Knowledge that Transforms

We model owners as solving a multidimensional problem when taking their firms public. Owners can affect the level of underpricing through the choices they make in promoting an issue, such as which underwriter to hire or on what exchange to list. The benefits of reducing underpricing in this way depend on the owners’ participation in the offering and the magnitude of the dilution they suffer on retained shares. We argue that the extent to which owners trade off underpricing and promotion is determined by the minimization of their wealth losses. Evidence from a sample of U.S. initial public offering confirms our empirical predictions.

This article analyzes optimal, dynamic portfolio and wealth/consumption policies of utility maximizing investors who must also manage market-risk exposure using Value-at-Risk (VaR). We find that VaR risk managers often optimally choose a larger exposure to risky assets than non-risk managers and consequently incur larger losses when losses occur. We suggest an alternative risk-management model, based on the expectation of a loss, to remedy the shortcomings of VaR. A general-equilibrium analysis reveals that the presence of VaR risk managers amplifies the stock-market volatility at times of down markets and attenuates the volatility at times of up markets.

This article examines the use of foreign currency derivatives (FCDs) in a sample of 720 large U.S. nonfinancial firms between 1990 and 1995 and its potential impact on firm value. Using Tobin’s Q as a proxy for firm value, we find a positive relation between firm value and the use of FCDs. The hedging premium is statistically and economically significant for firms with exposure to exchange rates and is on average 4.87% of firm value. We also find some evidence consistent with the hypothesis that hedging causes an increase in firm value.

Buying recent winners and shorting recent losers guarantees time-varying factor exposures in accordance with the performance of common risk factors during the ranking period. Adjusted for this dynamic risk exposure, momentum profits are remarkably stable across subperiods of the entire post-1926 era. Factor models can explain 95% of winner or loser return variability, but cannot explain their mean returns. Momentum strategies which base winner or loser status on stock-specific return components are more profitable than those based on total returns. Neither industry effects nor cross-sectional differences in expected returns are the primary cause of the momentum phenomenon.

Hedge fund strategies typically generate option-like returns. Linear-factor models using benchmark asset indices have difficulty explaining them. Following the suggestions in Glosten and Jagannathan (1994), this article shows how to model hedge fund returns by focusing on the popular “trend-following” strategy. We use lookback straddles to model trend-following strategies, and show that they can explain trend-following funds’ returns better than standard asset indices. Though standard straddles lead to similar empirical results, lookback straddles are theoretically closer to the concept of trend following. Our model should be useful in the design of performance benchmarks for trend-following funds.

We examine insider trading activities of all companies traded on the NYSE, AMEX, and Nasdaq during the 1975-95 period. In general, very little market movement is observed when insiders trade and when they report their trades to the SEC. Insiders in aggregate are contrarian investors. However, they predict market movements better than simple contrarian strategies. Insiders also seem to be able to predict cross-sectional stock returns. The result, however, is driven by insider's ability to predict returns in smaller firms. In addition, informativeness of insiders' activities is coming from purchases, while insider selling appears to have no predictive ability. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Journal Article Financial Constraints and Stock Returns Get access Owen Lamont, Owen Lamont University of Chicago and NBER Address correspondence to Owen Lamont, Graduate School of Business, University of Chicago, 1101 E. 58th St., Chicago, IL 60637, or e-mail: [email protected]. Search for other works by this author on: Oxford Academic Google Scholar Christopher Polk, Christopher Polk Northwestern University Search for other works by this author on: Oxford Academic Google Scholar Jesús Saaá-Requejo Jesús Saaá-Requejo Vega Asset Management Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 14, Issue 2, April 2001, Pages 529–554, https://doi.org/10.1093/rfs/14.2.529 Published: 21 June 2015

We develop a multiperiod market model describing both the process by which traders learn about their ability and how a bias in this learning can create overconfident traders. A trader in our model initially does not know his own ability. He infers this ability from his successes and failures. In assessing his ability the trader takes too much credit for his successes. This leads him to become overconfident. A trader’s expected level of overconfidence increases in the early stages of his career. Then, with more experience, he comes to better recognize his own ability. The patterns in trading volume, expected profits, price volatility, and expected prices resulting from this endogenous overconfidence are analyzed.

Shareholders of a Regional Bell Operating Company (RBOC) tend to live in the area which it serves, and an RBOC's customers tend to hold its shares rather than other RBOCs' equity. The geographic bias of the RBOC investors is closely related to the general tendency of households' portfolios to be concentrated, of employees' tendency to own their employers' stocks in their retirement accounts, and to the home country bias in the international arena. Together, these phenomena provide compeling evidence that people invest in the familiar while often ignoring the principles of portfolio theory.

This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.