Generalized Information Ratio

48 Pages Posted: 8 Mar 2018 Last revised: 29 Apr 2018

See all articles by Zhongzhi Lawrence He

Zhongzhi Lawrence He

Brock University, Goodman School of Business

Date Written: April 1, 2018

Abstract

Alpha-based performance evaluation may fail to capture correlated residuals due to model errors. This paper proposes using the Generalized Information Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by the theoretical link between abnormal returns and residual covariance matrix, GIR is derived as alphas scaled by the inverse square root of residual covariance matrix. GIR nests alphas and Information Ratio as special cases, depending on the amount of information used in the residual covariance matrix. We show that GIR is robust to various degrees of model misspecification and produces stable out-of-sample returns. Incorporating residual correlations leads to substantial gains that alleviate model error concerns of active management.

Keywords: Performance Evaluation, Mutual Funds, Information Ratio, Model Misspecification, Optimal Transport Mapping, Estimation of Covariance Matrix

JEL Classification: C11, G11, G12

Suggested Citation

He, Zhongzhi Lawrence, Generalized Information Ratio (April 1, 2018). Available at SSRN: https://ssrn.com/abstract=3130422 or http://dx.doi.org/10.2139/ssrn.3130422

Zhongzhi Lawrence He (Contact Author)

Brock University, Goodman School of Business ( email )

500 Glenridge Avenue
Finance
St. Catherine's, Ontario L2S 3A1
Canada

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