Academic Journal
Detection of stock price movements using chance discovery and genetic programming.
| Τίτλος: | Detection of stock price movements using chance discovery and genetic programming. |
|---|---|
| Συγγραφείς: | Garcia-Almanza, Alma Lilia, Tsang, Edward P. K. |
| Πηγή: | International Journal of Knowledge Based Intelligent Engineering Systems; Dec2007, Vol. 11 Issue 5, p329-344, 16p, 1 Diagram, 12 Charts, 6 Graphs |
| Θεματικοί όροι: | Genetic programming, Computer science, Computer programming, Electronic data processing, Stock prices, Prices of securities |
| Περίληψη: | The aim of this work is to detect important movements in financial stock prices that may indicate future opportunities or risks. The occurrence of such movements is scarce, thus this problem falls into the domain of Chance Discovery, a new research area whose objective is to identify rare events that may represent potential opportunities and risks. In this work we propose to capture patterns of the rare instances in different ways in order to increase the probability of identifying similar cases in the future. To generate more variety of solutions we evolve a genetic program, which is an evolutionary technique that is able to create multiple solutions for a single problem. The idea is to mine the knowledge acquired by the evolutionary process to extract and collect different rules that model the positive cases in several and novel ways. Once an important movement in financial markets has been discovered, human interaction is needed to analyze the markets conditions and determine if that movement could be a good opportunity to invest or could be the principle of a bubble or another critical event that represents a risk. Standard decision trees methods capture patterns from training data sets. However, when the chances are scare, some of the patters captured by the best rules may not repeat themselves in unseen cases. In this work we propose Repository Method which comprises multiple rules to form a more reliable classifier in rare cases. To illustrate our approach, it was applied to discover important movements in stock prices. From experimental results we showed that our approach can consistently detect rare cases in extreme imbalanced data sets. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Knowledge Based Intelligent Engineering Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 27971305 RelevancyScore: 833 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 832.930847167969 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detection of stock price movements using chance discovery and genetic programming. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Garcia-Almanza%2C+Alma+Lilia%22">Garcia-Almanza, Alma Lilia</searchLink><br /><searchLink fieldCode="AR" term="%22Tsang%2C+Edward+P%2E+K%2E%22">Tsang, Edward P. K.</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Knowledge Based Intelligent Engineering Systems; Dec2007, Vol. 11 Issue 5, p329-344, 16p, 1 Diagram, 12 Charts, 6 Graphs – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Stock+prices%22">Stock prices</searchLink><br /><searchLink fieldCode="DE" term="%22Prices+of+securities%22">Prices of securities</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The aim of this work is to detect important movements in financial stock prices that may indicate future opportunities or risks. The occurrence of such movements is scarce, thus this problem falls into the domain of Chance Discovery, a new research area whose objective is to identify rare events that may represent potential opportunities and risks. In this work we propose to capture patterns of the rare instances in different ways in order to increase the probability of identifying similar cases in the future. To generate more variety of solutions we evolve a genetic program, which is an evolutionary technique that is able to create multiple solutions for a single problem. The idea is to mine the knowledge acquired by the evolutionary process to extract and collect different rules that model the positive cases in several and novel ways. Once an important movement in financial markets has been discovered, human interaction is needed to analyze the markets conditions and determine if that movement could be a good opportunity to invest or could be the principle of a bubble or another critical event that represents a risk. Standard decision trees methods capture patterns from training data sets. However, when the chances are scare, some of the patters captured by the best rules may not repeat themselves in unseen cases. In this work we propose Repository Method which comprises multiple rules to form a more reliable classifier in rare cases. To illustrate our approach, it was applied to discover important movements in stock prices. From experimental results we showed that our approach can consistently detect rare cases in extreme imbalanced data sets. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Knowledge Based Intelligent Engineering Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=27971305 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/KES-2007-11509 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 329 Subjects: – SubjectFull: Genetic programming Type: general – SubjectFull: Computer science Type: general – SubjectFull: Computer programming Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Stock prices Type: general – SubjectFull: Prices of securities Type: general Titles: – TitleFull: Detection of stock price movements using chance discovery and genetic programming. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Garcia-Almanza, Alma Lilia – PersonEntity: Name: NameFull: Tsang, Edward P. K. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 13272314 Numbering: – Type: volume Value: 11 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Knowledge Based Intelligent Engineering Systems Type: main |
| ResultId | 1 |