Towards the Next Generation of Performance Attribution for Institutional Investment Management

Abstract

Proprietary information and data processing systems have become key competitive differentiators for investors in today’s complex financial marketplace; better systems provide better data, which, in turn, drive better investment decisions and superior investment performance. But while the best systems are multifaceted and touch all aspects of the investment organization (see Clark and Monk 2013), one component of such systems is increasingly important: the measurement and attribution of performance. Today’s leading investors view performance attribution as more than just explaining the past; it is seen as a tool for making better investment decisions in the future. Accurate, timely, and detailed attribution helps measure skill and sources of active return and can thus help strengthen investment teams and improve portfolio performance. In this brief article, we describe our experience at the Alberta Investment Management Corporation (AIMCo) in developing performance attribution as an investment management tool in the hope that it may become part of a broader institutional investor discussion on this topic.

Keywords: institutional investment, benchmarks, performance attribution, big data, asset management