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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbankfin.2010.06.001 Byline: Amir E. Khandani, Adlar J. Kim, Andrew W. Lo Keywords: Household behavior; Consumer credit risk; Credit card borrowing; Machine learning; Nonparametric estimation Abstract: We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R.sup.2's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk. Author Affiliation: MIT Sloan School of Management and Laboratory for Financial Engineering, United States Article History: Received 11 March 2010; Accepted 4 June 2010 Article Note: (footnote) [star] The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of AlphaSimplex Group, MIT, any of their affiliates and employees, or any of the individuals acknowledged below. We thank Jayna Cummings, Tanya Giovacchini, Alan Kaplan, Paul Kupiec, Frank Moss, Deb Roy, Ahktarur Siddique, Roger Stein, two referees, the editor Ike Mathur, and seminar participants at Citigroup, the FDIC, the MIT Center for Future Banking, the MIT Media Lab, and Moody's Academic Advisory and Research Committee for many helpful comments and discussion. Research support from the MIT Laboratory for Financial Engineering and the Media Lab's Center for Future Banking is gratefully acknowledged.

Although hedge funds have typically yielded double-digit returns, their dynamic investment strategies entail risks that differ in important ways from those of more traditional investments. The proliferation of hedge funds in recent years may have significant implications for systemic risk. This article considers hedge funds' impact on systemic risk by examining their unique risk-and-return profiles at both the individual-fund and the aggregate-industry levels. Because liquidity is one of the central aspects of systemic risk, the authors present several indirect measures for gauging illiquidity exposure in hedge funds and other asset classes. Systemic risk is directly related to hedge fund failures, so the authors investigate attrition rates of individual hedge funds and present a logit analysis that yields estimates of a fund's probability of liquidation as a function of various fund characteristics. To capture some dynamic factors unique to the hedge fund industry, the authors also present estimates of statistical regine-switching models for hedge fund indexes. The study's findings suggest that hedge funds may be facing a period of lower expected returns and that systemic risk may be rising. To address these challenges, the authors propose that. a new entity, patterned after the U.S. National Transportation Safety Board, be established to investigate hedge fund liquidations and provide public recommendations for improving the stability and robustness of global financial markets., The term 'systemic risk' is commonly used to describe the possibility of a series of correlated defaults among financial institutions--typically banks--that occurs over a short period of time, often caused [...]