Machine learning-based approaches for financial market prediction: A comprehensive review

  • Bhaskar Nandi Department of Computer Science, Seacom Engineering College, Howrah 711302, West Bengal, India
  • Subrata Jana Jadavpur University, Kolkata 700032, West Bengal, India
  • Krishna Pada Das Department of Mathematics, Mahadevananda Mahavidyalaya, Barracpore 700120, Kolkata, West Bengal, India
Ariticle ID: 134
329 Views, 67 PDF Downloads
Keywords: financial markets, literature survey, research paper, machine learning, deep learning

Abstract

This research paper investigates the use of machine learning techniques in financial markets. The paper provides a comprehensive literature review of recent research on machine learning applications in finance, including stock price prediction, financial time series forecasting, and portfolio optimization. Various machine learning techniques, such as regression analysis, decision trees, support vector machines, and deep learning, are discussed in detail, with a focus on their strengths, weaknesses, and potential applications. The paper also highlights the challenges associated with machine learning in finance, such as data quality, model interpretability, and ethical considerations. Overall, the paper demonstrates that machine learning has significant potential in finance but calls for further research to address these challenges and fully explore its potential in financial markets.

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Published
2023-08-28
How to Cite
Nandi, B., Jana, S., & Das, K. P. (2023). Machine learning-based approaches for financial market prediction: A comprehensive review. Journal of AppliedMath, 1(2), 134. https://doi.org/10.59400/jam.v1i2.134
Section
Review