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arXiv · 2104.11783

Form 10-Q Itemization

Yanci Zhang·Tianming Du·Yujie Sun·Lawrence Donohue·Rui Dai
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Abstract

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.

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Yanci Zhang, Tianming Du, Yujie Sun, Lawrence Donohue, Rui Dai. 2021-04-23. Form 10-Q Itemization. https://arxiv.org/abs/2104.11783

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