Research Landscape of Artificial Intelligence in Financial Disciplines: A Bibliometric Perspective
Abstract
The last 20 years have seen the artificial intelligence make considerable progress, especially in the finance field. By 2021, AI has become ubiquitous and the activity in the field of research has increased significantly. This project aims to engage in a systematic review of the existing research, see what has already been done, and what gaps that still exist in this field, namely in the area of finance. In this regard, I investigated a wide range of published articles that were published between 1992 and March 2021. The literature reviewed was divided into ten major research topics, which included: AI on stock market, algorithmic trading, volatility estimation, portfolio management, performance, risk and default analysis, cryptocurrencies, derivatives, bank credit risk, investor sentiment, and the foreign-exchange markets. Nevertheless, there are significant gaps in these endeavours, especially when it comes to the risk posed by recent technological upheavals on finance; this offers prospects of research in the future. I used both a bibliometric and content analysis methodologically to compile a global picture. These results indicate that, most of the studies on various applications of AI in the financial sector have a sharp rise since the beginning of the 21 st century around the world. Predictive and forecasting systems, classification and early-, warning model, and big-, data-, data-, and text-mining based analytical methods are the most common ones.
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