Machine Learning Models for Predicting Private Equity Fund Performance Using Historical Deal-Level and Macroeconomic Data

Abstract

The private equity sector has grown in significance within international financial markets due to its ability to generate high value long term investment returns. However predicting the returns of private equity funds is extremely challenging because of market volatility the nonlinear relationship between investments and returns and the opacity of alternative asset markets. This study aims to develop a predictive model for private equity fund performance using machine learning techniques based on Private Equity Data from Q1 2009 to Q2 2021. The framework includes data preprocessing temporal feature engineering ensemble learning hyperparameter optimization and explainable artificial intelligence XAI techniques implemented in a Google Colab environment. The models comparatively evaluated were Linear Regression Random Forest XGBoost and LightGBM using Mean Absolute Error MAE Root Mean Squared Error RMSE Mean Absolute Percentage Error MAPE and coefficient of determination R2 as evaluation metrics. Experimental results demonstrated that the XGBoost model achieved the best predictive performance with an MAE of 0.091 RMSE of 0.126 MAPE of 5.96 percent and R2 value of 0.919 outperforming Random Forest LightGBM and Linear Regression. SHAP based explainability analysis revealed that the variables most influencing private equity fund performance were investment growth in the fourth quarter capital deployment over the previous four quarters and transaction density. The findings support the effectiveness of ensemble learning algorithms in improving forecasting capability within the private equity domain while also providing transparent AI assisted investment decision support for private equity investors portfolio managers and financial analysts in alternative investment contexts.