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Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method
Online portfolio selection is one of the fundamental problems in the field of computational finance. Although existing online portfolio strategies have been shown to achieve good performance, we always have to set the values for different parameters of online portfolio strategies, where the optimal...
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| Published in: | Computational economics 2020-01, Vol.55 (1), p.231-251 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c409t-b8af74494762e25c07dfa8549c1f6a8f79bbf98c286d1d777bb0a40d468a5a6a3 |
| container_end_page | 251 |
| container_issue | 1 |
| container_start_page | 231 |
| container_title | Computational economics |
| container_volume | 55 |
| creator | Yang, Xingyu He, Jin’an Lin, Hong Zhang, Yong |
| description | Online portfolio selection is one of the fundamental problems in the field of computational finance. Although existing online portfolio strategies have been shown to achieve good performance, we always have to set the values for different parameters of online portfolio strategies, where the optimal values can only be known in hindsight. To tackle the limits of existing strategies, we present a new online portfolio strategy based on the online learning character of
Weak Aggregating Algorithm
(WAA). Firstly, we consider a number of
Exponential Gradient
(EG
(
η
)
) strategies of different values of parameter
η
as experts, and then determine the next portfolio by using the WAA to aggregate the experts’ advice. Furthermore, we theoretically prove that our strategy asymptotically achieves the same increasing rate as the best EG
(
η
)
expert. We prove our strategy, as EG
(
η
)
strategies, is universal. We present numerical analysis by using actual stock data from the American and Chinese markets, and the results show that it has good performance. |
| doi_str_mv | 10.1007/s10614-019-09890-2 |
| format | article |
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Weak Aggregating Algorithm
(WAA). Firstly, we consider a number of
Exponential Gradient
(EG
(
η
)
) strategies of different values of parameter
η
as experts, and then determine the next portfolio by using the WAA to aggregate the experts’ advice. Furthermore, we theoretically prove that our strategy asymptotically achieves the same increasing rate as the best EG
(
η
)
expert. We prove our strategy, as EG
(
η
)
strategies, is universal. We present numerical analysis by using actual stock data from the American and Chinese markets, and the results show that it has good performance.</description><identifier>ISSN: 0927-7099</identifier><identifier>EISSN: 1572-9974</identifier><identifier>DOI: 10.1007/s10614-019-09890-2</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Behavioral/Experimental Economics ; Computer Appl. in Social and Behavioral Sciences ; Distance learning ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Economics and Finance ; Experts ; Finance ; Machine learning ; Markets ; Math Applications in Computer Science ; Numerical analysis ; Operations Research/Decision Theory ; Parameters ; Strategy ; Values</subject><ispartof>Computational economics, 2020-01, Vol.55 (1), p.231-251</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Computational Economics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-b8af74494762e25c07dfa8549c1f6a8f79bbf98c286d1d777bb0a40d468a5a6a3</citedby><cites>FETCH-LOGICAL-c409t-b8af74494762e25c07dfa8549c1f6a8f79bbf98c286d1d777bb0a40d468a5a6a3</cites><orcidid>0000-0001-7023-0926</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2206828002/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2206828002?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml></links><search><creatorcontrib>Yang, Xingyu</creatorcontrib><creatorcontrib>He, Jin’an</creatorcontrib><creatorcontrib>Lin, Hong</creatorcontrib><creatorcontrib>Zhang, Yong</creatorcontrib><title>Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method</title><title>Computational economics</title><addtitle>Comput Econ</addtitle><description>Online portfolio selection is one of the fundamental problems in the field of computational finance. Although existing online portfolio strategies have been shown to achieve good performance, we always have to set the values for different parameters of online portfolio strategies, where the optimal values can only be known in hindsight. To tackle the limits of existing strategies, we present a new online portfolio strategy based on the online learning character of
Weak Aggregating Algorithm
(WAA). Firstly, we consider a number of
Exponential Gradient
(EG
(
η
)
) strategies of different values of parameter
η
as experts, and then determine the next portfolio by using the WAA to aggregate the experts’ advice. Furthermore, we theoretically prove that our strategy asymptotically achieves the same increasing rate as the best EG
(
η
)
expert. We prove our strategy, as EG
(
η
)
