Shun Dong

Shun Dong

Ph.D. Candidate in Analytics, Information and Operations
University of Kansas School of Business
shun.dong@ku.edu
CV · Google Scholar · KU Profile

About Me

I am a Ph.D. candidate in Analytics, Information and Operations at the University of Kansas School of Business. My research focuses on statistical and machine learning methods, optimization, and data-driven decision support, with particular interests in business analytics, information systems, predictive modeling, and AI-enabled decision making. My work spans methodological development and substantive applications in finance, risk analytics, healthcare, and other data-driven business settings.

Outside of research, I enjoy movies and music, playing table tennis and basketball, and experimenting with new technologies. I am especially interested in generative AI tools and computer hardware, and I enjoy building and upgrading custom computer systems.

Job Market Paper

Accounting for Industry Heterogeneity in Predicting Corporate Default Probability: A Semiparametric Learning Approach

with Shaobo Li, Ben Sherwood, and Xiaorui Zhu

Revise and Resubmit, European Journal of Operational Research

Abstract: Predicting corporate default is essential for risk management, credit allocation, and investment decision making, yet most bankruptcy models overlook heterogeneity in how financial distress manifests across industries. We propose the single-index hazard model with multiple links (SIML), a semiparametric learning method that combines a common financial risk index with industry-specific flexible link functions. This structure captures nonlinear industry effects and their interactions with firm-level financial characteristics while retaining an interpretable low-dimensional index. We also develop a two-stage simulation-based testing procedure for industry heterogeneity and nonlinearity. Using a comprehensive database of 18,651 U.S. publicly traded firms from 1980 to 2023, we find strong evidence of distinct nonlinear risk patterns across industries. SIML outperforms a broad set of benchmark models in both in-sample and out-of-sample evaluations. A simulated corporate loan market and an asset-pricing analysis of the distress risk anomaly further show the economic value of accounting for industry-specific default risk.

Awards & Fellowships

  • School of Business Best Paper Award, University of Kansas, 2026
  • Max E. Fessler Dissertation Support Award, University of Kansas, 2026
  • John O. Tollefson Best Paper Award, University of Kansas School of Business, 2026
  • INFORMS Fellowship
  • POMS Fellowship
  • Graduate Teaching Assistantship, University of Kansas, 2022–Present
  • Graduate Research Assistantship, University of Cincinnati, 2020–2022
  • Graduate Teaching Assistantship, University of Cincinnati, 2018–2020
  • Graduate Teaching Assistantship, Missouri State University, 2015–2017
  • 1st Place, CNAS Thesis Competition Research Award , Missouri State University, 2014
  • CSSA Outstanding Contribution Award, 2014