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Comparing model-specific and model-agnostic features importance methods using machine learning with technical indicators: A NASDAQ sector-based study
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DOI：10.1016/j.mlwa.2025.100799.png)
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Abstract
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Predicting stock prices is crucial for making informed investment decisions as stock markets significantly influence the global economy. Although previous studies have explored feature importance methods for stock price prediction, comprehensive comparisons of those methods have been limited. This study aims to provide a detailed comparison of different feature importance methods for selecting technical indicators to predict stock prices. Specifically, this research analyzed financial data from the 11 sectors of the NASDAQ. A moving window forecasting framework was implemented to dynamically capture the evolving patterns in financial markets over time. Model-specific feature importance methods were compared with model-agnostic approaches. Multiple machine learning algorithms, including Random Forest (RF), and Multi-layer Neural Network (MNNs), were employed to forecast stock prices. Additionally, extensive hyperparameter tuning was conducted to improve model explainability, contributing to the field of Explainable Artificial Intelligence (XAI). The results highlight the predictive effectiveness of different feature importance methods in selecting optimal technical indicators, thereby offering valuable insights for enhancing stock price forecasting accuracy and model transparency. In summary, this research offers a comprehensive comparison of feature importance methods, emphasizing their application in the selection of technical indicators in a dynamic, rolling prediction setting.
Keywords:
Feature importance
Technical indicators
Stock price prediction
Machine learning
Explainable artificial intelligence (XAI)
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