Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance

Autor:	Gandouet, Olivier, Belbahri, Mouloud, Jezequel, Armelle, Bodjov, Yuriy
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computational Engineering Finance and Science Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnings calls. We detail a training approach that merges knowledge distillation and transfer learning, resulting in lightweight topic and sentiment classification models without significant loss in accuracy. These models are assessed through a dataset annotated by experts. The paper also delves into two practical case studies, highlighting how the generated features can be effectively utilized in quantitative investing scenarios. Comment: Edge Intelligence Workshop at AAAI24
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.02185 Zobrazit plný text záznamu View this record from Arxiv