A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market

In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical ind...

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Detalles Bibliográficos
Autores: Alfonso Pérez, Gerardo, Rodríguez Ramírez, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/102044
Acceso en línea:https://hdl.handle.net/11441/102044
https://doi.org/10.3390/math8081301
Access Level:acceso abierto
Palabra clave:Feature selection
Neural networks
Stocks
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spelling A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock MarketAlfonso Pérez, GerardoRodríguez Ramírez, DanielFeature selectionNeural networksStocksIn this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen non-linear method. Stock market technical indicators can generate contradictory signals regarding the future performance of the stock analyzed. Furthermore, some non-linear methods, such as neural networks, can have poor generalization power when dealing with problems of high dimensionality due to the issue of local minima. Therefore, non-linear approaches that can identify appropriate combinations of input variables are of clear importance. It will be shown that the proposed approach, when using neural networks as classifiers, generates error rates lower than those using directly neural networks without dimensionality reduction. It will also be shown that merely increasing the number of neurons does not increase the accuracy. The approach proposed in this article is illustrated with an application to the stock market using neural networks but it could be applied to other fields and it can also be used with other non-linear techniques such as for instance support vector machines.Ministerio de Ciencia e Innovación PID2019-106212RB-C41.MDPIIngeniería de Sistemas y AutomáticaMinisterio de Ciencia e Innovación (MICIN). España2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/102044https://doi.org/10.3390/math8081301reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMathematics, 8 (8). Article number 1301.PID2019-106212RB-C41.10.3390/math8081301info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1020442026-06-17T12:51:07Z
dc.title.none.fl_str_mv A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
title A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
spellingShingle A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
Alfonso Pérez, Gerardo
Feature selection
Neural networks
Stocks
title_short A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
title_full A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
title_fullStr A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
title_full_unstemmed A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
title_sort A Nonlinear Technical Indicator Selection Approach for Stock Markets. Application to the Chinese Stock Market
dc.creator.none.fl_str_mv Alfonso Pérez, Gerardo
Rodríguez Ramírez, Daniel
author Alfonso Pérez, Gerardo
author_facet Alfonso Pérez, Gerardo
Rodríguez Ramírez, Daniel
author_role author
author2 Rodríguez Ramírez, Daniel
author2_role author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Feature selection
Neural networks
Stocks
topic Feature selection
Neural networks
Stocks
description In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen non-linear method. Stock market technical indicators can generate contradictory signals regarding the future performance of the stock analyzed. Furthermore, some non-linear methods, such as neural networks, can have poor generalization power when dealing with problems of high dimensionality due to the issue of local minima. Therefore, non-linear approaches that can identify appropriate combinations of input variables are of clear importance. It will be shown that the proposed approach, when using neural networks as classifiers, generates error rates lower than those using directly neural networks without dimensionality reduction. It will also be shown that merely increasing the number of neurons does not increase the accuracy. The approach proposed in this article is illustrated with an application to the stock market using neural networks but it could be applied to other fields and it can also be used with other non-linear techniques such as for instance support vector machines.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/102044
https://doi.org/10.3390/math8081301
url https://hdl.handle.net/11441/102044
https://doi.org/10.3390/math8081301
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Mathematics, 8 (8). Article number 1301.
PID2019-106212RB-C41.
10.3390/math8081301
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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