Knowledge that Transforms

We study the effects of physical distance on the acquisition and use of private information in informationally opaque credit markets. Using a unique data set of all loan applications by small firms to a large bank, we show that borrower proximity facilitates the collection of soft information, leading to a trade-off in the availability and pricing of credit, which is more readily accessible to nearby firms albeit at higher interest rates ceteris paribus. Analyzing loan rates and firms’ decision to switch lenders provides further evidence for banks’ strategic use of private information. However, distance erodes our lender’s ability to collect proprietary intelligence and to carve out local captive markets, suggesting that the requisite soft information is primarily local.

This paper investigates a little studied but common mechanism that firms use to obtain state favors: business owners themselves seeking election to top office. Using Thailand as a research setting, we find that the more business owners rely on government concessions or the wealthier they are, the more likely they are to run for top office. Once in power, the market valuation of their firms increases dramatically. Surprisingly, the political power does not influence the financing strategies of their firms. Instead, business owners in top office use their policy-decision powers to implement regulations and public policies favorable to their firms. Such policies hinder not only domestic competitors but also foreign investors. As a result, these politically connected firms are able to capture more market share.

One can consider the concept of market neutrality for hedge funds as having breadth and depth: "breadth" reects the number of market risks to which a fund is neutral, while "depth" reects the "completeness" of the neutrality of the fund to market risks. We focus on market neutrality depth, and propose ve different neutrality concepts. "Mean neutrality" nests the standard correlation-based denition of neutrality. "Variance neutrality", "Value-at-Risk neutrality" and "tail neutrality" all relate to the neutrality of the risk of the hedge fund to market risks. Finally, "complete neutrality" corresponds to independence of the fund to market risks. We suggest statistical tests for each neutrality concept, and apply the tests to a combined database of monthly "market neutral" hedge fund returns from the HFR and TASS hedge fund databases. We nd that around one-quarter of these funds exhibit some signicant exposure to market risk.

We model the demand-pressure effect on prices when options cannot be perfectly hedged. The model shows that demand pressure in one option contract increases its price by an amount proportional to the variance of the unhedgeable part of the option. Similarly, the demand pressure increases the price of any other option by an amount proportional to the covariance of their unhedgeable parts.

The authors show that the widening of a foreign firm's U.S. investor base and the improved information environment associated with cross-listing on a U.S. exchange each have a separately identifiable effect on a firm's valuation. The increase in valuation associated with cross-listing is transitory, not permanent. Valuations of Canadian firms peak in the year of cross-listing and fall monotonically thereafter, regardless of the level of U.S. investor holdings or the ownership structure of the firm. Cross-listed firms with a 20 per cent or more blockholder attract a similar number of U.S. institutional investors as widely held firms, on average, but experience a lower increase in valuation at high levels of investor recognition. While U.S. investors are less willing to invest in firms with dual-class shares, these firms benefit more from cross-listing even when they fail to widen their U.S. investor base, suggesting that the reduction in information asymmetry between controlling and minority investors has a separate impact on valuation for firms where agency problems are greatest.

When a firm exercises discretion to disclose or withhold information (strategic disclosure), risk-averse investors command higher expected returns when expected cash flows decrease, producing a negative correlation between these expectations. Moreover, stock returns exhibit stronger reversal than they do when full disclosure is enforced. We propose a model that makes these predictions and provide consistent evidence using a panel of foreign firms that list American Depositary Receipts (ADRs). We find significant shifts in the time-series properties of stock returns for firms that undergo large changes in disclosure environments, such as those cross-listing on the NYSE/AMEX/NASDAQ and those from less-developed/emerging markets and code-law countries.

Corporations uniquely have a tax preference for cash dividends. Nevertheless, dividends do not increase following trades of large-percentage blocks of stock from individuals to corporations. Moreover, although one-third of firms have corporate blockholders, 68% of these firms pay no dividends, and ownership is not clustered at levels that increase the tax benefits of dividends. These findings are not driven by the investing firms' tax rates or by agency problems. Instead, operating companies expand the target firms and pursue joint ventures. Dividends are lower with these investors. Financial investors are not attracted to dividend-paying firms and tend to be passive. 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.

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.