Predicting startup success using two bias-free machine learning: resolving data imbalance using generative adversarial networks
Jungryeol Park, Saesol Choi, Yituo Feng
Abstract
The success of newly established companies holds significant implications for community development and economic growth. However, startups often grapple with heightened vulnerability to market volatility, which can lead to early-stage failures. This study aims to predict startup success by addressing biases in existing predictive models. Previous research has examined external factors such as market dynamics and internal elements like founder characteristics.While such efforts have contributed to understanding success mechanisms, challenges persist, including predictor and learning data biases. This study proposes a novel approach by constructing independent variables using early-stage information, incorporating founder attributes, and mitigating class imbalance through generative adversarial networks (GAN). Our proposed model aims to enhance investment decision-making efficiency and effectiveness, offering a valuable decision support system for various venture capital funds.