Practical Applications of Alternative Data in Investment Management: Usage, Challenges, and Valuation

Abstract

In Alternative Data in Investment Management: Usage, Challenges, and Valuation, from the Fall 2021 issue of The Journal of Financial Data Science, Gene Ekster and Petter Kolm, both at New York University’s Courant Institute of Mathematical Sciences, provide insight into how to get the most out of this relatively new resource. Unlike traditional financial data used to analyze and manage investments, alternative data has unique technical challenges, an evolving industry of providers, and valuation challenges. Ekster and Kolm offer methods of dealing with these matters. They point out that it is crucial to understand the structure of the industry, particularly the difference between data originators and intermediaries. The authors also discuss entity mapping, tagging, and other ways of addressing technical issues with alternative data. Importantly, they provide investment professionals with methods of determining the likely value of an alternative dataset with a short history. They include a case study on predicting revenues of publicly traded companies, thus illustrating the design considerations for data processing pipelines and downstream analytics.