Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54414
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dc.contributor.authorSutthiporn Piamsuwannakiten_US
dc.contributor.authorKittawit Autchariyapanitkulen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorRujira Ouncharoenen_US
dc.date.accessioned2018-09-04T10:13:07Z-
dc.date.available2018-09-04T10:13:07Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-84951764722en_US
dc.identifier.other10.1007/978-3-319-25135-6_16en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951764722&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54414-
dc.description.abstract© Springer International Publishing Switzerland 2015. We used interval-valued data to predict stock returns rather than just point valued data. Specifically, we used these interval values in the classical capital asset pricing model to estimate the beta coefficient that represents the risk in the portfolios management analysis. We also use the method to obtain a point valued of asset returns from the intervalvalued data to measure the sensitivity of the asset return and the market return. Finally, AIC criterion indicated that this approach can provide us better results than use the close price for prediction.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleCapital asset pricing model with interval dataen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume9376en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsRajabhat Universityen_US
article.stream.affiliationsMaejo Universityen_US
Appears in Collections:CMUL: Journal Articles

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