RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data

ArXiv ID: 2404.07452 “View on arXiv”

Authors: Unknown

Abstract

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to financial risk prediction remains underexplored. Addressing this gap, in this paper, we introduce RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely integrates multimodal financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data to improve financial risk prediction. Empirical results demonstrate RiskLabs’ effectiveness in forecasting both market volatility and variance. Through comparative experiments, we examine the contributions of different data sources to financial risk assessment and highlight the crucial role of LLMs in this process. We also discuss the challenges associated with using LLMs for financial risk prediction and explore the potential of combining them with multimodal data for this purpose.

Keywords: Large Language Models (LLMs), Risk Prediction, Multimodal Learning, Volatility Forecasting, Equities

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced techniques like multimodal fusion with LLMs and multi-head self-attention, indicating substantial mathematical complexity. It also presents empirical results with comparative experiments on real financial data (earnings calls, news, time series), demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: Predict Financial Risk<br>using LLMs on Multimodal Data"] --> B

    subgraph B ["Data Acquisition & Integration"]
        direction LR
        B1["Textual Data<br>ECC Transcripts"]
        B2["Vocal Data<br>ECC Audio Tone"]
        B3["Market Data<br>Time Series"]
        B4["Contextual Data<br>News Sentiment"]
    end

    B --> C["LLM-based Feature Extraction<br>& Multimodal Fusion"]

    C --> D{"Prediction Tasks"}
    D --> E["Market Volatility<br>Forecasting"]
    D --> F["Variance Prediction"]

    E & F --> G["Key Outcomes"]
    
    subgraph G ["Comparative Analysis"]
        G1["Superior Performance<br>vs. Unimodal Models"]
        G2["LLMs are crucial<br>for text processing"]
        G3["Identified optimal<br>data source combinations"]
    end