Machine learning in business and finance: a literature review and research opportunities

Hanyao Gao, Gang Kou (), Haiming Liang, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li and Yucheng Dong ()
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Hanyao Gao: Sichuan University
Gang Kou: Southwestern University of Finance and Economics
Haiming Liang: Sichuan University
Hengjie Zhang: Hohai University
Xiangrui Chao: Sichuan University
Cong-Cong Li: Southwest Jiaotong University
Yucheng Dong: Sichuan University

Financial Innovation, 2024, vol. 10, issue 1, 1-35

Abstract: Abstract This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.

Keywords: Machine learning; Business; Finance; Marketing (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1186/s40854-024-00629-z

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