Category Archives: Working Papers

Price Competition and Active or Inactive Consumer Search

Koh, Yumi, Gea M. Lee, Gene Moo Lee (2023) “Price Competition and Active or Inactive Consumer Search”. Working Paper. [Latest version: May 31, 2023] [SSRN]

We propose a price-competition model in which prices are dispersed and a fraction of consumers decide whether to make an immediate purchase without actively searching for prices or to search sequentially. We use an incomplete-information setting with heterogeneous production costs and information frictions:  rms’ production cost types are drawn from an interval and are privately observed. The model includes active or inactive consumer search as an equilibrium outcome and allows a competition-induced switch between the two outcomes. We study how firms and consumers interact in determining prices and making an active or inactive search when competition intensifies with more firms.

IT Risk and Stock Price Crash Risk

Song, Victor, Hasan Cavusoglu, Jaecheol Park, Mary L. Z. Ma, Gene Moo Lee (2026) “IT Risk and Stock Price Crash Risk,” Under review.

This study examines whether and how firm-level information technology (IT) risk contributes to stock price crash risk. We construct a novel measure of ex-ante IT risk from risk factor disclosures in Item 1A of firms’ 10-K filings using advanced machine learning approaches. We find that higher IT risk is associated with greater stock price crash risk. Mechanism analyses indicate that this effect operates primarily through increased downside operating risk, rather than through heightened exposure to data breach events. We further document heterogeneity in the relationship between IT risk and stock price crash risk: (1) cybersecurity risk has a stronger effect than noncybersecurity IT risk; (2) the effect is stronger for newly disclosed IT risk factors; and (3) higher readability amplifies the crash risk effect. Together, these findings highlight IT risk as a previously underexplored determinant of stock price crash risk and offer new insights into the capital market consequences of firms’ IT-related disclosures.

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.