Previous title: Price Competition and Consumer Search
We propose a model of price competition in which firms select prices conditional on privately-observed production costs and a subset of consumers can choose to search sequentially given price dispersion. We investigate how competition affects the consumers’ choice of whether to purchase immediately from a randomly-selected first firm or engage in sequential search. We establish two types of equilibria, random equilibrium and searching equilibrium, based on the consumers’ search decision in equilibrium. We show that sequential search can be completely or at least partially inactivated in the market with a sufficiently large number of competing firms.
Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: An Unstructured Structural Hole-based Firm-specific Measure”, Under Review. [Submitted: May 13, 2022]
In this research methods paper, we propose a firm-specific strategic competitive positioning (SCP) measure to capture a firm’s unique competitive and strategic positioning based on annual corporate filings. Using an unsupervised machine learning approach, we use structural holes, a concept in network theory, to develop and operationalize an SCP measure derived from a strategic similarity matrix of all existing U.S. publicly traded firms. This enables us to construct a robust firm-level SCP measure with minimal human intervention. Our measure dynamically captures competitive positioning across different firms and years without using artificially bounded industry classification systems. We illustrate how the measure dynamically captures firm-level, industry-level, and cross-industry strategic changes. Then, we demonstrate the effectiveness of our measure with an empirical demonstration showing the imprinting effect of SCP at the time of initial public offering (IPO) on the subsequent performance of the firm. The results show that our unsupervised SCP measure predicts post-IPO performance. This paper makes a significant methodological contribution to the information systems and strategic management literature by proposing a network theory-based unsupervised approach to dynamically measure firm-level strategic competitive positioning. The measure can be easily applied to firm-specific, industry-level, and cross-industry research questions across a wide variety of fields and contexts.
As firms increasingly depend on Information Technology (IT), risks associated with IT have become one of the top concerns for managers and investors. This study examines the relation between the IT-related risk factor in Item 1A of the 10-K annual reports and a firm’s stock price crash risk, a firm-specific propensity to stock price crashes. Using topic modeling to identify IT-related risk factors, we find that IT risk emerges as one of the firms’ key risk categories and that IT risk factors are positively associated with a firm’s future stock price crash risk. We further separate IT risk factors into cybersecurity risk that potentially leads to a loss or leak of data, and non-cybersecurity IT risk that relates to a firm’s reliance on IT for its competitive advantage and value creation activities. We find that cybersecurity risk continues to affect crash risk, but non-cybersecurity IT risk does not, consistent with their different risk natures. We also find that the readability, novelty, and the order of appearance of the IT risk factor in Item 1A enhance the information content of IT risk factors and strengthen their relation with stock price crash risk.
Data: litigation risk scores for 6134 firms in 1996-2015 [link]
Research assistant: Raymond Situ
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.
Modern cyberattacks such as advanced persistent threats have become sophisticated. Hackers can stay undetected for an extended time and defenders do not have sufficient countermeasures to prevent advanced cyberattacks. Reflecting on this phenomenon, we propose a game-theoretic model in which a hacker launches stealthy cyberattacks for a long time and a defender’s actions are to monitor the activities and to disable a suspicious user. Damages caused by the hacker can be enormous if the defender does not immediately ban a suspicious user under certain circumstances, which can explain the emerging sophisticated cyberattacks with detrimental consequences. Our model also predicts that the hacker may opt to be behavioral to avoid the worst cases. This is because behavioral cyberattacks are less threatening and the defender decides not to immediately block a suspicious user to reduce the cost of false detection.