This paper attempts to determine whether the fluctuations of conditional first and second moments—which are observed for many assets—are consistent with the Sharpe-Lintner-Mossin capital asset pricing model. We test the mean-variance model under several different assumptions about the time variation of conditional second moments of returns, using weekly data from July 1974 to December 1986, that include returns on a portfolio composed of dollar, Deutsche mark, sterling, and Swiss franc assets, together with the U.S. stock market. The results indicate that estimated conditional variances cannot explain the observed time variation of risk premia.

This paper attempts to determine whether the fluctuations of conditional first and second moments—which are observed for many assets—are consistent with the Sharpe‐Lintner‐Mossin capital asset pricing model. We test the mean‐variance model under several different assumptions about the time variation of conditional second moments of returns, using weekly data from July 1974 to December 1986, that include returns on a portfolio composed of dollar, Deutsche mark, sterling, and Swiss franc assets, together with the U.S. stock market. The results indicate that estimated conditional variances cannot explain the observed time variation of risk premia.

Regulation Fair Disclosure, implemented on October 23, 2000, prohibits U.S. public companies from making selective, nonpublic disclosures to favored investment professionals. Regulation Fair Disclosure has a number of exclusions, however, including disclosure of nonpublic information to credit rating agencies. As a result, credit analysts at rating agencies have access to confidential information that is no longer made available to equity analysts, potentially increasing the information content of credit ratings. We examine the effect of credit rating changes on stock prices and find that the informational effect of downgrades and upgrades is much greater in the post-FD period.

Long‐term estimates of expected return on equities are typically derived from U.S. data only. There are reasons to suspect that these estimates are subject to survivorship, as the United States is arguably the most successful capitalist system in the world. We collect a database of capital appreciation indexes for 39 markets going back to the 1920s. For 1921 to 1996, U.S. equities had the highest real return of all countries, at 4.3 percent, versus a median of 0.8 percent for other countries. The high equity premium obtained for U.S. equities appears to be the exception rather than the rule.

This paper reexamines the ability of dividend yields to predict long‐horizon stock returns. We use the bootstrap methodology, as well as simulations, to examine the distribution of test statistics under the null hypothesis of no forecasting ability. These experiments are constructed so as to maintain the dynamics of regressions with lagged dependent variables over long horizons. We find that the empirically observed statistics are well within the 95% bounds of their simulated distributions. Overall there is no strong statistical evidence indicating that dividend yields can be used to forecast stock returns.

This paper reexamines the ability of dividend yields to predict long-horizon stock returns. The authors use the bootstrap methodology, as well as simulations, to examine the distribution of test statistics under the null hypothesis of no forecasting ability. These experiments are constructed so as to maintain the dynamics of regressions with lagged dependent variables over long horizons. They find that the empirically observed statistics are well within the 95 percent bounds of their simulated distributions. Overall there is no strong statistical evidence indicating that dividend yields can be used to forecast stock returns. Copyright 1993 by American Finance Association.(This abstract was borrowed from another version of this item.)(This abstract was borrowed from another version of this item.)

The voluntary nature of hedge fund database reporting creates strategic listing opportunities for hedge funds. However, little is known about how managers list funds across multiple databases or whether investors are fooled by funds’ listing decisions. In this paper, we find that hedge funds strategically list their small, best-performing funds in multiple outlets immediately while preserving the option to list their other funds in additional databases later. We generally find that investors react rationally to these fund listings based on the predictability of performance. Finally, our results lead to specific guidelines on handling backfilled returns to minimize biases.

A discontinuity, or kink, at zero in the hedge fund net return distribution has been interpreted as evidence of managers manipulating returns to avoid showing small losses. Instead, we propose alternative explanations for this phenomenon. In particular, we show that incentive fees can mechanistically create a kink in the net return distribution. This mechanism accounts for almost the entire kink observed in the large, liquid Long-Short Equity style. Furthermore, we show that asset illiquidity and the bounding of yields at zero can generate distribution discontinuities as well. Therefore, we conclude that the observed hedge fund return discontinuities are not direct proof of manipulation.

Researchers have long known about backfill bias in hedge fund databases. The most common treatments include either retaining all backfilled returns or truncating a fixed number of returns from each return series. However, we show that truncation largely preserves backfilled returns and document that either of these backfill treatments can lead to biased empirical findings, including cross-sectional results. Thus, our findings show that the best practice for empirical tests is to remove returns prior to the listing date. Because most databases do not have listing dates, we propose a novel method to infer unavailable listing dates. Received August 19, 2018; editorial decision December 4, 2018 by Editor Wei Jiang.