Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

Rong Liu; Feng Mai; Zhe Shan; Ying Wu

doi:10.1016/j.im.2020.103387

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Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

Journal article

Peer reviewed

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

Rong Liu, Feng Mai, Zhe Shan and Ying Wu

Information & management, Vol.57(8), p.103387

12/2020

DOI: 10.1016/j.im.2020.103387

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Abstract

The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing insider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading (using actual insider trading allegation by shareholders as the proxy) from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct.

Attention models

Deep learning

Insider trading

Predictive analytics

Text mining

Details

Title: Subtitle: Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach
Creators: Rong Liu - Stevens Institute of Technology
Feng Mai - Stevens Institute of Technology
Zhe Shan - Miami University
Ying Wu - Stevens Institute of Technology
Resource Type: Journal article
Publication Details: Information & management, Vol.57(8), p.103387
Publisher: Elsevier B.V
DOI: 10.1016/j.im.2020.103387
ISSN: 0378-7206
eISSN: 1872-7530
Language: English
Date published: 12/2020
Academic Unit: Business Analytics
Record Identifier: 9984701727502771

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