請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93233完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 胡明哲 | zh_TW |
| dc.contributor.advisor | Ming-Che Hu | en |
| dc.contributor.author | 趙俊程 | zh_TW |
| dc.contributor.author | Chun-Cheng Chao | en |
| dc.date.accessioned | 2024-07-23T16:25:22Z | - |
| dc.date.available | 2024-07-24 | - |
| dc.date.copyright | 2024-07-23 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-05 | - |
| dc.identifier.citation | [1] Harry Markowitz. Portfolio Selection. Journal of Finance, 7:77–91, March 1952.
[2] Thierry Roncalli. Introduction to risk parity and budgeting. Number 2. CRC Press, 2013. [3] Marcos Lopez de Prado. Building diversified portfolios that outperform out-of-sample. Journal of Portfolio Management, 2016. [4] Thomas Raffinot. The hierarchical equal risk contribution portfolio. Available at SSRN 3237540, 2018. [5] Robert Tibshirani, Guenther Walther, and Trevor Hastie. Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63:411–423, 2001. [6] Joseph B Kruskal and Myron Wish. Multidimensional scaling. Sage, 1978. [7] Joshua B Tenenbaum, Vin de Silva, and John C Langford. A global geometric frame- work for nonlinear dimensionality reduction. science, 290(5500):2319–2323, 2000. [8] Ruiling Liu, Hengjin Cai, and Cheng Luo. Clustering analysis of stocks of csi 300 index based on manifold learning. 2012. [9] Yan Huang, Gang Kou, and Yi Peng. Nonlinear manifold learning for early warnings in financial markets. European Journal of Operational Research, 258:692–702, 2017. [10] Indranil Ghosh, Esteban Alfaro-Cortés, Matías Gámez, and Noelia García-Rubio.Prediction and interpretation of daily nft and defi prices dynamics: Inspection through ensemble machine learning & xai. International Review of Financial Analysis, 87:102558, 2023. [11] Guido Previde Massara, Tiziana Di Matteo, and Tomaso Aste. Network filtering for big data: Triangulated maximally filtered graph. Journal of complex Networks, 5:161–178, 2016. [12] Longfeng Zhao, Gang-Jin Wang, Mingang Wang, Weiqi Bao, Wei Li, and H Eugene Stanley. Stock market as temporal network. Physica A: Statistical Mechanics and its Applications, 506:1104–1112, 2018. [13] Michele Tumminello, Tomaso Aste, Tiziana Di Matteo, and Rosario N Mantegna. A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102:10421–10426, 2005. [14] Hadi Esmaeilpour Moghadam, Teymour Mohammadi, Mohammad Feghhi Kashani, and Abbas Shakeri. Complex networks analysis in iran stock market: The application of centrality. Physica A: Statistical Mechanics and its Applications, 531:121800, 2019. [15] Shreya Patki, Roy H Kwon, and Yuri Lawryshyn. Centrality-based equal risk contribution portfolio. Risks, 12:8, 2024. [16] Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang, and Yuqi Liang. Temporal and heterogeneous graph neural network for financial time series prediction. In Proceedings of the 31st ACM international conference on information knowledge management, pages 3584–3593, 2022. [17] Clifford S Asness, Andrea Frazzini, and Lasse H Pedersen. Leverage aversion and risk parity. Financial Analysts Journal, 68:47–59, 2012. [18] Ali Namaki, Amir H Shirazi, R Raei, and GR Jafari. Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390:3835–3841, 2011. [19] Gustavo Peralta and Abalfazl Zareei. A network approach to portfolio selection. Journal of Empirical Finance, 38:157–180, 2016. [20] Gábor J Székely, Maria L Rizzo, and Nail K Bakirov. Measuring and testing dependence by correlation of distances. 2007. [21] Casimir Kuratowski. Sur le probleme des courbes gauches en topologie. Fundamenta mathematicae, 15:271–283, 1930. [22] Ernesto Estrada and Juan A Rodriguez-Velazquez. Subgraph centrality in complex networks. Physical Review E, 71:056103, 2005. [23] Victor DeMiguel, Lorenzo Garlappi, and Raman Uppal. Optimal versus naive diversification: How inefficient is the 1/n portfolio strategy? The review of Financial studies, 22:1915–1953, 2009. [24] David H Bailey and Marcos Lopez de Prado. The sharpe ratio efficient frontier. Journal of Risk, 15:13, 2012. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93233 | - |
| dc.description.abstract | 本研究結合流形學習與網絡分析方法，以資產間的網絡關係做為量化風險指標，作一創新的風險平價投資組合策略。本研究利用等距特徵映射來捕捉資產之間的非歐幾何距離，並利用距離相關度量揭示資產間的非線性關係，為資產配置提供更深入的分析基礎。
為了對相關係數矩陣進行降維，我們採用三角最大濾圖 (Triangulated Maximally Filtered Graph）對網絡進行過濾，保留更具有代表性的主要風險結構。利用圖論衡量資產節點的子圖中心性，以及關注負向風險損失的條件風險價值(Expected Shortfall)，構建考慮全局及個體的防護性資產配置。 在產業龍頭存股等權重指數作為回測資料上，我們的方法展現出更優異的風險調整後收益，突顯出使用非歐幾何與網絡分析在投資組合風險管理方面的潛力與創新性。 | zh_TW |
| dc.description.abstract | This study integrates manifold learning and network analysis to quantify risk using asset network relationships, creating an innovative risk parity portfolio strategy. Isometric Mapping (Isomap) is employed to capture non-Euclidean distances and reveal nonlinear relationships between assets.
