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    <abstract>Financial volatility prediction is vital for indicating a company’s risk profile. Transcripts of companies’ earnings calls are important unstructured data sources to be utilized to access companies’ performance and risk profiles. However, current works ignore the role of financial metrics knowledge (such as EBIT, EPS, and ROI) in transcripts, which is crucial for understanding companies’ performance, and little consideration is given to integrating text and price information. In this work, we statistic common financial metrics and make a special dataset based on these metrics. Then, we introduce a knowledge-enhanced financial volatility prediction method (KeFVP) to inject knowledge of financial metrics into text comprehension by knowledge-enhanced adaptive pre-training (KePt) and effectively incorporating text and price information by introducing a conditional time series prediction module. We conduct extensive experiments on three real-world public datasets, and the results indicate that KeFVP is effective and outperforms all the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T KeFVP: Knowledge-enhanced Financial Volatility Prediction
%A Niu, Hao
%A Xiong, Yun
%A Wang, Xiaosu
%A Yu, Wenjing
%A Zhang, Yao
%A Yang, Weizu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F niu-etal-2023-kefvp
%X Financial volatility prediction is vital for indicating a company’s risk profile. Transcripts of companies’ earnings calls are important unstructured data sources to be utilized to access companies’ performance and risk profiles. However, current works ignore the role of financial metrics knowledge (such as EBIT, EPS, and ROI) in transcripts, which is crucial for understanding companies’ performance, and little consideration is given to integrating text and price information. In this work, we statistic common financial metrics and make a special dataset based on these metrics. Then, we introduce a knowledge-enhanced financial volatility prediction method (KeFVP) to inject knowledge of financial metrics into text comprehension by knowledge-enhanced adaptive pre-training (KePt) and effectively incorporating text and price information by introducing a conditional time series prediction module. We conduct extensive experiments on three real-world public datasets, and the results indicate that KeFVP is effective and outperforms all the state-of-the-art methods.
%R 10.18653/v1/2023.findings-emnlp.770
%U https://aclanthology.org/2023.findings-emnlp.770
%U https://doi.org/10.18653/v1/2023.findings-emnlp.770
%P 11499-11513

Markdown (Informal)

[KeFVP: Knowledge-enhanced Financial Volatility Prediction](https://aclanthology.org/2023.findings-emnlp.770) (Niu et al., Findings 2023)

• KeFVP: Knowledge-enhanced Financial Volatility Prediction (Niu et al., Findings 2023)

ACL

• Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, and Weizu Yang. 2023. KeFVP: Knowledge-enhanced Financial Volatility Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11499–11513, Singapore. Association for Computational Linguistics.