Enhancing Volatility Forecasting in Financial Markets: A General Numeral Attachment Dataset for Understanding Earnings Calls
Journal
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Long Papers, IJCNLP-AACL 2023
Journal Volume
2
Start Page
37
End Page
42
ISBN
[9798891760141]
Date Issued
2023-11
Author(s)
Abstract
Volatility, a crucial statistical measure in the financial market, serves as an indicator of financial instrument risk. Accurate volatility capture aids in predicting stock movements and is valuable in derivative trading, such as options trading. While recent research focuses on volatility forecasting using earnings call transcriptions, most approaches rely on end-to-end models that directly process textual or vocal data. However, limited efforts have been made to simulate the reading and comprehension processes of financial professionals, thereby enhancing the capabilities of language models. To address this gap, we propose a general numeral attachment dataset designed to train language models to understand earnings calls with the expertise of professionals. Additionally, we introduce a pre-training process that improves the semantic understanding of earnings calls. Experimental results demonstrate that our pretrained language model enhances the accuracy of 3-day volatility forecasting.
Event(s)
13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, IJCNLP-AACL 2023
Publisher
Association for Computational Linguistics
Type
conference paper