ABSTRACT
Mobile stock trading apps with artificial intelligence (AI) capabilities are revolutionising retail investing and financial decision-making globally. These platforms have attracted a lot of interest from retail investors due to their rapid digitalization and AI-based investment capabilities, but the research that has been done so far is still dispersed across disciplines and geographical areas. To bridge this gap, the current study conducts a bibliometric and thematic analysis of research on mobile stock trading applications and AI-based investment decision-making. The research integrates the PRISMA framework with Khanra and Dhir s methodology. For the final synthesis, 243 Scopus-indexed articles that met the preset inclusion and exclusion criteria were included. The findings reveal that although the earliest publication related to retail investment appeared in 1983, literature specifically focused on mobile stock trading applications emerged after 2013, with a rapid rise in publications since 2021. China, the United States, and Turkey were the prominent contributors, with Finance Research Letters being the top leading journal. Based on the author co-authorship network analysis, a total of 18 authors are interconnected with each other, comprising four clusters with an inclusion threshold of 5 citations. The thematic analysis identified four major clusters: AI-driven investment and sustainability, behavioural biases in AI-assisted investing, fintech platform adoption, and retail investor decision-making through mobile trading applications. However, stronger regulatory frameworks, increased investor awareness, better AI literacy, ethical governance systems, and more thorough empirical research on post-adoption investment behaviour and long-term investor results are necessary for this sector to flourish sustainably.

Keywords: Mobile stock trading , Artificial intelligence, Retail investment, Fintech, Investment decisions
JEL codes: G11, G41,G23


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