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.
As firms increasingly depend on Information Technology (IT) in their business strategies and value creation activities, risks associated with IT have become one of the top concerns for managers and investors. This study examines the relation between IT-related risk factor information 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 the text-mining approach of Latent Dirichlet Allocation topic modeling to identify IT-related risk factors, we find that IT risk emerges as one of the firms’ key risk factors and that IT risk is 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 IT value 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 IT value 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 information, specifically cybersecurity risk, in Item 1A enhance the information content of 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 these 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. Focusing on cases in which the players sufficiently care about future payoffs, we find that if the defender does not immediately ban a suspicious user, damages caused by the hacker can be enormous. Therefore, the defender bans every suspicious user in equilibrium to avoid huge losses, resulting in the worst payoffs for both players. These results explain the emerging sophisticated cyberattacks with detrimental consequences. Our model also predicts that the hacker may opt to be non-strategic. This is because non-strategic cyberattacks are less threatening and the defender decides not to immediately block a suspicious user to reduce false detection, in which case both players become better off.
We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.