Zilong Liu
Clinical Assistant Professor of Business Administration and Deloitte Scholar
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Educational Background
- Ph.D., Business Administration, Risk Management, Kent State University, 2016
- M.A., Financial Economics, Ohio University, 2011
- B.S., Business Administration, Huazhong University of Science and Technology, 2009
Positions Held
- Clinical Assistant Professor, Business Administration, University of Illinois at Urbana-Champaign, 2022 to present
- Principal of Model Validation, Model Risk Management, Discover Financial Service, 2020-2022
- Lead Operational Risk Management, Corporate Risk Management, Discover Financial Service, 2018-2020
- Quantitative/modeling Associate, Model Risk Management, KeyBank National Association, 2016-2018
- Quantitative/modeling Analyst, Asset and Liability Management, PNC Financial Service, 2015-2016
Recent Publications
- Liu, Z., & Liang, H. (2022). Permanent layoff and consumer credit card loss forecasting. Managerial Finance, emerald insight. link >
- Shen, F., Guo, Q., Liang, H., & Liu, Z. (2022). Responses in Divergence of Opinion to Earnings Announcements: Evidence from American Depository Receipts. International Journal of Managerial Finance, Emerald Insight, (March 2022). link >
- Lai, S., Liang, H., Liu, Z., Pu, X., & Zhang, J. (2022). Ownership concentration among entrepreneurial firms: The growth-control trade-off. International Review of Economics & Finance, Elsevier, 78 (March 2022), 122-140. link >
- Liang, H., Guiffrida , A., Liu, Z., Patuwo , B., & Shanker, M. (2021). A Generalized Stochastic Cost–Volume–Profit Model. systems, MDPI, 9 (4). link >
- Liang, H., & Liu, Z. (2021). The impact of bank liquidity risk on risk-taking and bank lending: evidence from European bank. Journal of Finance & Banking Review, GATR Journals, 6 (2), 82-97. link >
Other Publications
Articles
- Baran, L., Li, Y., Liu, Z., Liu, C., & Pu, X. (2018). S&P 500 Index revisions and credit spreads. Review of Financial Economics, wiley, 36 (4), 348-363. link >
- Hu , M., Jiang, X., Liang, H., Liu, Z., & Song , C. (2017). Online Sales in Startups. Pan Pacific Journal of Business Research, Institute for Academic Research, 8 (2), 20-35.
- Liu, Z., Pu, X., & Zhao, X. (2015). What Moves the Correlation between Equity and CDS Markets? The Journal of Fixed Income, Euromoney Institutional Investor, 25 (2), 72-87. link >
Presentations
- Liu, Z., Liang, H., & Liu, C. (2022). The effects of debt liquidity risk on firms’ growth rate. Proceedings of the Academy of Finance 2022 Conference.
Honors and Awards
- Deloitte Scholar, University of Illinois at Urbana-Champaign, 2024 to present
Current Courses
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Business Analytics II (BADM 211) Builds on the foundation from the Business Analytics I (BADM 210), synthesizes concepts through hands-on application and project-based learning. Focuses on data acquisition, organization, analysis and visualization in a business setting. Expanding on the use of statistics in generating basic inferences to predictive modeling Identify opportunities for improving business decisions using data, conduct relevant analysis of the gathered and cleaned data, and finally, interpret and present analysis outcomes to decision makers. Using statistical tools and software applications to identify business problems, acquire relevant data, and generate analytic solutions using advanced analytics techniques and tools for generating insights. Introduces the students to analyzing, learning, and prediction using advanced analytics techniques and tools for generating business insights. This course will provide a practical introduction to various techniques regarding clustering, text mining, classification and decision trees, and time series analysis. Finally, the course will introduce advanced and emerging topics in predictive analytics.
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Database Design and Management (BADM 352) Introduce the modern concepts, techniques and management practices when dealing with data and use of data in organizations. Topics include data modeling, database logical and physical designs, implementation, database administration and web-based database environment. Students will be involved in constructing a database and researching an advanced topic to solidify the learning.
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Big Data Platforms (BADM 358) Provides students a deep understanding of the fundamentals of big data platforms and data engineering, data analytics and algorithms for analytical use cases. Experimenting end to end pipelines on cloud platforms: data collection to deployment
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Big Data Infrastructures (BADM 558) Provides students a thorough understanding of the fundamentals of big data platforms and technologies, data engineering, data analytics and algorithms for both operational and analytical use cases. Experimenting end to end pipelines on cloud platforms from data collection to presenting data driven insights for a nontechnical audience. Students will have the opportunity to understand both relational, analytical databases and NoSQL databases on the cloud as well as on premise from real-life datasets while leveraging programmatic or configuration driven data pipelines.
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