By Prof Meera Aranha & Prof Srinivasa Reddy, TAPMI
On the NCDEX, RAINMUMBAI, India’s first exchange-traded weather derivative, records daily turnover of Rs 20 crore. Farmer-producer organizations purchase it to hedge against an El Niño-affected monsoon. Its settlement price is based on IMD rainfall data, and no single participant can move that number. This market works because its design makes manipulation impossible. This demonstrates that the integrity of a prediction market (a marketplace where people buy and sell contracts based on the outcome of a future event) depends fundamentally on the architecture of the contract rather than on post-facto regulation alone. Unfortunately, not all prediction markets are built on such manipulation-resistant foundations. Indian regulators have an opportunity to address this before the prediction market industry grows in India.
To understand why architecture matters more than rules, consider what happened to the world’s largest prediction market platform. The Wall Street Journal’s recent investigation into Polymarket, the world’s largest prediction market platform, exposed paid-for fake trading videos, fabricated trading volumes, and content encouraging the misuse of insider information. The company paid content creators to film fictitious trades on replica websites, celebrating wins on bets that they had actually lost. The content creators fabricated nearly $1.9 million in trades. More damaging content, which explained how to exploit insider information (including classified intelligence and proprietary corporate data) on the platform, was available. They are symptoms of a market built on the wrong foundation.
Modigliani and Miller’s information hypothesis states that informed participants gain from uninformed participants through market manipulation, using prices that seem authoritative but are meaningless. The information hypothesis provides three essential design principles for prediction markets. First, outcome verifiability: the event being predicted must be beyond any participant’s ability to influence it. RAINMUMBAI satisfies this because a trader’s position can not move IMD data. Contracts on celebrity behavior, corporate announcements, or events susceptible to a single actor’s intervention fail this condition. Second, participant accountability: the identities of those trading must be verifiable, because anonymous participation enables coordinated manipulation. What India possesses, through Aadhaar-linked mobile numbers that verify 800 million WhatsApp users, is participant verifiability at an impressive scale. Third, strong enforcement by a regulator that strictly implements the rules rather than merely prescribing them.
The urgency for getting the architecture right is increasing as large technology platforms enter the prediction market space. Having failed to acquire Kalshi, Meta is reportedly developing Arena, a prediction-market application that uses play money and AI-generated puzzles. Although Arena currently falls outside financial regulation because it does not involve real money, Meta could rapidly scale such a platform, given its existing customer identity database, and Arena could migrate to real stakes. This would cause a massive scale-up in the prediction market. Even X, formerly Twitter, is planning to enter this space. These platforms will arrive in India regardless of whether a regulatory framework is ready for them.
S h platforms also illustrate why participant verification cannot be an afterthought. MeitY’s recent direction requiring WhatsApp to pause its username feature, which would have allowed users to communicate without revealing their phone numbers, was motivated by concerns over impersonation and fraud. The same principle applies to prediction markets: trustworthy markets require participants whose identities can be verified. SEBI already regulates commodity derivatives, while the RBI has taken a cautious approach to emerging financial instruments such as crypto. What India now needs is a coordinated framework that treats prediction markets as a distinct regulatory category. RAINMUMBAI shows what good design looks like: an honest market built on an honest underlying signal. That is the principle regulators should preserve. The credibility of ’India’s prediction markets will depend not on writing more rules after failures occur, but on building markets whose architecture makes those failures difficult to begin with.
