This paper presents a numerical method for valuing complex investments with multiple interacting options. The method is a log-transformed variation of binomial option pricing designed to overcome problems of consistency, stability, and efficiency encountered in the Cox, Ross, and Rubinstein (1979) and other numerical methods. This method handles well options with a series of exercise prices (compound options), nonproportional dividends, and interactions among a variety of real options. Comparisons with several existing numerical methods regarding accuracy, consistency, stability, and efficiency are given.

We investigate the (sell-side) analyst rankings of Institutional Investor (I/I) and The Wall Street Journal (WSJ), using data from 1993–2005. We find that factors with a primary component of recognition are the most important determinants of the rankings, although performance measures are statistically significant determinants in some cases. The single exception to this finding is with existing WSJ stars, where industry-adjusted investment-recommendation performance is the only significant determinant of repeating as a star. Further, in the year after becoming stars, the recommendations of WSJ stars are significantly worse than those of nonstars; and the recommendations and earnings forecasts of I/I stars, as well as the earnings forecasts of WSJ stars, are not significantly different from those of nonstars. We conclude that these rankings are largely “popularity contests.”

We solve for the optimal dynamic trading strategy of an investor who faces a leverage constraint, i.e., a limitation on his ability to borrow for the purpose of investing in a risky asset. We assume that the investor has constant relative risk aversion, and that the value of the risky asset follows a geometric Brownian motion. In the absence of the leverage constraint, the optimal strategy involves investing a fixed proportion of wealth in the risky asset. We prove that, in the presence of the leverage constraint, the optimal investment also involves investing a fixed proportion of wealth in the risky asset when the leverage constraint is not binding. However, the two proportions are different, reflecting the extent to which the investor alters his strategy even when the leverage constraint is not binding because of the possibility that the leverage constraint will become binding in the future.

We find that among firms that file Chapter 11 those that are smaller have better operating performance, and are in higher operating margin industries spend less time in Chapter 11. Firms are more likely to emerge as going concerns and to achieve positive post reorganization profitability if they significantly reduce assets and liabilities while in Chapter 11. Higher pre-bankruptcy industry-adjusted operating margins and improvements in margin are associated with post-reorganization profitability but do not impact the decision to reorganize. These results reveal characteristics and actions associated with successful reorganizations and, furthermore, suggest that Chapter 11 allows promising firms to successfully reorganize.

We find that firms that regularly access public debt (bond) markets are more likely to pay a dividend and subsequently follow a dividend smoothing policy than firms that rely exclusively on private (bank) debt. In particular, firms with bond ratings follow a traditional Lintner (1956) style dividend smoothing policy, where the influence of the prior dividend payment is very strong and the current dividend is relatively insensitive to current earnings. In contrast, firms without bond ratings flow through more of their earnings as dividends and display very little dividend smoothing behavior. In effect, they seem to follow a residual dividend policy.

We argue that in the after-market trading of an IPO, the underwriting syndicate, by standing ready to buy back shares at the offer price (“price stabilization”), compensates uninformed investors ex post for the adverse selection cost they face in bidding for IPOs. This dominates ex ante compensation by underpricing. The reason is that stabilization exploits ex post information about investor demand whereas underpricing must be based on ex ante information. However, liquidity and syndication costs constrain the use of stabilization which, in equilibrium, generates some underpricing as well. We develop a model that formalizes this intuition and generates several empirical implications.

Several recent studies of the capital asset pricing model were designed to improve the understanding of the pricing of capital assets by expanding the singlefactor market model to include macroeconomic information, industry influences and individual firm characteristics. Stone [20] has offered another means of expanding the market model. He has proposed a two-index model consisting of the traditional “equity market” index and a “debt market” index. Stone justified the model by arguing that individual equity securities exhibit varying degrees of sensitivity to interest rates and that the opportunity to invest in risky debt securities may represent an attractive alternative to riskless assets and risky equity securities. He indicated the incorporation of an index for the return on debt in the market model might improve its explanatory power for such securities as “…gold, bank, savings and loan, public utility, and similar stocks exhibiting considerable interest rate sensitivity [20, p. 710].”

This Paper explores the importance and price implications of style investing by institutional investors in the stock market. To analyze styles, we assign stocks to deciles or segments across three style dimensions: size, value/growth, and sector. we find strong evidence that institutional investors reallocate and sector. We find strong evidence that institutional investors reallocate across style groupings more intensively than across random stock groupings. In addition, we show that own segment style inflows and refurns positively forecast future stock returns, which distant segament style inflows and returns forecast negatively. We argue that behavioral theories play a role in explaining these results.

Recent studies of mutual funds have all arrived at the same conclusion: mutual fund performance has been inferior to the performance of the market indices. One of the most prominent of these studies was conducted by William F. Sharpe. He showed that if his measure of mutual fund performance, the reward-to-variability ratio, is calculated net of management expenses for each fund in his sample of thirty-four, then the average value of this ratio over the thirty-four funds is significantly less than the same measure applied to the Dow Jones Industrials over the 1954–1963 period. From this evidence, Sharpe concluded that average mutual fund performance was distinctly inferior to an investment in the Dow Jones Industrial Average. It is the intent of this paper to show that if another variable, namely the third moment of the fund's annual rate of return, is introduced into the investor's decision process, Sharpe's conclusion must be altered.

