Stock Market Prediction with Machine Learning using Fundamental Analysis: A Review
Keywords:
Feature engineering, Financial ratios, Fundamental analysis, LSTM, Machine learning, Portfolio optimization, Random forest, Stock market predictionAbstract
Stock market prediction has moved much beyond short-term technical trading signals toward deeper, value-oriented approaches grounded in fundamental analysis. This review synthesizes the findings of the previous article and studies that explore how machine learning models will utilize financial statements and accounting ratios and macroeconomic indicators to forecast stock returns, trends, and risk. Across the literature, several algorithms-including Random Forest, Support Vector Regression, Logistic Regression, Gradient Boosting techniques, and deep learning models such as LSTM and GRU-have been applied to fundamental data. The available proofs suggest that models built on fundamental indicators can perform better than those relying only on technical features, especially in long-term forecasting. Hybrid approaches that combine fundamental signals with technical signals or sentiment data further improve predictive robustness. Tree-based ensemble models are widely known for their interpretability and stability, while the deep learning architectures display strength in capturing temporal patterns in multi-year financial data, albeit with higher computational requirements and overfitting risks. Despite good outcomes, common challenges remain: limited data availability, quarterly reporting constraints, sector-specific variations, and insufficient integration of real-time macroeconomic factors. In general, this review of the literature highlights that integrating machine learning with structured fundamental analysis provides a reliable foundation for long-term stock prediction and informed portfolio decision-making, while highlighting the need for scalable, interpretable, and hybrid modeling frameworks for real-world financial applications.
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