Form 10-Q Itemization
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.
Keywords
Cite
@article{arxiv.2104.11783,
title = {Form 10-Q Itemization},
author = {Yanci Zhang and Tianming Du and Yujie Sun and Lawrence Donohue and Rui Dai},
journal= {arXiv preprint arXiv:2104.11783},
year = {2021}
}
Comments
6 pages, 3 figures, 3 tables, http://review10q.ddns.net/