An effective approach for predicting daily stock trading decisions using fuzzy inference systems
To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of mark...
Gespeichert in:
| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2024-02, Vol.28 (4), p.3301-3319 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3319 |
|---|---|
| container_issue | 4 |
| container_start_page | 3301 |
| container_title | Soft computing (Berlin, Germany) |
| container_volume | 28 |
| creator | Venugopal, R. Veeramani, C. Muruganandan, S. |
| description | To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted
t
-tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the
t
-tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO. |
| doi_str_mv | 10.1007/s00500-023-09383-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2921589930</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2921589930</sourcerecordid><originalsourceid>FETCH-LOGICAL-c200t-687c7d10386c8966b93a70998c3bc6db45a0955f65e0616169ba78759abd66f3</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIPcLLE2bCOYzs-VhUvqRKX3o3j2CWlTYKdIKVfj9sgcUN72N3RzKx2ELqlcE8B5EME4AAEMkZAsYIRdoZmNGeMyFyq89OcESlydomuYtwCZFRyNkPviwY7753t62-HTdeF1tgP7NuAu-CqOuHNBlem3o049q39xH0w1Qlzto5120Q8xOPuh8NhxHXjXXCNdTiOsXf7eI0uvNlFd_Pb52j99LhevpDV2_PrcrEiNgPoiSiklRUFVghbKCFKxYwEpQrLSiuqMucGFOdecAeCplKlkYXkypSVEJ7N0d1kmx74Glzs9bYdQpMu6kxllBdKMUisbGLZ0MYYnNddqPcmjJqCPgappyB1ClKfgtQsidgkioncbFz4s_5H9QN9v3ad</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2921589930</pqid></control><display><type>article</type><title>An effective approach for predicting daily stock trading decisions using fuzzy inference systems</title><source>SpringerLink (Online service)</source><creator>Venugopal, R. ; Veeramani, C. ; Muruganandan, S.</creator><creatorcontrib>Venugopal, R. ; Veeramani, C. ; Muruganandan, S.</creatorcontrib><description>To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted
t
-tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the
t
-tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-023-09383-3</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial Intelligence ; Computational Intelligence ; Control ; Decision making ; Decisions ; Engineering ; Fuzzy logic ; Fuzzy systems ; Genetic algorithms ; Inference ; Information sources ; Investments ; Investors ; Literature reviews ; Market prices ; Mathematical Logic and Foundations ; Mechatronics ; Robotics ; Securities markets ; Soft Computing in Decision Making and in Modeling in Economics ; Stock exchanges ; Stock prices ; System effectiveness ; Trends ; Volatility</subject><ispartof>Soft computing (Berlin, Germany), 2024-02, Vol.28 (4), p.3301-3319</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c200t-687c7d10386c8966b93a70998c3bc6db45a0955f65e0616169ba78759abd66f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-023-09383-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00500-023-09383-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Venugopal, R.</creatorcontrib><creatorcontrib>Veeramani, C.</creatorcontrib><creatorcontrib>Muruganandan, S.</creatorcontrib><title>An effective approach for predicting daily stock trading decisions using fuzzy inference systems</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted
t
-tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the
t
-tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO.</description><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Decision making</subject><subject>Decisions</subject><subject>Engineering</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Genetic algorithms</subject><subject>Inference</subject><subject>Information sources</subject><subject>Investments</subject><subject>Investors</subject><subject>Literature reviews</subject><subject>Market prices</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Robotics</subject><subject>Securities markets</subject><subject>Soft Computing in Decision Making and in Modeling in Economics</subject><subject>Stock exchanges</subject><subject>Stock prices</subject><subject>System effectiveness</subject><subject>Trends</subject><subject>Volatility</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIPcLLE2bCOYzs-VhUvqRKX3o3j2CWlTYKdIKVfj9sgcUN72N3RzKx2ELqlcE8B5EME4AAEMkZAsYIRdoZmNGeMyFyq89OcESlydomuYtwCZFRyNkPviwY7753t62-HTdeF1tgP7NuAu-CqOuHNBlem3o049q39xH0w1Qlzto5120Q8xOPuh8NhxHXjXXCNdTiOsXf7eI0uvNlFd_Pb52j99LhevpDV2_PrcrEiNgPoiSiklRUFVghbKCFKxYwEpQrLSiuqMucGFOdecAeCplKlkYXkypSVEJ7N0d1kmx74Glzs9bYdQpMu6kxllBdKMUisbGLZ0MYYnNddqPcmjJqCPgappyB1ClKfgtQsidgkioncbFz4s_5H9QN9v3ad</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Venugopal, R.</creator><creator>Veeramani, C.</creator><creator>Muruganandan, S.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>20240201</creationdate><title>An effective approach for predicting daily stock trading decisions using fuzzy inference systems</title><author>Venugopal, R. ; Veeramani, C. ; Muruganandan, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-687c7d10386c8966b93a70998c3bc6db45a0955f65e0616169ba78759abd66f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Decision making</topic><topic>Decisions</topic><topic>Engineering</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Genetic algorithms</topic><topic>Inference</topic><topic>Information sources</topic><topic>Investments</topic><topic>Investors</topic><topic>Literature reviews</topic><topic>Market prices</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Robotics</topic><topic>Securities markets</topic><topic>Soft Computing in Decision Making and in Modeling in Economics</topic><topic>Stock exchanges</topic><topic>Stock prices</topic><topic>System effectiveness</topic><topic>Trends</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Venugopal, R.</creatorcontrib><creatorcontrib>Veeramani, C.</creatorcontrib><creatorcontrib>Muruganandan, S.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Venugopal, R.</au><au>Veeramani, C.</au><au>Muruganandan, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An effective approach for predicting daily stock trading decisions using fuzzy inference systems</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2024-02-01</date><risdate>2024</risdate><volume>28</volume><issue>4</issue><spage>3301</spage><epage>3319</epage><pages>3301-3319</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted
t
-tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the
t
-tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-023-09383-3</doi><tpages>19</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-7643 |
| ispartof | Soft computing (Berlin, Germany), 2024-02, Vol.28 (4), p.3301-3319 |
| issn | 1432-7643 1433-7479 |
| language | eng |
| recordid | cdi_proquest_journals_2921589930 |
| source | SpringerLink (Online service) |
| subjects | Artificial Intelligence Computational Intelligence Control Decision making Decisions Engineering Fuzzy logic Fuzzy systems Genetic algorithms Inference Information sources Investments Investors Literature reviews Market prices Mathematical Logic and Foundations Mechatronics Robotics Securities markets Soft Computing in Decision Making and in Modeling in Economics Stock exchanges Stock prices System effectiveness Trends Volatility |
| title | An effective approach for predicting daily stock trading decisions using fuzzy inference systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T10%3A45%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20effective%20approach%20for%20predicting%20daily%20stock%20trading%20decisions%20using%20fuzzy%20inference%20systems&rft.jtitle=Soft%20computing%20(Berlin,%20Germany)&rft.au=Venugopal,%20R.&rft.date=2024-02-01&rft.volume=28&rft.issue=4&rft.spage=3301&rft.epage=3319&rft.pages=3301-3319&rft.issn=1432-7643&rft.eissn=1433-7479&rft_id=info:doi/10.1007/s00500-023-09383-3&rft_dat=%3Cproquest_cross%3E2921589930%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2921589930&rft_id=info:pmid/&rfr_iscdi=true |