Knowledge that Transforms

This paper documents that hedge funds did not exert a correcting force on stock prices during the technology bubble. Instead, they were heavily invested in technology stocks. This does not seem to be the result of unawareness of the bubble: Hedge funds captured the upturn, but, by reducing their positions in stocks that were about to decline, avoided much of the downturn. Our findings question the efficient markets notion that rational speculators always stabilize prices. They are consistent with models in which rational investors may prefer to ride bubbles because of predictable investor sentiment and limits to arbitrage.

We model consumption and dividend growth rates as containing (1) a small long‐run predictable component, and (2) fluctuating economic uncertainty (consumption volatility). These dynamics, for which we provide empirical support, in conjunction with Epstein and Zin's (1989) preferences, can explain key asset markets phenomena. In our economy, financial markets dislike economic uncertainty and better long‐run growth prospects raise equity prices. The model can justify the equity premium, the risk‐free rate, and the volatility of the market return, risk‐free rate, and the price–dividend ratio. As in the data, dividend yields predict returns and the volatility of returns is time‐varying.

This paper examines the relation between net buying pressure and the shape of the implied volatility function (IVF) for index and individual stock options. We find that changes in implied volatility are directly related to net buying pressure from public order flow. We also find that changes in implied volatility of S&P 500 options are most strongly affected by buying pressure for index puts, while changes in implied volatility of stock options are dominated by call option demand. Simulated delta‐neutral option‐writing trading strategies generate abnormal returns that match the deviations of the IVFs above realized historical return volatilities.

We examine whether the market interprets changes in dividends as a signal about the persistence of past earnings changes. Prior to observing this signal, investors may believe that past earnings changes are not necessarily indicative of future earnings levels. We empirically investigate whether a change in dividends alters investors' assessments about the valuation implications of past earnings. Results confirm the hypothesis that changes in dividends cause investors to revise their expectations about the persistence of past earnings changes. This effect varies predictably with the magnitude of the dividend change and the sign of the past earnings change.

This study empirically evaluates the certification and value‐added roles of reputable venture capitalists (VCs). Using a novel sample of entrepreneurial start‐ups with multiple financing offers, I analyze financing offers made by competing VCs at the first professional round of start‐up funding, holding characteristics of the start‐up fixed. Offers made by VCs with a high reputation are three times more likely to be accepted, and high‐reputation VCs acquire start‐up equity at a 10–14% discount. The evidence suggests that VCs' “extra‐financial” value may be more distinctive than their functionally equivalent financial capital. These extra‐financial services can have financial consequences.

This paper analyzes the role of jumps in continuous‐time short rate models. I first develop a test to detect jump‐induced misspecification and, using Treasury bill rates, find evidence for the presence of jumps. Second, I specify and estimate a nonparametric jump‐diffusion model. Results indicate that jumps play an important statistical role. Estimates of jump times and sizes indicate that unexpected news about the macroeconomy generates the jumps. Finally, I investigate the pricing implications of jumps. Jumps generally have a minor impact on yields, but they are important for pricing interest rate options.

We test overreaction theories of short‐run momentum and long‐run reversal in the cross section of stock returns. Momentum profits depend on the state of the market, as predicted. From 1929 to 1995, the mean monthly momentum profit following positive market returns is 0.93%, whereas the mean profit following negative market returns is −0.37%. The up‐market momentum reverses in the long‐run. Our results are robust to the conditioning information in macroeconomic factors. Moreover, we find that macroeconomic factors are unable to explain momentum profits after simple methodological adjustments to take account of microstructure concerns.

Chapter 11 structures complex negotiations between creditors and debtors that are overseen by a bankruptcy court. We identify conditions where the court should sometimes err in determining which firms should be liquidated. Such errors affect actions by both good and bad entrepreneurs. We first characterize the optimal error rate without renegotiation. When creditors and debtors can renegotiate to circumvent an error‐riven court, for one class of actions a blind court that ignores all information is optimal. For another class, the court should place the burden of proof on the entrepreneur. The robust feature is that the court should sometimes err.

S&P 500 index funds represent one of the simplest vehicles for examining rational behavior. They hold virtually the same securities, yet their returns differ by more than 2 percent per year. Although the relative returns of alternative S&P 500 funds are easily predictable, the relationship between cash flows and performance is weaker than rational behavior would lead us to expect. We show that selecting funds based on low expenses or high past returns outperforms the portfolio of index funds selected by investors. Our results exemplify the fact that, in a market where arbitrage is not possible, dominated products can prosper.

This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state‐dependent arrival intensity. We discuss how to perform likelihood‐based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously.