IEEE Access (Jan 2023)

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis

  • Bilal Hassan Ahmed Khattak,
  • Imran Shafi,
  • Abdul Saboor Khan,
  • Emmanuel Soriano Flores,
  • Roberto Garcia Lara,
  • Md. Abdus Samad,
  • Imran Ashraf

DOI
https://doi.org/10.1109/ACCESS.2023.3330156
Journal volume & issue
Vol. 11
pp. 125359 – 125380

Abstract

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Artificial intelligence (AI)-based models have emerged as powerful tools in financial markets, capable of reducing investment risks and aiding in selecting highly profitable stocks by achieving precise predictions. This holds immense value for investors, as it empowers them to make data-driven decisions. Identifying current and future trends in multi-class forecasting techniques employed within financial markets, particularly profitability analysis as an evaluation metric is important. The review focuses on examining stud-ies conducted between 2018 and 2023, sourced from three prominent academic databases. A meticulous three-stage approach was employed, encompassing the systematic planning, conduct, and analysis of the se-lected studies. Specifically, the analysis emphasizes technical assessment, profitability analysis, hybrid mod-eling, and the type of results generated by models. Articles were shortlisted based on inclusion and exclusion criteria, while a rigorous quality assessment through ten quality criteria questions, utilizing a Likert-type scale was employed to ensure methodological robustness. We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. Most studies only employ performance metrics and lack utilization of profitability metrics or investment or trading strategy (simulated or real-time). Similarly, research on multi-class or output is severely lacking in financial forecasting and can be a good avenue for future research.

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