This paper tests the Kraus-Litzenberger (1976) three-moment capital asset pricing model using Hansen's (1982) generalized method-of-moments (GMM). The GMM approach does not impose strong distributional assumptions on the asset returns. This is an interesting issue since there is no obvious multivariate distribution for returns that also exhibits co-skewness. Using monthly stock returns to test the model, there is some evidence that systematic skewness is priced.

We propose an investor attention index based on proxies in the literature and find that it predicts the stock market risk premium significantly, both in sample and out of sample, whereas every proxy individually has little predictive power. The index is extracted using partial least squares, but the results are similar by the scaled principal component analysis. Moreover, the index can deliver sizable economic gains for mean-variance investors in asset allocation. The predictive power of the investor attention index stems primarily from the reversal of temporary price pressure and from the stronger forecasting ability for high-variance stocks.

We test for the presence of a systematic tail risk premium in the cross section of expected returns by applying a measure of the sensitivity of assets to extreme market downturns, the tail beta. Empirically, historical tail betas help predict the future performance of stocks in extreme market downturns. During a market crash, stocks with historically high tail betas suffer losses that are approximately 2 to 3 times larger than their low-tail-beta counterparts. However, we find no evidence of a premium associated with tail betas. The theoretically additive and empirically persistent tail betas can help assess portfolio tail risks.

This study focuses on the risk-return characteristics of investments in the common stocks of U.S.–based multinational corporations (MNCs) and U.S. national corporations (NATLs). Findings follow from a comparison of the risk-adjusted performance of MNCs and NATLs using the framework of the capital asset pricing model (CAPM). Results of this comparison challenge assertions of earlier writers that marginal benefits or advantages accrue from investments in MNCs as compared to NATLs.

A contingent claims approach to capital budgeting may be preferable to traditional methods where uncertainty and managers' strategic reactions to changing conditions are important. As an example of such a case, we solve the classical problem of the duration of an investment in forestry resources (i.e., when to cut down the trees) in the general case of stochastic output prices and stochastic natural growth rate and timber inventories. A contingent claims approach is used to value the forestry resources as a function of: (1) stochastic prices and inventories, and (2) an asymmetric, optimal production policy that incorporates the option to halt timber production temporarily.

Motivated by economic models of sequential trade, empirical analyses of market dynamics frequently estimate liquidity as the coefficient of signed order flow in a price change regression. This paper implements such an analysis for futures transaction data from pit trading. To deal with the absence of timely bid and ask quotes (which are used to sign trades in most equity market studies), this paper proposes new techniques based on Markov chain Monte Carlo estimation. The model is estimated for four representative Chicago Mercantile Exchange contracts. The highest liquidity (lowest order flow coefficient) is found for the S&P 500 index. Liquidity for the Euro and U.K. £ contracts is somewhat lower. The pork belly contract exhibits the least liquidity.

Using data on over 4,000 individual residential addresses, we find that geographic distance between directors and corporate headquarters is related to information acquisition and board decisions. The fraction of a board’s unaffiliated directors who live near headquarters is higher when information-gathering needs are greater. When the fraction of unaffiliated directors living near headquarters is lower, nonroutine chief executive officer (CEO) turnover is more sensitive to stock performance. Also, the level, intensity, and sensitivity of CEO equity-based pay increase with board distance. Overall, our results suggest that geographic location is an important dimension of board structure that influences directors’ costs of gathering information.

Recently, there has been rapid growth in the assets managed by “hedged mutual funds”—mutual funds mimicking hedge fund strategies. We examine the performance of these funds relative to hedge funds and traditional mutual funds. Despite using similar trading strategies, hedged mutual funds underperform hedge funds. We attribute this finding to hedge funds’ lighter regulation and better incentives. Conversely, hedged mutual funds outperform traditional mutual funds. Notably, this superior performance is driven by managers with experience implementing hedge fund strategies. Our findings have implications for investors seeking hedge-fund-like payoffs at a lower cost and within the comfort of a regulated environment.

Representative agent models are inconsistent with existing empirical evidence for steep demand curves for individual stocks. This paper resolves the puzzle by proposing that stock prices are instead set by two separate classes of investors. While the market portfolio is still priced by individual investors based on their collective risk aversion, those individual investors also delegate part of their wealth to active money managers, who use that capital to price stocks in the cross section. In equilibrium, the fee charged by active managers has to equal the before-fee alpha they earn. This endogenously determines the amount of active capital and the slopes of demand curves. A calibration of the model reveals that demand curves can be steep enough to match the magnitude of many empirical findings, including the price effects for stocks entering or leaving the S&P 500 index.

We examine whether political corruption impedes innovation. Using a comprehensive sample of U.S. firms, we find that corruption has a substantial, negative relation with the quantity and quality of innovation. These results are robust to using various fixed effects, proxies for corruption and innovation, and subsamples. To establish causality, we employ 2 instruments for corruption: local ethnic diversity and the corruption of the state a firm’s founder grew up in. Corruption appears to reduce innovation output both on average and for the most innovative firms. Overall, this evidence is consistent with the notion that corruption reduces social welfare by impeding innovation.

Aggregate idiosyncratic volatility spiked nearly fivefold during the Internet boom of the late 1990s, dwarfing in magnitude a moderately increasing trend. While some researchers argue that this rise in idiosyncratic risk was the result of changes in the characteristics of public firms, others argue that it was driven by the changing sentiment of irrational traders. We present evidence that the marketwide decline in maturity of the typical public firm can explain most of the increase in firm-specific risk during the Internet boom. Controlling for firm maturity, we find no evidence that investor sentiment drives idiosyncratic risk throughout the Internet boom.