Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy
Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into acc...
Gespeichert in:
Veröffentlicht in: | Security and communication networks 2022-07, Vol.2022, p.1-18 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 18 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | Security and communication networks |
container_volume | 2022 |
creator | Wang, Qinan Jiang, Weiwei |
description | Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy. |
doi_str_mv | 10.1155/2022/3598285 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2699541992</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2699541992</sourcerecordid><originalsourceid>FETCH-LOGICAL-c267t-9f4916bef10d13d2b63ba9afc4d7bd25021c2995fd09bb8de8409828ea0c13e53</originalsourceid><addsrcrecordid>eNp9kD1PwzAQhi0EEqWw8QMsMUKo7dhpPNKqfEgFhhZ1jC6x07iEuNgOpf-elFaMTO8Nz3u6exC6pOSWUiEGjDA2iIVMWSqOUI_KWEaEMnb8N1N-is68XxGSUD7kPbQYaQh4Ab6KZlBrPIdvvDGhws9tHczSwbrCY9t82boNxjZQ4xfdut8IG-vePR6B1wrPHSjTLPEsOAh6uT1HJyXUXl8cso_e7ifz8WM0fX14Gt9No4IlwxDJkkua5LqkRNFYsTyJc5BQFlwNc8UEYbRgUopSEZnnqdIpJ7v3NJCCxlrEfXS137t29rPVPmQr27ruTp-xpCtyKiXrqJs9VTjrvdNltnbmA9w2oyTbqct26rKDug6_3uOVaRRszP_0DzWQbb4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2699541992</pqid></control><display><type>article</type><title>Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley-Blackwell Open Access Titles</source><source>Alma/SFX Local Collection</source><creator>Wang, Qinan ; Jiang, Weiwei</creator><contributor>Khan, Mohammad Ayoub ; Mohammad Ayoub Khan</contributor><creatorcontrib>Wang, Qinan ; Jiang, Weiwei ; Khan, Mohammad Ayoub ; Mohammad Ayoub Khan</creatorcontrib><description>Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2022/3598285</identifier><language>eng</language><publisher>London: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Artificial neural networks ; Automation ; Constraint modelling ; Discriminant analysis ; Forecasting ; Machine learning ; Neural networks ; Profits ; Regression analysis ; Stock exchanges ; Support vector machines ; Taxes</subject><ispartof>Security and communication networks, 2022-07, Vol.2022, p.1-18</ispartof><rights>Copyright © 2022 Qinan Wang and Weiwei Jiang.</rights><rights>Copyright © 2022 Qinan Wang and Weiwei Jiang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c267t-9f4916bef10d13d2b63ba9afc4d7bd25021c2995fd09bb8de8409828ea0c13e53</citedby><cites>FETCH-LOGICAL-c267t-9f4916bef10d13d2b63ba9afc4d7bd25021c2995fd09bb8de8409828ea0c13e53</cites><orcidid>0000-0002-1653-5232</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Khan, Mohammad Ayoub</contributor><contributor>Mohammad Ayoub Khan</contributor><creatorcontrib>Wang, Qinan</creatorcontrib><creatorcontrib>Jiang, Weiwei</creatorcontrib><title>Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy</title><title>Security and communication networks</title><description>Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Constraint modelling</subject><subject>Discriminant analysis</subject><subject>Forecasting</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Profits</subject><subject>Regression analysis</subject><subject>Stock exchanges</subject><subject>Support vector machines</subject><subject>Taxes</subject><issn>1939-0114</issn><issn>1939-0122</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kD1PwzAQhi0EEqWw8QMsMUKo7dhpPNKqfEgFhhZ1jC6x07iEuNgOpf-elFaMTO8Nz3u6exC6pOSWUiEGjDA2iIVMWSqOUI_KWEaEMnb8N1N-is68XxGSUD7kPbQYaQh4Ab6KZlBrPIdvvDGhws9tHczSwbrCY9t82boNxjZQ4xfdut8IG-vePR6B1wrPHSjTLPEsOAh6uT1HJyXUXl8cso_e7ifz8WM0fX14Gt9No4IlwxDJkkua5LqkRNFYsTyJc5BQFlwNc8UEYbRgUopSEZnnqdIpJ7v3NJCCxlrEfXS137t29rPVPmQr27ruTp-xpCtyKiXrqJs9VTjrvdNltnbmA9w2oyTbqct26rKDug6_3uOVaRRszP_0DzWQbb4</recordid><startdate>20220730</startdate><enddate>20220730</enddate><creator>Wang, Qinan</creator><creator>Jiang, Weiwei</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</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>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-1653-5232</orcidid></search><sort><creationdate>20220730</creationdate><title>Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy</title><author>Wang, Qinan ; Jiang, Weiwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c267t-9f4916bef10d13d2b63ba9afc4d7bd25021c2995fd09bb8de8409828ea0c13e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Automation</topic><topic>Constraint modelling</topic><topic>Discriminant analysis</topic><topic>Forecasting</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Profits</topic><topic>Regression analysis</topic><topic>Stock exchanges</topic><topic>Support vector machines</topic><topic>Taxes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qinan</creatorcontrib><creatorcontrib>Jiang, Weiwei</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</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 Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Security and communication networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qinan</au><au>Jiang, Weiwei</au><au>Khan, Mohammad Ayoub</au><au>Mohammad Ayoub Khan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy</atitle><jtitle>Security and communication networks</jtitle><date>2022-07-30</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>18</epage><pages>1-18</pages><issn>1939-0114</issn><eissn>1939-0122</eissn><abstract>Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy.</abstract><cop>London</cop><pub>Hindawi</pub><doi>10.1155/2022/3598285</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-1653-5232</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1939-0114 |
ispartof | Security and communication networks, 2022-07, Vol.2022, p.1-18 |
issn | 1939-0114 1939-0122 |
language | eng |
recordid | cdi_proquest_journals_2699541992 |
source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley-Blackwell Open Access Titles; Alma/SFX Local Collection |
subjects | Algorithms Artificial intelligence Artificial neural networks Automation Constraint modelling Discriminant analysis Forecasting Machine learning Neural networks Profits Regression analysis Stock exchanges Support vector machines Taxes |
title | Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T19%3A18%3A54IST&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=Beat%20Wash-Sale%20Tax%20with%20Multigraph%20Convolutional%20Neural%20Networks%20Based%20Trading%20Strategy&rft.jtitle=Security%20and%20communication%20networks&rft.au=Wang,%20Qinan&rft.date=2022-07-30&rft.volume=2022&rft.spage=1&rft.epage=18&rft.pages=1-18&rft.issn=1939-0114&rft.eissn=1939-0122&rft_id=info:doi/10.1155/2022/3598285&rft_dat=%3Cproquest_cross%3E2699541992%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=2699541992&rft_id=info:pmid/&rfr_iscdi=true |