This paper tests whether the persistence of the momentum and reversal effects is the result of idiosyncratic risk limiting arbitrage. Idiosyncratic risk deters arbitrage, regardless of the arbitrageur’s diversification. Reversal is prevalent only in high idiosyncratic risk stocks, suggesting that idiosyncratic risk limits arbitrage in reversal mispricing. This finding is robust to controls for transaction costs, informed trading, and systematic relations between idiosyncratic risk and subsequent returns. Momentum is not related to idiosyncratic risk. Momentum generates a smaller aggregate return than reversal, so the findings along with those in related studies suggest that transaction costs are sufficient to prevent arbitrageurs from eliminating momentum mispricing.

We examine how shocks to the supply of credit impact corporate financing and investment using the collapse of Drexel Burnham Lambert, Inc.; the passage of the Financial Institutions Reform, Recovery, and Enforcement Act of 1989; and regulatory changes in the insurance industry as an exogenous contraction in the supply of below-investment-grade credit after 1989. A difference-in-differences empirical strategy reveals that substitution to bank debt and alternative sources of capital (e.g., equity, cash balances, and trade credit) was limited, leading to an almost one-for-one decline in net investment with the decline in net debt issuances. Despite this sharp change in behavior, corporate leverage ratios remained relatively stable, a result of the contemporaneous decline in debt issuances and investment. Overall, our findings highlight how even large firms with access to public credit markets are susceptible to fluctuations in the supply of capital.

While the signaling hypothesis has played a prominent role as the economic rationale associated with the initial public offering (IPO) underpricing puzzle (Welch (1989)), the empirical evidence on it has been mixed at best (Jegadeesh, Weinstein, and Welch (1993), Michaely and Shaw (1994)). This paper revisits the issue from the vantage point of close to two decades of additional experience by examining a sample of foreign IPOs—firms from both financially integrated and segmented markets—in U.S. markets. The evidence indicates that signaling does matter in determining IPO underpricing, especially for firms domiciled in countries with segmented markets, which as a result face higher information asymmetry and lack access to external capital markets. We find a significant positive and robust relationship between the degree of IPO underpricing and segmented-market firms’ seasoned equity offering (SEO) activities. For firms from integrated markets, in contrast, the analyst-coverage purchase hypothesis appears to matter more in explaining IPO underpricing, and the aftermarket price appreciation explains these firms’ SEO activities. The evidence, therefore, clearly supports the notion that some firms are willing to leave money on the table voluntarily to get a more favorable price at seasoned offerings when they are substantially wealth constrained, a prediction embedded in the signaling hypothesis.

Existing empirical research investigating the size of the equity premium has largely consisted of a series of innovations around a common theme: producing a better estimate of the equity premium by using better data or a better estimation technique. The equity premium estimate that emerges from most of this work matches one moment of the data alone: the mean difference between an estimate of the return to holding equity and a risk-free rate. We instead match multiple moments of U.S. market data, exploiting the joint distribution of the dividend yield, return volatility, and realized excess returns, and find that the equity premium lies within 50 basis points of 3.5%, a range much narrower than was achieved in previous studies. Additionally, statistical tests based on the joint distribution of these moments reveal that only those models of the conditional equity premium that embed time variation, breaks, and/or trends are supported by the data. In order to develop the joint distribution of the dividend yield, return volatility, and excess returns, we need a model of price and return fundamentals. We document that even recently developed analytically tractable models that permit autocorrelated dividend growth rates and discount rates impose restrictions that are rejected by the data. We therefore turn to a wider range of models, requiring numerical solution methods and parameter estimation by the simulated method of moments.