AlphaVC - A Reinforcement Learning-based Venture Capital Investment Strategy

Venture capital investments play a powerful role in fueling the emergence and growth of early-stage startups. However, only a small fraction of venture-backed startups can survive and exit successfully. Prior data-driven prediction-based or recommendation-based solutions are incapable of providing effective and actionable strategies on proper investment timing and amounts for startups across different investment rounds. In this paper, we develop a novel reinforcement learning-based method, AlphaVC, to facilitate venture capitalists’ decision-making. Our policy-based RL agents can dynamically identify the best candidates and sequentially place the optimal investment amounts at proper rounds to maximize financial returns for a given portfolio. We retrieve company demographics and investment activity data from Crunchbase and test our model in two industry sectors: Financial Services and Information Technology. Our methodology demonstrates its efficacy and superiority in both ranking and portfolio-based performance metrics in comparison with various SOTA baseline methods.

Hao (Howard) ZHONG
Hao (Howard) ZHONG
Assistant Professor | Scientific Co-director of MSc in Big Data and Business Analytics