@inproceedings{zhang-etal-2026-sae,
title = "{SAE}-{F}i{RE}: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection",
author = "Zhang, Huopu and
Liu, Yanguang and
Zhang, Miao and
He, Zirui and
Du, Mengnan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.265/",
pages = "5363--5382",
ISBN = "979-8-89176-395-1",
abstract = "Predicting earnings surprises through the analysis of earnings conference call transcripts has attracted increasing attention from the financial research community. Conference calls serve as critical communication channels between company executives, analysts, and shareholders, offering valuable forward-looking information. However, these transcripts present significant analytical challenges, typically containing over 5,000 words with substantial redundancy and industry-specific terminology that creates obstacles for language models. In this work, we propose the Sparse Autoencoder for Financial Representation Enhancement (SAE-FiRE) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to efficiently identify patterns and filter out noises, and focusing specifically on capturing nuanced financial signals that have predictive power for earnings surprises. Experimental results indicate that the proposed method can significantly outperform comparing baselines."
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<abstract>Predicting earnings surprises through the analysis of earnings conference call transcripts has attracted increasing attention from the financial research community. Conference calls serve as critical communication channels between company executives, analysts, and shareholders, offering valuable forward-looking information. However, these transcripts present significant analytical challenges, typically containing over 5,000 words with substantial redundancy and industry-specific terminology that creates obstacles for language models. In this work, we propose the Sparse Autoencoder for Financial Representation Enhancement (SAE-FiRE) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to efficiently identify patterns and filter out noises, and focusing specifically on capturing nuanced financial signals that have predictive power for earnings surprises. Experimental results indicate that the proposed method can significantly outperform comparing baselines.</abstract>
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%0 Conference Proceedings
%T SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection
%A Zhang, Huopu
%A Liu, Yanguang
%A Zhang, Miao
%A He, Zirui
%A Du, Mengnan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-sae
%X Predicting earnings surprises through the analysis of earnings conference call transcripts has attracted increasing attention from the financial research community. Conference calls serve as critical communication channels between company executives, analysts, and shareholders, offering valuable forward-looking information. However, these transcripts present significant analytical challenges, typically containing over 5,000 words with substantial redundancy and industry-specific terminology that creates obstacles for language models. In this work, we propose the Sparse Autoencoder for Financial Representation Enhancement (SAE-FiRE) framework to address these limitations by extracting key information while eliminating redundancy. SAE-FiRE employs Sparse Autoencoders (SAEs) to efficiently identify patterns and filter out noises, and focusing specifically on capturing nuanced financial signals that have predictive power for earnings surprises. Experimental results indicate that the proposed method can significantly outperform comparing baselines.
%U https://aclanthology.org/2026.findings-acl.265/
%P 5363-5382
Markdown (Informal)
[SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection](https://aclanthology.org/2026.findings-acl.265/) (Zhang et al., Findings 2026)
ACL