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Forecasting Financial Time Series Movements with Rough Sets and Fuzzy Rough Sets

Mariusz Podsiadło

Abstract

The feasibility of rough sets and fuzzy rough sets in building trend prediction models for financial time series is experimentally investigated. An adaptive time-weighted rule voting method is proposed, where the rule voting weight depends from the age of supporting data. The efficiency of the proposed models is verified and compared with the one of rough sets with equal-weighted rule voting algorithm, as well as support vector machines models. Aside of the standard classification accuracy measures, financial profit and loss backtesting using a sample market timing strategy was performed, and compared with the performance of the buy and hold strategy based on market data of multiple well known indices S&P500, DAX, and HSI. The experiments show that the proposed models using rough sets enhanced with the adaptive time-weighted rule voting, as well as fuzzy rough sets, delivered on pair or better performance than the used benchmark models, and the buy and hold strategy.
Record ID
WUTe07a02e5329f41e89ca0f5f017970a24
Diploma type
Doctor of Philosophy
Author
Mariusz Podsiadło Mariusz Podsiadło,, Undefined Affiliation
Title in Polish
Prognozowanie ruchu finansowych szeregów czasowych z wykorzystaniem zbiorów przybliżonych i rozmytych zbiorów przybliżonych
Title in English
Forecasting Financial Time Series Movements with Rough Sets and Fuzzy Rough Sets
Language
(en) English
Certifying Unit
Faculty of Electronics and Information Technology (FEIT)
Discipline
information science / (technology domain) / (technological sciences)
Status
Finished
Start date
26-04-2016
Defense Date
17-01-2017
Supervisor
External reviewers
Andrzej Skowron Andrzej Skowron,, Undefined Affiliation
Alicja Wakulicz-Deja Alicja Wakulicz-Deja,, Undefined Affiliation
Pages
122
Keywords in English
artificial intelligence, rough sets, fuzzy rough sets, financial time series prediction
Abstract in English
The feasibility of rough sets and fuzzy rough sets in building trend prediction models for financial time series is experimentally investigated. An adaptive time-weighted rule voting method is proposed, where the rule voting weight depends from the age of supporting data. The efficiency of the proposed models is verified and compared with the one of rough sets with equal-weighted rule voting algorithm, as well as support vector machines models. Aside of the standard classification accuracy measures, financial profit and loss backtesting using a sample market timing strategy was performed, and compared with the performance of the buy and hold strategy based on market data of multiple well known indices S&P500, DAX, and HSI. The experiments show that the proposed models using rough sets enhanced with the adaptive time-weighted rule voting, as well as fuzzy rough sets, delivered on pair or better performance than the used benchmark models, and the buy and hold strategy.
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/WUTe07a02e5329f41e89ca0f5f017970a24/
URN
urn:pw-repo:WUTe07a02e5329f41e89ca0f5f017970a24

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