From Votes to Volatility Predicting the Stock Market on Election Day

Paper: arXiv 2412.11192

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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces advanced architectural extensions (LLM agents, political signal integration) but relies on existing frameworks with moderate mathematical depth, while demonstrating strong empirical rigor through a specific high-frequency backtest on recent election data with detailed feature engineering and ablation studies.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Predict S&P 500 on Election Day"] --> B["Data Sources"]
  B --> C["Data Inputs:<br>Historical Price + Election News + Volatility Indices"]
  C --> D["Methodology:<br>EDSMF Model Architecture"]
  subgraph D ["Computational Process"]
      D1["Large Language Models<br>for Contextual Analysis"]
      D2["Specialized Agents<br>for Political/Economic Impact"]
  end
  D --> E["Model Training & Optimization"]
  E --> F["Key Outcomes:<br>Improved Predictive Accuracy"]
  F --> G["Impact:<br>Enhanced Volatility Forecasting"]

Cited References (22)

1. StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting (2024) — link
2. Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction (2022) — link
3. HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information (2021) — link
4. Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach (2021) — link
5. Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction (2020) — link
6. HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction (2019) — link
7. Temporal Relational Ranking for Stock Prediction (2018) — link
8. Deep learning with long short-term memory networks for financial market predictions (2017) — link

0 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.