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研究生:薛勝斌
研究生(外文):Sheng-Pin Hsuen
論文名稱:基金持股影響因素之探討-可數追蹤資料模型之分析
論文名稱(外文):Modeling Mutual Fund Manager's Stock Holding Decision:Evidence from Count Panel Data Models
指導教授:邱魏頌正邱魏頌正引用關係
學位類別:碩士
校院名稱:南華大學
系所名稱:經濟學研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:40
中文關鍵詞:基金持股決策可數追蹤資料模型重複間斷選擇模型準最大概似估計法一般動差估計法
外文關鍵詞:pseudo maximum likelihoodgeneralized moment methodrepeated discrete choice modelcount panel data modelmutual fund manager's stock holding decision
相關次數:
  • 被引用被引用:11
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  • 下載下載:81
  • 收藏至我的研究室書目清單書目收藏:6
論文摘要

解釋基金投資活動一直都是經濟學者們所關心及注目的課題﹐近年來基金投資之研究主要是使用連續型變數模型估計投資之資料﹐但投資之資料常屬間斷且非負整數之型態。若以連續型數量研究方法估計間斷型資料﹐模型規格設定的錯誤與資料處理上可能產生的重大遺漏﹐均可能導致最終之實證推論發生偏誤﹐因而使研究成果之可信度及參考之價值降低。

本文研究之重心在於基金之持股﹐而研究之目的則在於運用可數追蹤資料模型與重複間斷選擇模型等間斷數量研究方法﹐深入檢驗暨探討可能影響基金持股的重要個總體經濟因素。實證之樣本為台灣地區2000-2002年間﹐成立三年以上的科技型基金對上市電子類股持股之月資料。實證結果歸納如下:

(一) 模型規格之檢定:
Hausman 檢定值為36.825﹐表模式中之解釋變數與干擾項間存在顯著之關聯性﹐實證分析之模式應設定為具有固定效果的追蹤資料模型。

(二) 個體面變數之實證結果:
1.個股前期報酬率與基金持股間呈顯著正相關之結論顯示出基金經理人偏好增加前期報酬率高個股之持股數﹐隱含科技型基金經理人間普遍存在追漲操作之投資行為﹐支持Grinblatt et al.(1995)實證之結果。
2.公司規模與基金持股間呈負相關間接隱含經理人認同股票市場存在規模效果﹐支持Banz(1981)對規模效果之實證。
3.本益比對基金持股之影響為顯著負相關﹐支持Basu(1977)與Reinganum(1981)研究之發現。
4.個股標準差與基金持股二者間呈現顯著正相關之實證結論並不支持Campbell與Hentschel(1992)的波動性回饋假說。我們這或許是因為本文實證分析之樣本僅限於科技型基金所造成。科技型基金多屬積極成長型基金﹐偏好持有價格波動幅度較大之個股。
5.個股之週轉率對基金持股之影響唯有準概似估計法之估計結果呈顯著正相關﹐且估計之參數值較小﹐表示個股週轉率對本研究中基金持股之影響甚低﹐這似乎與Falkenstein(1996)研究之結論並不一致﹐然此二者間其實並無衝突與矛盾之處。因相較於Falkenstein對所有類型基金之全面性研究﹐本文只侷限於研究科技型基金。因該類基金持有科技類個股之比重甚高﹐而科技類股在台灣股市屬熱門交易之類股﹐不易產生週轉率低落之情形﹐故對基金持股之影響並不顯著。

(三) 總體面變數之實證結果:
1.基金持股顯著受貨幣供給成長率與利率等二貨幣政策指標變數之影響﹐與貨幣供給成長率間呈顯著正相關而與利率間呈顯著之負相關﹐這表示政府寬鬆貨幣政策所增加的資金可能流入基金市場進而導致經理人增加整體持股。
2.基金持股與工業生產成長率成正相關且與消費者物價上漲率呈負相關﹐但均不顯著。造成此二變數解釋能力低落之原因可能為台灣證期會對基金投資金額之限制﹐其規定基金持股至少須達至一定之比例方能收取全額之管理費用﹐否則將只能收取半數管理費。此一規定可能使經理人不理會經濟景氣與物價之變動﹐為求收取全額之管理費用而經常維持一定比例之持股﹐導致此二變數對基金持股之影響均不顯著之實證結果。
3.匯率與基金持股間成正相關隱含台灣高科技公司屬於出口傾向﹐然估計之結果不顯著可能是因為匯率對國內型基金之影響力有限所致。
目錄
摘要 1
 
