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

Paper: arXiv 2404.18470

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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (132)

1. Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility (2009)
2. Deep learning for event-driven stock prediction (2015)
3. Artificial Intelligence, Machine Learning and Big Data in Finance Opportunities, Challenges, and Implications for Policy Makers (2021)
4. Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance (2021)
5. The effect of conference calls on analyst and market underreaction to earnings announcements (2005)
6. Text-based network industries and endogenous product differentiation (2016)
7. Twitter mood predicts the stock market (2011)
8. The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices (2017)

6 of 132 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.