Improving Investment Operations Through Data Science: A Case Study of Innovation in Valuation

Abstract

New technologies in data science are allowing long-term investors to bring much more rigor to their operations. This paper thus shows empirical examples in support of the data-driven advances, demonstrating their practical applications. We use the UC Investments office as our case study, and we discuss how adoption of advanced data science techniques can move organizations past the current unsatisfactory state of the art, to an unprecedented level of operational finesse. Specifically, we focus on a methodological innovation in fair valuation of illiquid assets that is supported by an automated, rigorous process. We test this process in a real-world setting, and find, at least in this case, that these advances can enhance roll forward outputs in terms of timeliness, accuracy and granularity. This has several potential impacts, not only for reporting, but also for investment, risk management, actuarial purposes and even personal compensation of teams.

Keywords: Fair Value, Private Equity, Valuation, Alternatives, Operations, Data Science, Roll Forward, Reporting, Modern Proxy Benchmarks, NAV