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Applying Supervised Machine Learning Techniques to Municipal Bond Trading
Jacobus, Roland (2017)
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http://hdl.handle.net/11299/188792
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Title
Applying Supervised Machine Learning Techniques to Municipal Bond Trading
Authors
Issue Date
2017-05
Type
Thesis or Dissertation
Abstract
In this paper we will examine how artificial intelligence or machine learning can be used to make better municipal bond trading decisions. The paper will examine a variety of classification models trained in a supervised environment. The paper will discuss: i. How to prepare data for machine learning analysis ii. The basic mathematical concepts of each model iii. The results of each model and how to interpret them iv. How to fine‐tune parameters for optimal performance
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Master's Theses (Plan A and Professional Engineering Design Projects) [3734]
Description
University of Minnesota M.S. thesis. May 2017. Major: Applied and Computational Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); x, 55 pages.
Suggested Citation
Jacobus, Roland. (2017). Applying Supervised Machine Learning Techniques to Municipal Bond Trading. Retrieved from the University of Minnesota Digital Conservancy, http://hdl.handle.net/11299/188792.
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.