Shi, Ming-XuanMing-XuanShiChen, Chung-ChiChung-ChiChenHuang, Hen-HsenHen-HsenHuangChen, Hsin-HsiHsin-HsiChen2026-03-112026-03-112023-11[9798891760141]https://www.scopus.com/record/display.uri?eid=2-s2.0-105027194235&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/736221Volatility, 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.trueconference paper10.18653/v1/2023.ijcnlp-short.52-s2.0-105027194235