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Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts...

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Detalles Bibliográficos
Autores principales: Pyo, Sujin, Lee, Jaewook, Cha, Mincheol, Jang, Huisu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685607/
https://www.ncbi.nlm.nih.gov/pubmed/29136004
http://dx.doi.org/10.1371/journal.pone.0188107
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author Pyo, Sujin
Lee, Jaewook
Cha, Mincheol
Jang, Huisu
author_facet Pyo, Sujin
Lee, Jaewook
Cha, Mincheol
Jang, Huisu
author_sort Pyo, Sujin
collection PubMed
description The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.
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spelling pubmed-56856072017-11-30 Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets Pyo, Sujin Lee, Jaewook Cha, Mincheol Jang, Huisu PLoS One Research Article The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction. Public Library of Science 2017-11-14 /pmc/articles/PMC5685607/ /pubmed/29136004 http://dx.doi.org/10.1371/journal.pone.0188107 Text en © 2017 Pyo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pyo, Sujin
Lee, Jaewook
Cha, Mincheol
Jang, Huisu
Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title_full Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title_fullStr Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title_full_unstemmed Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title_short Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets
title_sort predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the korean stock markets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685607/
https://www.ncbi.nlm.nih.gov/pubmed/29136004
http://dx.doi.org/10.1371/journal.pone.0188107
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