TY - GEN
T1 - AlphaVC
T2 - 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
AU - Zhong, Hao
AU - Yuan, Zixuan
AU - Zhang, Denghui
AU - Jiang, Yi
AU - Zhang, Shengming
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2024 IEEE Computer Society. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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 reinforcement learning 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. Our methodology demonstrates its efficacy and superiority in both ranking and portfolio-based performance metrics in comparison with various state-of-the-art baseline methods. Through sensitivity and ablation analyses, our research highlights the significance of factoring in the distal outcome and acknowledging the learning effect when making decisions at different time points. Additionally, we observe that AlphaVC concentrates on a select number of high-potential companies, but distributes investments evenly across various stages of the investment process.
AB - 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 reinforcement learning 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. Our methodology demonstrates its efficacy and superiority in both ranking and portfolio-based performance metrics in comparison with various state-of-the-art baseline methods. Through sensitivity and ablation analyses, our research highlights the significance of factoring in the distal outcome and acknowledging the learning effect when making decisions at different time points. Additionally, we observe that AlphaVC concentrates on a select number of high-potential companies, but distributes investments evenly across various stages of the investment process.
KW - portfolio optimization
KW - reinforcement learning
KW - venture capital
UR - http://www.scopus.com/inward/record.url?scp=85199796860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85199796860&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85199796860
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 4333
EP - 4342
BT - Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
A2 - Bui, Tung X.
Y2 - 3 January 2024 through 6 January 2024
ER -