1.前言 4
 
2.模型設定 10
基金持股模型 10
實證模型 13
 
3.估計方法 15
條件最大概似估計法 16
準最大概似估計法 17
一般動差估計法 18
 
4.資料說明 21
基金持股資料 21
個體面經濟因素資料 24
總體面經濟因素資料 26
 
5.實證研究 28
個體面經濟因素之實證結果 28
個體面經濟因素之實證結果 31
6.結論 32
 
7.參考文獻 33
Adriaan, S. K., 2000, the Effects of Female Emploment Status on the Presence and Number of Children, Journal of Population Economics, 13, 221-239.
 
Anderson, E. B., 1970, Asymptotic Properties of Condictional Maximum Likelihood Estimators, Journal of the Royal Statistical Society, 32, 283-301.
 
Banz, R., 1981, the Relationship between Return and Market Value of Common Stocks, Journal of Financial Economics, 9, 1-8.
 
Bowker J. M., and Leeworthy V. R., 1998, Accounting for Ethnicity in Recreation Demand: A Flexible Count Data Approach, Journal of Leisure Research, 30, 64-78.
 
Bruce, M. and Yacov, T. and Eithan, H. and David, Z., 1998, Count Data Regression Models of the Time to Adopt New Technologies, Applied Economics Letters, 5, 369-373.
 
Bruno, C. and Emmanuel, D., 1997, Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data, Journal of Applied Econometrics, 12, 243-263.
 
Bruno, C. and Emmanuel, D., 1997, Research and Development, Competetion and Innovation: Pseudo-maximum Likelihood and Simulated Maximum Likelihood Methods Applied to Count Data Models with Heterogeneity, Journal of Econometrics, 79, 355-378.
 
Cameron, A. C., and Trivedi P. K., 1986, Econometric Models Based on Count Data: Comparison and Application of Some Estimators and Tests, Journal of Applied Econometrics, 1, 29-53.
Cameron, A. C., and Johansson, P., 1997, Count Data Regression Using Series Expensions: with Applications, Journal of applied Econometrics, 12, 203-223.
 
Chib, S., Greenberg, E., and Winkelmann, R., 1998, Posterior Simulation and Bayes Factors in Count Data Models, Journal of Econometrics, 86, 33-54.
 
Colin, C. and Frank, A. G. W., 1996, R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization, Journal of Business and Economic Statistics, 14, 209-220.
Crouchley R., and Davies R. B., 1999, Acomparison of Population Average and Redom Effect Models for the Analysis of Longitudinal Count Data with Base-Line Information, Journal of Royal Statistical Society A, 162, 331-347.
 
Daniel, H. and Robert, M., 1993, A Theoretical Foundation for Count Data Model, American Journal of Agricultural Economics, 75, 604-611.
Gourieroux, C., Monfort, A., and Trognon, A., 1984, Pseudo Maximum Likelihood Methods: Theory, Econometrica, 52, 681-700.
 
Gourieroux, C., Monfort, A., and Trognon, A., 1984, Pseudo Maximum Likelihood Methods: Applications to Poisson Models, Econometrica, 701-720.
 
Gourieroux, C., and Magnac, T., 1997, Duration, Transition and Count Data Models Introduction, Journal of Econometrics, 79, 195-199.
 
Hall, B. H., Hausman, J., and Griliches, Z., 1984, Econometric Models for Count Data with an Application to the Patents-R&D Relactionship, Econometrica, 52, 909-938.
 
Hansen, L., 1982, Large Sample Properties of Generalized Method of Moments Estimators, Econometrica, 50, 1029-54.
 
Hausman, J., and Griliches Z., 1984, Econometric Models for Count Data with an Application to the Patent-R&D Relactionship, Econometrica, 52, 909-938.
 
Hellerstein, J., 1993, Using Count Data Models in Travel Cost Analysis with Aggregate Data, American Journal of Agriculture Economics, 75, 604-611.
 
