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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Author

Listed:
  • Saeed Nosratabadi

    (Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary)

  • Amirhosein Mosavi

    (Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Puhong Duan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Pedram Ghamisi

    (Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, D-09599 Freiberg, Germany)

  • Ferdinand Filip

    (Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia)

  • Shahab S. Band

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Uwe Reuter

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany)

  • Joao Gama

    (Faculty Laboratory of Artificial Intelligence and Decision Support (LIAAD)-INESC TEC, Campus da FEUP, Rua Roberto Frias, 4200-465 Porto, Portugal)

  • Amir H. Gandomi

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the 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 broad 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, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Suggested Citation

  • Saeed Nosratabadi & Amirhosein Mosavi & Puhong Duan & Pedram Ghamisi & Ferdinand Filip & Shahab S. Band & Uwe Reuter & Joao Gama & Amir H. Gandomi, 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods," Mathematics, MDPI, vol. 8(10), pages 1-25, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1799-:d:428986
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    References listed on IDEAS

    as
    1. Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Hyun Sik Sim & Hae In Kim & Jae Joon Ahn, 2019. "Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?," Complexity, Hindawi, vol. 2019, pages 1-10, February.
    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Salim Lahmiri & Stelios Bekiros & Anastasia Giakoumelou & Frank Bezzina, 2020. "Performance assessment of ensemble learning systems in financial data classification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 3-9, January.
    5. Hew, Jun-Jie & Leong, Lai-Ying & Tan, Garry Wei-Han & Ooi, Keng-Boon & Lee, Voon-Hsien, 2019. "The age of mobile social commerce: An Artificial Neural Network analysis on its resistances," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 311-324.
    6. Yao-Zhi Xu & Jian-Lin Zhang & Ying Hua & Lin-Yue Wang, 2019. "Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    7. Daigo Tashiro & Hiroyasu Matsushima & Kiyoshi Izumi & Hiroki Sakaji, 2019. "Encoding of high-frequency order information and prediction of short-term stock price by deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1499-1506, September.
    8. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
    9. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    10. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo & Rocha, Ana Paula, 2019. "Deep learning in exchange markets," Information Economics and Policy, Elsevier, vol. 47(C), pages 38-51.
    11. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    12. Judit Oláh & Eszter Krisán & Anna Kiss & Zoltán Lakner & József Popp, 2020. "PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector," Energies, MDPI, vol. 13(9), pages 1-35, May.
    13. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    14. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    15. Catalin Stoean & Wiesław Paja & Ruxandra Stoean & Adrian Sandita, 2019. "Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    16. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
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    2. David G. Green, 2023. "Emergence in complex networks of simple agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 419-462, July.
    3. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.
    4. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    5. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    6. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.

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