Knowledge that Transforms

Over the past decade, commercial banks have devoted many resources to developing internal models to better quantify their financial risks and assign economic capital. These efforts have been recognized and encouraged by bank regulators. Recently, banks have extended these efforts into the field of credit risk modeling. However, an important question for both banks and their regulators is evaluating the accuracy of a model’s forecasts of credit losses, especially given the small number of available forecasts due to their typically long planning horizons. Using a panel data approach, we propose evaluation methods for credit risk models based on cross-sectional simulation. Specifically, models are evaluated not only on their forecasts over time, but also on their forecasts at a given point in time for simulated credit portfolios. Once the forecasts corresponding to these portfolios are generated, they can be evaluated using various statistical methods.

This paper derives the optimal size of the financial sector using a general equilibrium framework that is an extension of the paper of Holmstrom and Tirole (1997) [Quarterly Journal of Economics 112, 663–691]. We show that the financial sector has a unique optimal size relative to the size of the economy as a whole. Creating and maintaining this sector requires diversion of some physical capital from production of output to monitoring that production. However, the efficiency gain in output production brought about by monitoring warrants the diversion. It is also found that the optimal size of the financial sector is independent of the state of the economy and does not vary over the business cycle.

We investigate how banking market competition, informational opacity, and sensitivity to shocks have changed over the last three decades by examining the persistence of firm-level rents. We develop propagation mechanisms with testable implications to isolate the sources of persistence. Our analysis suggests that different processes underlie persistence at the high and low ends of the performance distribution. Our tests suggest that impediments to competition and informational opacity continue to be strong determinants of persistence; that the reduction in geographic regulatory restrictions had little effect on competitiveness; and that persistence remains sensitive to regional/macroeconomic shocks. The findings also suggest reasons for the recent record profitability of the industry.

We analyze the effect of tax-based transaction structure on the acquisition price of corporate subsidiaries. For a sample of 200 subsidiary stock acquisitions, the evidence weakly supports the conclusion that acquisition premiums are higher in transactions accompanied by an I.R.C. §338(h)(10) election. We find that the tax benefits generated by the §338(h)(10) election are positively correlated with acquisition premiums. Overall, this study indicates that the tax structure of a subsidiary sale influences the price paid in the transaction, and the tax structure selected is a function of a divesting parent's tax basis in the subsidiary's stock and net assets.

M. H. Franco Wong, The Association between SFAS No. 119 Derivatives Disclosures and the Foreign Exchange Risk Exposure of Manufacturing Firms, Journal of Accounting Research, Vol. 38, No. 2 (Autumn, 2000), pp. 387-417

Kevin C. W. Chen, Michael P. Schoderbek, The 1993 Tax Rate Increase and Deferred Tax Adjustments: A Test of Functional Fixation, Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 23-44

Andrew D. Cuccia, Gary A. Mc Gill, The Role of Decision Strategies in Understanding Professionals' Susceptibility to Judgment Biases, Journal of Accounting Research, Vol. 38, No. 2 (Autumn, 2000), pp. 419-435

William H. Beaver, Stephen G. Ryan, Biases and Lags in Book Value and Their Effects on the Ability of the Book-to-Market Ratio to Predict Book Return on Equity, Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 127-148

This paper explores how Bayes-rational individuals learn sequentially from the discrete actions of others. Unlike earlier informational herding papers, we admit heterogeneous preferences. Not only may type-specific ‘herds’ eventually arise, but a new robust possibility emerges: confounded learning. Beliefs may converge to a limit point where history offers no decisive lessons for anyone, and each type's actions forever nontrivially split between two actions. To verify that our identified limit outcomes do arise, we exploit the Markov-martingale character of beliefs. Learning dynamics are stochastically stable near a fixed point in many Bayesian learning models like this one.

A valid Edgeworth expansion is established for the limit distribution of density-weighted semiparametric averaged derivative estimates of single index models. The leading term that corrects the normal limit varies in magnitude, depending on the choice of bandwidth and kernel order. In general this term has order larger than the n−1/2 that prevails in standard parametric problems, but we find circumstances in which it is O(n−1/2), thereby extending the achievement of an n−1/2 Berry-Esseen bound in Robinson (1995a). A valid empirical Edgeworth expansion is also established. We also provide theoretical and empirical Edgeworth expansions for a studentized statistic, where some correction terms are different from those for the unstudentized case. We report a Monte Carlo study of finite sample performance.