Meng-Chen (Melinda) Hsieh, Ph.D., is currently an Associate Professor of Management Sciences in the Department of Information Systems, Analytics, and Supply Chain Management. She received her Ph.D. in Statistics from the Stern School of Business, New York University. Before joining Rider, she had over eight years of applied research experience at diverse private organizations, including IBM Research, Morgan Stanley, Credit Suisse and Activision. She teaches statistics and business analytics courses for undergraduate and graduate programs at the Norm Brodsky College of Business.
High Frequency Financial Econometrics, Applied Time Series Models, Applied Time Series Models in Supply Chain Management, Data-Driven Analytics for Business Decisions
Statistics, Business Analytics, Predictive Models
- Hsieh, M. (in press, 2021). Data-Driven Portfolio Optimization with Drawdown Constraints Utilizing Machines Learning. Contemporary Perspectives in Data Mining, 4. https://www.infoagepub.com/products/Contemporary-Perspectives-in-Data-Mining-Vol-4
- M. Hsieh, A. Giloni, C. Hurvich, The Propagation and Identification of ARMA Demand Under Exponential Smoothing. IMA Journal of Management Mathematics, Volume 31, Issue 3, July 2020, pp. 307-344. https://academic.oup.com/imaman/article-abstract/31/3/307/5826539
- M. Hsieh, C. Hurvich, P. Soulier, Modeling Leverage and Long Memory Volatility in a Pure Jump Process. High Frequency. Vol. 2, Issue 3-4, 2019, pp. 124-131. https://onlinelibrary.wiley.com/doi/10.1002/hf2.10042
- M. Hsieh, C. Hurvich, R. Deo, Long Memory in Intertrade Durations, Counts and Realized Volatility of NYSE Stocks. Journal of Statistical Planning and Inference. Vol. 140, Issue 12, December 2010, pp. 3715-3733.
- M. Hsieh, C. Hurvich and P. Soulier, Asymptotics for Duration-Driven Long Memory Processes. Journal of Econometrics. Vol. 141, December 2007, pp. 913-949.
Presentation of Refereed Papers
- Return Predictability from Realized Volatility in Pure Jump Point Processes, 13th The Society of Financial Econometrics Annual Conference, June 2021.
- Enhanced Modeling and Parameter Estimation for Long-Horizon Return Predictability in Pure Jump Point Processes, Joint Statistical Meetings, August 2020.
- Long-Horizon Return Predictability with Realized Volatility from Pure Jump Point Process, Joint Statistical Meetings, Colorado, CO, August 2019.
- Data-Driven Portfolio Optimization with Drawdown Constraints Utilizing Machine Learning, INFORMS, Phoenix, November 2018.
- Machine Learning and Portfolio Optimization with Drawdown Constraints, ASA Symposium on Data Science and Statistics, Reston, VA, May 2018.
- Statistical Learning on Optimal Decisions, poster presentation at INFORMS, Nashville, TN, November 2016.
- Impact of Exponential Smoothing on Inventory Costs in Supply Chains, POMS 27th Annual Conference, Orlando, FL, May 2016.
- Long-Horizon Return Predictability from Realized Volatility in Pure Jump Point Processes, Conference on Intrinsic Time in Finance, Germany, May 2022
- 2020-2022: Davis Fellowship. Norm Brodsky College of Business
- 2017-2019: Davis Fellowship. College of Business Administration
- 2017: Summer Research Fellowship. Rider University
- 2016: Summer Research Fellowship. Rider University