The paper examines the relevance of information asymmetry between the managers of the firm and the market for the equity issue process. It uses four proxies for information asymmetry and presents three groups of tests: cross-sectional regressions of the reaction at equity issue announcements, comparisons of information asymmetry before and after the announcements, and analyses of the timing behavior observed during the equity issue process. The results show that information asymmetry is a significant variable for equity issues.

We observe significant post-split excess returns of 7.93 percent in the first year and 12.15 percent in the first three years for a sample of 1,275 two-for-one stock splits. These excess returns follow an announcement return of 3.38 percent, indicating that the market underreacts to split announcements. The evidence suggests that splits realign prices to a lower trading range, but managers self-select by conditioning the decision to split on expected future performance. Presplit runup and post-split excess returns are inversely related, indicating that our results are not caused by momentum.

“Don't put all your eggs in one basket,” is a familiar adage. Economists, such as Marschak, Markowitz, and Tobin, who work only with mean income and its variance, can give specific content to this rule—namely, putting a fixed total of wealth equally into independently, identically distributed investments will leave the mean gain unchanged and will minimize the variance.

This paper uses investor-level data to provide direct evidence for an intuitive but surprisingly untested proposition that investors make larger investment mistakes when valuation uncertainty is higher and stocks are more difficult to value. Using multiple measures of valuation uncertainty and multiple behavioral bias proxies, I show that individual investors exhibit stronger behavioral biases when stocks are harder to value and when market-level uncertainty is higher. I also find that informed trading intensity is higher among stocks where individual investors exhibit stronger behavioral biases. Collectively, these results indicate that uncertainty at both stock and market levels amplifies individual investors’ behavioral biases and that relatively better informed investors attempt to exploit those biases.

After Markowitz [14, p. 100] and Sharpe [19, 20] suggested estimating the beta systematic risk coefficient for market assets, finance professors, stock brokers, investment managers, and others began expending large quantities of resources each year on estimating betas. Unfortunately however, it appears that the ordinary least-squares (OLS) regressions used in nearly every instance may be inappropriate. This paper suggests that many stocks' beta coefficients move randomly through time rather than remain stable as the OLS model presumes.

I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend-price (DP) and earnings-price (EP) ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, I develop new methods for predictive regressions with panel data. Inference based on the standard fixed effects estimator is shown to suffer from severe size distortions in the typical stock return regression, and an alternative robust estimator is proposed. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in developed markets. In contrast, no strong or consistent evidence of predictability is found when considering the EP and DP ratios as predictors.

In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.

This paper presents new tests of the mixture of distributions hypothesis. Previous tests examined security prices and volume measured only at daily intervals. Here, differential implications of the hypothesis for transaction data are derived and tested. The new predictions emanate from the assumption that prices and volume evolve at uniform rates in transaction time. The results support this assumption and the mixture of distributions hypothesis in general. In addition, the tests suggest that the daily transaction-count may be a useful instrumental variable for estimating unobserved realizations of stochastic price variances.

We compare average trade execution costs during 1994 for sets of large, medium, and small capitalization stocks listed on the New York and NASDAQ stock markets. All measures of execution costs examined, including quoted bid-ask spreads, effective spreads (which allow for executions within the quotes), and realized spreads (which measure price reversal after trades), are larger for NASDAQ-listed than for NYSE-listed stocks. The differentials in average trading costs across exchanges are greater for medium and small capitalization issues than for large capitalization stocks and are greater for small compared to large trades. These differentials cannot be attributed to cross-exchange differences in the adverse selection costs of market-making. Furthermore, we find no evidence that average execution costs on NASDAQ declined after the publicized events of May 1994.

Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. See Stambaugh (1999) for the single regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least-squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients, we develop a heuristic estimator that performs well in simulations, for both the single predictor model and an important specification of the multiple predictor model. The effectiveness of our method is demonstrated by simulations and empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.

This paper describes the application of the Principle of Maximum Entropy to the estimation of the distribution of an underlying asset from a set of option prices. The resulting distribution is least committal with respect to unknown or missing information and is, hence, the least prejudiced. The maximum entropy distribution is the only information about the asset that can be inferred from the price data alone. An extension to the Principle of Minimum Cross-Entropy allows the inclusion of prior knowledge of the asset distribution. We show that the maximum entropy distribution is able to accurately fit a known density, given simulated option prices at different strikes.

We examine the cash flow signaling, overinvestment, and dividend clientele explanations for the information content of dividend change announcements. After simultaneously controlling for the standardized dividend change, dividend yield, and Tobin's Q, we find that announcement period excess returns are positively related to the magnitude of the standardized dividend change and to the dividend yield, but unrelated to Tobin's Q. We provide further evidence on the cash flow signaling and overinvestment hypotheses by examining revisions in analysts' earnings forecasts and changes in capital expenditures following dividend change announcements. We find that analysts significantly revise their earnings forecasts following dividend changes and that Q < 1 firms actually increase their capital expenditures following dividend increases and decrease them following dividend decreases. Overall, our findings support the cash flow signaling and dividend clientele hypotheses for stock price reactions to dividend change announcements, but provide little support for the overinvestment hypothesis.

