Using a new Bayesian method for the analysis of diffusion processes, this article finds that the nonlinear drift in interest rates found in a number of previous studies can be confirmed only under prior distributions that are best described as informative. The assumption of stationarity, which is common in the literature, represents a nontrivial prior belief about the shape of the drift function. This belief and the use of "flat" priors contribute strongly to the finding of nonlinear mean reversion. Implementation of an approximate Jeffreys prior results in virtually no evidence for mean reversion in interest rates unless stationarity is assumed. Finally, the article documents that nonlinear drift is primarily a feature of daily rather than monthly data, and that these data contain a transitory element that is not reflected in the volatility of longer-maturity yields.

[We investigate the asset pricing and macroeconomic implications of the ratio of new orders (NO) to shipments (S) of durable goods. NO/S measures investment commitments by firms, and high values of NO/S are associated with a business cycle peak. We find that NO/S proxies for a short-horizon component of risk premia not identified in prior work. Higher levels of NO/S forecast lower excess returns on equities and many types of bonds, at horizons from one month to one year. These effects are generally robust to the inclusion of common return predictors and are significant on an out-of-sample basis as well. We also address the term structure of risk premia by constructing a similar ratio to measure longer-term investment commitments, which predicts returns primarily at longer horizons.]

We argue that anomalies may experience prolonged decay after discovery and propose a Bayesian framework to study how that impacts portfolio decisions. Using the January effect and short-term index autocorrelations as examples of disappearing anomalies, we find that prolonged decay is empirically important, particularly for small-cap anomalies. Papers that document new anomalies without accounting for such decay may actually underestimate the original strength of the anomaly and imply an overstated level of the anomaly out of sample. We show that allowing for potential decay in the context of portfolio choice leads to out-of-sample outperformance relative to other approaches.

This paper proposes an alternative to the asymptotic principal components procedure of Connor and Korajczyk (J. Financial Econom. 15 (1986) 373) that is robust to time series heteroskedasticity in the factor model residuals. The new method is simple to use and requires no assumptions stronger than those made by Connor and Korajczyk. It is demonstrated through simulations and analysis of actual stock market data that allowing heteroskedasticity sometimes improves the quality of the extracted factors quite dramatically. Over the period from 1989 to 1993, for example, a single factor extracted using the Connor and Korajczyk method explains only 8.2% of the variation of the CRSP value-weighted index, while the factor extracted allowing heteroskedasticity explains 57.3%. Accounting for heteroskedasticity is also important for tests of the APT, with p-values sometimes depending strongly on the factor extraction method used.

Using a new Bayesian method for the analysis of diffusion processes, this article finds that the nonlinear drift in interest rates found in a number of previous studies can be confirmed only under prior distributions that are best described as informative. The assumption of stationarity, which is common in the literature, represents a nontrivial prior belief about the shape of the drift function. This belief and the use of “flat” priors contribute strongly to the finding of nonlinear mean reversion. Implementation of an approximate Jeffreys prior results in virtually no evidence for mean reversion in interest rates unless stationarity is assumed. Finally, the article documents that nonlinear drift is primarily a feature of daily rather than monthly data, and that these data contain a transitory element that is not reflected in the volatility of longer-maturity yields.

Growing evidence suggests that extraordinary average returns may be obtained by trading equity index options, and that at least part of this abnormal performance is attributable to volatility and jump risk premia. This paper asks whether such priced risk factors are alone sufficient to explain these average returns. To provide an answer in as general as possible a setting, I estimate a flexible class of nonlinear models using all S&P 500 Index futures options traded between 1986 and 2000. The results show that priced factors contribute to these expected returns but are insufficient to explain their magnitudes, particularly for short‐term out‐of‐the‐money puts.

We examine the relation between inventory investment and the cost of capital in the time series and the cross section. We find consistent evidence that risk premiums, rather than real interest rates, are strongly negatively related to future inventory growth at the aggregate, industry, and firm levels. The effect is stronger for firms in industries that produce durables rather than nondurables, exhibit greater cyclicality in sales, require longer lead times, and are subject to more technological innovation. We then construct a production-based asset pricing model with two types of capital, fixed capital and inventories, to explain these empirical findings. Convex adjustment costs and a countercyclical price of risk lead to negative time series and cross-sectional relations between expected returns and inventory growth.

We analyze the out-of-sample performance of variables shown to forecast future mutual fund alphas. The degree of predictability, as measured by alpha spreads from quintile sorts or cross-sectional regression slopes, falls by at least half post-sample. These declines appear to be primarily the result of changes in the level of arbitrage activity in the market, with mutual fund competition appearing to play a secondary role. We find no evidence that the declines are the result of data snooping or learning by investors or fund managers. Finally, we show that corporate bond fund performance exhibits similar dependence on measures of bond market arbitrage activity.

This paper investigates the behavior of the ratio of new orders to shipments of durable goods (NO/S). High levels of NO/S are associated with a business cycle peak. They predict a short-run increase in employment and fixed and inventory investment but a dramatic long-run decline in employment, fixed investment, inventories, and GDP as a whole. We also find that NO/S captures time-varying risk premia. Higher levels of NO/S forecast lower excess returns on a broad set of assets, including equities, longand intermediate-term Treasury bonds, and high- and low-grade corporate bonds, at horizons from one month to one year. These effects are robust to the inclusion of all common return predictors. We then construct an equilibrium model of investment with time to plan and a countercyclical price of risk which implies that the ratio of new orders to shipments is procyclical and predicts lower excess returns.

We find that option returns are significantly lower over nontrading periods, the vast majority of which are weekends. Our evidence suggests that nontrading returns cannot be explained by risk, but rather are the result of widespread and highly persistent option mispricing driven by the incorrect treatment of stock return variance during periods of market closure. The size of the effect implies that the broad spectrum of finance research involving option prices should account for nontrading effects. Our study further suggests how alternative industry practices could improve the efficiency of option markets in a meaningful way.