Alternative Data in Investment Management: Usage, Challenges and Valuation

Abstract

Alternative data in finance is an umbrella term for diverse non-traditional datasets used by quantitative and fundamental institutional investors to enhance portfolio returns. While the use of alternative data is a recent phenomenon, it was not until the last five years that it gained widespread acceptance and the sector started to evolve into a complex ecosystem of data originators, intermediaries and investors. The alternative data industry faces several obstacles, including difficulty estimating a dataset’s value to investors and technical challenges in leveraging these datasets efficiently at large scale. In this article, the authors provide an up-to-date description of the alternative data space as it relates to the institutional investment industry. The authors elaborate on what alternative data is and how it is used in investment management. The authors identify and discuss some of the key challenges that arise when working with alternative data. In particular, they address issues such as entity mapping, ticker-tagging, panel stabilization and debiasing with modern statistical and machine learning approaches. The authors advance several methodologies for the valuation of alternative datasets, including an event study methodology they refer to as the Golden Triangle, the application of report cards, and the relationship between a datasets’ structure of information content its potential to enhance investment returns. To illustrate the effectiveness of the methods, they apply them to a case study analysis of real-world healthcare data, delivering an improvement of revenue prediction accuracy from an 88% mean absolute error to a 2.6% mean absolute error.