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https://ah.lib.nccu.edu.tw/handle/140.119/111463| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 黃泓智 | zh_TW |
| dc.contributor.author | 于孟玉 | zh_TW |
| dc.contributor.author | Yu, Meng Yu | en_US |
| dc.creator | 于孟玉 | zh_TW |
| dc.creator | Yu, Meng Yu | en_US |
| dc.date | 2017 | en_US |
| dc.date.accessioned | 2017-07-31T03:01:26Z | - |
| dc.date.available | 2017-07-31T03:01:26Z | - |
| dc.date.issued | 2017-07-31T03:01:26Z | - |
| dc.identifier | G0104358017 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/111463 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 風險管理與保險學系 | zh_TW |
| dc.description | 104358017 | zh_TW |
| dc.description.abstract | 台灣現在已進入高齡化社會，該怎麼在退休前為自己累積一份財富為一重要議題，而以往許多研究都只針對不同類型資產進行資產配置分析，因此本研究便對於其中的股票進行投資組合的配置。首先透過個股篩選，利用基本面財報單因子以及財務特徵指標找出具有潛力的個股，再根據馬可維茲投資組合理論，找出在平均數－變異數投資組合模型(Mean-Variance Portfolio Model；MV模型)與平均數－條件風險值(Mean-Conditional Value at Risk Portfolio Model；MCVaR模型)下的最適投資組合與最小風險投資組合，並透過不同穩健共變異數估計方法來改善樣本變異數估計的偏誤，希望能藉由此資產配置策略達到良好的投資績效。\n 研究結果發現透過基本面選股後，不論是MV模型或MCVaR模型在資產配置上都有良好的效果，與等權重投資組合相比甚至可以達到兩倍的績效，而穩健共變異數估計中以Shrink估計表現最為優異，且發現MCD與MVE估計並不是一個能夠改善樣本估計的良好方法，最後提出最適投資組合在獲利性財務指標篩股下有最好的績效，夏普比率為1.4544，而最小風險投資組合在品質性財務指標篩股下有最好的績效，夏普比率為1.7933。 | zh_TW |
| dc.description.tableofcontents | 第一章 緒論 6\n第一節 研究背景 6\n第二節 研究目的 8\n第三節 研究架構 10\n第二章 文獻探討 11\n第一節 基本面文獻探討 11\n第二節 風險衡量指標文獻探討 12\n第三節 投資組合理論文獻探討 14\n第三章 研究方法 18\n第一節 基本面單因子介紹 19\n第二節 財務特徵指標介紹 20\n第三節 投資組合理論介紹 21\n第四章 實證結果分析 29\n第一節 實證分析資料來源與說明 29\n第二節 績效指標說明 29\n第三節 投資組合績效分析 31\n第五章 結論與未來方向建議 58\n第一節 結論 58\n第二節 未來方向建議 59\n參考文獻 60\n附錄 62 | zh_TW |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0104358017 | en_US |
| dc.subject | 投資組合理論 | zh_TW |
| dc.subject | MV模型 | zh_TW |
| dc.subject | MCVaR模型 | zh_TW |
| dc.subject | 共變異數估計 | zh_TW |
| dc.subject | Portfolio theory | en_US |
| dc.subject | MV model | en_US |
| dc.subject | MCVaR model | en_US |
| dc.subject | Covariance estimator | en_US |
| dc.title | 以基本面與投資組合理論建構台灣股票市場最適資產配置 | zh_TW |
| dc.title | Using fundamental analysis with portfolio theory to construct the optimal asset allocation in Taiwan stock market | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | 許偉倫，2011。穩健型投資組合建構與回溯測試之探討。碩士論文。\n盧泰源，2016。最適化Smart Beta 策略組合型基金之應用—以台灣股票市場之交易策略研究。碩士論文。\nAbarbanell, J. S., &Bushee, B. J. (1998). Abnormal returns to a fundamental analysis strategy. Accounting Review, 19-45.\nArtzner, P., F. Delbaen, J. Eber, and D. Health, (1999), Coherent measure of Risk. Mathematical Finance, Vol.2, pp.95-121.\nBrianton, G. (1998). Portfolio optimization. Risk Management and Financial Derivatives: A Guide to the Mathematics, 1st edition, Palgrave (trade), 4, 39-44.\nBrinson, G. P., Singer, B. D., &Beebower, G. L. (1991). Determinants of portfolio performance II: An update. Financial Analysts Journal, 40-48.\nDeMiguel, V., & Nogales, F. J. (2009). Portfolio selection with robust estimation. Operations Research, 57(3), 560-577.\nGraham, B., & Dodd, D. L. (1934). Security analysis: Principles and technique. McGraw-Hill.\nGrubel, H. G. (1968). Internationally diversified portfolios: welfare gains and capital flows. The American Economic Review, 58(5), 1299-1314.\nLedoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of empirical finance, 10(5), 603-621.\nLev, B., &Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of Accounting research, 190-215.\nLi, D., & Ng, W. L. (2000). Optimal dynamic portfolio selection: Multiperiod mean‐variance formulation. Mathematical Finance, 10(3), 387-406.\nMarkowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.\nOu, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of accounting and economics, 11(4), 295-329.\nPflug, G. C. (2000). Some remarks on the value-at-risk and the conditional value-at-risk. In Probabilistic constrained optimization (pp. 272-281). Springer US.\nRockafellar, R. &Uryasev, S. (2000). Optimization of conditional value-at-risk. The Journal of Risk, 2(3), 21–41.\nRousseeuw, P. J. (1985). Multivariate estimation with high breakdown point. Mathematical statistics and applications, 8, 283-297.\nRousseeuw, P. & Van Driessen, K. (1999). A fast algorithm for the minimumcovariance determinant estimator. Technometrics, 41, 212–223.\nRousseeuw, P., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M., Verbeke, T., & Maechler, M. (2008). The robustbase package: Basic Robust Statistics. cran.r-project.org. \nSchaefer, J., Opgen-Rhein, R., & Strimmer, K. (2008). The corpcor package:Efficient Estimation of Covariance and (Partial) Correlation. cran.r-project.org.\nSloan, R. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting review, 71(3), 289-315.\nVenables, W. N. & Ripley, B. D. (2008). The MASS Package. cran.r-project.org. | zh_TW |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.fulltext | No Fulltext | - |
| item.openairetype | thesis | - |
| item.grantfulltext | none | - |
| item.cerifentitytype | Publications | - |
| Appears in Collections: | 學位論文 | |
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