Many evaluation techniques typically measure performance as deviations of average returns on actively managed funds from those predicted by some asset pricing model. Empirical evidence, however, has so far suggested that all asset pricing models lack empirical support, implying that the models contain mis-specification errors to various degrees. Evaluating mutual fund performance relative to any of these models thus becomes problematic. In this paper, we propose an approach to performance measurement that emphasizes minimizing explicitly the pricing error associated with an asset pricing function which is employed to compute performance measures. This approach is henceforth called the minimum specification-error (MSE) method. We also discuss the statistical properties for implementing MSE performance measure. To demonstrate the significance of the pricing error confounded in evaluation measurement, we contrast our methodology with the Grinblatt and Titman (1989) period weighting approach and with the empirical implementation of Chen and Knez (1996). We find that the greater the pricing error of passive assets, the larger the performance measures. Given the average pricing error generated from a collection of 163 diverse passive portfolios used in this analysis the performance values assigned to a large number of the funds become statistically and economically insignificant.

A recent study by Goyal and Santa-Clara [J. Finance 58 (2003) 975] finds a significantly positive relationship between average stock returns and pre-determined average return volatility measures, while finding no relationship between the average return and its own volatility. The result is interpreted as evidence that idiosyncratic risk matters in asset pricing. We re-examine the issue in extended sample periods and find the proclaimed positive relationship is not substantiated. Our analysis indicates that the above-mentioned positive relationship is mainly driven by the data in the 1990s. The trading strategy suggested by Goyal and Santa-Clara to exploit the return predictability by pre-determined volatility does not yield sustained economic gains.

It is well documented that the time-varying bond excess returns can be explained by predetermined variables such as information in the term structure and macro economic variables. Recent studies suggest that demand and supply of bonds influence bond excess returns. We extend the literature and find that monetary system attributes affect return dynamics in the bond market. By introducing a theoretical model to forecast excess returns on Treasury bonds in the context of China’s unique monetary system, this paper attributes the predicted components of bond excess returns mainly to the inflexible term structures of official interest rates set by China’s central bank.

In this paper we investigate the properties of the standard two‐pass methodology of testing beta pricing models with misspecified factors. In a setting where a factor is useless, defined as being independent of all the asset returns, we provide theoretical results and simulation evidence that the second‐pass cross‐sectional regression tends to find the beta risk of the useless factor priced more often than it should. More surprisingly, this misspecification bias exacerbates when the number of time series observations increases. Possible ways of detecting useless factors are also examined.

Using within-loan estimations to remove the impact of demand-side factors, we find that the capital levels of banks participating in the same syndicated loan are positively associated with the banks’ contributions to the loan. Consistent with the argument that higher capital reduces the cost of uninsured debt, the positive effect of bank capital on lending is stronger among banks that rely more on wholesale funding. Furthermore, we find that banks increase their contributions to syndicated loans after receiving Troubled Asset Relief Program (TARP) funding. Taken together, we provide new evidence on the importance and causal effect of bank capital on lending.

We propose a new, price-based measure of information risk called abnormal idiosyncratic volatility (AIV) that captures information asymmetry faced by uninformed investors. AIV is the idiosyncratic volatility prior to information events in excess of normal levels. Using earnings announcements as information events, we show that AIV is positively associated with informed return run-ups, abnormal insider trading, short selling, and institutional trading during pre-earnings-announcement periods. We find that stocks with high AIV earn economically and statistically larger future returns than stocks with low AIV. Taken together, our findings support the notion that information risk is priced.

In this article we generalize Harvey's (1989) empirical specification of conditional asset pricing models to allow for both time‐varying covariances between stock returns and marketwide factors and time‐varying reward‐to‐covariabilities. The model is then applied to examine the effects of firm size and book‐to‐market equity ratios. We find that the traditional asset pricing model with commonly used factors can only explain a small portion of the stock returns predicted by firm size and book‐to‐market equity ratios. The results indicate that allowing time‐varying covariances and time‐varying reward‐to‐covariabilities does little to salvage the traditional asset pricing models.