Financial time series forecasting using rough sets with time-weighted rule voting

Mariusz Podsiadlo , Henryk Rybiński


In the paper we investigate experimentally the feasibility of rough sets in building profitable trend prediction models for financial time series. In order to improve the decision process for long time series, a novel time-weighted rule voting method, which accounts for information aging, is proposed. The experiments have been performed using market data of multiple stock market indices. The classification efficiency and financial performance of the proposed rough sets models was verified and compared with that of support vector machines models and reference financial indices. The results showed that the rough sets approach with time weighted rule voting outperforms the classical rough sets and support vector machines decision systems and is profitable as compared to the buy and hold strategy. In addition, with the use of variable precision rough sets, the effectiveness of generated trading signals was further improved.

Author Mariusz Podsiadlo - [Warsaw University of Technology (PW)]
Mariusz Podsiadlo,,
- Politechnika Warszawska
, Henryk Rybiński (FEIT / IN)
Henryk Rybiński,,
- The Institute of Computer Science
Journal seriesExpert Systems With Applications, ISSN 0957-4174
Issue year2016
ASJC Classification1702 Artificial Intelligence; 1706 Computer Science Applications; 2200 General Engineering
Languageen angielski
Score (nominal)35
Score sourcejournalList
ScoreMinisterial score = 35.0, 10-09-2020, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 10-09-2020, ArticleFromJournal
Publication indicators Scopus Citations = 16; WoS Citations = 12; GS Citations = 24.0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 2.446; WoS Impact Factor: 2016 = 3.928 (2) - 2016=3.526 (5)
Citation count*24 (2020-09-17)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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