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Individual investment decision behaviors based on demographic characteristics: Case from China
Predicting and analyzing behaviors of investors is of great value to financial institutions. This paper uses survey data from about 9,000 individual investors across China to explore the predictability of decision behaviors by studying demographic characteristics that are relatively easy to obtain....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6085059/ https://www.ncbi.nlm.nih.gov/pubmed/30092101 http://dx.doi.org/10.1371/journal.pone.0201916 |
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author | Lan, Qiujun Xiong, Qingyue He, Linjie Ma, Chaoqun |
author_facet | Lan, Qiujun Xiong, Qingyue He, Linjie Ma, Chaoqun |
author_sort | Lan, Qiujun |
collection | PubMed |
description | Predicting and analyzing behaviors of investors is of great value to financial institutions. This paper uses survey data from about 9,000 individual investors across China to explore the predictability of decision behaviors by studying demographic characteristics that are relatively easy to obtain. After applying Pearson’s chi-squared test, Spearman rank correlation test, and several data mining methods, we verified that demographic characteristics are closely linked to decision behaviors, and it would be an economical and feasible solution for financial organizations to build initial behavioral prediction models especially when investors’ behavioral data are insufficient. |
format | Online Article Text |
id | pubmed-6085059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60850592018-08-18 Individual investment decision behaviors based on demographic characteristics: Case from China Lan, Qiujun Xiong, Qingyue He, Linjie Ma, Chaoqun PLoS One Research Article Predicting and analyzing behaviors of investors is of great value to financial institutions. This paper uses survey data from about 9,000 individual investors across China to explore the predictability of decision behaviors by studying demographic characteristics that are relatively easy to obtain. After applying Pearson’s chi-squared test, Spearman rank correlation test, and several data mining methods, we verified that demographic characteristics are closely linked to decision behaviors, and it would be an economical and feasible solution for financial organizations to build initial behavioral prediction models especially when investors’ behavioral data are insufficient. Public Library of Science 2018-08-09 /pmc/articles/PMC6085059/ /pubmed/30092101 http://dx.doi.org/10.1371/journal.pone.0201916 Text en © 2018 Lan 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 Lan, Qiujun Xiong, Qingyue He, Linjie Ma, Chaoqun Individual investment decision behaviors based on demographic characteristics: Case from China |
title | Individual investment decision behaviors based on demographic characteristics: Case from China |
title_full | Individual investment decision behaviors based on demographic characteristics: Case from China |
title_fullStr | Individual investment decision behaviors based on demographic characteristics: Case from China |
title_full_unstemmed | Individual investment decision behaviors based on demographic characteristics: Case from China |
title_short | Individual investment decision behaviors based on demographic characteristics: Case from China |
title_sort | individual investment decision behaviors based on demographic characteristics: case from china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6085059/ https://www.ncbi.nlm.nih.gov/pubmed/30092101 http://dx.doi.org/10.1371/journal.pone.0201916 |
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