Navigating the Spectrum of Unexpected Events in Private Equity: Beyond Black Swans

This study proposes a quantitative framework for classifying and managing a spectrum of unexpected market events in private equity (PE), extending beyond traditional Black Swan theory. We hypothesize that PE firms’ performance correlates with their ability to identify and respond to various intensities of market anomalies. Using a mixed-methods approach, we analyzed historical performance data from PE firms over a 20-year period and conducted semi-structured interviews with industry experts (n=100). We employed machine learning algorithms to categorize market events and multivariate regression to correlate event response strategies with firm performance. Results show a significant relationship between comprehensive anomaly management and superior returns. Our classification model demonstrated moderate predictive power for firm performance based on their event response strategies.