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

arXiv:2404.18470v1 Announce Type: cross
Abstract: In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. …

abstract academics analytics arxiv challenge conference cs.ai cs.ce cs.cl data deep learning earnings extract financial forecast investors language language model large language large language model performance prediction q-fin.rm q-fin.tr realm signal stock stock performance studies trading type unstructured unstructured data while