Pune City Retail Investors' Familiarity With Algorithmic Trading: A Survey in Light of India’s 2025 SEBI Framework
Year: 2025
Type: article
Study design: Observational Study
Abstract: The transition of algorithmic trading from the institutional domain to widespread retail access in India is underpinned by the regulatory framework set forth by SEBI in 2025. This study quantifies the familiarity of retail investors in Pune with algorithmic trading using a standardized cross-sectional survey method (N = 213). We developed a five-layered instrument to assess familiarity, encompassing the concepts and constructs related to AT, regulatory knowledge, operational knowledge, risk awareness, and information received from media/messaging sources. Our results provide a 0–100 index score on which retail investors in Pune can be said to have a moderate to low familiarity with the algorithmic trading concept (58.46/100). Retail investors in Pune are not totally aware of the safety and soundness rules governing the new algorithmic trading access methods mandated by SEBI. We find significant differences in familiarity based on who is using the newer API access method to reach the trading platform (8.1 points more familiar); and we find a few significant differences in familiarity based on whom you might be asking in the retail investor space (IT professionals being more familiar than others). Overall, our findings suggest familiarity with algorithmic trading among Pune's retail investors is somewhat uneven and modest at best. The study's practical implications suggest that we can nudge most broker-dealer firms into or near compliance with the help of several nudges employed where most retail investors interact with their brokerage firms—inside the broker-dealer apps. This is what much of the study is about. It is about nudging these firms to conditionally become "by design" compliant with the way established law and regulation (SR, R, and DP) operate. It's about hoeing the compliance pathway with several types of nudges.
Author: Amit Vitthal Gogawale
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4415093784
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