Explainable Machine Learning for Cross-Sectional Stock Return Prediction and Mean–Variance Portfolio Optimization

Abstract

Portfolio construction aims to balance expected return and risk through effective asset allocation. This study proposes a portfolio formation framework that integrates machine learning-based return prediction with Markowitz mean–variance portfolio optimization. Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression models are employed to predict the cross-sectional excess returns of stocks using financial indicators derived from technical and macroeconomic variables. These predictions are incorporated into the portfolio optimization process to determine portfolio weights. The resulting strategies are evaluated against benchmark portfolios including an equal-weighted portfolio and the BIST 100 index. Empirical results show that machine learning-based stock selection improves portfolio performance. In particular, the XGBoost-based portfolio achieves the best results with an annual return of 75.30% and a Sharpe ratio of 1.80. SHAP analysis further indicates that momentum and price-based technical indicators play a dominant role in model predictions.