A hybrid decision tree-Markowitz framework for intelligent trading portfolio optimization

Predicting stock returns is difficult due to numerous dynamic and interrelated factors such as interest rates, exchange rates, economic conditions, corporate policies, investor behavior, and political developments. This study proposes a hybrid trading strategy that integrates the Decision Tree (DT) algorithm with the Markowitz Mean-Variance (MV) portfolio optimization model to improve prediction accuracy and optimize investment returns. Using data from 94 companies continuously listed on the NASDAQ 100 (NDX100) index between 2015 and 2024, the model uses technical analysis indicators as input for the DT to predict short-term stock returns and then applies MV optimization to allocate weights within dynamically generated portfolios. The empirical analysis includes 64 portfolio variations derived from different parameter settings and is evaluated using the Sharpe ratio, Sortino ratio, maximum drawdown, total return, and turnover ratios. The results show that portfolios created with higher trading target ratios and moderate MV optimization significantly outperform the NDX100 index, providing superior risk-adjusted returns and better stability. These findings highlight the effectiveness of combining machine learning-based regressor with classical optimization for dynamic portfolio creation and underscore its practical potential for improving trading decisions in volatile markets.