Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

Gaurang Sonkavde, Deepak Sudhakar Dharrao (), Anupkumar M. Bongale (), Sarika T. Deokate, Deepak Doreswamy and Subraya Krishna Bhat
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Gaurang Sonkavde: Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, Maharashtra, India
Deepak Sudhakar Dharrao: Department of Computer Science & Engineering, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, Maharashtra, India
Anupkumar M. Bongale: Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune 412115, Maharashtra, India
Sarika T. Deokate: Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044, Maharashtra, India
Deepak Doreswamy: Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
Subraya Krishna Bhat: Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India

IJFS, 2023, vol. 11, issue 3, 1-22

Abstract: The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.

Keywords: stock market; finance; linear regression; random forest; XG-Boost; FB Prophet; LSTM; ensemble learning; blending ensemble (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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