Machine Learning-Based Investigation on the Impact of Chinese Venture Capital Institutions’ Performance: Evaluation Factors of Venture Enterprises to Venture Capital Institutions

Abstract

Studying the influencing factors of venture capital fund investment performance is crucial for the decision making of venture capital institutions. This paper explored the influencing factors of venture capital institutions from the perspective of startups, aiming to elucidate the mechanisms of these factors on the performance of venture capital funds and to propose a novel and effective predictive model of investment performance. Linear regression and one-way ANOVA were used to analyze the influence of each variable on investment performance, and the weight proportion of each influencing factor was obtained under the linear model. Two machine learning models, including the random forest algorithm and extreme learning machine algorithm, are established, and the particle swarm algorithm and machine learning algorithm were combined to optimize the random parameters in the two models. Compare the reliability and accuracy of machine learning models and multivariate linear regression models. The analysis results indicate that the PSO-ELM hybrid model has a better predictive performance than other prediction models. A convenient machine learning algorithm provided in this paper can quickly and effectively predict the investment performance of various investment portfolios and provide investors with decision-making assistance.