Abstract
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.
Abstract (translated)
在这项研究中，我们使用ChatGPT来创建具有清晰可解释特性的简化模型。这些特性然后用于从电话会议中评估财务结果。我们详细介绍了一种结合知识蒸馏和迁移学习的方法，导致没有显著准确度损失的轻量级主题和情感分类模型。这些模型通过由专家标注的数据集进行评估。此外，本文还深入研究了两个实际案例，阐明生成的特征如何有效地用于量化投资场景。
URL
https://arxiv.org/abs/2403.02185
