Information Coefficient as a Performance Measure of Stock Selection Models

TL;DR

This paper investigates the effectiveness of the Information Coefficient (IC) as a performance metric for stock selection models, revealing its limitations and proposing practical monitoring procedures.

Contribution

It provides a detailed analysis of IC behavior in realistic settings and introduces two new procedures for ongoing performance evaluation of stock selection models.

Findings

IC often remains near zero with high volatility in realistic models

Proposes two practical IC-based performance monitoring procedures

Highlights limitations of IC as a sole performance measure

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.