Research Trends from Classical Artificial Intelligence to Deep Learning in Stock Market Prediction: A Scientometric Perspective
Keywords:
Bibliometric Technique, CiteSpace, Research Trends, Scientometric Analysis, Stock Market Prediction, Visualize Research Trends.Abstract
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is a rapidly growing and unexplored field with diverse applications across industries, especially those requiring complex analysis, inference, and prediction from large and diverse datasets. This research paper addresses several key inquiries related to the application of CiteSpace for stock market prediction (SMP). The key objectives include identifying trends and findings in the literature, exploring limitations and future research directions, identifying prominent authors in the field, and determining active countries engaged in cooperative SMP research. The study employs scientometric analysis as the primary methodology, offering a powerful and versatile approach to literature reviews. The analysis encompasses citation analysis, author influence, relevant journals, co-citation patterns, bibliometric coupling, and co-occurrence analysis. The dataset consists of 10,976 articles published between 2013 and 2023, sourced from Scopus-indexed journals, and CiteSpace software tools are used for data analysis. The analysis reveals that Sweden has made outstanding contributions to SMP research, with a total citation count of 2,664, followed by the United States with 2,369 citations. The primary research institutions that have made significant contributions are PLOS ONE, with a citation count of 2,219, and IEEE ACCESS with 2,110 citations. In terms of individual authors, Xiao, Ming and Wang, Lihui are ranked as the top-cited authors, with 53 and 41 citations respectively.
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