Forecasting US Presidential Election 2024 using multiple machine learning algorithms

Pankaj Sinha, Amit Kumar, Sumana Biswas and Chirag Gupta

MPRA Paper from University Library of Munich, Germany

Abstract: The outcome of the US presidential election is one of the most significant events that impacts trade, investment, and geopolitical policies on the global stage. It also sets the direction of the world economy and global politics for the next few years. Hence, it is of prime importance not just for the American population but also to shape the future well-being of the masses worldwide. Therefore, this study aims to forecast the popular vote share of the incumbent party candidate in the Presidential election of 2024. The study applies the regularization-based machine learning algorithm of Lasso to select the most important economic and non-economic indicators influencing the electorate. The variables identified by lasso were further used with lasso (regularization), random forest (bagging) and gradient boosting (boosting) techniques of machine learning to forecast the popular vote share of the incumbent party candidate in the 2024 US Presidential election. The findings suggest that June Gallup ratings, average Gallup ratings, scandal ratings, oil price indicator, unemployment indicator and crime rate impact the popular vote share of the incumbent party candidate. The prediction made by Lasso emerges as the most consistent estimate of the popular vote share forecast. The lasso-based prediction model forecasts that Kamala Harris, the Democratic Party candidate, will receive a popular vote share of 47.04% in the 2024 US Presidential Election.

Keywords: US Presidential Election; Machine Learning; Lasso; Random Forest (search for similar items in EconPapers)
JEL-codes: C1 C10 C15 C6 C63 G0 (search for similar items in EconPapers)
Date: 2024-10-20, Revised 2024-10-22
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pol
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