This paper proposes a measure of ex ante equity premium, IPOFDR, which is the average difference between the initial public offering (IPO) offer price and the 1st-trading-day close price. I test the idea in 3 ways. First, there is a positive relation between IPOFDR and future market returns. Second, changes in IPOFDR help explain the cross section of stock returns. Third, the predictive power of IPOFDR for stock returns reflects mainly its close relation with market variance and average idiosyncratic variance—arguably measures of systematic risk. These results cast doubt on the notion that the IPO 1st-day return is a measure of investor sentiment.

This paper presents a consumption-based model that explains the equity premium puzzle through two channels. First, because of borrowing constraints, the shareholder cannot completely diversify his income risk and requires a sizable risk premium on stocks. Second, because of limited stock market participation, the precautionary saving demand lowers the risk-free rate but not stock return and generates a substantial liquidity premium. This model also replicates many other salient features of the data, including the first two moments of the risk-free rate, excess stock volatility, stock return predictability, and the unstable relation between stock volatility and the dividend yield.

This paper develops and estimates a heteroskedastic variant of Campbell’s [Campbell, J., 1993. Intertemporal asset pricing without consumption data. American Economic Review 83, 487–512] ICAPM, in which risk factors include a stock market return and variables forecasting stock market returns or variance. Our main innovation is the use of a new set of predictive variables, which not only have superior forecasting abilities for stock returns and variance, but also are theoretically motivated. In contrast with the early authors, we find that Campbell’s ICAPM performs significantly better than the CAPM. That is, the additional factors account for a substantial portion of the two CAPM-related anomalies, namely, the value premium and the momentum profit.

We argue that changes in average idiosyncratic volatility provide a proxy for changes in the investment opportunity set and that this proxy is closely related to the book-to-market factor. We test this idea in two ways using G7 countries' data. First, we show that idiosyncratic volatility has statistically significant predictive power for aggregate stock market returns over time. Second, we show that idiosyncratic volatility performs just as well as the book-to-market factor in explaining the cross section of stock returns. Our results suggest that the hedge against changes in investment opportunities is an important determinant of asset prices. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected], Oxford University Press.

Consistent with the post-1962 US evidence by Ang et al. [Ang, A., Hodrick, R., Xing Y., Zhang, X., 2006. The cross-section of volatility and expected returns. Journal of Finance 51, 259–299], we find that stocks with high idiosyncratic variance (IV) have low CAPM-adjusted expected returns in both pre-1962 US and modern G7 data. We also test in three ways the conjecture that IV is a proxy of systematic risk. First, the return difference between low and high IV stocks – that we dub as IVF – is a priced factor in the cross-section of stock returns. Second, loadings on lagged market variance and lagged average IV account for a significant portion of variation in average returns on portfolios sorted by IV. Third, the variance of IVF correlates closely with average IV, and the two variables have similar explanatory power for the time-series and cross-sectional stock returns.

Average idiosyncratic stock volatility forecasts the bilateral exchange rates of the US dollar against major foreign currencies in and out of sample. The US dollar tends to appreciate after an increase in US idiosyncratic volatility. Similarly, ceteris paribus, German and Japanese idiosyncratic volatilities positively and significantly correlate with future US dollar prices of the Deutsche mark and the Japanese yen, respectively. Our results suggest that exchange rates are predictable.

Although many studies show that the presence of institutional investors facilitates the incorporation of accounting information into financial markets, the evidence of informed trading by institutions is rather limited in the extant literature. We address these inconsistent findings by proposing PC _ NII , percentage changes in the number of a stock's institutional investors, as a novel informed trading measure. PC _ NII is better able to detect informed trading than are changes in institutional ownership ( Δ IO )—the measure commonly used in previous studies—because (i) entries and exits are usually triggered by substantive private information and (ii) only a small fraction of institutions have superior information. As conjectured, PC _ NII subsumes the information content of Δ IO and other institutional trading and herding measures in the forecast of stock returns, and its strong predictive power for stock returns reflects mainly its close correlation with future earnings surprises. We also show that PC _ NII helps address empirical issues that require a reliable measure of institutional informed trading.

The aggregate default probability is significantly priced in equities because of its close relation with uncertainty. Ceteris paribus, the aggregate default probability positively predicts stock market returns, and loadings on its changes correlate negatively with the cross-section of expected stock returns. These findings are consistent with multifactor models in which aggregate uncertainty is a determinant of the conditional equity premium and innovations in aggregate uncertainty are a systematic risk factor. By contrast, credit spreads have negligible explanatory power for expected stock returns. Contrary to the conventional wisdom, credit spreads are poor proxies for aggregate credit risk.

Using options-implied variance, a forward-looking measure of conditional variance, we revisit the debate on the idiosyncratic risk-return relation. In both cross-sectional (for individual stocks) and time-series (for the market index) regressions, we find a negative relation between options-implied variance and future stock returns. Consistent with Miller’s (1977) divergence of opinion hypothesis, the negative relation gets stronger (1) for stocks with more stringent short-sale constraints or (2) when shorting stocks becomes more difficult. Moreover, the negative correlation of realized idiosyncratic variance or analyst forecast dispersion with future stock returns mainly reflects their close correlation with our conditional idiosyncratic variance measure.

A spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected month t idiosyncratic volatility and month t stock returns arises when the month t return is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected month t idiosyncratic volatility is estimated using returns only up to month t − 1.