Individuals’ personality traits drive their decision-making and ultimately their behavior. While the importance of a match between investors and investees is often highlighted in prior research, only limited evidence exists regarding the implications of a match in terms of personality, especially for performance outcomes. Drawing on the attractiveness paradigm in combination with the notions of complementary and supplementary fit, we argue that similarity in specific personality traits within the business angel-founder dyad can support or harm the performance of a new venture. Building on this theoretical argumentation, we use a machine learning (ML) algorithm to predict the Big Five personality traits of business angels and founders collaborating in an investment round based on their Twitter messages. Using a sample of 10,416 business angel-founder dyads that include 1,358 business angels who invested in 2,928 ventures of 3,651 founders, our results show that similarity in extraversion is negatively associated with venture performance, while similarity in agreeableness is positively associated with venture performance. Our findings contribute to the entrepreneurial finance literature that explores the performance of new ventures and show the potential of new artificial intelligence (AI)-based methods for advancing entrepreneurship research.
