Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama and Amir H. Gandomi

No yc6e2, OSF Preprints from Center for Open Science

Abstract: This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Date: 2020-10-15
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-pay
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:yc6e2

DOI: 10.31219/osf.io/yc6e2

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