Tag Archives: compustat

Corporate Social Network Analysis: A Deep Learning Approach

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu (2020) “Corporate Social Network Analysis: A Deep Learning Approach,” Working Paper.

Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.

Structural Hole-based Measures of Firm’s Strategic Competitive Positioning

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Structural Hole-based Measures of Firm’s Strategic Competitive Positioning”, Working Paper.

The theory of network opportunity emergence holds that as the overall industry network structure becomes centralized, opportunities emerge for new entrants. However, new entrants must correctly strategically position themselves in the market to be properly valued. This creates tensions for entrepreneurial ventures considering going public around how to craft their strategic posture to take advantage of differentiating opportunities in the market structure while still being familiar enough to customers and investors. In this paper, we propose a theory of IPO strategic posture to unpack these dynamics. We empirically test our theory using a machine learning approach called doc2vec to create a similarity matrix of all existing U.S. publicly traded companies based upon self-provided business descriptions provided in their 10-K annual reports. This enables us to measure existing companies’ similarities of strategic postures and identify where industry-level structural holes emerge. We then use these structural hole signatures of potential market entry opportunities to predict how new companies strategically posture an IPO. We then follow the trajectories of those newly listed companies to see how their strategic posture impacts growth and ultimate survival. We conclude with a discussion of how the institutional pressures of the venture capital industry create pressure for ventures to self-present their IPO strategic postures as too distinct for their own long-term survival.

Predicting Litigation Risk via Machine Learning

Lee, Gene Moo*, James Naughton*, Xin Zheng*, Dexin Zhou* (2020) “Predicting Litigation Risk via Machine Learning,” Working Paper. [SSRN] (* equal contribution)

This study examines whether and how machine learning techniques can improve the prediction of litigation risk relative to the traditional logistic regression model. Existing litigation literature has no consensus on a predictive model. Additionally, the evaluation of litigation model performance is ad hoc. We use five popular machine learning techniques to predict litigation risk and benchmark their performance against the logistic regression model in Kim and Skinner (2012). Our results show that machine learning techniques can significantly improve the predictability of litigation risk. We identify two best-performing methods (random forest and convolutional neural networks) and rank the importance of predictors. Additionally, we show that models using economically-motivated ratio variables perform better than models using raw variables. Overall, our results suggest that the joint consideration of economically-meaningful predictors and machine learning techniques maximize the improvement of predictive litigation models.