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

2020 | Information & Management | Citations: 2
Authors: Liu, Rong

Abstract: The detrimental effects of insider trading on the financial markets and the econ ... Expand

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 in sider 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. Collapse

Semantic filters: Keras behavioral theory

Topics: database system accounting Python open source application programming interface

Methods: machine learning deep learning experiment artificial neural network word embedding

Theories: information manipulation theory behavioral theory
Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis
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2020 | Information Systems Research | Citations: 0
Authors: Liu, Xiaomo; Wang, G. Alan; Fan, Weiguo; Zhang, Zhongju

Abstract: Online communities and social collaborative platforms have become an increasingl ... Expand

Abstract: Online communities and social collaborative platforms have become an increasingly popular avenue for knowledge sharing and exchange. In these communities, users often engage in informal conversations responding to questions and answers, and over time, they produce a huge amount of highly unstructured and implicit knowledge. How to effectively manage the knowledge repository and identify useful solutions thus becomes a major challenge. In this study, we propose a novel text analytic framework to extract important features from online forums and apply them to classify the usefulness of a solution. Guided by the design science research paradigm, we utilize a kernel theory of the knowledge adoption model, which captures a rich set of argument quality and source credibility features as the predictors of information usefulness. We test our framework on two large-scale knowledge communities: the Apple Support Community and Oracle Community. Our extensive analysis and performance evaluation illustrate that the proposed framework is both effective and efficient in predicting the usefulness of solutions embedded in the knowledge repository. We highlight the theoretical implications of the study as well as the practical applications of the framework to other domains. Collapse

Semantic filters: Keras behavioral theory

Topics: Oracle database online community information retrieval problem solving knowledge repository

Methods: computational algorithm machine learning natural language processing classification method experiment

Theories: behavioral theory

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Found 2 articles

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

Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis