Comparative Analysis of LSTM, RNN, CNN and MLP Machine Learning Algorithms for Stock Value Prediction

Dattatray G. Takale; Aditya A. Wattamwar; Saksham S. Saipatwar; Harshwardhan V. Saindane; Tushar B. Patil

doi:10.48001/jofsn.2024.211-10

Authors

Dattatray G. Takale Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
Aditya A. Wattamwar Research Intern, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
Saksham S. Saipatwar Research Intern, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
Harshwardhan V. Saindane Research Intern, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
Tushar B. Patil Research Intern, Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

DOI:

https://doi.org/10.48001/jofsn.2024.211-10

Keywords:

Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Machine learning algorithms, Multilevel perceptron, Recurrent Neural Network (RNN), Stock price prediction

Abstract

In the context of the increasingly globalized financial markets, this review paper compares four prominent ML algorithms for stock value prediction: LSTM, CNN, RNN, and MLP. Through rigorous evaluations using diverse historical stock price data and key metrics like MAE, RMSE, and precision, the study reveals unique strengths and weaknesses in each algorithm's ability to capture stock price dynamics. LSTM emerges as the top performer, followed by CNN, RNN, and MLP. However, the authors emphasize that the selection of the best algorithm relies on certain data characteristics and desired accuracy levels.

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