請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8089完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王藹農(Ai-Nung Wang) | |
| dc.contributor.author | Chun-Te Su | en |
| dc.contributor.author | 蘇俊德 | zh_TW |
| dc.date.accessioned | 2021-05-20T00:48:50Z | - |
| dc.date.available | 2021-02-26 | |
| dc.date.available | 2021-05-20T00:48:50Z | - |
| dc.date.copyright | 2021-02-26 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-02-18 | |
| dc.identifier.citation | J. L. Kelly, A new interpretation of information rate, Bell System Techn. Journal. 35, 917–926, (1956). J. B. Williams, Speculation and the carryover, The Quarterly Journal of Economics. 50(3), 436–455, (1936). L. Breiman, Investment policies for expanding businesses optimal in a longrun sense, Naval Research Logistics Quarterly. 7(4), 647–651, (1960). L. Breiman, Optimal gambling systems for favorable games, 4th Berkeley Symposium on Probability and Statistics. 1, 65–78, (1961). R. Bellman and R. Kalaba, Dynamic programming and statistical communication theory, Proceedings of the National Academy of Sciences of the United States of America. 43(8), 749–751, (1957). E. J. Elton and M. J. Gruber, On the maximization of the geometric mean with log-normal return distribution, Management Science. 21(4), 483–488, (1974). S. Maier, D. Peterson, and J. V. Weide, A strategy which maximizes the geometric mean return on portfolio investments, Management Science. 23 (10), 1117–1123, (1977). R. Merton, Lifetime portfolio selection under uncertainty: The continuous time case, Review of Economics and Statistics. 51(3), 247–257, (1969). T. Cover, An algorithm for maximizing expected log investment return, IEEE Transactions on Information Theory. 30(2), 369–373, (1984). Bierens, Herman J, The Nadaraya–Watson kernel regression function estimator. Topics in Advanced Econometrics. New York: Cambridge University Press. pp. 212–247, (1994). Cleveland, William S.; Devlin, Susan J, Locally-Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association. 83(403): 596–610, (1988) D. Basak, S. Pal and D. Patranabis, Support vector regression, Neural Information Processing – Letters and Reviews 11(10) (2007). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8089 | - |
| dc.description.abstract | 最佳成長投資組合（GOP）是在任何時間範圍內都具有最大期望增長率的投資組合，隨著時間範圍的增加，這種投資組合肯定會勝過其他任何不同的投資策略。在本文中，我使用了非母數統計方法和統計機器學習工具來估計市場數據的分佈。此外，模擬了平穩型數據和非平穩型數據以表示市場數據，通過了解市場分佈，我建立了最佳成長投資組合。GOP的文獻綜述在第1節中、理論研究在第2節中介紹、方法將在第3節中簡要介紹且在第4節中做出模擬結果。 | zh_TW |
| dc.description.abstract | The growth-optimal portfolio (GOP) is a portfolio which has a maximal expected growth rate over any time horizon. As a consequence, this portfolio is certain to outperform any other significantly different strategy as the time horizon increases. In this thesis, I used nonparametric statistic and machine learning tools to estimate the distribtuion of market data. Also, simulated both stationary and nonstationary data to represent market data. Through understanding the distribution of market, I built up the growth-optimal portfolio. The literature reviewing of GOP are in section 1. The theoretical studieso are presented in section 2. Methodology will be briefly introduced in section 3. Simulation results are in section 4. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T00:48:50Z (GMT). No. of bitstreams: 1 U0001-1802202115082400.pdf: 1894444 bytes, checksum: c0400353a47918d2716698bd43a3f8d3 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii 英文摘要 iv 1 Introduction 1 1.1 Literature Review 1 1.2 Growth-Optimal Portfolios 1 2 Emperical Log-Optimal Portfolio Selection 4 2.1 Constantly-Rebalanced Portfolio Selection 4 2.2 Time-Varying Portfolio Selection 5 3 Methodology 7 3.1 Mixing condtions 7 3.2 Kernel Regression Smoother 8 3.3 Local Polynomial Regression 9 3.4 Support Vector Regression 10 4 Simulation 11 參考文獻 13 | |
| dc.language.iso | en | |
| dc.title | 統計機器學習在最佳成長投資組合之應用 | zh_TW |
| dc.title | Statistical Machine Learning in Growth Optimal Portfolio | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳其誠(Ki-Seng Tan),謝春忠(Chun-Chung Hsieh) | |
| dc.subject.keyword | Kelly公式,最佳成長投資組合,核迴歸,局部多項式估計,支持向量迴歸, | zh_TW |
| dc.subject.keyword | Kelly Formula,Growth Optimal Portfolio,kernel regression,local polynomial,support vector regression, | en |
| dc.relation.page | 13 | |
| dc.identifier.doi | 10.6342/NTU202100742 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-02-19 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 應用數學科學研究所 | zh_TW |
| 顯示於系所單位： | 應用數學科學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1802202115082400.pdf | 1.85 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。