An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange

Abstract

Frequent market instability and the lack of rigorously validated forecasting frameworks pose significant challenges for predicting market stress in the Dhaka Stock Exchange (DSE). This study proposes a leakage-safe machine learning framework for next-day market stress prediction using historical trading data from 2008 to 2022. Technical indicators representing trend, momentum, volatility, and volume are extracted, while market conditions are classified into three categories: Normal, High-Volatility, and Crash. Seven models, namely Logistic Regression, Random Forest, XGBoost, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Hidden Markov Model (HMM), and GJR-GARCH, are evaluated using expanding-window walk-forward cross-validation with embargo periods. Random Forest achieves the best crash prediction performance with a crash PR-AUC of $$0.1697 \pm 0.0134$$ , followed by XGBoost and LSTM. At the same time, the Friedman test confirms statistically significant differences among the models ( $$\chi ^2 = 19.11$$ , $$p = 0.004$$ ). Permutation Importance and SHAP identify Bollinger Bandwidth and rolling volatility as the most influential predictors. The proposed framework demonstrates that leakage-safe and interpretable machine learning can effectively support market stress prediction and risk-aware decision-making in the DSE.