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

arXiv: https://arxiv.org/abs/2404.18470 · HTML: https://mlsanity.com/paper/2404.18470/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [musicnn: Pre-trained convolutional neural networks for music audio tagging](https://mlsanity.com/paper/1909.06654/) (2019)
- [Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition](https://mlsanity.com/paper/2104.06517/) (2021)
- [Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models](https://mlsanity.com/paper/1702.01978/) (2017)

## Built on by (most cited first, 9 of 9)

- [FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance](https://mlsanity.com/paper/2508.05201/) (2025)
- [FinAudio: A Benchmark for Audio Large Language Models in Financial Applications](https://mlsanity.com/paper/2503.20990/) (2025)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?](https://mlsanity.com/paper/2507.21055/) (2025)
- [Extracting the Structure of Press Releases for Predicting Earnings Announcement Returns](https://mlsanity.com/paper/2509.24254/) (2025)
- [Interpreting Public Sentiment in Diplomacy Events: A Counterfactual Analysis Framework Using Large Language Models](https://mlsanity.com/paper/2509.20367/) (2025)
- [Fast Numbers, Slow Language: Bridging Quantitative and Qualitative Earnings Signals](https://mlsanity.com/paper/2606.29734/) (2026)
- [LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification](https://mlsanity.com/paper/2608.11753/) (2026)
- [A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls](https://mlsanity.com/paper/2608.28040/) (2026)

## Abstract (arXiv)

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: 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.