Form 10-Q, the quarterly financial statement,  is one of the most crucial filings for US public firms to disclose their financial and other relevant business operation information. Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of formatgiven company-specific nature , it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information . In this paper , we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model. The implementation is an integrated pipeline which provides a solution to the item retrieval on a large scale. This would enable cross sectional and longitudinal textual analysis on massive number of companies .
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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Form 10-Q, the
The
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or Form 10-Q,
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crucial
frequently required
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firms to disclose their
companies to disclose
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relevant business operation
important business
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gigantic number
massive volume
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prevailing in the market for each quarter and diverse
and the enormous
R
implementation of formatgiven company-specific nature
reporting format
R
long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information . In this paper , we create a tool to itemize
been a long-standing challenge to retrieve item-specific information from
R
filings using multi-stage processes, blending
filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing
R
CNN deep learning model. The implementation is an integrated pipeline which provides a solution to the item retrieval on a large scale. This would enable cross sectional and longitudinal textual analysis on massive number of companies
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