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

• URL: http://arxiv.org/abs/2404.18470v2
• Date: Thu, 29 Aug 2024 23:13:56 GMT
• Title: ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction
• Authors: Yupeng Cao, Zhi Chen, Qingyun Pei, Nathan Jinseok Lee, K. P. Subbalakshmi, Papa Momar Ndiaye,
• Abstract summary: This research introduces a novel framework: textbfECC Analyzer, which utilizes large language models (LLMs) to extract richer, more predictive content from ECCs. 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. Experimental results demonstrate that our model outperforms traditional analytical benchmarks.
• Score: 7.358590821647365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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