This paper examines the performance of momentum trading strategies in foreign exchange markets. We find the well-documented profitability of momentum strategies during the 1970s and the 1980s has continued throughout the 1990s. Our approach and findings are insensitive to the specification of the trading rule and the base currency for analysis. Finally, we show that the performance is not due to a time-varying risk premium but rather depends on the underlying autocorrelation structure of the currency returns. In sum, the results lend further support to prior momentum studies on equities. The profitability to momentum-based strategies holds for currencies as well.

Public discussion about corporate use of derivatives focuses on whether firms use derivatives to reduce or increase firm risk. In contrast, empirical academic studies of corporate dervatives use take it for granted that firms hedge with derivatives. Using data from financial statements of 425 large U.S. corporations, we investigate whether firms systematically reduce or increase their riskiness with derivatives. We find that many firms manage their exposures with large derivatives positions. Nonetheless, compared to firms that do not use financial derivatives, firms that use derivatives display few, if any, measurable differences in risk that are associated with the use of derivatives.

This paper provided a pure financial explanation for the existence of trade credit and for the values of the credit terms offered to customers. Two motives for extending trade credit were identified. The pure operating flexibility motive arises because the opportunity to change credit policy provides the seller an efficient way to respond to fluctuations in demand. This motive was eliminated from consideration in this paper by assuming constant demand. The seller must hold a liquid reserve when the financial markets are imperfect and the desire to earn an excess rate of return on this reserve explains the pure financial intermediary motive for trade credit.

The pure financial incentive to lend this liquid reserve to customers was examined by viewing a market borrowing rate of interest that exceeds the market lending rate of interest as a hindrance to trade or, equivalently, as a financial market tariff. This tariff imposes a wedge between the market prices paid and received for the product plus a loan and thereby inflicts a loss of surplus on the seller and buyers. Trade credit lending enables the seller and/or the buyers to recapture at least part of this loss when the source of the tariff does not apply to direct loans to customers. Financial market tariffs caused by transactions costs fulfill this requirement because the trade credit lender's familiarity with its customers and product provide it with information and collection cost advantages over financial intermediaries. Tariffs caused by financial intermediary rents fulfill this requirement as well because the parties to a trade credit loan do not employ the services of a financial intermediary.

Increasing opportunity costs and financial market imperfections in addition to the ones described above establish the limits of credit policy. The optimal amount of accounts receivable is derived from the condition that the marginal revenue of trade credit lending is equal to the marginal cost. This condition combined with factoring costs produces a unique, finite optimal credit period. Accrual accounting for income tax purposes imposes an additional restriction because the firm is taxed on the recovery of its opportunity costs. These limitations on credit policy were examined separately in this paper for clarity but they are in effect simultaneously in practice.

The Monte Carlo method is used to solve for the price of a call when the variance is changing stochastically.

Firms reduce leverage following credit rating downgrades. In the year following a downgrade, downgraded firms issue approximately 1.5%–2.0% less net debt relative to net equity as a percentage of assets compared to other firms. This relationship persists within an empirical model of target leverage behavior. The effect of a downgrade is larger at downgrades to a speculative grade rating and if commercial paper access is affected. In particular, firms downgraded to speculative are about twice as likely to reduce debt as other firms. Rating upgrades do not affect subsequent capital structure activity, suggesting that firms target minimum rating levels.

Fama and French (1992) report that size and the book-to-market ratio capture the cross-sectional variation of average stock returns for the universe of NYSE, Amex, and Nasdaq securities. This paper, in providing an exhaustive exploration of book-to-market across the dimensions of firm size, exchange listing, and calendar seasonally, reports that Fama and French's empirical findings are driven by two features of the data: a January seasonal in the book-to-market effect, and exceptionally low returns on small, young, growth stocks. In the largest size quintile of all firms (accounting for 73% of the total market value of all publicly traded firms), book-to-market has no significant explanatory power on the cross-section of realized returns during the 1963–1995 period. Thus, book-to-market as such would have less importance to money managers than the literature would have led us to believe.

We show that contractual risk-taking incentives for chief executive officers (CEOs) increased at large U.S. commercial banks around 2000, when industry deregulation expanded these banks’ growth opportunities. Our econometric models indicate that CEOs responded positively to these incentives, especially at the larger banks best able to take advantage of these opportunities. Our results also suggest that bank boards responded to higher-than-average levels of risk by moderating CEO risk-taking incentives; however, this feedback effect is absent at the very largest banks with strong growth opportunities and a history of highly aggressive risk-taking incentives.

We investigate the claim that hedge funds offer investors a superior risk-return tradeoff. We do so using a continuous-time version of Dybvig's (1988a), (1988b) payoff distribution pricing model. The evaluation model, which does not require any assumptions with regard to the return distribution of the funds to be evaluated, is applied to the monthly returns of 77 hedge funds and 13 hedge fund indices from May 1990–April 2000. The results show that, as a stand-alone investment, hedge funds do not offer a superior risk-return profile. We find 12 indices and 72 individual funds to be inefficient, with the average efficiency loss amounting to 2.76% per annum for indices and 6.42% for individual funds. Part of the inefficiency cost of individual funds can be diversified away. Funds of funds, however, are not the preferred vehicle for this as their performance appears to suffer badly from their double fee structure. Looking at hedge funds in a portfolio context results in a marked improvement in the evaluation outcomes. Seven of the 12 hedge fund indices and 58 of the 72 individual funds classified as inefficient on a stand-alone basis are capable of producing an efficient payoff profile when mixed with the S&P 500. The best results are obtained when 10%–20% of the portfolio value is invested in hedge funds.