| dc.contributor.advisor | 顏佑銘 | zh_TW |
| dc.contributor.advisor | Yen, Yu-Min | en_US |
| dc.contributor.author (Authors) | 戴晨宇 | zh_TW |
| dc.contributor.author (Authors) | Tai, Chen-Yu | en_US |
| dc.creator (作者) | 戴晨宇 | zh_TW |
| dc.creator (作者) | Tai, Chen-Yu | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 1-Jul-2025 14:37:01 (UTC+8) | - |
| dc.date.available | 1-Jul-2025 14:37:01 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Jul-2025 14:37:01 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112351010 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/157735 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國際經營與貿易學系 | zh_TW |
| dc.description (描述) | 112351010 | zh_TW |
| dc.description.abstract (摘要) | 本研究旨在探討加權範數懲罰函數於最小變異數投資組合建構中的應用與實證成效，並檢視其於市場動盪期間的穩健性與報酬潛力。傳統最小變異數法於高維度資產結構中易受估計誤差影響，導致樣本外表現不穩與權重極端化現象。為克服此問題，本文採用加權範數最小變異數投資組合，透過同時引入l_1與l_2^2，達成投資組合權重的稀疏化與穩定性。
本研究以四十三間台灣市值排名前段之上市公司為樣本，分析期間涵蓋2004年至2024年，並特別納入2008年金融海嘯與2020年新冠肺炎疫情等重大市場事件進行分段分析，使用十項績效指標評估加權範數最小變異數投資組合與1/N、NSMVP、GMVP的樣本外表現，並擴充分析設定目標報酬限制與替代範數懲罰之應用成效。
實證結果顯示，加權範數最小變異數投資組合於市場正常時在風險調整後報酬表現優異，市場波動時則有助於降低投組風險，此外，設定目標報酬條件反而導致報酬下降與波動上升，而三種替代範數懲罰整體表現與加權範數最小變異數投組接近。 | zh_TW |
| dc.description.abstract (摘要) | This study investigates the application and empirical performance of the weighted-norm penalty function in constructing minimum-variance portfolios (MVP), with particular focus on its robustness and return potential during periods of market turbulence. Traditional MVPs are prone to estimation errors, especially in high-dimensional asset spaces, which often results in unstable out-of-sample performance and extreme portfolio weights. To address these issues, this research adopts the Weighted-Norm Minimum-Variance Portfolio (WNMVP), which incorporates both l_1 and l_2^2 norm penalties to simultaneously achieve sparsity and stability in portfolio weights.
The empirical analysis is based on 43 publicly listed Taiwanese large-cap companies, covering the period from 2004 to 2024. The study includes sub-period analyses of major market disruptions, such as the 2008 Global Financial Crisis and the 2020 COVID-19 pandemic. Out-of-sample performance is evaluated across ten performance metrics, comparing WNMVP with the 1/N strategy, the No-Short-Sale Minimum-Variance Portfolio (NSMVP), and the Global Minimum-Variance Portfolio (GMVP). In addition, the effectiveness of target return constraints and alternative norm penalty functions is also examined.
The results indicate that WNMVP outperforms other strategies in terms of risk-adjusted returns under normal market conditions and contributes to risk reduction during turbulent periods. However, imposing a target return constraint tends to reduce returns and increase volatility. Furthermore, the three alternative norm penalty strategies exhibit overall performance comparable to that of WNMVP. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究架構 2
第二章 文獻探討 4
第一節 投資組合理論發展脈絡 4
第二節 其他投資組合 5
第三章 研究方法 7
第一節 加權範數最小變異數投資組合 7
第二節 替代範數懲罰 8
第三節 績效評估指標 10
第四章 績效實證分析 16
第一節 樣本資料敘述 16
第二節 樣本資料敘述性統計分析 17
第三節 實證分析結果 21
第五章 研究結論與研究建議 35
第一節 研究結論 35
第二節 研究建議 36
參考文獻 38 | zh_TW |
| dc.format.extent | 2017815 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112351010 | en_US |
| dc.subject (關鍵詞) | 加權範數懲罰函數 | zh_TW |
| dc.subject (關鍵詞) | 最小變異數投資組合 | zh_TW |
| dc.subject (關鍵詞) | 2008年金融海嘯 | zh_TW |
| dc.subject (關鍵詞) | 新冠肺炎疫情 | zh_TW |
| dc.subject (關鍵詞) | Weighted-norm penalty function | en_US |
| dc.subject (關鍵詞) | Minimum-variance portfolio | en_US |
| dc.subject (關鍵詞) | 2008 financial crisis | en_US |
| dc.subject (關鍵詞) | COVID-19 pandemic | en_US |
| dc.title (題名) | 以加權範數懲罰函數建構之投資組合實證研究：以2008金融海嘯與新冠肺炎時間區間為例 | zh_TW |
| dc.title (題名) | An Empirical Study on Portfolio Construction Using Weighted Norm Penalty Functions: Evidence from the 2008 Financial Crisis and the COVID-19 Pandemic Periods | en_US |
| dc.type (資料類型) | thesis | en_US |
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10.Yen, Y. M. （2015）. Sparse Weighted-Norm Minimum Variance Portfolios. Review of Finance, 20, 1259-1287. | zh_TW |
