SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection
This paper introduces SAE-FiRE, a framework using sparse autoencoders for earnings surprise prediction based on conference call transcripts.
What it examines
The paper introduces SAE-FiRE, a framework using sparse autoencoders to extract important signals from long earnings call transcripts while reducing redundant noise. It addresses overfitting in language models by applying systematic feature selection with statistical tests and tree-based methods for robust earnings surprise predictions.
What it concludes
The results show that SAE-FiRE outperforms baseline models in predicting earnings surprises by effectively filtering noise. Applications include financial forecasting, risk assessment, and smarter investment decisions. Future work may involve multimodal inputs and domain-specific adaptations to further enhance financial prediction accuracy.
Evidence objects
Researchers introduce the innovative SAE-FiRE framework that leverages sparse autoencoders to analyze lengthy earnings call transcripts, achieving enhanced prediction of earnings surprises with improved weighted F1, AUC, and accuracy rates.
key_findings bullet 1 · key_findings · validation V0
Employing SAEs to disentangle noisy, redundant financial texts, the framework isolates critical patterns and discriminative features, thereby enabling the model to capture both high-level semantic trends and fine-grained financial details.
key_findings bullet 2 · key_findings · validation V0
Tree-based feature selection filters noise as traditional methods like ANOVA F-tests integrated with deep learning enrich predictions, though reliance on pre-trained SAEs excluding near-zero surprises could limit overall variability exploration.
key_findings bullet 3 · key_findings · validation V0
The paper employs a moderately novel application of sparse autoencoders to predict earnings surprises from conference call transcripts. Although its core idea extends existing methods, the inclusion of a novel feature selection process offers fresh insights in processing lengthy, noisy financial text, making this work compelling and of moderate originality for financial analysis.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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, typi… ▽ More 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. △ Less
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