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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
| dc.contributor.author | Chen-Yu Wang | en |
| dc.contributor.author | 汪宸宇 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:06:16Z | - |
| dc.date.copyright | 2022-07-05 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-06-29 | |
| dc.identifier.citation | [1] Borodin, A., El-Yaniv, R., and Gogan, V. (2003). Can we learn to beat the best stock. Advances in Neural Information Processing Systems, 16. [2] Chen, G.-H., Kao, M.-Y., Lyuu, Y.-D., and Wong, H.-K. (2001). Optimal buy-andhold strategies for financial markets with bounded daily returns. SIAM Journal on Computing, 31(2):447–459. [3] Cover, T. M. (2011). Universal portfolios. In The Kelly Capital Growth Investment Criterion: Theory and Practice, pages 181–209. World Scientific. [4] Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006). Online passive aggressive algorithms. Journal of Machine Learning Research, 7:551–585. [5] Dochow, R. (2016). Online algorithms for the portfolio selection problem. Springer Wiesbaden. [6] Duchi, J., Shalev-Shwartz, S., Singer, Y., and Chandra, T. (2008). Efficient projections onto the l 1-ball for learning in high dimensions. In Proceedings of the 25th International Conference on Machine Learning, pages 272–279. [7] Gao, L. and Zhang, W. (2013). Weighted moving average passive aggressive algorithm for online portfolio selection. In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, volume 1, pages 327–330. [8] Huang, D., Zhou, J., Li, B., Hoi, S. C., and Zhou, S. (2016). Robust median reversion strategy for online portfolio selection. IEEE Transactions on Knowledge and Data Engineering, 28(9):2480–2493. [9] Intercontinental Exchange, Inc (2022). The history of NYSE. https://www.nyse.com/history-of-nyse. Last accessed on May 15, 2022. [10] Karp, R. M. (1992). On-line algorithms versus offline algorithms, how much is it worth to know the future? In Proc. IFIP 12th World Computer Congress, 1992, volume 1, pages 416–429. [11] Lai, Z.-R., Yang, P.-Y., Fang, L., and Wu, X. (2018). Reweighted price relative tracking system for automatic portfolio optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(11):4349–4361. [12] Li, B. and Hoi, S. C. (2014). Online portfolio selection: A survey. ACM Computing Surveys (CSUR), 46(3):1–36. [13] Li, B., Hoi, S. C., Sahoo, D., and Liu, Z.-Y. (2015). Moving average reversion strategy for on-line portfolio selection. Artificial Intelligence, 222:104–123. [14] Li, B., Hoi, S. C., Zhao, P., and Gopalkrishnan, V. (2011). Confidence weighted mean reversion strategy for on-line portfolio selection. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 434–442. [15] Li, B., Zhao, P., Hoi, S. C., and Gopalkrishnan, V. (2012). Pamr: Passive aggressive mean reversion strategy for portfolio selection. Machine Learning, 87(2):221–258. [16] Magdon-Ismail, M., Atiya, A. F., Pratap, A., and Abu-Mostafa, Y. S. (2004). On the maximum drawdown of a Brownian motion. Journal of Applied Probability, 41(1):147–161. [17] Marek, P., Ťoupal, T., and Vávra, F. (2016). Efficient distribution of investment capital. In Proceedings of the 34th International Conference Mathematical Methods in Economics. [18] Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91. [19] Tharp, V. K., Chabot, C., and Tharp, K. (2007). Trade your way to financial freedom. McGraw-Hill New York. [20] Vecer, J. (2006). Maximum drawdown and directional trading. Risk, 19(12):88–92. [21] Williams, L. (2011). Long-term secrets to short-term trading. John Wiley & Sons New York. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84204 | - |
| dc.description.abstract | 投資組合最佳化和部位管理是兩個投資中重要但不同的概念，過去針對投資組合選擇的研究，大多為可以及時處理最新資料、且無法預見未來的線上算法，且大多不會考慮部位管理，使所有資產在任何情況下都百分之百投入標的物，這不一定是對長期報酬最佳的選項。本研究對既有的被動攻擊型投資組合策略進行改寫，得到具備部位管理功能的新算法架構。在此一架構底下，使用者可以自行設計部位管理策略、或投資組合最佳化目標，形成自己的投資組合最佳化策略。基於此架構，我們接著以資料學習的方式找尋適當且單純的條件，作為部位調整的根據，套用於四個既有的被動攻擊型投資組合策略之上。實證研究顯示，在測試的六個資料集中，使用部位管理後，這些策略都能在其中的五個資料集上產生更高的最終獲利，並降低最大回檔比例。 | zh_TW |