The Triangulated Maximally Filtered Graph (TMFG) is applied to filter the network, retaining the most representative risk structures. By measuring assets' subgraph centrality and considering the Expected Shortfall, we construct a defensive asset allocation. As for backtesting part, we using the Taiwan Industrial Leaders Dividend Equal Weight Index demonstrates superior risk-adjusted returns, highlighting the potential of non-Euclidean geometry and network analysis in portfolio risk management. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-23T16:25:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-23T16:25:22Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 iii 摘要 v Abstract vii 目次 ix 圖次 xiii 表次 xv 第一章 緒論 1 1.1 研究背景 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究動機與目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 研究架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章 文獻回顧 5 2.1 流形學習 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 網絡分析於金融市場的應用 . . . . . . . . . . . . . . . . . . . . . . 8 2.3 投資組合模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.1 平均 ─ 變異數投資組合模型 . . . . . . . . . . . . . . . . . . . . 10 2.3.2 風險平價策略 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2.1 等風險貢獻組合 . . . . . . . . . . . . . . . . . . . . 12 2.3.3 逆變異數組合 (Inverse Variance Portfolio, IVP) . . . . . . . . . . 14 2.3.4 階層風險平價 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.4.1 條件風險價值（Expected Shortfall, ES） . . . . . . . 18 第三章 研究方法 19 3.1 流形學習與網絡分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 等距特徵映射 Isometric Mapping (ISOMAP) . . . . . . . . . . . . 20 3.2 非歐式距離估計 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.1 距離相關係數與距離共變異數 . . . . . . . . . . . . . . . . . . . 23 3.2.2 網絡過濾技術 Network Filtering . . . . . . . . . . . . . . . . . . . 25 3.2.2.1 最 大 平 面 過 濾 圖 （planar maximally filtered graph, PMFG) . . . . . . . . . . . . . . . 25 3.2.2.2 Kuratowski 定理 . . . . . . . . . . . . . . . . . . . . 26 3.2.3 三角最大過濾圖 Triangulated maximally filtered graph . . . . . . 26 3.2.4 子圖中心性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 非歐式幾何風險網絡平衡方法 . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 資產分配過程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 流程圖 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 第四章 實證結果 35 4.1 資料來源與樣本期間說明 . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 實驗設計與相關指標 . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 實驗結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 探索式資料分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 策略回測比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3.2.1 熊市市場比較 . . . . . . . . . . . . . . . . . . . . . . 48 4.3.3 機率性夏普比率檢定 . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.4 敏感度分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 第五章 實證結果 57 5.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 未來研究方向 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 參考文獻 61 附錄 A — 技術指標生成 65 A.1 技術指標公式 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 附錄 B — 特選臺灣產業龍頭存股等權重指數內容重點整理 69 B.1 成分股遴選機制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 B.1.1 選股母體定義 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 B.1.2 流動性和財務指標篩選 . . . . . . . . . . . . . . . . . . . . . . . 69 B.1.3 產業分類及成分股排序 . . . . . . . . . . . . . . . . . . . . . . . 70 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 風險平價 | zh_TW |
| dc.subject | 流形學習 | zh_TW |
| dc.subject | 非歐幾何 | zh_TW |
| dc.subject | 金融網絡分析 | zh_TW |
| dc.subject | 三角最大過濾圖 | zh_TW |
| dc.subject | 投資組合 | zh_TW |
| dc.subject | Triangulated Maximally Filtered Graph | en |
| dc.subject | Non-Euclidean Geometry | en |
| dc.subject | Financial Network Analysis | en |
| dc.subject | Risk Parity | en |
| dc.subject | Manifold Learning | en |
| dc.title | 非歐幾何之投資組合風險平衡策略：流形學習與網絡分析 | zh_TW |
| dc.title | Non-Euclidean geometric portfolio theory and risk parity: Manifold learning and network analysis | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 童慶斌;蔡政安;鄭克聲;楊豐安 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Pin Tung;Chen-An Tsai;Ke-Sheng Zheng;Feng-An Yang | en |
| dc.subject.keyword | 流形學習,非歐幾何,金融網絡分析,三角最大過濾圖,投資組合,風險平價, | zh_TW |
| dc.subject.keyword | Manifold Learning,Non-Euclidean Geometry,Financial Network Analysis,Triangulated Maximally Filtered Graph,Risk Parity, | en |
| dc.relation.page | 70 | - |
| dc.identifier.doi | 10.6342/NTU202401526 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-07-06 | - |
| dc.contributor.author-college | 共同教育中心 | - |
| dc.contributor.author-dept | 統計碩士學位學程 | - |
| dc.date.embargo-lift | 2029-07-05 | - |
| 顯示於系所單位： | 統計碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf 此日期後於網路公開 2029-07-05 | 11.94 MB | Adobe PDF |
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。