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DC Field | Value | Language |
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dc.contributor.author | Sutthiporn Piamsuwannakit | en_US |
dc.contributor.author | Kittawit Autchariyapanitkul | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.contributor.author | Rujira Ouncharoen | en_US |
dc.date.accessioned | 2018-09-04T10:13:13Z | - |
dc.date.available | 2018-09-04T10:13:13Z | - |
dc.date.issued | 2015-01-01 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-84951080741 | en_US |
dc.identifier.other | 10.1007/978-3-319-25135-6-16 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951080741&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/54420 | - |
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 interval-valued 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.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Capital asset pricing model with interval data | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | en_US |
article.volume | 9376 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
article.stream.affiliations | Rajabhat University | en_US |
article.stream.affiliations | Maejo University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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