Knowledge that Transforms

We provide experimental evidence that relaxing margin restrictions to allow more short selling can exacerbate overpricing, even though it reduces equilibrium price levels. This is because smart-money traders initially profit more by front-running optimistic investor sentiment than by disciplining prices. When short selling is not possible, competitive pressures among arbitrageurs rapidly drive prices to the equilibrium. However, the risk of margin calls slows the convergence process, because arbitrageurs who sell short too early face substantial losses if they are unable to synchronize their trades with other arbitrageurs (as in Abreu and Brunnermeier. 2002. Journal of Financial Economics 66(2--3):341--60; 2003. Econometrica 71(1):173--204). The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

I examine a series of stock splits in Japan in which firms restrict the ability of their investors to sell their shares for a period of approximately 2 months. By removing potential sellers from the market, the restrictions have the effect of increasing the impact of trading on prices. The greater the desire of investors to trade, and the greater the restrictions, the larger the impact of the restrictions. In the data, particularly severe restrictions are associated with returns of over 30% around the ex-date, most of which are reversed when investors are allowed to sell again. Firms are more likely to issue equity or redeem convertible debt during the restricted period, suggesting strong incentives for manipulation.

We study an equilibrium model with restricted investor participation in which strategic arbitrageurs reap profits by exploiting mispricings across different market segments. We endogenize the asset structure as the outcome of a security design game played by the arbitrageurs. The equilibrium asset structure depends realistically upon considerations such as depth and gains from trade. It is neither complete nor socially optimal in general; the degree of inefficiency depends upon the heterogeneity of investors. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

This paper develops a rational, liquidity-based model of closed-end funds (CEFs) that provides an economic motivation for the existence of this organizational form: They offer a means for investors to buy illiquid securities, without facing the potential costs associated with direct trading and without the externalities imposed by an open-end fund structure. Our theory predicts the patterns observed in CEF initial public offerings (IPOs) and the observed behavior of the CEF discount, which results from a trade-off between the liquidity benefits of investing in the CEF and the fees charged by the fund's managers. In particular, the model explains why IPOs occur in waves in certain sectors at a time, why funds are issued at a premium to net asset value (NAV), and why they later usually trade at a discount. We also conduct an empirical investigation, which, overall, provides more support for a liquidity-based model than for an alternative sentiment-based explanation.

Hedge funds have become important investors in public companies raising equity privately. Hedge funds tend to finance companies that have poor fundamentals and pronounced information asymmetries. To compensate for these shortcomings, hedge funds protect themselves by requiring substantial discounts, negotiating repricing rights, and entering into short positions of the underlying stocks. We find that companies that obtain financing from hedge funds significantly underperform companies that obtain financing from other investors during the following two years. We argue that hedge funds are investors of last resort and provide funding for companies that are otherwise constrained from raising equity capital. (JELG14, G23, G32) Hedge funds have recently become an important source of funding for pub-lic companies raising equity privately. Financing young companies with severe information asymmetries is an important investment strategy for some hedge funds. Since 1995, hedge funds have participated in more than 50 % of the private placements of equity securities and have contributed

A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and bond option pricing model. It is especially favored when the bias in ML is large due to strong persistence in the data or strong nonlinearity in pricing functions. Monte Carlo studies show that the proposed procedures achieve bias reductions over ML estimation in pricing contingent claims when ML is biased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U.S. Treasury bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML. Some consequences for the statistical testing of contingent-claim pricing models are discussed.

Although it is of interest to test whether or not a particular asset pricing model is literally true, a more useful task for empirical researchers is to determine how wrong a model is and to compare the performance of competing asset pricing models. In this paper, we propose a new methodology to test whether or not two competing linear asset pricing models have the same Hansen-Jagannathan distance. We show that the asymptotic distribution of the test statistic depends on whether the competing models are correctly specified or misspecified, and on whether the competing models are nested or non-nested. In addition, given the increasing interest in misspecified models, we propose a simple methodology for computing the standard errors of the estimated stochastic discount factor parameters that are robust to model misspecification. Using monthly data on 25 size and book-to-market ranked portfolios and the one-month T-bill, we show that the commonly used returns and factors are, for the most part, too noisy for us to conclude that one model is superior to the other models in terms of Hansen-Jagannathan distance. Specifically, there is little evidence that conditional and intertemporal capital asset pricing model (CAPM)-type specifications outperform the simple unconditional CAPM. In addition, we show that many of the macroeconomic factors commonly used in the literature are no longer priced once potential model misspecification is taken into account.

This study demonstrates the impact of systematic risk on the prices of individual equity options. The option prices are characterized by the level and slope of implied volatility curves, and the systematic risk is measured as the proportion of systematic variance in the total variance. Using daily option quotes on the S, and P 100 index and its 30 largest component stocks, we show that after controlling for the underlying asset's total risk, a higher amount of systematic risk leads to a higher level of implied volatility and a steeper slope of the implied volatility curve. Thus, systematic risk proportion can help differentiate the price structure across individual equity options.

We present a dynamic contracting model in which the principal and the agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, a shift that changes incentives. In our dynamic setting, the interaction between incentive provision and learning creates an intertemporal source of "disagreement risk" that alters optimal risk sharing. An endogenous regime shift between economies with small and large belief differences is present, and an early shock to beliefs can lead to large persistent differences in variable pay even after beliefs have converged. Under risk-neutrality, "selling the firm" to the agent does not implement the first-best outcome because it precludes state-contingent trades.

Motivated by the insight of Keynes (1936) on the importance of higher-order beliefs in financial markets, we examine the role of such beliefs in generating drift in asset prices. We show that in a dynamic setting, a higher-order difference of opinions is necessary for heterogeneous beliefs to generate price drift. Such drift does not arise in standard difference of opinion models, since investors' beliefs are assumed to be common knowledge. Our results stand in contrast to those of Allen, Morris, and Shin (2006) and others, as we argue that in rational expectation equilibria, heterogeneous beliefs do not lead to price drift.