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Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction

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  • Chopra, Ritika
  • Sharma, Gagan Deep
  • Pereira, Vijay

Abstract

The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI) has become overwhelming, making it quite challenging for academics and relevant researchers to gain an elaborative, structured, and organised overview of the relevant information. We fill this gap by contributing and conducting a robust bibliometric review on the application of AI innovations for stock market prediction. More specifically, we conducted a bibliometric review by identifying 241 relevant papers related to stock forecasting using AI by taking a quantitative approach. A quantitative approach uses an examination of linked articles to look at the development of research topics and the structure of existing knowledge. We identified five significant themes based on exploratory factor and hierarchical cluster analyses. We posited that successful AI-based models could aid stock traders, brokers, and investors in better decision-making, a task that had previously been fraught with difficulties. Overall, this paper is aimed at benefiting stock traders, brokers, businesses, investors, government, financial institutions, depositories, and banks. This paper concludes with a future research agenda.

Suggested Citation

  • Chopra, Ritika & Sharma, Gagan Deep & Pereira, Vijay, 2024. "Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction," Technovation, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:techno:v:135:y:2024:i:c:s0166497224001172
    DOI: 10.1016/j.technovation.2024.103067
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    as
    1. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    2. Choijil, Enkhbayar & Méndez, Christian Espinosa & Wong, Wing-Keung & Vieito, João Paulo & Batmunkh, Munkh-Ulzii, 2022. "Thirty years of herd behavior in financial markets: A bibliometric analysis," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. Sarat Chandra Nayak & Bijan Bihari Misra & Himansu Sekhar Behera, 2016. "An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 7(2), pages 39-57, April.
    4. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
    7. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    8. van Eck, Nees Jan & Waltman, Ludo, 2014. "CitNetExplorer: A new software tool for analyzing and visualizing citation networks," Journal of Informetrics, Elsevier, vol. 8(4), pages 802-823.
    9. Bing Wang & Su-Yan Pan & Ruo-Yu Ke & Ke Wang & Yi-Ming Wei, 2014. "An overview of climate change vulnerability: a bibliometric analysis based on Web of Science database," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1649-1666, December.
    10. Smidt, Seymour, 1968. "A New Look at the Random Walk Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 235-261, September.
    11. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    12. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    13. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    14. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    15. Paul, Justin & Criado, Alex Rialp, 2020. "The art of writing literature review: What do we know and what do we need to know?," International Business Review, Elsevier, vol. 29(4).
    16. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    17. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    18. Dabić, Marina & Marzi, Giacomo & Vlačić, Božidar & Daim, Tugrul U. & Vanhaverbeke, Wim, 2021. "40 years of excellence: An overview of Technovation and a roadmap for future research," Technovation, Elsevier, vol. 106(C).
    19. Chen, Tai-Liang & Cheng, Ching-Hsue & Jong Teoh, Hia, 2007. "Fuzzy time-series based on Fibonacci sequence for stock price forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 377-390.
    20. Li, Yelin & Bu, Hui & Li, Jiahong & Wu, Junjie, 2020. "The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1541-1562.
    21. Taylor, Stephen J., 1982. "Tests of the Random Walk Hypothesis Against a Price-Trend Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(1), pages 37-61, March.
    22. Yi-Ming Guo & Zhen-Ling Huang & Ji Guo & Hua Li & Xing-Rong Guo & Mpeoane Judith Nkeli, 2019. "Bibliometric Analysis on Smart Cities Research," Sustainability, MDPI, vol. 11(13), pages 1-18, June.
    23. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    24. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    25. Burton G. Malkiel, 2005. "Reflections on the Efficient Market Hypothesis: 30 Years Later," The Financial Review, Eastern Finance Association, vol. 40(1), pages 1-9, February.
    26. Tihana Škrinjarić & Zrinka Orlović, 2020. "Economic Policy Uncertainty and Stock Market Spillovers: Case of Selected CEE Markets," Mathematics, MDPI, vol. 8(7), pages 1-33, July.
    27. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    28. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    29. Andreas Neuhierl & Michael Weber, 2016. "Monetary Policy and the Stock Market: Time-Series Evidence," NBER Working Papers 22831, National Bureau of Economic Research, Inc.
    30. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    31. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    32. Nees Jan Eck & Ludo Waltman, 2017. "Citation-based clustering of publications using CitNetExplorer and VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1053-1070, May.
    33. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    34. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    35. L.A. Smales, 2017. "The importance of fear: investor sentiment and stock market returns," Applied Economics, Taylor & Francis Journals, vol. 49(34), pages 3395-3421, July.
    36. Chen, Xuehui & Zhu, Hongli & Zhang, Xinru & Zhao, Lutao, 2022. "A novel time-varying FIGARCH model for improving volatility predictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    37. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    38. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    39. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    40. Katherine W. McCain, 1990. "Mapping authors in intellectual space: A technical overview," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 433-443, September.
    41. Howard D. White, 2001. "Authors as citers over time," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(2), pages 87-108.
    42. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    43. Walid Chkili & Manel Hamdi, 2021. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing Limited, vol. 14(5), pages 853-873, May.
    44. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    45. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
    46. Nijole Maknickiene & Indre Lapinskaite & Algirdas Maknickas, 2018. "Application of ensemble of recurrent neural networks for forecasting of stock market sentiments," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 13(1), pages 7-27, March.
    47. Wen-Lung Shiau & Yogesh K. Dwivedi, 2013. "Citation and co-citation analysis to identify core and emerging knowledge in electronic commerce research," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1317-1337, March.
    48. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
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