TLDR: Indian trader Rahul Mathur recently experimented with the Perplexity Finance chatbot to develop a Futures & Options (F&O) trading strategy for the Indian stock market. While the AI tool demonstrated powerful information synthesis and personalized tracking capabilities, it failed to account for crucial market mechanics like minimum lot sizes and F&O eligibility, leading to a strategy deemed ‘not viable’ for real-world trading. The experiment highlights both the immense potential and current limitations of AI in financial markets.
Indian financial markets are witnessing a new wave of innovation as prominent trader Rahul Mathur recently put artificial intelligence to the test in the realm of Futures & Options (F&O) trading. Mathur utilized the Perplexity Finance chatbot, which recently launched its Indian market data feature, to formulate an F&O strategy with a corpus of ₹25 lakh. The results of this pioneering experiment, documented and shared by Mathur, have been described as mixed, offering valuable insights into the current capabilities and shortcomings of AI in high-stakes financial applications.
Mathur’s objective was straightforward: to have the AI chatbot generate a viable F&O trading plan. While the Perplexity Finance tool successfully produced a detailed strategy, its practical application revealed significant disconnects with real-world market mechanics. A critical flaw identified was the AI’s failure to incorporate fundamental rules of the Indian F&O segment, such as mandated minimum lot sizes for contracts and even recommending stocks that are not listed on the F&O segment at all. Mathur explicitly stated that the AI-suggested F&O plan ‘was not viable’ for actual trading, underscoring a crucial gap in the AI’s understanding of regulatory and structural market nuances.
Furthermore, the experiment exposed a ‘recency bias’ within the chatbot’s recommendations. Mathur observed that the AI disproportionately suggested stocks he had recently inquired about, rather than providing a holistic, data-driven analysis of the broader market. He cautioned that such bias could potentially lead novice investors down a narrow and risky path, causing them to miss out on more lucrative or stable opportunities.
Despite these significant limitations, Mathur did not dismiss the Perplexity Finance chatbot entirely. He highlighted several promising features that position it as a potentially powerful tool for retail investors. The ability to create a personalized tracker for Indian stocks and the tool’s remarkable speed in synthesizing vast amounts of information were lauded as ‘very powerful’ for a new release. Mathur noted that the chatbot, made freely accessible through an Airtel partnership, effectively provides ‘every Indian retail investor a superhuman investment researcher in their pocket.’ He believes the tool is democratizing access to a professional level of financial analysis, a significant step forward for individual investors.
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This experiment by Rahul Mathur serves as a compelling case study, showcasing the dual nature of AI in finance. While AI platforms like Perplexity Finance offer unprecedented speed and access to information, their current iteration still requires human oversight to navigate the intricate and often nuanced realities of live market trading. The findings suggest that while AI can significantly enhance research and analytical capabilities, human expertise remains indispensable for strategic execution and risk management in complex financial instruments like F&O.
