Epic Signal! Prediction Markets Show "Efficiency Trap," Are $BTC, $ETH Traders Being Quietly Harvested by These Three Major Biases?

Can prediction markets tell us the truth? They rely on efficiency, but efficiency itself is not reliable. Market structures can operate, but they are made up of too many components to consistently provide accurate probabilities. Expecting these systems to be perfectly efficient is an unrealistic illusion.

Previously, we discussed how prediction markets serve as beacons amid informational chaos and identified three fallacies that hinder their ability to reach true probabilities. Today, we delve into these biases: bias bias, hedging bias, and time bias.

Market efficiency is the foundation of prediction market accuracy; without efficiency, probabilities become distorted. A simple example is a coin flip market. Suppose a market maker offers a coin flip at odds of 55 cents. They actually hold a 10% advantage because the true probability is worth 50 cents. Buyers expect to lose 5 cents per flip.

Then competition appears. A second market maker lowers the odds to 52.5 cents, reducing the advantage to 5%. A third joins, offering 51 cents, leaving only a 2% advantage. The key point is that in an efficient market, profit margins are squeezed until only risk premiums remain. For high-certainty events like coin flips, risk premiums are very low, and markets can be highly efficient. But for high-uncertainty events like forest fire insurance, risk premiums must be high enough to attract underwriters.

Bias bias occurs when, in the absence of pure efficiency, prediction probabilities tend to skew upward. People tend to favor outcomes they hope will happen, indirectly inflating their prices. For example, Chelsea fans are more willing to bet on Chelsea winning the Champions League than Arsenal fans. The problem is that in inefficient markets, no one may be willing to push Chelsea’s odds back to the “true” level.

A real-world example is the current US presidential election. The mainstream crypto prediction market currently estimates Trump’s chances at about 57%, and Harris’s at around 39.5%. Compared to other prediction tools: Silver Bulletin shows Trump at 56.9%, Harris at 42.5%; Manifold Markets has Trump at 54%, Harris at 43%; Metaculus estimates Trump at 55%, Harris at 45%; PredictIt shows Harris at 51% and Trump at 50%.

This crypto prediction market boasts the strongest liquidity worldwide, with total trading volume exceeding $4.6 billion for this election. If markets were perfectly efficient, it should be so. But it’s still not efficient enough. Its core user base leans toward the political right, which directly influences its higher probability assigned to Trump’s victory compared to other platforms.

If prediction markets rely on efficiency but cannot self-correct when biases distort odds, can we still regard them as the ultimate probability source?

Time bias: Market efficiency isn’t as simple as flipping a coin. If someone wants to correct a market bias, the benefit they gain must justify the cost. Suppose a market has a 1% upward bias, but the outcome is only revealed after six months. An arbitrageur correcting the market might earn an annualized return of only about 2%, even less than the risk-free rate.

Unless the bias widens or the correction time shortens so that market-making profits exceed the risk-free rate, markets won’t automatically restore efficiency. The only force that can immediately correct the market is the emergence of traders with genuine demand in the opposite direction.

Hedging bias: Hedging behavior can directly push odds up or down, distorting actual probabilities. For example, a trader buys $1 million worth of S&P 500 ETF call options on the morning of a Federal Reserve meeting. They believe rate cuts will push the index higher, while maintaining rates will suppress it. At that time, the market prices for the two outcomes are roughly even.

As the decision approaches, the trader hesitates to reduce directional risk. Due to poor options liquidity, they choose to buy $200,000 worth of “NO” options in the rate prediction market, lowering the probability of rate cuts from 50% to 48%.

If the market consensus is 50:50, and the prediction market shows 48:52, market efficiency theory suggests traders should buy “YES” to restore balance. But this often doesn’t happen. There are at least two reasons.

First, and most directly: traders may be unwilling to take on the directional risk for a small advantage. Unlike a coin flip that can be repeated infinitely, the Fed meets only 12 times a year. This low frequency significantly increases risk premiums because each event has a major impact. Using the expected value formula for buying at 48 cents, the average return is only about 2 cents. Given the infrequency of such opportunities, it’s hard to find risk-takers willing to step in.

Second, the informational asymmetry dilemma: if prediction markets are viewed as the sole truth source, traders may avoid arbitrage because they cannot determine whether their counterparties have insider information. They don’t know if the other side is just hedging their ETF options. This means arbitrageurs face not only directional risk but also must bet that their opponents lack informational advantages.

My view is that I still trust prediction markets quite a bit. But it’s a mistake to see them as the sole source of probability truth. They excel at information discovery, and I believe they will become the primary platform for viewing real-time odds on any event.

At the same time, I disagree with the idea that they are always completely accurate. For major events, I think it’s prudent to include an error margin in predictions, which helps absorb biases caused by bias bias, hedging, or time factors.

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