Jeffrey, E. and J. S. Shonkwiler, 1995, Estimating Social Wealfare Using Count Data Models: An Application to Long-Run Recreation Demand User Condictions of Endogenous Stratification and Trucation, Review of Economics and Statistics, 104-112.
 
Jerry, A. H. and Gregory, K. L. and Daniel, M., 1995, A Utility-Consistent, combined Discrete Choice and Count Data Model- Assessing Recreational Use Losses due to Natural Resource Damage, Journal of Public Economics, 56, 1-30.
Jerry, H. and Bronwyn H. H. and Zvi G., 1984, Econometric Models for Count Data with an Application to the Patents-R&D Relationship, Econometrica, 52, 909-938.
 
Jochen, M. and Regina, T. R., 2000, Fertility Assimilation of Immigrants: Evidence from Count Data Models, Journal of Population Economics, 13, 241-261.
 
John, A. L., 2001, US County-Level Determinants of Inbound FDI: Evidence from a Two-Step Modified Data Model, International Journal of Industrial Organization, 19, 953-973.
 
John, Mullahy., 1997, Instrumental Variable Estimation of Count Data Models: Applicatipns to Models of Cigarette Smoking Behavior, Review of Economics and Statistics, 586-593.
 
Jose, G. M., 1997, GMM Estimation of Count Panel Data Models with Fixed Effects and Predetermined Instruments, Journal of Business and Economic Statistic, 15, 82-89.
 
Kasuandra, M. T., 2000, The Effects of Model Specification on Foreign Direct Investment Models: An Application of Count Data Models, Southern Economic Journal, 67, 460-468.
 
Kert, B., 1995, Prediction and Control for a Time Series Count Data Model, International Journal of Forecasting, 11, 263-270.
 
Maria, M. and Dan-Olof, R., 2000, Modeling Femel Fertility Using Inflated Count Data Models, Journal of Population Economics, 13, 189-203.
 
Michael, D. Creel, and John, B. Loomis., 1990, Theoretical and Empirical Advantage of Truncated Count Data Estimators for Analysis of Deer Hunting in California, American Journal of Agriculture Economic, 434-441.
 
Michele, C., 1997, Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data, Journal of Applied Econometrics, 12, 265-280.
 
Miguel, A. D. and Thomas, J. K., 1997, Count Data Models with Variance of Unknown Form: An Application to a Hedonic Model of Work Absenteeism, the Review of Economics and Statistics, 41-49.
 
Mullahy, J., 1986, Specification and Testing of Some Modified Count Data Models, Journal of Econometrics, 33, 341-365.
 
Ramaswamy, V., and Anderson, E. W., and Desarbo, W. S., 1994, A Disaggregate Negative Binominal Regression Procedure for Count Data Analysis, Management Science, 40, 405-417.
 
Richard, B. and Rachel, G. and John, V. R., 1995, Dynamic Count Data Models of Technologocal innovation, the Economic Journal, 105, 333-344.
 
Shiferaw, G. and John, E., 2000, Generalized Bivariate Count Data Regression Models, Economics Letters, 68, 31-36.
 
Steven, B. C., and Franklin, G. M. Jr., 1995, Modeling Household Fertility Decision: Estimation and Testing of Censored Regression Models for Count Data, Empirical Economics, 20, 183-196.
 
Steven, T. Yen.,Gaussian versus Count Data Hurdle Models: Cigarettee Consumption by Women in the US, Applied Economics Letters, 6, 73-76.
 
Sulayman, A., 1998, the Demand for Children in Arab Countries: Evidence from Panel and Count Data Models, Journal of population Economics, 11, 435-452.
 
Timothy, C. H. and Kenneth, E. M., 1996, Count Data Models and Problem of Zeros in Recreation Demand Analysis, American Agricultural Economics Association, 78, 89-102.
 
Wedel, M., and Desarbo, W. S., and Bult, J. R., and Ramaswamy, V., 1993, A Latent Class Poisson Regression Model for Heterogeneous Count Data, Journal of Applied Econometrics, 8, 397-411.
 
Winkelmann, R.,1995, Duration Dependence and Dispersion in Count Data Models, Journal of Business and Economic Statistic, 4, 467-474.
 
Wooldridge, J., 1990, Distribution Free Estimators of Some Nonlinear Panel Data Models, MIT Working Paper, Department of Economics 564.
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