AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING
TL;DR
An explainable ensemble learning framework to forecast stocks’ market-relative performance and guide portfolio rebalancing in Borsa Istanbul (BIST), which identifies profitability and valuation indicators, especially return on assets, return on equity, and key valuation ratios, as the main drivers of future stock performance.
Abstract
This research develops an explainable ensemble learning framework to forecast stocks’ market-relative performance and guide portfolio rebalancing in Borsa Istanbul (BIST). The analysis covers quarterly data from 20 firms between 2009-Q1 and 2025-Q1 and employs Random Forest, XGBoost, LightGBM, and CatBoost trained on thirteen financial ratios. Hyperparameters are selected using grid search with time-series cross-validation, and performance is evaluated out-of-sample. Portfolios are rebalanced quarterly using predicted returns and benchmarked against an equally weighted portfolio and the BIST 100 Index. Results show that model-based portfolios generally outperform benchmarks in total return and risk-adjusted performance, with stronger statistically supported results in broader Top-k configurations, particularly CatBoost Top-7. SHAP identifies profitability and valuation indicators, especially return on assets, return on equity, and key valuation ratios, as the main drivers of future stock performance. These findings support explainable ensemble learning for fundamental-based portfolio management in emerging markets.