A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-13
Main Authors: Liu, Xiaqun, Li, Jinsheng, Zhuang, Yaming
Format: Article
Language:English
Subjects:
Behavior
Behavior evolution
Decision making
Evolution
Financial institutions
Financial markets
Heterogeneity
Information dissemination
Institutional investments
Investment
Investment policy
Investments
Investor behavior
Learning
Learning strategies
Learning theory
Mathematical analysis
Network topologies
Numerical analysis
Securities markets
Social networks
Stock exchanges
Stocks
Volatility
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163
cites cdi_FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163
container_end_page 13
container_issue 2020
container_start_page 1
container_title Complexity (New York, N.Y.)
container_volume 2020
creator Liu, Xiaqun
Li, Jinsheng
Zhuang, Yaming
description This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior. The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors. Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.
doi_str_mv 10.1155/2020/3561538
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_fb1ebd0a83d14429a1299c1ae3f66d8c</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A697096600</galeid><doaj_id>oai_doaj_org_article_fb1ebd0a83d14429a1299c1ae3f66d8c</doaj_id><sourcerecordid>A697096600</sourcerecordid><originalsourceid>FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163</originalsourceid><addsrcrecordid>eNqFkUtv1DAUhSMEEmVgxxpZYglp7fiReDmtSjvSFCQea-vGvp56HnZxMlPx73FIBUvkha3r7x4dnVNVbxk9Z0zKi4Y29IJLxSTvnlVnjGpdU9mo59O7VXXTdu3L6tUwbCmlWvH2rPJLcpcc7olPmVyf0v44hhRJ8mQVTziMKQ_kEu_hFMp_iOTbmOyO3EHe4UguYUBHCv4VQywCFg8YR7JGyDHEzcR_xvEx5d3r6oWH_YBvnu5F9ePT9fer23r95WZ1tVzXVig51k0vgXLpUDfWQ2-17bVUPbVUadXrTjnai8Y6KqUTsi2odWA57VgHwjPFF9Vq1nUJtuYhhwPkXyZBMH8GKW8M5DHYPRrfM-wdhY47JkSjgTVaWwbIvVKus0Xr_az1kNPPY8nCbNMxx2LfNEJozZksQS-q85naQBGdYhgz2HIcHoJNEX0o86XSbUlcUVoWPs4LNqdhyOj_2mTUTDWaqUbzVGPBP8z4fYgOHsP_6HczjYVBD_9oJopdwX8DMQylQw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2449931515</pqid></control><display><type>article</type><title>A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network</title><source>Wiley Online Library Open Access</source><creator>Liu, Xiaqun ; Li, Jinsheng ; Zhuang, Yaming</creator><contributor>Zhou, Wei ; Wei Zhou</contributor><creatorcontrib>Liu, Xiaqun ; Li, Jinsheng ; Zhuang, Yaming ; Zhou, Wei ; Wei Zhou</creatorcontrib><description>This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior. The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors. Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.</description><identifier>ISSN: 1076-2787</identifier><identifier>EISSN: 1099-0526</identifier><identifier>DOI: 10.1155/2020/3561538</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Behavior ; Behavior evolution ; Decision making ; Evolution ; Financial institutions ; Financial markets ; Heterogeneity ; Information dissemination ; Institutional investments ; Investment ; Investment policy ; Investments ; Investor behavior ; Learning ; Learning strategies ; Learning theory ; Mathematical analysis ; Network topologies ; Numerical analysis ; Securities markets ; Social networks ; Stock exchanges ; Stocks ; Volatility</subject><ispartof>Complexity (New York, N.Y.), 2020, Vol.2020 (2020), p.1-13</ispartof><rights>Copyright © 2020 Xiaqun Liu et al.</rights><rights>COPYRIGHT 2020 John Wiley &amp; Sons, Inc.</rights><rights>Copyright © 2020 Xiaqun Liu et al. 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-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163</citedby><cites>FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163</cites><orcidid>0000-0002-5883-0520 ; 0000-0003-4461-471X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4023,27922,27923,27924</link.rule.ids></links><search><contributor>Zhou, Wei</contributor><contributor>Wei Zhou</contributor><creatorcontrib>Liu, Xiaqun</creatorcontrib><creatorcontrib>Li, Jinsheng</creatorcontrib><creatorcontrib>Zhuang, Yaming</creatorcontrib><title>A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network</title><title>Complexity (New York, N.Y.)</title><description>This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior. The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors. Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.