I sat at my desk, watching the market predictions flicker across my screen. The Iowa Electronic Market (IEM) futures for the 2024 U.S. Presidential Election displayed Vice President Harris at 53% and Former President Trump at 48.1% in the vote-share market. A close race, I thought. But something stood out—the winner-take-all market showed a dramatically different outcome, giving Harris an 89.4% chance and Trump just 12.2%.
IEM as of 10/17/24
This puzzling discrepancy between markets got me thinking. The tight race in vote shares contrasted sharply with traders' overwhelming confidence in a Democratic victory, leading me to Paul Samuelson’s Theory of Revealed Preferences and Nassim Taleb’s Skin in the Game. These concepts shed light on how financial risk, voter behavior, and complex election structures like the Electoral College can create such stark differences in predicted outcomes.
About the author: Jeff Hulett leads Personal Finance Reimagined, a decision-making and financial education platform. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions.
Jeff is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM.
Political disclosure: To maintain transparency and provide readers with a full understanding of potential influences on this article, the author has provided a political disclosure outlining past experiences and affiliations that may shape the perspectives shared. This commitment to openness ensures readers can engage with the content in a fully informed way.
A Quick Primer on the Iowa Electronic Markets
Before diving deeper, let me set the stage for how I got here. The Iowa Electronic Markets (IEM) are unique real-money futures markets, designed to predict election outcomes. Traders buy and sell contracts based on their expectations for real-world results, and the prices of these contracts reflect the collective market view.
In the current IEM setup for the 2024 U.S. Presidential Election, there are two key markets:
Pres24_VS (Vote Share Market): This market tracks the vote share each of the two major candidates—one from the Democratic Party and one from the Republican Party—are expected to win. The prices indicate the percentage of the vote each candidate is likely to receive.
Pres24_WTA (Winner-Take-All Market): A winner-take-all market where traders bet on which candidate will win the popular vote plurality. The contract for the winning candidate pays out $1, while the losing candidate’s contract is worth nothing.
Currently, the Pres24_VS market shows Vice President Harris at 53% and Former President Trump at 48.1%. However, the Pres24_WTA market paints a much more one-sided picture, with traders giving Harris nearly a 90% chance of winning the popular vote.
These numbers puzzled me — why the stark contrast? It wasn’t until I thought back to Samuelson’s theory that the answer became clearer.
The Light Bulb Moment: Samuelson’s Theory of Revealed Preferences
Samuelson’s theory is beautifully simple: Instead of asking people what they prefer, we can infer their true preferences by observing what they actually do. It’s like going to a cocktail party and asking your friends, “Who do you think will win the election?” or “Which candidate will be better for the country?”—very different from asking, “Which candidate are you willing to bet $1,000 on to win?” These questions engage different parts of our brain.
This difference arises because speculative thinking activates regions like the prefrontal cortex, which handles abstract thought and hypothetical reasoning. However, making a high-stakes decision, like betting money, engages the amygdala and insula, which are responsible for processing risk, reward, and emotional investment. Studies in neuroscience show that financial decisions activate areas tied to risk assessment, making people more cautious and realistic when money is involved.
In a marketplace, whether it’s for goods or contracts, people reveal their beliefs through the choices they make—especially when those choices have a financial impact. It’s not about what they say they believe; it’s about where they’re willing to put their money.
I realized that this is exactly what’s happening in the IEM presidential markets. Traders are revealing their preferences—or more accurately, their beliefs—through their actions. By buying contracts in either the vote-share or the winner-take-all markets, they are giving us valuable insights into what they think will happen.
Connecting the Dots Between Prices and Preferences
With Samuelson’s theory in mind, I revisited the two markets. The Pres24_VS prices — 53% for the Democrat and 48.1% for the Republican — indicate that traders believe the race will be relatively tight in terms of the percentage of votes each candidate will receive. A 53% to 48.1% split suggests a close race, but not one where either candidate runs away with the vote share.
The Pres24_WTA market, however, focuses on the overall winner. With Harris at 89.4%, traders clearly believe she will secure a win, even if the margins are narrow. In essence, the VS market reflects how close the race will be, while the WTA market reflects traders’ confidence in the final result. It’s similar to how scientists reject the null hypothesis—traders are 90% confident in betting against a Republican victory, even if the race looks close. Their revealed preferences show their true expectations through their financial commitments, reflecting a strong belief in a Democratic win despite the narrow vote share predictions.
A Comparison with the Latest Polls
Curious about how these market predictions stack up against public polling, I turned to the most recent NBC poll, which reveals a deadlocked race, with both Harris and Trump receiving 48% support from registered voters.
The tight polling data somewhat mirrors the Pres24_VS market, where Harris and Trump are expected to earn similar vote shares, though the Pres24_VS market shows a slightly less competitive race. In contrast, the Pres24_WTA market paints a very different picture, heavily favoring a Democratic win, with traders giving Harris a much greater chance of securing the popular vote despite the close percentages in the vote-share market.
