Distilled ChatGPT Topic & Sentiment Modeling with Applications in
  Finance
Olivier Gandouet Mouloud Belbahri Armelle Jezequel Yuriy Bodjov
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.