Abstract
The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.
Highlights
Gagan Deep Sharma,1 Burak Erkut,2,3 Mansi Jain,1 Tugberk Kaya,2,4 Mandeep Mahendru,5,6 Mrinalini Srivastava,1 Raminder Singh Uppal,7 and Sanjeet Singh 8
Econometric modeling, such as autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) model, buying and hold (B and H) strategy, random walk (RW), stochastic volatility (SV) model, has been employed for predicting stock returns [10], which is being complemented by artificial intelligence and machine learning systems including artificial neural networks (ANNs), multilayer perceptron (MLP), fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector modeling [11, 12]
The research on the applications of artificial intelligence in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. erefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. is paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of Artificial intelligence (AI) in stock market predictions and interpreting the results in a narrative review
Summary
Gagan Deep Sharma ,1 Burak Erkut ,2,3 Mansi Jain ,1 Tugberk Kaya ,2,4 Mandeep Mahendru ,5,6 Mrinalini Srivastava ,1 Raminder Singh Uppal ,7 and Sanjeet Singh 8. Is unprecedented crisis calls for unusual solutions to help sustain people’s well-being Using techniques such as artificial intelligence for predictions is emerging as one key idea in such a situation. Advancements in computing technologies and econometric methodologies are changing the face of stock market predictions in recent times Econometric modeling, such as autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) model, buying and hold (B and H) strategy, random walk (RW), stochastic volatility (SV) model, has been employed for predicting stock returns [10], which is being complemented by artificial intelligence and machine learning systems including artificial neural networks (ANNs), multilayer perceptron (MLP), fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector modeling [11, 12]
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