Identifying Potential Investors with Data Driven Approaches

Bo Yang, Kejun Huang, Nicholas D. Sidiropoulos

Abstract: Seeking outside funding is a critical task for many companies, but it is also challenging to identify the potential investors given the heterogeneity in size, interests, and expertise. In this work, we propose to tackle this problem via data-driven approaches. Towards this end, we first harvest relevant publicly available data about institutional investors and their holdings in publicly traded companies, as well as key financial metrics on the same set of public companies. Using these data, we approach the problem of interest as a recommender system with side information. We formulate two principal goals: predicting “missing” (potential) investor holding positions; and providing top-K investor recommendations. For each goal, custom recommendation algorithms are proposed to achieve the corresponding objective. Numerical experiments are carefully designed to validate the effectiveness of the proposed algorithms, which exhibit good performance in practice.