The answer lies in what these markets are measuring. Polls reflect the current sentiment of voters, but the IEM markets reflect traders' beliefs about the final outcome. Traders are not simply betting on the current state of public opinion — they are weighing factors like voter turnout, momentum, and last-minute shifts in the electorate. This difference explains why the Pres24_WTA market favors the Democratic candidate despite the close polling numbers.
Polls can fluctuate, but futures markets are forward-looking, incorporating more nuanced predictions about how the election will play out when all is said and done.
Why the IEM Prediction Could Be Right But Still Miss the Winner
As I was thinking this through, I realized something important: the IEM market could be entirely correct in predicting the popular vote but still fail to predict the winning candidate. Why? Because of the Electoral College. The IEM focuses on the popular vote, but as we've seen in past elections, like 2016, it’s possible to win the popular vote and lose the election due to how electoral votes are distributed across states. So, even if the IEM prediction is spot-on about who will win the most votes nationwide, it doesn’t guarantee that candidate will become president. The Electoral College adds a layer of complexity that can make it hard to predict the ultimate outcome based on popular vote predictions alone.
The Nate Silver Comparison: Polls vs. Market Predictions
As I reflected on these market predictions, I thought about Nate Silver, the founder of FiveThirtyEight. Silver’s cautious approach in 2016 gave Donald Trump a higher chance of winning than many other outlets, though his models didn’t fully capture late voter turnout shifts or biases in state-level polling. However, Silver left FiveThirtyEight in 2023, marking a significant shift for the platform. While he no longer leads the analysis, the 2016 election underscored the challenges of relying heavily on polling data for forecasts.
The Iowa Electronic Market (IEM) takes a different approach—one that relies on real-money trading. Here, participants put their own money on the line, revealing what they believe will actually happen, rather than simply responding to a poll. While Silver’s approach uses aggregated polling data and advanced statistical modeling, the IEM taps into real-time market sentiment, factoring in economic and behavioral signals that might not be immediately visible in polling data.
That said, both Silver’s models and the IEM face challenges when it comes to predicting the Electoral College, which adds complexity to the popular vote predictions. This comparison shows how polling models and market predictions can complement each other, each offering unique insights and facing their own limitations, especially in unpredictable elections like 2016.
Conclusion: The Power of Revealed Preferences and Skin in the Game
As I stepped back and considered the IEM markets, the NBC poll, Nate Silver’s models, and the Electoral College, I realized how powerful Samuelson’s theory of revealed preferences truly is. By observing how traders invest—putting their own skin in the game, as Nassim Nicholas Taleb would say—we gain deeper insights into their true beliefs. Real-money markets like the IEM reflect genuine risk-taking, not just opinions, and since these markets move in real time, predictions will adjust as we approach Election Day. Yet, even with these insights, the Electoral College’s complexity reminds us that predicting outcomes remains a challenging process.
The Iowa Electronic Markets offer a fascinating glimpse into how the 2024 election might unfold. Engaging with these markets allows you to see how real-time trading reveals the beliefs of participants with something to lose. Revealed preferences help separate who people believe will win from who they want to win—a distinction often blurred in polls. It’s amazing how the risk of losing money focuses the mind. Despite these insights, predicting elections remains intricate due to factors like the Electoral College.
So, whether you’re a seasoned trader or simply curious, I encourage you to dive into the IEM. The answers may be right in front of you, revealed through people’s choices and the risks they take.
Sources:
Samuelson, Paul. "A Note on the Pure Theory of Consumer's Behaviour." Economica, 1938.
Forsythe, R., Nelson, F., Neumann, G., & Wright, J. "Anatomy of an Experimental Political Stock Market." The American Economic Review, 1992.
Iowa Electronic Markets. "2024 U.S. Presidential Election Markets." University of Iowa, 2024.
Knutson, Brian, and Kuhnen, Camelia M. “The Neural Basis of Financial Risk Taking.” Neuron, vol. 47, no. 5, 2005, pp. 763–770.
NBC News. "A Deadlocked Race, With a Sliver of Undecideds Left." NBC News, 2024.
Wolfers, Justin, and Eric Zitzewitz. "Prediction Markets." Journal of Economic Perspectives, 2004.
Bipartisan Policy Center. The Electoral College, Simplified, 2024. Bipartisan Policy Center https://bipartisanpolicy.org/explainer/the-electoral-college-simplified/)
Harvard Business School. FiveThirtyEight and the Big Data Fail: Election 2016, 2017. Digital Data Design Institute at Harvard https://d3.harvard.edu/platform-digit/submission/fivethirtyeight-and-the-big-data-fail-election-2016/)
Taleb, Nassim Nicholas. Skin in the Game: Hidden Asymmetries in Daily Life, 2018.
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