財務市場狀態轉換模型之應用- Smart Beta策略基金績效分析

dc.contributor蔡蒔銓zh_TW
dc.contributor賴慧文zh_TW
dc.contributorTsai, Shih-Chuanen_US
dc.contributorLai, Whuei-Wenen_US
dc.contributor.author陳永孝zh_TW
dc.contributor.authorChen, Yung-Shiauen_US
dc.date.accessioned2019-09-03T09:57:53Z
dc.date.available2023-12-31
dc.date.available2019-09-03T09:57:53Z
dc.date.issued2018
dc.description.abstract近年來因子投資法逐漸成為基金投資組合的主流,透過對特定股票篩選機制及調整投資組合權重,擇時放大特定因子的曝險程度,使投資組合呈現特定風格主題。自2008年全球金融風暴後投資者情緒(Investor sentiment)衡量指標逐漸受到理財顧問及基金經理人所重視,並作為判斷投資市場氣氛的依據標準,本篇論文使用投資者情緒指標建立馬可夫轉換模型作出財務市場狀態轉折時點預測,並採用美國晨星公司資料庫中的Smart Beta策略基金為樣本,觀察不同的市場狀態期間,是否使得風格主題策略為基金帶來更高的額外報酬。zh_TW
dc.description.abstractIn recent years, factor-based investing has become one of the mainstreams in portfolio management. In particular, specific stocks are selected or portfolio weights are adjusted to enlarge the exposure of a portfolio to some specific factors. The purpose of this study is to examine the performance of smart beta strategies in various financial states. Since investors’ sentiment indicators have been widely used by financial advisors and fund managers to predict phases of the financial cycle after the global financial crisis in 2008. This thesis uses these indicators to identify the turning points of financial market in a Markov switching framework and examines the performance of smart beta strategies in different financial states.en_US
dc.description.sponsorship管理研究所zh_TW
dc.identifierG060555002O
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060555002O%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/94670
dc.language中文
dc.subjectSmart Beta策略基金zh_TW
dc.subject因子投資法zh_TW
dc.subject馬可夫轉換模型zh_TW
dc.subject投資者情緒衡量指標zh_TW
dc.subjectSmart Beta strategy fundsen_US
dc.subjectFactor-Based Investment methoden_US
dc.subjectMarkov Switching modelen_US
dc.subjectInvestor sentimenten_US
dc.title財務市場狀態轉換模型之應用- Smart Beta策略基金績效分析zh_TW
dc.titleThe Performance of Smart Beta Funds in Financial Markets with Regime Switchingen_US

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