Form 10-Q Itemization

TL;DR

This paper introduces a hybrid approach combining rule-based methods and CNN image classification to effectively itemize and extract structured information from unstructured 10-Q filings, facilitating improved data retrieval and NLP applications.

Contribution

It presents a novel pipeline that leverages typographic features and CNNs to automate itemization of 10-Q filings, addressing challenges of unstructured financial documents.

Findings

Effective itemization of 10-Q filings demonstrated

Pipeline enables rapid data retrieval from textual data

Extracted data supports training transformer-based NLP models

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.