Feasibility of Machine Learning and Algorithms in Real World Stock Market Applications - Liang, Wenye (Transactions on Computer Science and Intelligent Systems Research, 2025)
The stock market is one of the most important parts of the global economy, and predicting its movements has long been a challenge. Traditional methods relied on mathematical models and historical data, but advances in computing have introduced machine learning and deep learning approaches. This paper reviews four common methods used in stock prediction: Random Forest, XGBoost, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. Each method has strengths, such as Random Forest’s stability, XGBoost’s strong pattern recognition, CNN’s ability to process visual patterns, and LSTM’s capability to capture temporal dependencies. However, challenges remain, including limited interpretability, poor generalization to new markets, and reliance on single-modality data. To address these issues, future research should focus on combining machine learning with expert systems for better interpretability, applying domain adaptation for cross-market generalization, and using multimodal learning to integrate numerical, textual, and sentiment data. These strategies may help improve the feasibility of machine learning in real-world stock market applications.