Machine Learning in Financial Markets: A Review of Predictive Modeling Techniques

Abstract

This review article examines the application of machine learning (ML) techniques to predictive modeling in financial markets, synthesizing methodological trends and presenting an illustrative empirical comparison across a curated dataset of N = 410 observations drawn from daily equity index and firm-level trading records. Traditional statistical approaches to market prediction, including autoregressive and econometric models, are contrasted with modern supervised, ensemble, and deep learning architectures. The study evaluates seven modeling families linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost/LightGBM), support vector machines, long short-term memory (LSTM) networks, and Transformer-based sequence models using an advanced analytical toolchain comprising Python, scikit-learn, TensorFlow/Keras, XGBoost, and SHAP for model interpretability. Results indicate that ensemble and deep-sequence models outperform classical linear benchmarks on directional accuracy and risk-adjusted return metrics, though gains diminish once transaction costs and overfitting risk are accounted for. The article concludes with a discussion of persistent challenges non-stationarity, low signal-to-noise ratios, and interpretability and outlines directions for future research, including hybrid econometric-ML frameworks and explainable AI in trading systems.