Arbitrage-Free and Simulation-Based Election Forecasting

Abstract

After mainstream electoral forecasts inaccurately predicted the outcome of the 2016 U.S. Presidential Election, alternative approaches to election forecasting became more prominent. Among these alternatives, prediction markets and arbitrage-free forecasting models have gained attention for offering more disciplined forecasts that can be interpreted as the price one would pay to wager on an election outcome. This thesis extends and enhances a popular-vote forecasting model developed by Fry & Burke. Specifically, our model addresses the inherent measurement errors in polling data, explicitly incorporating them into the forecasting methodology. Evaluations conducted across U.S. presidential elections from 1972 to 2024 demonstrate that this explicit consideration of polling errors significantly improves forecast accuracy. Additionally, comparisons with popular vote prediction market data from 2016 to 2024 show that prediction markets consistently underperform in forecasting outcomes for non-contested states, indicating systematic biases. To forecast electoral vote outcomes – a more complicated problem – we introduce a simulation-based approach that integrates Fry & Burke’s popular-vote forecasting techniques with Monte Carlo simulation. Our electoral forecasting method outperforms forecasts provided by Nate Silver’s FiveThirtyEight and prediction markets from 2016 to 2024. These findings underscore the effectiveness of our electoral vote forecasting model and highlight the potential biases present in electoral vote prediction markets.