Machine Learning Approaches in Stock Market Prediction: A Systematic Literature Review

Abstract

This paper provides a comprehensive review of the use of machine learning (ML) models in stock market prediction, focusing on the effectiveness of various approaches and the integration of external data sources. The review covers widely used ML models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and hybrid models. It compares the performance of these models, highlighting key metrics such as accuracy, precision, and Root Mean Square Error (RMSE).

Introduction

1. Background & Motivation

Stock market prediction is complex due to its volatile, non-linear, and dynamic nature. Traditional methods like:

• Fundamental Analysis (financial reports, economic indicators)

• Technical Analysis (price trends, volume)

…are valuable but limited in processing vast, high-frequency, and unstructured data.

2. Rise of Machine Learning (ML)

Advancements in ML and computing power have revolutionized financial forecasting. ML algorithms can:

• Learn from historical data

• Detect hidden patterns

• Model non-linear relationships

• Adapt to new data inputs

?? Widely used ML techniques include:

• Artificial Neural Networks (ANN)

• Support Vector Machines (SVM)

• Long Short-Term Memory (LSTM) networks

• Hybrid models like CNN-LSTM or GA-ANN

3. Systematic Literature Review (SLR)

The study conducted an SLR of 30 peer-reviewed papers (2012–2020) using PRISMA guidelines to:

• Assess ML model performance

• Identify trends in research

• Highlight strengths and limitations of various models

4. Key Findings from Literature Review

• LSTM models consistently outperformed other ML models for time-series forecasting, due to their ability to capture long-term dependencies.

• ANNs are suitable for modeling non-linear relationships, especially for short-term predictions.

• SVMs are effective when combined with external data like sentiment analysis.

• Hybrid models (e.g., CNN-LSTM, GA-ANN) achieve higher accuracy and lower RMSE.

5. Methodology Overview

• Databases searched: Google Scholar, IEEE Xplore, ScienceDirect, ACM Digital Library

• Inclusion criteria: Peer-reviewed, English, published 2012–2020, focused on ML in stock prediction

• Selection process: Identification → Screening → Eligibility → 30 final studies

• Data extracted: ML model used, data types (price, sentiment), performance metrics

6. Comparative Model Performance

• LSTM: Best for long-term predictions and capturing temporal dependencies

• ANN: Better suited for short-term, rapid fluctuations

• Hybrid models (CNN-LSTM): Strongest performance overall

• Sentiment-based models: Enhanced accuracy using news/social media inputs

7. Research Trends

• Surge in ML-based studies between 2013–2017, with peak interest in 2015

• Transition from classical ML models (ANN, SVM) to deep learning and hybrid models

• Increasing incorporation of external/alternative data: News, Twitter, ESG metrics

8. Challenges & Limitations

• Overfitting: ML models often fail to generalize well on unseen data

• Interpretability: Deep learning models are black boxes, hard for investors to trust

• Data dependency: Large, clean datasets are required for training deep models

• Computational cost: Real-time prediction demands significant resources

• Practical application gap: High theoretical accuracy doesn’t always translate into real-world success

9. Future Directions

• Integration of alternative data sources (e.g., ESG, social media)

• Greater use of reinforcement learning for adaptive trading

• Development of interpretable AI models for financial decision-making

• Focus on real-time, robust systems suitable for live trading environments

Conclusion

In conclusion, machine learning (ML) models have made significant strides in stock market prediction, with notable advancements in both model complexity and the inclusion of diverse data sources. Among the models reviewed, Long Short-Term Memory (LSTM) networks stand out for their ability to effectively handle time-series data and capture long-term dependencies, making them highly suitable for long-term forecasting. Artificial Neural Networks (ANNs), on the other hand, excel in short-term predictions where rapid fluctuations are more pronounced. Hybrid models, such as CNN-LSTM and GA-ANN, have demonstrated promising results by combining the strengths of multiple techniques to improve accuracy and generalization. Additionally, the integration of external data sources, particularly sentiment analysis from financial news and social media, has proven crucial in enhancing prediction accuracy. However, several challenges persist, including issues related to overfitting, model interpretability, and the computational complexity of deep learning models. These limitations often hinder the practical deployment of these models in real-world financial environments. Despite these challenges, there are exciting opportunities for future research, particularly in the development of real-time prediction models, Explainable AI (XAI) for better model interpretability, and the integration of alternative data sources such as satellite imagery and Environmental, Social, and Governance (ESG) metrics. Ultimately, the future of stock prediction lies in the continued evolution of hybrid models, which leverage the strengths of multiple algorithms and data sources, combined with advancements in real-time processing and model transparency. As these models become more robust and interpretable, they will likely offer more accurate and reliable predictions, making them invaluable tools for investors, financial analysts, and researchers in the field.

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Copyright

Copyright © 2025 Divyansh Bhardwaj, Aditi Verma, Harsh Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.