Election Forecasting Using Macroeconomic and Social Indicators via Machine Learning

Abstract

A comparative analysis of machine learning models is executed for the forecasting of incumbent party losses during federal elections in democratic countries. A proprietary dataset that encompasses a wide array of potential economic and social factors affecting election outcomes is compiled, and the most significant factors are identified and evaluated. A myriad of the most popular machine learning models for supervised learning is applied to the dataset, utilizing them as classifiers to predict whether the incumbent party stays in power during federal elections for eleven of the world’s most populous and democratic countries: the United States, Canada, the United Kingdom, the Netherlands, Austria, Norway, Sweden, Denmark, Australia, India, and New Zealand. The results show that the most significant factors for election outcomes are inflation growth rate, unemployment growth rate, and voter turnout growth rate. Multilayer perceptron produces the most accurate classifications. Additionally, Gaussian models such as Gaussian process classifier and Gaussian naive Bayes have the poorest classification accuracy.