Page 109 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.83, # 1, 2026, pp. 107-131

mark in October 2025, compared to around 4 crore in 2020, indicating rapid expansion
within a short period (The Economic Times, 2025). This surge has been further
accelerated in the post-COVID-19 period, where digital adoption, work-from-home
conditions, and increased financial awareness encouraged individuals to explore
equity investments as an alternative avenue for wealth creation. According to the
National Stock Exchange, there would be over 13 crore unique investors and over 25
crore customer accounts by 2026, demonstrating the ongoing expansion of the
investor base (NSE, 2026).

Artificial Intelligence has emerged as a crucial enabler within this ecosystem, enhancing
the decision-making capabilities of retail investors. However AI recommendations has
significantly transformed traditional investment practices, particularly among young,
digitally active, and first-time retail investors using mobile trading platforms. Moreover,
fintech innovations and neo-broker applications are increasingly encouraging retail
investor participation by providing user-friendly interfaces, lower transaction costs,
accessibility, and AI-driven personalized investment support (Phan et al., 2025). New AI-
powered features, such as robo-advisory services, algorithmic trading support and news
& social media data-driven sentiment analysis, are radically changing the landscape of
interaction of investors and financial markets (Acunto et al., 2019).

Nevertheless, the current literature is still dispersed despite the quick growth of AI-
enabled mobile trading platforms and the exponential rise in retail investor involvement
in India. Particularly in developing nations like India, there is a dearth of thorough
knowledge regarding how AI affects the behaviour of retail investors in the context of
mobile trading applications. Furthermore, there is still a lack of research on topics like
behavioural biases, financial literacy, and trust in algorithmic recommendations in the
context of AI-driven investment settings (Arslan & Kekeç, 2023; Saraçlı et al., 2023;
Xu et al., 2023). In this context, this study aims to map the intellectual structure, identify
important research themes, and highlight emerging trends, research gaps, contributions,
and future obstacles. A bibliometric analysis of artificial intelligence applications in
mobile stock trading for retail investor behaviour, as well as a descriptive framework
are provided (Bhopal et al., 2023). The results are expected to provide insightful
information to scholars, professionals, and policymakers, especially when it comes to
comprehending how AI and digital transformation are changing Indian retail investment
behaviour through the use of mobile stock trading apps (Imran & Rehman, 2024; Kaur
et al., 2024; Inder et al., 2022).

RQ1: What is trend of publications on investment decisions in the context of Mobile
stock trading applications?

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