Abstract

http://ssrn.com/abstract=2263944
 


 



Performance Measurement with Uncertain Risk Loadings


Francesco A. Franzoni


University of Lugano; Swiss Finance Institute

Martin C. Schmalz


University of Michigan, Stephen M. Ross School of Business

January 28, 2016

Swiss Finance Institute Research Paper No. 13-41
Ross School of Business Paper No. 1194

Abstract:     
The paper establishes a new empirical fact: mutual funds’ flow-performance sensitivity is a hump-shaped function of aggregate risk realizations. We then propose an explanation to rationalize this finding. We posit that rational investors use past performance to learn not only about manager skill but also about the fund risk loading. It follows that when the realization of aggregate risk is larger in absolute value, the signal about skill is noisier, and investors react less strongly to performance. Besides validating the theory as an explanation for the empirical fact, the data also provide support for some of the model's out-of-sample predictions.

Number of Pages in PDF File: 80

Keywords: Bayesian learning, parameter uncertainty, mutual funds, flow-performance, Kalman filter, beta

JEL Classification: G00, G20


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Date posted: May 13, 2013 ; Last revised: February 3, 2016

Suggested Citation

Franzoni, Francesco A. and Schmalz, Martin C., Performance Measurement with Uncertain Risk Loadings (January 28, 2016). Swiss Finance Institute Research Paper No. 13-41; Ross School of Business Paper No. 1194. Available at SSRN: http://ssrn.com/abstract=2263944 or http://dx.doi.org/10.2139/ssrn.2263944

Contact Information

Francesco A. Franzoni
University of Lugano ( email ) ( email )
University of Lugano
Via G. Buffi 13
Lugano, 6904
Switzerland
Swiss Finance Institute ( email ) ( email )
University of Lugano
Via G. Buffi 13
Lugano, 6904
Switzerland
Martin C. Schmalz (Contact Author)
University of Michigan, Stephen M. Ross School of Business ( email )
701 Tappan St
R5456
Ann Arbor, MI 48109-1234
United States
7347630304 (Phone)
HOME PAGE: http://https://sites.google.com/site/martincschmalz/
Feedback to SSRN


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