From Votes to Volatility Predicting the Stock Market on Election Day

TL;DR

This paper introduces the EDSMF model that combines large language models and specialized agents to predict stock market volatility on Election Day, improving accuracy during this critical period.

Contribution

The paper presents a novel model integrating language models and agents specifically designed for election-related market prediction, addressing a gap in existing forecasting methods.

Findings

Enhanced prediction accuracy for S&P 500 on Election Day

Effective modeling of political and economic impacts

Demonstrated superiority over baseline models

Abstract

Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election Day represents one such critical scenario, characterized by intensified market volatility, as the winning candidate's policies significantly impact various economic sectors and companies. To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model. Our approach leverages the contextual capabilities of large language models alongside specialized agents designed to analyze the political and economic consequences of elections. By building on a state-of-the-art architecture, we demonstrate that EDSMF improves the predictive performance of the S&P 500 during this uniquely volatile day.