Comparative Study of Different Machine Learning Methodology Used for Stock Price Prediction

Abstract

Stock price prediction has been a topic of interest for investors and researchers for a long time because of the monetary returns that can be achieved using correct forecasts. Over the last few years, machine learning (ML) approaches have become increasingly popular for modelling non-linear and complex financial time series data. This article offers a comparative study of three highly acclaimed machine learning approaches Long-Short-Term-Memory (LSTM) networks, Support Vector Machines (SVM), and Random Forests (RF) for forecasting stock prices. LSTM, an RNN variant, is designed to recognize long-term dependence in sequential information and thus becomes particularly appropriate to model time series trends. SVM, a versatile supervised learning approach, is characterized by its capacity to handle high-dimensional spaces with ease and express non-linear dependencies via kernel maps. Random Forest, an approach to ensemble-based learning, predicts multiple decision tree outputs to circumvent overfitting and provide better generalizability. Historical stock prices are employed to train and test each model in this research. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²) Score are used to measure prediction accuracy. The findings show that all three models excel in various contexts, but LSTM performs better in identifying temporal patterns, SVM is good with smaller datasets and evident margins, and Random Forest provides robustness and interpretability. This comparative study sheds light on the strengths and weaknesses of each method, helping practitioners and researchers choose the most suitable model for stock market forecasting applications.

Introduction

The stock market is complex and volatile, making accurate stock price prediction challenging yet crucial for investors and analysts. Machine Learning (ML) techniques have shown promise in improving forecast accuracy by identifying patterns in historical data. This research compares three ML methods—Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Random Forests (RF)—for stock price prediction.

• LSTM is a specialized Recurrent Neural Network designed to capture long-term dependencies in sequential data, making it well-suited for time series like stock prices.

• SVM excels in handling both linear and nonlinear relationships using kernel functions but is sensitive to parameter tuning.

• Random Forest is an ensemble of decision trees that manages complex, high-dimensional data and reduces overfitting through averaging.

The study involves preprocessing historical stock data, training each model, and evaluating performance using metrics like RMSE, MAE, and R². Visualization of predicted versus actual prices helps assess model effectiveness.

The literature review supports the selection of these methods, highlighting their respective strengths and limitations in financial forecasting. The proposed system integrates these models into a unified framework, leveraging their complementary advantages to enhance prediction accuracy.

Implementation details include data normalization, model training, and evaluation using Python and relevant ML libraries, with stock data sourced from Yahoo Finance. Results show that combining these models provides a robust and scalable solution for stock price forecasting, aiding better investment decisions and risk management.

Conclusion

This project aimed at using machine learning algorithms to forecast stock market trends based on global financial information. We investigated three leading models—Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and Random Forests (RF)to make daily market forecasts. The outcome proved the potential of these models, as their forecasts outperformed traditional benchmarks, with promise for real-world trading strategies to be more profitable. LSTM had the highest accuracy in recognizing time-series patterns, SVM worked best with smaller, high-dimensional datasets, and Random Forest delivered consistent, interpretable results. Increasing dataset size, trying other algorithms, and applying different evaluation measures might lead to greater accuracy and reliability.

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Copyright

Copyright © 2025 Prof. Junaid Mandviwala, Shivam Samir Shere, Krishnakant Narsing Choudhary, Sohail Sabir Ansari, Saif Azhar Ansari. 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.