A survey of fundamental indicators, machine learning techniques and feature extraction techniques for stock price prediction

Abstract Machine learning (ML), more recently, deep learning, in combination with the two mainstream approaches of technical and fundamental analysis, has been widely applied to stock price prediction. Since technical analysis remains the dominant paradigm in this literature, comprehensive surveys centered exclusively on fundamental indicators are notably absent. This study addresses this gap through a systematic review of 54 empirical studies published between 2014 and 2025, identified from an initial pool of 2263 papers indexed in Scopus and Web of Science. The review catalogs 544 fundamental indicators across ten categories and examines their alignment with seven model families, alongside feature selection techniques, evaluation criteria, and data sources. Valuation metrics and profitability measures dominate the literature, with the P/E ratio and ROA being the most frequently employed indicators across price, return, and signal prediction tasks. Among model architectures, ANNs and Random Forest lead traditional ML approaches, while LSTM networks dominate deep learning applications. Using chi-square tests of independence, point-biserial correlations, and adjusted standardized residuals, we find a significant association between model category and prediction task, LSTM networks show a strong affinity for price prediction, Random Forests for return prediction, and boosting methods a negative association with price prediction. Transformer-based architectures show promise but have yet to establish dominance, and LLMs currently function more effectively in auxiliary roles such as financial statement interpretation and sentiment extraction than as standalone predictive engines. Accuracy, RMSE, and the Sharpe Ratio serve as the standard evaluation metrics across the reviewed literature. A sensitivity analysis across three inclusion scenarios confirms the robustness of core findings. This survey provides a unified framework for model-indicator alignment and offers practical guidance for feature selection, evaluation standards, and future research priorities.