RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)
Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging t...
Gespeichert in:
| Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2021-05, Vol.12 (5), p.4819-4826 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4826 |
|---|---|
| container_issue | 5 |
| container_start_page | 4819 |
| container_title | Journal of ambient intelligence and humanized computing |
| container_volume | 12 |
| creator | Ananthi, M. Vijayakumar, K. |
| description | Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms. |
| doi_str_mv | 10.1007/s12652-020-01892-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2920103442</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2920103442</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1645-aab86c93e142f710322ea55ec912d3dd101b49ce0016686b536a4e4e9ab306ad3</originalsourceid><addsrcrecordid>eNp9kEtPwzAQhC0EElXpH-AUiQscAl6_mnCrQniIIqQQzpbrbEvakhQ7PfTf4xIeN3yxV_5mtDOEnAK9BErHVx6YkiymjMYUkpTF8oAMIFFJLEHIw983Hx-TkfdLGg5POQAMyFuRl8UkK_ObaFKUD9k0v45eutauonfjVthFpjHrna99tPV1s4isaao1-q4OhMOFQ-_rtglU9SPoHIZh47Cqbbf_O88ei6eLE3I0N2uPo-97SF5v8zK7j6fPdw_ZZBpbUELGxswSZVOOINh8DJQzhkZKtCmwilcVUJiJ1CKloFSiZpIrI1BgamacKlPxITnrfTeu_diGTfWy3boQwmuWMhochWCBYj1lXeu9w7neuDoE2Gmgel-q7kvVoVT9VaqWQcR7kQ9ws0D3Z_2P6hMtuHfp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2920103442</pqid></control><display><type>article</type><title>RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)</title><source>SpringerNature Journals</source><source>ProQuest Central UK/Ireland</source><source>ProQuest Central</source><creator>Ananthi, M. ; Vijayakumar, K.</creator><creatorcontrib>Ananthi, M. ; Vijayakumar, K.</creatorcontrib><description>Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.</description><identifier>ISSN: 1868-5137</identifier><identifier>EISSN: 1868-5145</identifier><identifier>DOI: 10.1007/s12652-020-01892-5</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Artificial Intelligence ; Computational Intelligence ; Deep learning ; Engineering ; Genetic algorithms ; Investments ; Literature reviews ; Machine learning ; Market analysis ; Neural networks ; Original Research ; Predictions ; Robotics and Automation ; Securities markets ; Sentiment analysis ; Social networks ; Stock exchanges ; Stock prices ; Support vector machines ; Time series ; User Interfaces and Human Computer Interaction ; Wavelet transforms</subject><ispartof>Journal of ambient intelligence and humanized computing, 2021-05, Vol.12 (5), p.4819-4826</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1645-aab86c93e142f710322ea55ec912d3dd101b49ce0016686b536a4e4e9ab306ad3</citedby><cites>FETCH-LOGICAL-c1645-aab86c93e142f710322ea55ec912d3dd101b49ce0016686b536a4e4e9ab306ad3</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/s12652-020-01892-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920103442?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Ananthi, M.</creatorcontrib><creatorcontrib>Vijayakumar, K.</creatorcontrib><title>RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)</title><title>Journal of ambient intelligence and humanized computing</title><addtitle>J Ambient Intell Human Comput</addtitle><description>Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Genetic algorithms</subject><subject>Investments</subject><subject>Literature reviews</subject><subject>Machine learning</subject><subject>Market analysis</subject><subject>Neural networks</subject><subject>Original Research</subject><subject>Predictions</subject><subject>Robotics and Automation</subject><subject>Securities markets</subject><subject>Sentiment analysis</subject><subject>Social networks</subject><subject>Stock exchanges</subject><subject>Stock prices</subject><subject>Support vector machines</subject><subject>Time series</subject><subject>User Interfaces and Human Computer Interaction</subject><subject>Wavelet transforms</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtPwzAQhC0EElXpH-AUiQscAl6_mnCrQniIIqQQzpbrbEvakhQ7PfTf4xIeN3yxV_5mtDOEnAK9BErHVx6YkiymjMYUkpTF8oAMIFFJLEHIw983Hx-TkfdLGg5POQAMyFuRl8UkK_ObaFKUD9k0v45eutauonfjVthFpjHrna99tPV1s4isaao1-q4OhMOFQ-_rtglU9SPoHIZh47Cqbbf_O88ei6eLE3I0N2uPo-97SF5v8zK7j6fPdw_ZZBpbUELGxswSZVOOINh8DJQzhkZKtCmwilcVUJiJ1CKloFSiZpIrI1BgamacKlPxITnrfTeu_diGTfWy3boQwmuWMhochWCBYj1lXeu9w7neuDoE2Gmgel-q7kvVoVT9VaqWQcR7kQ9ws0D3Z_2P6hMtuHfp</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Ananthi, M.</creator><creator>Vijayakumar, K.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20210501</creationdate><title>RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)</title><author>Ananthi, M. ; Vijayakumar, K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1645-aab86c93e142f710322ea55ec912d3dd101b49ce0016686b536a4e4e9ab306ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Genetic algorithms</topic><topic>Investments</topic><topic>Literature reviews</topic><topic>Machine learning</topic><topic>Market analysis</topic><topic>Neural networks</topic><topic>Original Research</topic><topic>Predictions</topic><topic>Robotics and Automation</topic><topic>Securities markets</topic><topic>Sentiment analysis</topic><topic>Social networks</topic><topic>Stock exchanges</topic><topic>Stock prices</topic><topic>Support vector machines</topic><topic>Time series</topic><topic>User Interfaces and Human Computer Interaction</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ananthi, M.</creatorcontrib><creatorcontrib>Vijayakumar, K.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of ambient intelligence and humanized computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ananthi, M.</au><au>Vijayakumar, K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2021-05-01</date><risdate>2021</risdate><volume>12</volume><issue>5</issue><spage>4819</spage><epage>4826</epage><pages>4819-4826</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-020-01892-5</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1868-5137 |
| ispartof | Journal of ambient intelligence and humanized computing, 2021-05, Vol.12 (5), p.4819-4826 |
| issn | 1868-5137 1868-5145 |
| language | eng |
| recordid | cdi_proquest_journals_2920103442 |
| source | SpringerNature Journals; ProQuest Central UK/Ireland; ProQuest Central |
| subjects | Accuracy Algorithms Artificial Intelligence Computational Intelligence Deep learning Engineering Genetic algorithms Investments Literature reviews Machine learning Market analysis Neural networks Original Research Predictions Robotics and Automation Securities markets Sentiment analysis Social networks Stock exchanges Stock prices Support vector machines Time series User Interfaces and Human Computer Interaction Wavelet transforms |
| title | RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM) |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T15%3A03%3A56IST&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=RETRACTED%20ARTICLE:%20Stock%20market%20analysis%20using%20candlestick%20regression%20and%20market%20trend%20prediction%20(CKRM)&rft.jtitle=Journal%20of%20ambient%20intelligence%20and%20humanized%20computing&rft.au=Ananthi,%20M.&rft.date=2021-05-01&rft.volume=12&rft.issue=5&rft.spage=4819&rft.epage=4826&rft.pages=4819-4826&rft.issn=1868-5137&rft.eissn=1868-5145&rft_id=info:doi/10.1007/s12652-020-01892-5&rft_dat=%3Cproquest_cross%3E2920103442%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=2920103442&rft_id=info:pmid/&rfr_iscdi=true |