Forecasting US Presidential Election 2024 using multiple machine learning algorithms

Sinha, Pankaj and Kumar, Amit and Biswas, Sumana and Gupta, Chirag (2024): Forecasting US Presidential Election 2024 using multiple machine learning algorithms.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: US Presidential Election, Machine Learning, Lasso, Random Forest
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; G - Financial Economics > G0 - General

References

Campbell, J. E. (2005). The fundamentals in US presidential elections: Public opinion, the economy and incumbency in the 2004 presidential election. Journal of Elections, Public Opinion & Parties, 15(1), 73-83.

Erikson, R. S., & Palfrey, T. R. (1998). Campaign spending and incumbency: An alternative simultaneous equations approach. The Journal of Politics, 60(2), 355-373.

Erikson, R. S., & Wlezien, C. (2014). Forecasting US presidential elections using economic and noneconomic fundamentals. PS: Political Science & Politics, 47(2), 313-316.

Funck, A. S., & McCabe, K. T. (2022). Partisanship, information, and the conditional effects of scandal on voting decisions. Political Behavior, 44(3), 1389-1409.

Gelman, A., & King, G. (1993). Why are American presidential election campaign polls so variable when votes are so predictable? British Journal of Political Science, 23(4), 409-451.

Graefe, A. (2013). Issue and leader voting in US presidential elections. Electoral Studies, 32(4), 644-657.

Guntermann, E., Lenz, G. S., & Myers, J. R. (2021). The impact of the economy on presidential elections throughout US history. Political Behavior, 43, 837-857.

Hibbs Jr, D. A. (2000). Bread and peace voting in US presidential elections. Public Choice, 104(1), 149-180.

Holbrook, T. M. (2009). Economic considerations and the 2008 presidential election. PS: Political Science & Politics, 42(3), 473-478.

Jacobson, G. C. (2006). Campaign spending effects in US Senate elections: evidence from the National Annenberg Election Survey. Electoral Studies, 25(2), 195-226.

Lau, R. R., Andersen, D. J., & Redlawsk, D. P. (2008). An exploration of correct voting in recent US presidential elections. American Journal of Political Science, 52(2), 395-411.

Lewis-Beck, C., & Martini, N. F. (2020). Economic perceptions and voting behaviour in US presidential elections. Research & Politics, 7(4), 2053168020972811.

Martinez, M. D., & Gill, J. (2005). The effects of turnout on partisan outcomes in US presidential elections 1960–2000. The Journal of Politics, 67(4), 1248-1274.

Nagler, J., & Leighley, J. (1992). Presidential campaign expenditures: Evidence on allocations and effects. Public Choice, 73(3), 319-333.

Nyhan, B. (2015). Scandal potential: How political context and news congestion affect the president's vulnerability to media scandal. British Journal of Political Science, 45(2), 435-466.

Owens, J. R. (1984). Economic influences on elections to the US Congress. Legislative Studies Quarterly, 123-150.

Praino, R., Stockemer, D., & Moscardelli, V. G. (2013). The lingering effect of scandals in congressional elections: Incumbents, challengers, and voters. Social Science Quarterly, 94(4), 1045-1061.

Rottinghaus, B. (2023). Do Scandals Matter? Political Research Quarterly, 76(4), 1932-1943.

Schuster, S. S. (2020). Does campaign spending affect election outcomes? New evidence from transaction-level disbursement data. The Journal of Politics, 82(4), 1502-1515.

Shaffer, S. D., & Chressanthis, G. A. (1991). Accountability and US Senate elections: A multivariate analysis. Western Political Quarterly, 44(3), 625-639.

Sinha, Pankaj & Verma, Kaushal & Biswas, Sumana & Tyagi, Shashank & Gogia, Shaily & Singh, Aakhyat & Kumar, Amit, 2024. "Modeling and forecasting US presidential election 2024," MPRA Paper 122319, University Library of Munich, Germany, revised 08 Oct 2024.

Sinha, Pankaj & Srinivas, Sandeep & Paul, Anik & Chaudhari, Gunjan, 2016. "Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model," MPRA Paper 74618, University Library of Munich, Germany, revised 17 Oct 2016.

Sinha, P., Verma, A., Shah, P., Singh, J., & Panwar, U. (2022). Prediction for the 2020 United States presidential election using machine learning algorithm: Lasso regression. Journal of Prediction Markets, 16(1).

Von Sikorski, C. (2018). Political scandals as a democratic challenge| The aftermath of political scandals: A meta-analysis. International journal of communication, 12, 25.

Von Sikorski, C., Heiss, R., & Matthes, J. (2020). How political scandals affect the electorate. Tracing the eroding and spillover effects of scandals with a panel study. Political Psychology, 41(3), 549-568.

Walker, D. A. (2006). Predicting presidential election results. Applied Economics, 38(5), 483-490.

Wlezien, C. (2017). Policy (mis) representation and the cost of ruling: US presidential elections in comparative perspective. Comparative Political Studies, 50(6), 711-738.

Wurgler, E., & Brooks, C. (2014). Out of step? Voters and social issues in US presidential elections. The Sociological Quarterly, 55(4), 683-704.