Original title: Strojové učení s aplikacemi ve financích
Translated title: Machine learning with applications to finance
Authors: Mešša, Samuel ; Hurt, Jan (advisor) ; Večeř, Jan (referee)
Document type: Bachelor's theses
Year: 2018
Language: eng
Abstract: The impact of data driven, machine learning technologies across a wide variety of fields is undeniable. The financial industry, which relies heavily on predictive modeling being no exception. In this work we summarize two widely used machine learning models: support vector machines and neural networks, discuss their limitations and compare their performance to a more traditionally used method, namely logistic regression. Evaluation was done on two real world datasets, which were used to predict default of loan applicants and credit card holders formulated as a binary classification task. Neural networks and support vector machines either outperformed or showed comparable results to logistic regression with performance measured in receiver operator characteristic area under curve. In the second task neural networks outperformed both other models by a significant margin.
Keywords: finance; machine learning,classification; prediction; finance; klasifikace; predikce; strojové učení

Institution: Charles University Faculties (theses) (web)
Document availability information: Available in the Charles University Digital Repository.
Original record: http://hdl.handle.net/20.500.11956/99641

Permalink: http://www.nusl.cz/ntk/nusl-383896


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Universities and colleges > Public universities > Charles University > Charles University Faculties (theses)
Academic theses (ETDs) > Bachelor's theses