</description><subject>Behavior</subject><subject>Behavior evolution</subject><subject>Decision making</subject><subject>Evolution</subject><subject>Financial institutions</subject><subject>Financial markets</subject><subject>Heterogeneity</subject><subject>Information dissemination</subject><subject>Institutional investments</subject><subject>Investment</subject><subject>Investment policy</subject><subject>Investments</subject><subject>Investor behavior</subject><subject>Learning</subject><subject>Learning strategies</subject><subject>Learning theory</subject><subject>Mathematical analysis</subject><subject>Network topologies</subject><subject>Numerical analysis</subject><subject>Securities markets</subject><subject>Social networks</subject><subject>Stock exchanges</subject><subject>Stocks</subject><subject>Volatility</subject><issn>1076-2787</issn><issn>1099-0526</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqFkUtv1DAUhSMEEmVgxxpZYglp7fiReDmtSjvSFCQea-vGvp56HnZxMlPx73FIBUvkha3r7x4dnVNVbxk9Z0zKi4Y29IJLxSTvnlVnjGpdU9mo59O7VXXTdu3L6tUwbCmlWvH2rPJLcpcc7olPmVyf0v44hhRJ8mQVTziMKQ_kEu_hFMp_iOTbmOyO3EHe4UguYUBHCv4VQywCFg8YR7JGyDHEzcR_xvEx5d3r6oWH_YBvnu5F9ePT9fer23r95WZ1tVzXVig51k0vgXLpUDfWQ2-17bVUPbVUadXrTjnai8Y6KqUTsi2odWA57VgHwjPFF9Vq1nUJtuYhhwPkXyZBMH8GKW8M5DHYPRrfM-wdhY47JkSjgTVaWwbIvVKus0Xr_az1kNPPY8nCbNMxx2LfNEJozZksQS-q85naQBGdYhgz2HIcHoJNEX0o86XSbUlcUVoWPs4LNqdhyOj_2mTUTDWaqUbzVGPBP8z4fYgOHsP_6HczjYVBD_9oJopdwX8DMQylQw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Liu, Xiaqun</creator><creator>Li, Jinsheng</creator><creator>Zhuang, Yaming</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley &amp; Sons, Inc</general><general>Hindawi Limited</general><general>Hindawi-Wiley</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>AHMDM</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</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>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5883-0520</orcidid><orcidid>https://orcid.org/0000-0003-4461-471X</orcidid></search><sort><creationdate>2020</creationdate><title>A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network</title><author>Liu, Xiaqun ; Li, Jinsheng ; Zhuang, Yaming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Behavior</topic><topic>Behavior evolution</topic><topic>Decision making</topic><topic>Evolution</topic><topic>Financial institutions</topic><topic>Financial markets</topic><topic>Heterogeneity</topic><topic>Information dissemination</topic><topic>Institutional investments</topic><topic>Investment</topic><topic>Investment policy</topic><topic>Investments</topic><topic>Investor behavior</topic><topic>Learning</topic><topic>Learning strategies</topic><topic>Learning theory</topic><topic>Mathematical analysis</topic><topic>Network topologies</topic><topic>Numerical analysis</topic><topic>Securities markets</topic><topic>Social networks</topic><topic>Stock exchanges</topic><topic>Stocks</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xiaqun</creatorcontrib><creatorcontrib>Li, Jinsheng</creatorcontrib><creatorcontrib>Zhuang, Yaming</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>قاعدة العلوم الإنسانية - e-Marefa Humanities</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; 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>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; 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><collection>ProQuest Central Basic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Complexity (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xiaqun</au><au>Li, Jinsheng</au><au>Zhuang, Yaming</au><au>Zhou, Wei</au><au>Wei Zhou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network</atitle><jtitle>Complexity (New York, N.Y.)</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1076-2787</issn><eissn>1099-0526</eissn><abstract>This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior. The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors. Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2020/3561538</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-5883-0520</orcidid><orcidid>https://orcid.org/0000-0003-4461-471X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1076-2787
ispartof Complexity (New York, N.Y.), 2020, Vol.2020 (2020), p.1-13
issn 1076-2787
1099-0526
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_fb1ebd0a83d14429a1299c1ae3f66d8c
source Wiley Online Library Open Access
subjects Behavior
Behavior evolution
Decision making
Evolution
Financial institutions
Financial markets
Heterogeneity
Information dissemination
Institutional investments
Investment
Investment policy
Investments
Investor behavior
Learning
Learning strategies
Learning theory
Mathematical analysis
Network topologies
Numerical analysis
Securities markets
Social networks
Stock exchanges
Stocks
Volatility
title A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T17%3A57%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Model%20for%20Evolution%20of%20Investors%20Behavior%20in%20Stock%20Market%20Based%20on%20Reinforcement%20Learning%20in%20Network&rft.jtitle=Complexity%20(New%20York,%20N.Y.)&rft.au=Liu,%20Xiaqun&rft.date=2020&rft.volume=2020&rft.issue=2020&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=1076-2787&rft.eissn=1099-0526&rft_id=info:doi/10.1155/2020/3561538&rft_dat=%3Cgale_doaj_%3EA697096600%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c465t-2b5a035de92cfabc9cb956b0c0696b986d0b42cd055d457035cdac30818a4f163%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2449931515&rft_id=info:pmid/&rft_galeid=A697096600&rfr_iscdi=true