Prediction the Choice of Financing for Start-ups using Machine Learning Algorithms and Behavioral Biases

The aim of this paper is to predict financing methods to support decision-making for startup founders and their investors. Initially, factors influencing the choice of financing methods, including structural, demographic, and behavioral factors, were identified. These factors were then assessed using a questionnaire consisting of 32 items, which was sent online to startup founders. Based on 70 responses received and using algorithms including binary matching, classification chains, label power set, K-nearest neighbors, extreme gradient boosting, cluster boosting algorithm and random forest, the financing methods chosen by startups were predicted. Comparison of the results from the algorithms shows that the boosting ensemble algorithm, with an F1 score of 89 and precison of 85%, predicts the selected financing methods on the test dataset better than other algorithms. Additionally, data analysis indicates that startups are more inclined towards personal funding methods, which aligns with the prevalence of loss aversion bias among entrepreneurs. Following loss aversion, overconfidence, anchoring, and illusion of control biases were the most frequent among entrepreneurs.