Mapping the research landscape of artificial neural networks in stock market applications: a bibliometric analysis and future research directions
by Manpreet Kaur; Amit Kumar; Anil Kumar Mittal
International Journal of Bibliometrics in Business and Management (IJBBM), Vol. 2, No. 4, 2023

Abstract: Artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear and chaotic data. The present study aims to systematically map the conceptual and intellectual structure of ANN research in the entire domain of the stock market based on bibliometric analysis and network visualisation of 1,483 articles published during the period 1992-2022. The analysis revealed exponential growth in articles since 2018, with China as the major contributor. The upward publication trend evinces the contemporary relevance of the concerned field and its growing fascination in researchers' community. Furthermore, the co-word analysis demonstrated seven thematic clusters and the cluster 'stock price forecasting' remained the dominant one. In addition, the current study uncovered the challenges and knowledge gaps by intensively reviewing the relevant literature in the field. Based on the findings, the study provides valuable recommendations for future researchers and stock market practitioners regarding emerging research areas, the input selection approaches, parameter optimisation methods, and hybridisation of ANN models, and thus enables them to enhance the functional efficiency of models. Moreover, the study can also help regulators and policymakers in managing the risks caused by uncertainties in the stock market by designing proactive strategies.

Online publication date: Wed, 27-Mar-2024

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