ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction

ArXiv ID: 2404.18470 “View on arXiv”

Authors: Unknown

Abstract

In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock volatility is a critical challenge that has attracted both academics and investors. While previous studies have used multimodal deep learning-based models to obtain a general view of ECCs for volatility predicting, they often fail to capture detailed, complex information. Our research introduces a novel framework: \textbf{“ECC Analyzer”}, which utilizes large language models (LLMs) to extract richer, more predictive content from ECCs to aid the model’s prediction performance. We use the pre-trained large models to extract textual and audio features from ECCs and implement a hierarchical information extraction strategy to extract more fine-grained information. This strategy first extracts paragraph-level general information by summarizing the text and then extracts fine-grained focus sentences using Retrieval-Augmented Generation (RAG). These features are then fused through multimodal feature fusion to perform volatility prediction. Experimental results demonstrate that our model outperforms traditional analytical benchmarks, confirming the effectiveness of advanced LLM techniques in financial analysis.

Keywords: Large Language Models (LLMs), Earnings Conference Calls, Volatility Forecasting, Retrieval-Augmented Generation (RAG), Multimodal Feature Fusion, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs advanced LLM techniques and multimodal feature fusion, but the mathematical complexity is moderate (primarily involving standard ML architectures and loss functions) rather than dense theoretical derivations. It demonstrates strong empirical rigor by using a real-world S&P 500 dataset, reporting specific MSE reductions (27.7%), and outlining a reproducible pipeline with hierarchical extraction and RAG, making it backtest-ready despite relying on public financial data.

  flowchart TD
    A["Research Goal<br>Predict Stock Volatility<br>using ECCs"] --> B["Data Collection<br>Audio & Text from ECCs"]
    B --> C["Hierarchical Feature Extraction"]
    C --> C1["Paragraph Level: Summarization"]
    C --> C2["Fine-grained Level: RAG"]
    D["Multimodal Feature Fusion<br>Text + Audio"] --> E["Model Training & Prediction"]
    E --> F["Outcome: Volatility Prediction"]
    subgraph ECC Analyzer Framework
        direction TB
        B
        C
        C1
        C2
        D
    end
    C1 --> D
    C2 --> D
    F --> G["Key Finding<br>LLM + RAG outperforms<br>traditional benchmarks"]