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Meng-Chen Hsieh

Faculty / Meng-Chen Hsieh

Assistant Professor II

Office Location 
Sweigart Hall 313
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Headshot of Meng-Chen Hsieh

Meng-Chen (Melinda) Hsieh, Ph.D., is currently an assistant 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 in 2006. She has over eight years of applied research experience at diverse private organizations, including IBM Research, Morgan Stanley, Credit Suisse and Activision. Her current research interests are applied statistics in supply chain management and predictive analytics. Her research interests include time series analysis, financial econometric and predictive analytics. She was awarded the Davis Fellowship from the Rider University College of Business Administration in 2017 and 2018 and was awarded a Summer Research Fellowship from the Rider University College of Business Administration in 2017 and 2018.

Research Interests

Time Series Analysis, Financial Econometrics, Predictive Analytics

Teaching Interests

Statistics, Regression Model and Data Analysis, Forecasting Time Series Data

Intellectual Contributions:

Refereed Articles

  • Hsieh, M. (in press, 2019). Data-Driven Portfolio Optimization with Drawdown Constraints Utilizing Machines Learning. Contemporary Perspectives in Data Mining, 4.
  • Hsieh, M., Hurvich, C., & Deo, R. (2010). Long Memory in Intertrade Durations, Courts and Realized Volatility of NYSE Stocks. Journal of Statistical Planning and Interferance, 140 (12), 3715-3733.
  • Hsieh, M., Hurvich, C., & Soulier, P. (2007). Asymptotics for Duration-Driven Long Memory Process. Journal of Exonometrics, 141, 913-949.

Presentation of Refereed Papers:

National

  • Hsieh, M. (2018, November). Data-Driven Optimization with Drawdown Constraints Utilizing Machine Learning. INFORMS Workshop on Data Mining and Decision Analytics, Phoenix, Arizona.
  • Hsieh, M. (2018, May). Machine Learning and Portfolio Optimization with Drawdown Constraints. ASA Symposium on Data Science and Statistics, Reston, Virginia.
  • Hsieh, M. (2018, May). Data-Driven Portfolio Optimization Using Machine Learning. Symposium on Data Science and Statistics (SDSS), Reston, Virginia.
  • Hsieh, M., Giloni, A., Hurvich, C., & Simonoff, J. (2016, November). Statistical Learning and Optimal Decisions. INFORMS, Nashville, Tennessee.
  • Hsieh, M. Giloni, A., & Hurvich, C. (2016, May). Impact of Exponential Smoothing on Inventory Costs in Supply Chains. POMS Annual Meeting, Orlando, Florida.

Papers Under Review

  • Hsieh, M., Hurvich, C., & Soulier, P. (2019). "Modeling Leverage and Long Memory Volatility in a Pure Jump Process."
  • Hsieh, M., Giloni, A., & Hurvich, C. (2019). "Impact of Exponential Smoothing on Inventory Costs in Supply Chains."

Other Intellectual Activities:

Basic or Discovery Scholarship

  • 2017: Hsieh, M., Research Projects with faculty in New York University.
  • 2016: Hsieh, M., Engaged in a project. I have been engaged in research projects with researchers in New York University, University of Paris X, and Yeshiva University

Awards

  • 2018: Davis Fellowship, Rider University Norm Brodsky College of Business
  • 2018: Summer Research Fellowship, Rider University Norm Brodsky College of Business
  • 2017: David Fellowship, Rider University Norm Brodsky College of Business
  • 2017: Summer Research Fellowship, Rider University Norm Brodsky College of Business