Can Interest-Bearing Positions Solve the Long-Horizon Problem in Prediction Markets?

TL;DR

This paper demonstrates that interest-bearing positions significantly improve long-horizon prediction market accuracy and participation, challenging the notion that long horizons inherently cause substantial issues.

Contribution

It provides empirical evidence that interest-bearing positions mitigate the long-horizon problem and increase market participation, using agent-based simulations with LLM traders.

Findings

Interest-bearing positions reduce horizon effect on accuracy by 83%.

Market participation increases from 17% to 62% with interest.

Long-horizon problem may be overstated in prior literature.

Abstract

Prediction markets suffer from reduced liquidity and price accuracy for long-horizon events due to the opportunity cost of committed capital. Recently, major platforms have introduced interest-bearing positions to mitigate this "long-horizon problem." I evaluate this policy using agent-based simulations with large language model (LLM) traders in a 2 x 2 factorial design, varying time horizon (4 days vs. 2 years) and the presence of interest. While long horizons degrade accuracy, the observed pricing bias (0.72 percentage points) is significantly smaller than theoretical and prior empirical estimates. Paying interest eliminates approximately 83% of the horizon effect on accuracy and more than triples market participation (from 17% to 62% of wealth). These findings suggest the long-horizon problem may be overstated in existing literature and that interest-bearing positions are a highly effective intervention, primarily by incentivizing participation rather than correcting bias.