Prediction markets have been a hot topic, especially in the wake of the recent U.S. presidential election. On Election Day, we saw something curious: the odds for Donald Trump remained relatively stable throughout the day, hovering around 57% to 58%. It wasn’t until the polls closed and results started pouring in that we witnessed a dramatic shift, with Trump’s odds skyrocketing to 92.5% by midnight. This phenomenon raises a fascinating question about market efficiency and the flow of information.
The stability of prediction market odds during the day aligns with the idea of semi-strong form market efficiency. Essentially, this suggests that all publicly available information was already priced in before Election Day. Polls, news reports, and early voting data had been digested in the days leading up to the election, but on the day itself, not much new information emerged to sway the markets. Nate Silver, a well-known polling expert, echoed this sentiment, noting that Election Day can be relatively quiet until results start coming in.
However, one might wonder if campaign insiders had access to information that could have influenced their confidence throughout the day. Campaign teams often have large staffs monitoring voting sites and gathering data on turnout. They might be privy to exit polls conducted by major media organizations, although these results are typically kept under wraps until polls close. It’s plausible that campaign staffers could have had a more nuanced understanding of voter sentiment, yet this information didn’t seem to impact the prediction markets.
This leads us to two possible explanations. First, it could be that no one truly knows anything useful until the polls close. Alternatively, perhaps there is valuable information out there, but those who possess it choose not to trade, whether due to ethical concerns, the busyness of Election Day, or simply the lack of financial incentive in making small bets on prediction markets.
The recent election highlighted the growing significance of prediction markets. Just a couple of months ago, real-money election prediction markets were nearly illegal in the U.S., but now they are gaining traction. As reported by Bloomberg, these markets demonstrated their predictive power, often showing Trump as a favorite even when traditional polls suggested a closer race. This has led to speculation that prediction markets could soon become as normalized as sports betting.
Yet, the question remains: will prediction markets ever achieve widespread popularity? Some experts argue that there’s limited natural demand for prediction market contracts. Savers, gamblers, and sharps—the three main types of market participants—don’t seem to find a compelling reason to engage with prediction markets. They lack the appeal of traditional savings devices or the thrill associated with gambling on sports events.
Despite these challenges, the potential for prediction markets to evolve remains. With the right regulatory environment, there’s an opportunity for these markets to attract more participants. The experience of legalized sports gambling suggests that there’s a sizable market for betting on various events, and prediction markets could tap into that demand. As we’ve seen with other financial instruments, innovative product designs could entice retail investors to engage with prediction markets in ways we haven’t yet imagined.
Interestingly, the presence of sharps—those who enter markets to profit from superior analysis—could also change the landscape. For instance, the story of Théo, a pseudonymous Frenchman who placed a massive bet on Trump, illustrates how informed trading can lead to significant profits. He conducted his own polling and identified trends that traditional polls missed, ultimately making a substantial return on his investment. If prediction markets can attract more participants like Théo, we could see a rise in the quality of information and analysis driving these markets.
