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Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

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  • Ritika Chopra

    (University School of Management Studies, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India)

  • Gagan Deep Sharma

    (University School of Management Studies, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India)

Abstract

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

Suggested Citation

  • Ritika Chopra & Gagan Deep Sharma, 2021. "Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:526-:d:672223
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    References listed on IDEAS

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    1. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    2. Anderson, Nicola & Noss, Joseph, 2013. "Financial Stability Paper No 23: The Fractal Market Hypothesis and its implications for the stability of financial markets," Bank of England Financial Stability Papers 23, Bank of England.
    3. Sanchita Bansal & Isha Garg & Gagan Deep Sharma, 2019. "Social Entrepreneurship as a Path for Social Change and Driver of Sustainable Development: A Systematic Review and Research Agenda," Sustainability, MDPI, vol. 11(4), pages 1-28, February.
    4. Fouad Ben Abdelaziz & Mohamed Amer & Hazim El-Baz, 2014. "An Epsilon constraint method for selecting Indicators for use in Neural Networks for stock market forecasting," Post-Print hal-01223453, HAL.
    5. Vijay Desai & Rakesh Bharati, 1998. "A comparison of linear regression and neural network methods for predicting excess returns on large stocks," Annals of Operations Research, Springer, vol. 78(0), pages 127-163, January.
    6. Cheng, Pao L & Deets, M King, 1971. "Portfolio Returns and the Random Walk Theory," Journal of Finance, American Finance Association, vol. 26(1), pages 11-30, March.
    7. M. Rusydi & Sardar M. N. Islam, 2007. "Market Models and Applications," Palgrave Macmillan Books, in: Quantitative Exchange Rate Economics in Developing Countries, chapter 4, pages 45-62, Palgrave Macmillan.
    8. Stelios Bekiros, 2007. "A neurofuzzy model for stock market trading," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 53-57.
    9. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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    1. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.

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