Using Long Short-Term Memory Neural Networks to Analyze Sec 13D Filings: A Recipe for Human and Machine Interaction

Intelligent Systems in Accounting, Finance and Management, 26, 153–163, 2019

Posted: 13 Apr 2020

See all articles by Murat Aydogdu

Murat Aydogdu

Rhode Island College - Department of Economics and Finance

Hakan Saraoglu

Bryant University - Department of Finance

David A. Louton

Bryant University - Department of Finance

Date Written: 2019

Abstract

We implement an efficient methodology for extracting themes from Securities Exchange Commission 13D filings using aspects of human‐assisted active learning and long short‐term memory (LSTM) neural networks. Sentences from the ‘Purpose of Transaction’ section of each filing are extracted and a randomly chosen subset is labelled based on six filing themes that the existing literature on shareholder activism has shown to have an impact on stock returns. We find that an LSTM neural network that accepts sentences as input performs significantly better, with precision of 77%, than an alternately specified neural network that uses the common bag of words approach. This indicates that both sentence structure and vocabulary are important in classifying SEC 13D filings. Our study has important implications, as it addresses the recent cautions raised in the literature that analysis of finance and accounting‐related text sources should move beyond bag‐of‐words approaches to alternatives that incorporate the analysis of word sense and meaning reflecting context.

Keywords: Active Learning, Computational Linguistics, Neural Networks, Shareholder Activism

JEL Classification: C81, C88, G30, G38

Suggested Citation

Aydogdu, Murat and Saraoglu, Hakan and Louton, David A., Using Long Short-Term Memory Neural Networks to Analyze Sec 13D Filings: A Recipe for Human and Machine Interaction (2019). Intelligent Systems in Accounting, Finance and Management, 26, 153–163, 2019. Available at SSRN: https://ssrn.com/abstract=3555826

Murat Aydogdu

Rhode Island College - Department of Economics and Finance ( email )

Alger Hall 237
Providence, RI 02908
United States

Hakan Saraoglu

Bryant University - Department of Finance ( email )

1150 Douglas Pike
Smithfield, RI 02917
United States

David A. Louton (Contact Author)

Bryant University - Department of Finance ( email )

1150 Douglas Pike
Smithfield, RI 02917
United States
401-232-6343 (Phone)
401-232-6319 (Fax)

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