We show that stock market liquidity is an important determinant of the cost of raising external capital. Using a large sample of seasoned equity offerings, we find that, ceteris paribus, investment banks' fees are significantly lower for firms with more liquid stock. We estimate that the difference in the investment banking fee for firms in the most liquid vs. the least liquid quintile is about 101 basis points or 21% of the average investment banking fee in our sample. Our findings suggest that firms can reduce the cost of raising capital by improving the market liquidity of their stock.

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.

This article examines the international determinants of capital structure using a large sample of firms from 37 countries. The reliable determinants for leverage are firm size, tangibility, industry leverage, profits, and inflation. The quality of the countries’ institutions affects leverage and the adjustment speed toward target leverage in significant ways. High-quality institutions lead to faster leverage adjustments, whereas laws and traditions that safeguard debt holders relative to stockholders (e.g., more effective bankruptcy procedures and stronger creditor protection) lead to higher leverage.

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.

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.

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.

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.

“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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

Using a novel database of firm patents and board characteristics across 45 countries, we examine both within- and cross-country determinants of board gender diversity and its relation to corporate innovation. Boards are more likely to include women in countries with narrower gender gaps, higher female labor market participation, and less masculine cultures. Firms with gender diverse boards have more patents and novel patents, and a higher innovative efficiency. Further analyses suggest that gender diverse boards are associated with more failure-tolerant and long-term chief executive officer (CEO) incentives, more innovative corporate cultures, and more diverse inventors, characteristics that are conducive to an improved innovative performance.

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