Knowledge that Transforms

Hansen and Jagannathan (1991) (hereafter HJ) derive restrictions on the volatility of stochastic discount factors that price a given set of returns. This article studies the sampling properties of HJ bounds that use conditioning information. One approach is to multiply the returns by the lagged variables. We also study optimized HJ bounds with conditioning information from Gallant, Hansen, and Tauchen (1990) and based on portfolios derived in Ferson and Siegel (2001). We document striking finite-sample biases in the HJ bounds, where the bounds reject asset-pricing models too often. We provide a useful bias correction. We also evaluate asymptotic standard errors for the bounds from Hansen, Heaton, and Luttmer (1995).

I test the hypothesis that short debt maturity attenuates the negative effect of growth opportunities on leverage. Using simultaneous equations with leverage and maturity endogenous, I find strong support for an economically significant attenuation effect. The negative effect of growth opportunities on leverage for firms with all shorter-term debt is less than one-sixth as large as the effect for firms with all longer-term debt. Short maturity also increases liquidity risk, however, which negatively affects leverage. The results suggest that firms trade off the cost of underinvestment problems against the cost of liquidity risk when choosing short maturity.

We develop a model of informal financial networks and present corroborating evidence by studying the role of property brokers in the U.S. commercial real estate market. Our model demonstrates that service intermediaries, who do not themselves supply loans, can facilitate their clients' access to finance through informal relationships with lenders. Empirically we find that, controlling for endogenous broker selection, hiring a broker strikingly increases the probability of obtaining bank finance. Our results demonstrate that even in the United States, with its well-developed capital markets, informal networks play an important role in controlling access to finance.

This article introduces the concept of a "statistical arbitrage opportunity" (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a "pure arbitrage opportunity", a SAO can have negative payoffs provided that the average payoff in each final state is non-negative. If the pricing kernel in the economy is path independent, then no SAOs can exist. Furthermore, ruling out SAOs imposes a novel martingale-type restriction on the dynamics of securities prices. The important properties of the restriction are that it (1) is model-free, in the sense that it requires no parametric assumptions about the true equilibrium model, (2) can be tested in samples affected by selection biases, such as the peso problem, and (3) continues to hold when investors' beliefs are mistaken. The article argues that one can use the new restriction to empirically resolve the joint hypothesis problem present in the traditional tests of the efficient market hypothesis.