@inproceedings{wang-etal-2024-ama,
title = "{AMA}-{LSTM}: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction",
author = "Wang, Shengkun and
Ji, Taoran and
He, Jianfeng and
ALMutairi, Mariam and
Wang, Dan and
Wang, Linhan and
Zhang, Min and
Lu, Chang-Tien",
editor = "Yang, Yi and
Davani, Aida and
Sil, Avi and
Kumar, Anoop",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-industry.32/",
doi = "10.18653/v1/2024.naacl-industry.32",
pages = "379--386",
abstract = "Stock volatility prediction is an important task in the financial industry. Recent multimodal methods have shown advanced results by combining text and audio information, such as earnings calls. However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks."
}
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<abstract>Stock volatility prediction is an important task in the financial industry. Recent multimodal methods have shown advanced results by combining text and audio information, such as earnings calls. However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.</abstract>
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%0 Conference Proceedings
%T AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction
%A Wang, Shengkun
%A Ji, Taoran
%A He, Jianfeng
%A ALMutairi, Mariam
%A Wang, Dan
%A Wang, Linhan
%A Zhang, Min
%A Lu, Chang-Tien
%Y Yang, Yi
%Y Davani, Aida
%Y Sil, Avi
%Y Kumar, Anoop
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-ama
%X Stock volatility prediction is an important task in the financial industry. Recent multimodal methods have shown advanced results by combining text and audio information, such as earnings calls. However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.
%R 10.18653/v1/2024.naacl-industry.32
%U https://aclanthology.org/2024.naacl-industry.32/
%U https://doi.org/10.18653/v1/2024.naacl-industry.32
%P 379-386
Markdown (Informal)
[AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction](https://aclanthology.org/2024.naacl-industry.32/) (Wang et al., NAACL 2024)
ACL
- Shengkun Wang, Taoran Ji, Jianfeng He, Mariam ALMutairi, Dan Wang, Linhan Wang, Min Zhang, and Chang-Tien Lu. 2024. AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 379–386, Mexico City, Mexico. Association for Computational Linguistics.