An Automated System for Stock Market Trading Based on Logical Clustering
In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analys...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
|
| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/300918 |
| _version_ | 1797207598940815360 |
|---|---|
| author | Aleksandar Rakićević Vlado Simeunović Bratislav Petrović Sanja Milić |
| author_facet | Aleksandar Rakićević Vlado Simeunović Bratislav Petrović Sanja Milić |
| author_sort | Aleksandar Rakićević |
| collection | DOAJ |
| description | In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the system’s investment decision making process which reveals some exciting insights. |
| first_indexed | 2024-04-24T09:25:28Z |
| format | Article |
| id | doaj.art-2fd25a9ed38241c8a749ed8a5119eb94 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:25:28Z |
| publishDate | 2018-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-2fd25a9ed38241c8a749ed8a5119eb942024-04-15T14:56:36ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392018-01-0125497097810.17559/TV-20160318145514An Automated System for Stock Market Trading Based on Logical ClusteringAleksandar Rakićević0Vlado Simeunović1Bratislav Petrović2Sanja Milić3University of Belgrade, Faculty of Organizational Sciences, Jove Ilića 154, 11000 Belgrade, SerbiaUniversity of Istočno Sarajevo, Faculty of Education in Bijeljina, Semberskih ratara bb, 76300 Bijeljina, Bosnia and HerzegovinaUniversity of Belgrade, Faculty of Organizational Sciences, Jove Ilića 154, 11000 Belgrade, SerbiaUniversity of Istočno Sarajevo, Faculty of Education in Bijeljina, Semberskih ratara bb, 76300 Bijeljina, Bosnia and HerzegovinaIn this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the system’s investment decision making process which reveals some exciting insights.https://hrcak.srce.hr/file/300918automated trading systemfundamental analysislinterpolative Boolean algebraogical clusteringstock market |
| spellingShingle | Aleksandar Rakićević Vlado Simeunović Bratislav Petrović Sanja Milić An Automated System for Stock Market Trading Based on Logical Clustering Tehnički Vjesnik automated trading system fundamental analysis linterpolative Boolean algebra ogical clustering stock market |
| title | An Automated System for Stock Market Trading Based on Logical Clustering |
| title_full | An Automated System for Stock Market Trading Based on Logical Clustering |
| title_fullStr | An Automated System for Stock Market Trading Based on Logical Clustering |
| title_full_unstemmed | An Automated System for Stock Market Trading Based on Logical Clustering |
| title_short | An Automated System for Stock Market Trading Based on Logical Clustering |
| title_sort | automated system for stock market trading based on logical clustering |
| topic | automated trading system fundamental analysis linterpolative Boolean algebra ogical clustering stock market |
| url | https://hrcak.srce.hr/file/300918 |
| work_keys_str_mv | AT aleksandarrakicevic anautomatedsystemforstockmarkettradingbasedonlogicalclustering AT vladosimeunovic anautomatedsystemforstockmarkettradingbasedonlogicalclustering AT bratislavpetrovic anautomatedsystemforstockmarkettradingbasedonlogicalclustering AT sanjamilic anautomatedsystemforstockmarkettradingbasedonlogicalclustering AT aleksandarrakicevic automatedsystemforstockmarkettradingbasedonlogicalclustering AT vladosimeunovic automatedsystemforstockmarkettradingbasedonlogicalclustering AT bratislavpetrovic automatedsystemforstockmarkettradingbasedonlogicalclustering AT sanjamilic automatedsystemforstockmarkettradingbasedonlogicalclustering |