完整后设资料纪录
DC 栏位语言
dc.contributor.author樓玉梅en_US
dc.contributor.authorLou, Yu-Meien_US
dc.contributor.author謝國文en_US
dc.contributor.authorGwowen Shiehen_US
dc.date.accessioned2014-12-12T02:15:50Z-
dc.date.available2014-12-12T02:15:50Z-
dc.date.issued1995en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT840457054en_US
dc.identifier.urihttp://hdl.handle.net/11536/60884-
dc.description.abstract本研究的目的在於探討總體經濟因素和股價間的關係,並試圖尋找股
價走勢之最佳預測模型。和相關文獻主要不同之處在於本研究以預測的角
度來進行模型的評比和選擇,這將直接反應單純以傳統方法決定之主成份
模型在預測上的表現。另外,本研究同時也引用統計決策理論中著名的
Stien估計式以及目前極為盛行的模糊線性迴歸來進行整合和改良,藉以
尋找優於傳統主成份迴歸之可能模型,以利投資者對股價走勢的瞭解,並
成為股價預測上的重要依據。除此之外,本研究的結果也提供其他需要進
行構面縮減和預測評比之研究的參考。研究結果顯示,整體而言以 Stein
估計式預測結果最具穩健性,非常值得採用。而以傳統方法決定之主成份
模型,在所有的主成份模型中幾乎敬陪末座。另外,模糊線性迴歸模型在
本研究的預測評估下,並沒有很好的表現。
The purpose of this paper is to find the best prediction
model of Taiwan'sstock prices from twelve highly correlated
macro-economic variables. Principalcomponents regression is one
of the common methods of overcoming seriousmulticollinearity
problem by modifying the method of ordinary least squares.But
unfortunately principal components estimator may do much worse
than ordinaryleast squares estimator if its components are not
sufficiently close to the truedimensional space. Therefore
Stein shrinkage estimator is constructed bycontracting the
ordinary least squares estimator to the principal
componentsestimator. However, in such a practical application
of linear regression model,the linearity assumption for the
particular relationship may be imprecise.Fuzzy methods have been
proposed to represent a phenomenon that is imprecise andvague in
nature. Under the criteria of prediction mean squared error,
prediction mean absolute error and multivariate tests, we
compare the ordinaryleast squares regression, principal
components regression, Stein estimation withtheir fuzzy
counterparts. In addition, the principal components models with
theprincipal components retained by the Kaiser's rule, scree
test and cumulatedproportion of variance rule are also
discussed.
zh_TW
dc.language.isozh_TWen_US
dc.subject總體經濟因素zh_TW
dc.subject股價預測zh_TW
dc.subject主成份迴歸zh_TW
dc.subject模糊線性迴歸zh_TW
dc.subjectStein估計式zh_TW
dc.subjectmacro-economic factorsen_US
dc.subjectstock price predictionen_US
dc.subjectprincipal component regressionen_US
dc.subjectfuzzy linear regressionen_US
dc.subjectStein estimatoren_US
dc.title總體經濟因素與股價關聯性之分析及預測-主成份迴歸、模糊線性迴歸及 Stein估計式zh_TW
dc.titleThe Analysis and Prediction of Stock Price from Macro-Economic Factors: An Application of Principal Component Regression, Fuzzy Linear Regression and Stein Estimationen_US
dc.typeThesisen_US
dc.contributor.department管理科學系所zh_TW
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