, and cash flow yield have significant explanatory power with respect to the cross-section of realized stock returns during the period from July 1940 through June 1963. There is a strong January seasonal in the explanatory power of these variables, even though small stocks are, by construction, excluded from the sample.

The authors investigate the cross-sectional relation between industry-sorted stock returns and expected inflation, and they find that this relation is linked to cyclical movements in industry output. Stock returns of noncyclical industries tend to covary positively with expected inflation, while the reverse holds for cyclical industries. From a theoretical perspective, the authors describe a model that captures both (1) the cross-sectional variation in these relations across industries and (2) the negative and positive relation between stock returns and inflation at short and long horizons, respectively. The model is developed in an economic environment in which the spirit of the Fisher model is preserved.

This article develops a new method for inferring risk-neutral probabilities (or state-contingent prices) from the simultaneously observed prices of European options. These probabilities are then used to infer a unique fully specified recombining binomial tree that is consistent with these probabilities (and, hence, consistent with all the observed option prices). A simple backwards recursive procedure solves for the entire tree. From the standpoint of the standard binomial option pricing model, which implies a limiting risk-neutral lognormal distribution for the underlying asset, the approach here provides the natural (and probably the simplest) way to generalize to arbitrary ending risk-neutral probability distributions.