Machine learning with applications to finance
Strojové učení s aplikacemi ve financích
bakalářská práce (OBHÁJENO)
Zobrazit/ otevřít
Trvalý odkaz
http://hdl.handle.net/20.500.11956/99641Identifikátory
SIS: 194901
Kolekce
- Kvalifikační práce [10690]
Autor
Vedoucí práce
Oponent práce
Večeř, Jan
Fakulta / součást
Matematicko-fyzikální fakulta
Obor
Finanční matematika
Katedra / ústav / klinika
Katedra pravděpodobnosti a matematické statistiky
Datum obhajoby
21. 6. 2018
Nakladatel
Univerzita Karlova, Matematicko-fyzikální fakultaJazyk
Angličtina
Známka
Výborně
Klíčová slova (česky)
strojové učení, klasifikace, predikce, financeKlíčová slova (anglicky)
machine learning,classification, prediction, financeThe 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.