| dc.description.abstract | Portfolio optimization and position sizing are two distinct and crucial concepts in investing. Past studies on portfolio selection are often online algorithms, meaning they process only past information in order to make decisions; past studies mostly assumed that captial is fully invested, which might be harmful to long-term returns. This thesis adjusts the formulas of some passive-aggressive algorithms for online portfolio selection to make them capable of incorporating a position sizing scheme. Then, to achieve better performance, an investor can develop their own position sizing strategy or their own optimization criteria for portfolio allocation. We then propose a simple position sizing strategy, based on insights from inspecting six benchmark datasets, to obtain better trading results in terms of terminal wealth and maximum drawdown when applied to four existing passive-aggressive algorithms. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:06:16Z (GMT). No. of bitstreams: 1 U0001-2504202216463100.pdf: 728737 bytes, checksum: a6bcb2380907ca39bc0ccc574defa7ce (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 致謝i 摘要ii Abstract iii 目錄iv 圖目錄vi 表目錄vii 第一章緒論1 1.1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 相關研究. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章背景知識4 2.1 符號定義. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 績效計算方式. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 最終獲利倍數. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2 最大回檔比例. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.3 給定手續費下的最終獲利倍數. . . . . . . . . . . . . . . . . . . 7 第三章模型設計10 3.1 設計概念. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.1 PAMR 算法之幾何意義. . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 部位管理機制. . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 套用部位管理後之最佳化問題. . . . . . . . . . . . . . . . . . . . . 14 3.3 演算法設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 第四章實證研究17 4.1 資料來源與實驗流程. . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 市況條件搜尋. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.1 市況條件定義. . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.2 市況條件效果. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3.1 演算法重述. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3.2 參數搜尋與績效對照. . . . . . . . . . . . . . . . . . . . . . . . 25 4.3.3 加入手續費後最終獲利. . . . . . . . . . . . . . . . . . . . . . . 27 4.4 績效討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 第五章結論31 參考文獻32 附錄A — 推導最佳化問題之解35 A.1 情況1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 A.2 情況2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 A.2.1 列出Lagrangian . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 A.2.2 將Lagrangian 對b 進行偏微分. . . . . . . . . . . . . . . . . . . 36 A.2.3 將Lagrangian 對λ 進行偏微分. . . . . . . . . . . . . . . . . . . 36 | |
| dc.language.iso | zh-TW | |
| dc.subject | 投資組合最佳化 | zh_TW |
| dc.subject | 線上投資組合選擇 | zh_TW |
| dc.subject | 被動攻擊算法 | zh_TW |
| dc.subject | 部位管理 | zh_TW |
| dc.subject | 條件機率 | zh_TW |
| dc.subject | 量化交易 | zh_TW |
| dc.subject | quantitative trading | en |
| dc.subject | online portfolio selection | en |
| dc.subject | conditional probability | en |
| dc.subject | position sizing | en |
| dc.subject | passive-aggressive algorithms | en |
| dc.subject | portfolio optimization | en |
| dc.title | 套用部位管理於被動攻擊型線上投資組合策略之研究 | zh_TW |
| dc.title | Applying Position Sizing to Passive-Aggressive Algorithms for Online Portfolio Selection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 金國興(Gow-Hsing King),張經略(Ching-Lueh Chang),陸裕豪(U Hou Lok) | |
| dc.subject.keyword | 線上投資組合選擇,投資組合最佳化,被動攻擊算法,部位管理,條件機率,量化交易, | zh_TW |
| dc.subject.keyword | online portfolio selection,portfolio optimization,passive-aggressive algorithms,position sizing,conditional probability,quantitative trading, | en |
| dc.relation.page | 38 | |
| dc.identifier.doi | 10.6342/NTU202200720 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-07-01 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-05 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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