Alternative Data in Investment Management: Usage, Challenges, and Valuation

Abstract

<i>Alternative data</i> in finance is an umbrella term for diverse nontraditional datasets used by quantitative and fundamental institutional investors to enhance portfolio returns. Although 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 a 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 are and how they are 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 <i>golden triangle</i>, the application of report cards, and the relationship between a dataset’s structure of information content and its potential to enhance investment returns. To illustrate the effectiveness of these methods, they apply them to a case study analysis of real-world healthcare data, delivering an improvement in revenue prediction accuracy from an 88% mean absolute error to a 2.6% mean absolute error. <b>Key Findings</b> ▪ The authors describe the alternative data industry in an investment context. They discuss the commercial incentives for different categories of organizations within the industry and methodologies to valuate alternative datasets. ▪ They identify key technical challenges in working with alternative data, including entity mapping, ticker-tagging, panel stabilization, and debiasing. ▪ Through a case study, they demonstrate how to use alternative data combined with imputation and optimization techniques to predict revenues of publicly traded companies within the healthcare sector.