Paper: arXiv 2411.08404

Abstract

Recent advancements in Large Language Models (LLMs) have the potential to transform financial analytics by integrating numerical and textual data. However, challenges such as insufficient context when fusing multimodal information and the difficulty in measuring the utility of qualitative outputs, which LLMs generate as text, have limited their effectiveness in tasks such as financial forecasting. This study addresses these challenges by leveraging daily reports from securities firms to create high-quality contextual information. The reports are segmented into text-based key factors and combined with numerical data, such as price information, to form context sets. By dynamically updating few-shot examples based on the query time, the sets incorporate the latest information, forming a highly relevant set closely aligned with the query point. Additionally, a crafted prompt is designed to assign scores to the key factors, converting qualitative insights into quantitative results. The derived scores undergo a scaling process, transforming them into real-world values that are used for prediction. Our experiments demonstrate that LLMs outperform time-series models in market forecasting, though challenges such as imperfect reproducibility and limited explainability remain.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The methodology uses advanced LLM prompting and statistical aggregation (median of multiple trials) but is light on traditional mathematical formulas, while the empirical design includes real-world securities firm reports, backtesting comparison with time-series models, and reproducibility considerations.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Quantify Qualitative Insights for Market Prediction"] --> B["Data Acquisition<br>Securities Reports & Price Data"]
  B --> C["Context Set Construction<br>Dynamic Few-shot & Multimodal Fusion"]
  C --> D["LLM Processing<br>Prompt-based Scoring of Key Factors"]
  D --> E["Quantification<br>Scaling Scores to Real-World Values"]
  E --> F["Prediction<br>Market Forecast Generation"]
  F --> G{"Key Outcomes"}
  G --> H["LLMs Outperform Time-series Models"]
  G --> I["Remaining Challenges:<br>Reproducibility & Explainability"]

Cited References (37)

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and 29 more
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  2. Time series analysis: forecasting and control (2015)
  3. Hybridization of ARIMA with learning models for forecasting of stock market time series (2024)
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6 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.