strategies, is universal. We present numerical analysis by using actual stock data from the American and Chinese markets, and the results show that it has good performance.</description><subject>Algorithms</subject><subject>Behavioral/Experimental Economics</subject><subject>Computer Appl. in Social and Behavioral Sciences</subject><subject>Distance learning</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Economics</subject><subject>Economics and Finance</subject><subject>Experts</subject><subject>Finance</subject><subject>Machine learning</subject><subject>Markets</subject><subject>Math Applications in Computer Science</subject><subject>Numerical analysis</subject><subject>Operations Research/Decision Theory</subject><subject>Parameters</subject><subject>Strategy</subject><subject>Values</subject><issn>0927-7099</issn><issn>1572-9974</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNp9kM1OxCAUhYnRxPHnBVyRuEYvDC3grprxJxkzJuqa0BZqTYUR0OjO1_D1fBKro3Hn6p7F-c5NPoT2KBxQAHGYKJSUE6CKgJIKCFtDE1oIRpQSfB1NQDFBBCi1ibZSugeAgjI2Qf44hJR73-HZyzJ463NvBnwWTduPGV_naLLtXrELES_80HuLr0LMLgx9wNd2sE3ugz_ClcdV10Xbmd8xG3P6eHvHVfvcNxZf2nwX2h204cyQ7O7P3Ua3p7Obk3MyX5xdnFRz0nBQmdTSOMG54qJklhUNiNYZWXDVUFca6YSqa6dkw2TZ0lYIUddgOLS8lKYwpZluo_3V7jKGxyebsr4PT9GPLzVjUEomAdjYYqtWE0NK0Tq9jP2Dia-agv7yqlde9ehVf3vVX9B0BaWx7Dsb_6b_oT4Bn_99Ig</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Yang, Xingyu</creator><creator>He, Jin’an</creator><creator>Lin, Hong</creator><creator>Zhang, Yong</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AO</scope><scope>8BJ</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>M0C</scope><scope>P5Z</scope><scope>P62</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-7023-0926</orcidid></search><sort><creationdate>202001</creationdate><title>Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method</title><author>Yang, Xingyu ; He, Jin’an ; Lin, Hong ; Zhang, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-b8af74494762e25c07dfa8549c1f6a8f79bbf98c286d1d777bb0a40d468a5a6a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Behavioral/Experimental Economics</topic><topic>Computer Appl. in Social and Behavioral Sciences</topic><topic>Distance learning</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Economics</topic><topic>Economics and Finance</topic><topic>Experts</topic><topic>Finance</topic><topic>Machine learning</topic><topic>Markets</topic><topic>Math Applications in Computer Science</topic><topic>Numerical analysis</topic><topic>Operations Research/Decision Theory</topic><topic>Parameters</topic><topic>Strategy</topic><topic>Values</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Xingyu</creatorcontrib><creatorcontrib>He, Jin’an</creatorcontrib><creatorcontrib>Lin, Hong</creatorcontrib><creatorcontrib>Zhang, Yong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</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>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>ProQuest SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Computational economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Xingyu</au><au>He, Jin’an</au><au>Lin, Hong</au><au>Zhang, Yong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method</atitle><jtitle>Computational economics</jtitle><stitle>Comput Econ</stitle><date>2020-01</date><risdate>2020</risdate><volume>55</volume><issue>1</issue><spage>231</spage><epage>251</epage><pages>231-251</pages><issn>0927-7099</issn><eissn>1572-9974</eissn><abstract>Online portfolio selection is one of the fundamental problems in the field of computational finance. Although existing online portfolio strategies have been shown to achieve good performance, we always have to set the values for different parameters of online portfolio strategies, where the optimal values can only be known in hindsight. To tackle the limits of existing strategies, we present a new online portfolio strategy based on the online learning character of
Weak Aggregating Algorithm
(WAA). Firstly, we consider a number of
Exponential Gradient
(EG
(
η
)
) strategies of different values of parameter
η
as experts, and then determine the next portfolio by using the WAA to aggregate the experts’ advice. Furthermore, we theoretically prove that our strategy asymptotically achieves the same increasing rate as the best EG
(
η
)
expert. We prove our strategy, as EG
(
η
)
strategies, is universal. We present numerical analysis by using actual stock data from the American and Chinese markets, and the results show that it has good performance.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10614-019-09890-2</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0001-7023-0926</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0927-7099 |
| ispartof | Computational economics, 2020-01, Vol.55 (1), p.231-251 |
| issn | 0927-7099 1572-9974 |
| language | eng |
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| source | International Bibliography of the Social Sciences (IBSS); ABI/INFORM Global; Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List; EconLit |
| subjects | Algorithms Behavioral/Experimental Economics Computer Appl. in Social and Behavioral Sciences Distance learning Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Experts Finance Machine learning Markets Math Applications in Computer Science Numerical analysis Operations Research/Decision Theory Parameters Strategy Values |
| title | Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method |
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