#预测市场 Seeing this research report from Kalshi, I feel a sense of familiarity. This is not the first time that market intelligence has outperformed expert consensus.

I still remember around 2008, when models used by traditional financial institutions failed one after another, while truly perceptive traders sensed risks through price fluctuations. At that time, no one called it a "prediction market," but essentially it was the same—dispersed, incentivized participants who often react to reality earlier than centralized expert teams.

This data is particularly interesting: a 40% reduction in error margin, with even more pronounced advantages during periods of economic volatility. What does this indicate? In highly certain times, experts can do quite well relying on heuristics. But when variables increase and noise grows, those who truly bet real money on predictions tend to be calmer and more rational than analysts who only write reports. Decision-making involving vested interests is often more trustworthy than theoretical logic on paper.

Kalshi opening its internal data to researchers is noteworthy. The concept of prediction markets has been neglected in traditional finance for many years, but now blockchain provides a new stage for it. Using data to demonstrate its value feels like a cyclical return in history. Tools once marginalized are now poised to teach the textbooks.

That said, prediction accuracy does not equal investment opportunity. I’ve seen many "discoveries" supported by historical data that ultimately failed in new cycles. Kalshi’s advantages are real, but the true test always lies in the next unfamiliar market environment.