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

Saeed Nosratabadi · Amir Mosavi · Puhong Duan · Pedram Ghamisi · Ferdinánd Filip · Shahab S. Band +3 more

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Stock Market Forecasting Methods Artificial Intelligence Applications Data Mining and Machine Learning Applications

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.

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2009The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration 2017Deep learning with long short-term memory networks for financial market predictions 2017A survey on deep learning for big data 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2018Deep learning-based feature engineering for stock price movement prediction 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2019Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques

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