TY - JOUR
T1 - Cost-sensitive machine learning to support startup investment decisions
AU - Setty, Ronald
AU - Elovici, Yuval
AU - Schwartz, Dafna
N1 - Publisher Copyright:
© 2024 The Authors. Intelligent Systems in Accounting, Finance and Management published by John Wiley & Sons Ltd.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - In 2022, global startup investments exceeded US$445 billion, sourced from entities like venture capital (VC) funds, angel investors, and equity crowdfunding. Despite their role in driving innovation, startup investments often fall short of S&P 500 returns. Surprisingly, the potential of artificial intelligence (AI) remains untapped by investors, despite AI's growing sway in financial decision-making. Our empirical analysis predicts the success of 10,000 Israeli startups, utilizing diverse machine learning models. Unlike prior research, we employ the MetaCost algorithm to convert models into cost-sensitive variants, minimizing total cost instead of total error. This innovative approach enables varied costs linked to different prediction errors. Our results underscore that these cost-sensitive machine learning models significantly reduce risk for VC funds and startup investors compared to traditional ones. Furthermore, these models provide investors with a distinct capability to tailor their risk profiles, aligning predictions with their risk appetite. However, while cost-sensitive machine learning reduces risk, it may limit potential gains by predicting fewer successful startups. To address this, we propose methods to enhance successful startup identification, including aggregating outcomes from multiple MetaCost models, particularly advantageous for smaller deal flows. Our research advances AI's role in startup investing, presenting a pivotal tool for investors navigating this domain.
AB - In 2022, global startup investments exceeded US$445 billion, sourced from entities like venture capital (VC) funds, angel investors, and equity crowdfunding. Despite their role in driving innovation, startup investments often fall short of S&P 500 returns. Surprisingly, the potential of artificial intelligence (AI) remains untapped by investors, despite AI's growing sway in financial decision-making. Our empirical analysis predicts the success of 10,000 Israeli startups, utilizing diverse machine learning models. Unlike prior research, we employ the MetaCost algorithm to convert models into cost-sensitive variants, minimizing total cost instead of total error. This innovative approach enables varied costs linked to different prediction errors. Our results underscore that these cost-sensitive machine learning models significantly reduce risk for VC funds and startup investors compared to traditional ones. Furthermore, these models provide investors with a distinct capability to tailor their risk profiles, aligning predictions with their risk appetite. However, while cost-sensitive machine learning reduces risk, it may limit potential gains by predicting fewer successful startups. To address this, we propose methods to enhance successful startup identification, including aggregating outcomes from multiple MetaCost models, particularly advantageous for smaller deal flows. Our research advances AI's role in startup investing, presenting a pivotal tool for investors navigating this domain.
KW - AI in finance
KW - cost-sensitive machine learning
KW - imbalanced data analysis
KW - startup success prediction
KW - venture capital
UR - http://www.scopus.com/inward/record.url?scp=85184889983&partnerID=8YFLogxK
U2 - 10.1002/isaf.1548
DO - 10.1002/isaf.1548
M3 - Article
AN - SCOPUS:85184889983
SN - 1550-1949
VL - 31
JO - Intelligent Systems in Accounting, Finance and Management
JF - Intelligent Systems in Accounting, Finance and Management
IS - 1
M1 - e1548
ER -