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dc.contributor.authorNguyen Dan, Vy
dc.date.accessioned2017-04-10T19:49:10Z
dc.date.accessioned2018-06-07T02:08:33Z
dc.date.available2017-04-10T19:49:10Z
dc.date.available2018-06-07T02:08:33Z
dc.date.issued2015
dc.identifier.other022002188
dc.identifier.urihttp://10.8.20.7:8080/xmlui/handle/123456789/1607
dc.description.abstractOne of the main goals of investors is maximizing their securities’ possible returns, so choosing a good model to find the relationship between risk factors and expected return is very necessary and important. Since 1952, there have been many different models built to find the relationship between portfolio return and the whole market return: Capital Asset Pricing Model, Fama-French Three-Factor model and the most recent model is FamaFrench Five-Factor model. This study target is to test the validity of Fama-French Five-Factor asset pricing model on the manufacturing sector of Ho Chi Minh Stock Exchange (HOSE). The daily stocks’ returns of 94 firms during the period from January 2014 to December 2014 are used in the analysis. By using Panel data method, the test shows the optimal regression among five explainatory factors: risk premium, size premium, value premium, profitability premium and investment premium; and the explained variable which is the returns of securities. Based on the results of the regression, this study states that, the first four factors: risk premium, size premium, value premium and profitability premium are directly proportional to the changing of the securities’ returns. On the other hand, it can not give any surely conclusion for the relation between the investment premium factor and the return of the securities throughout the period test on HOSE.en_US
dc.description.sponsorshipDr. Ho Diepen_US
dc.language.isoen_USen_US
dc.publisherHCMC - International Universityen_US
dc.relation.ispartofseries;022002188
dc.subjectFinancial institutionen_US
dc.titleApplying Fama-French five-factor model on stocks of manufacturing sector on Ho Chi Minh Stock Enchangeen_US
dc.typeThesisen_US


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