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.
Mustafa Etcil, Burak Kolukısa, Burcu Güngör· Kahramanmaraş Sütçü İmam Üni...· 0 citations
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.
Mustafa Etcil, Hüseyin Akkaş, Burak Kolukısa et al.· Signal Processing and Commun...· 0 citations
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