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Information Coefficient as a Performance Measure of Stock Selection Models

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  • Feng Zhang
  • Ruite Guo
  • Honggao Cao

Abstract

Information coefficient (IC) is a widely used metric for measuring investment managers' skills in selecting stocks. However, its adequacy and effectiveness for evaluating stock selection models has not been clearly understood, as IC from a realistic stock selection model can hardly be materially different from zero and is often accompanies with high volatility. In this paper, we investigate the behavior of IC as a performance measure of stick selection models. Through simulation and simple statistical modeling, we examine the IC behavior both statically and dynamically. The examination helps us propose two practical procedures that one may use for IC-based ongoing performance monitoring of stock selection models.

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  • Feng Zhang & Ruite Guo & Honggao Cao, 2020. "Information Coefficient as a Performance Measure of Stock Selection Models," Papers 2010.08601, arXiv.org.
    • Handle: RePEc:arx:papers:2010.08601
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    References listed on IDEAS

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    1. Ding, Zhuanxin & Martin, R. Douglas, 2017. "The fundamental law of active management: Redux," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 91-114.
    2. Gillam, Robert A. & Guerard, John B. & Cahan, Rochester, 2015. "News volume information: Beyond earnings forecasting in a global stock selection model," International Journal of Forecasting, Elsevier, vol. 31(2), pages 575-581.
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