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## Penalty function in SVM classifiers applied to automated trading strategies

### Kamil Piotr Żbikowski

#### Abstract

This thesis is dedicated to the problem of machine learning algorithms applications to the area of financial time series prediction.

A new formula for constructing a penalty function that varies over time was proposed. It was used then to create an upgraded Support Vector Machine classifier. The idea of coupling penalty function and the underlying times series is motivated by the specific characteristics of financial time series. Its distribution, expected value and variance change over time. These properties directly cause difficulties in static analysis of classifications errors. Much more robust techniques should be applied.

Analysis of such data requires a dedicated computational system that would allow to adequately test the proposed classifier with the use of walk forward analysis method. Such a system was developed for the purpose of verification of the hypothesis presented in the presented thesis.

Experiments that were conducted showed that the results of short-term trends predictions with the use of proposed penalty function are better than with the use of plain penalty function . The statistical significance of the results was verified. The penalty function was then used for the purpose of constructing trading strategy. The data used for the experiments consisted of US stock and bitcoin quotations. Experiments show significant improvement in terms of the annual rate of return and the risk measured by the maximal drawdown over the test period.

Record ID
WUTf9d7ed681a43410bb36dc427980ef551
Diploma type
Doctor of Philosophy
Author
Title in Polish
Konstrukcja funkcji kary dla klasyfikatorów SVM w automatycznych strategiach inwestycyjnych
Title in English
Penalty function in SVM classifiers applied to automated trading strategies
Language
(pl) Polish
Certifying Unit
Faculty of Electronics and Information Technology (FEIT)
Discipline
information science / (technology domain) / (technological sciences)
Status
Finished
Start date
23-09-2014
Defense Date
12-06-2017
Title date
27-06-2017
Supervisor
External reviewers
Jacek Koronacki Jacek Koronacki,, Undefined Affiliation
Andrzej Skowron Andrzej Skowron,, Undefined Affiliation
Pages
128
Keywords in English
support vector machine, penalty function, time series, investment strategies
Abstract in English
This thesis is dedicated to the problem of machine learning algorithms applications to the area of financial time series prediction.

A new formula for constructing a penalty function that varies over time was proposed. It was used then to create an upgraded Support Vector Machine classifier. The idea of coupling penalty function and the underlying times series is motivated by the specific characteristics of financial time series. Its distribution, expected value and variance change over time. These properties directly cause difficulties in static analysis of classifications errors. Much more robust techniques should be applied.

Analysis of such data requires a dedicated computational system that would allow to adequately test the proposed classifier with the use of walk forward analysis method. Such a system was developed for the purpose of verification of the hypothesis presented in the presented thesis.

Experiments that were conducted showed that the results of short-term trends predictions with the use of proposed penalty function are better than with the use of plain penalty function . The statistical significance of the results was verified. The penalty function was then used for the purpose of constructing trading strategy. The data used for the experiments consisted of US stock and bitcoin quotations. Experiments show significant improvement in terms of the annual rate of return and the risk measured by the maximal drawdown over the test period.

PKT classification
4100
KBN classification
28 Informatyka
EU classification
80-30
Thesis file
Reviews
Other files

Uniform Resource Identifier
https://repo.pw.edu.pl/info/phd/WUTf9d7ed681a43410bb36dc427980ef551/
URN
urn:pw-repo:WUTf9d7ed681a43410bb36dc427980ef551

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