Stock Market Prediction Using Machine Learning: A Review of Techniques, Applications, Challenges, and Future Directions

Machine learning has become a prominent tool in stock market forecasting due to its flexible techniques used to analyze the past prices, technical indicators, fundamental features, macroeconomic factors, and sentiment of investors in the financial market. Some examples of machine learning algorithms include Support Vector Machines, Random Forest, Gradient Boosting, Artificial Neural Networks, and Long Short Term Memory networks. The literature presented in this paper shows that machine learning techniques are capable of being applied in financial prediction, but the effectiveness of these algorithms is highly dependent on the dataset characteristics, prediction horizon, chosen features, and evaluation technique. There is no one particular technique which could be called the best for all cases and markets. This review emphasizes some methodological problems that should be solved in order to properly interpret results obtained while using machine learning algorithms, such as noise in the dataset, changes in market, overfitting, data leakage, limited interpretability, and the difference between statistics and profitable trading. Interpretable AI will provide researchers with extra methods for exploring what drives the predictions made by the models. If used together with validation and proper benchmarking, it can help researchers better understand how machine learning algorithms behave. The future research should concentrate on standardized comparison of models, use of multiple sources of financial data, market regimes analysis, interpretability of predictions, and financial evaluation under realistic assumptions. Such studies will enable us to create forecasting systems that are both sophisticated and reliable. In general, machine learning is a tool for studying financial uncertainty, not an instrument for getting rid of it. The utility of a forecasting system lies in its predictive relationships and their applicability outside of the data that were used to estimate the model.

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