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    题名: 基於移動平均和移動標準差的CORN專家切換策略
    An CORN expert switching strategy based on moving averages and moving standard deviations
    作者: 黃旻宜
    Huang, Min-Yi
    贡献者: 黃子銘
    鄭宇翔
    Huang, Zhi-Min
    ZHENG,YU-XIANG
    黃旻宜
    Huang, Min-Yi
    关键词: 投資組合
    相關係數學習無母數方法
    移動標準差
    移動平均值
    交叉驗證
    Portfolio selection
    Correlation-driven Nonparametric Learning Approach
    Moving standard deviation
    Moving average
    Cross-validation
    日期: 2023
    上传时间: 2023-08-02 13:05:31 (UTC+8)
    摘要: 本研究著重於探討無母數學習的投資組合選擇方法，即Correlation-driven Nonparametric Learning Approach (CORN)。考慮到相關係數門檻與市場視窗選擇對策略表現的關鍵影響，我們提出了兩種策略的改進方法，包括新增辨識歷史相似資料的權重參數、基於移動平均和移動標準差視窗的專家切換策略，並基於定期交叉驗證選擇視窗組合。

    實證研究顯示，新引進的權重參數對投資績效確實具有影響力，但過度依賴近期數據可能導致表現不佳。在固定視窗參數的情況下，切換策略可有效地提高累積績效，而且定期進行交叉驗證更能減少策略在某些參數上表現不佳的問題，進而提升模型的穩健性。然而，我們也必須指出，在面對整個市場下跌時，這些策略同時也承受相對較大的損失。因此，在進行投資決策時，我們需要綜合考慮各種因素，並對策略的優缺點做出謹慎評估。
    This research explores the Correlation-driven Nonparametric Learning Approach for portfolio selection (CORN). Considering the significant impact of the correlation coefficient threshold and market window selection on strategy performance, we propose two improvements: introducing a new weight parameter for charactering time effect, and a switching strategy based on moving average and moving standard deviation windows with parameters selected using cross validation.

    Empirical studies indicate that the new weight parameter impacts performance, but excessive weighting on recent historical data may result in worse performance. Under fixed window parameters, switching strategies effectively enhance cumulative performance. Applying cross-validation for parameter selection can help stabilize the performance of the strategy, increase the robustness of the model. However, during market slumps, these strategies have a higher risk of losses. Therefore, investment decision should be made carefully, taking into consideration of various factors and the pros and cons of the strategies.
    參考文獻: Borodin, A., El-Yaniv, R., and Gogan, V. (2004). Can we learn to beat the best stock. Journal of Artificial Intelligence Research 21:579–594.

    Cover, T. M. (1991). Universal portfolios. Mathematical finance, 1(1):1–29.

    Gaivoronski, A. A. and Stella, F. (2000). Stochastic nonstationary optimization for finding universal portfolios. Annals of Operations Research, 100:165–188.

    Györfi, L., Lugosi, G., and Udina, F. (2006). Nonparametric kernel-based sequential in- vestment strategies. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 16(2):337–357.

    Györfi, L., Udina, F., and Walk, H. (2008). Nonparametric nearest neighbor based empir- ical portfolio selection strategies. 26(2):145–157.

    Helmbold, D. P., Schapire, R. E., Singer, Y., and Warmuth, M. K. (1998). On-line portfolio selection using multiplicative updates. Mathematical Finance, 8(4):325–347.
    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.

    Kelly, J. L. (1956). A new interpretation of information rate. the Bell system technical journal, 35(4):917–926.

    Kozat, S. S. and Singer, A. C. (2007). Universal constant rebalanced portfolios with switching. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages 1129–1132.

    Li, B. and Hoi, S. C. (2014). Online portfolio selection: A survey. ACM Computing Surveys (CSUR), 46(3):1–36.

    Li, B., Hoi, S. C., and Gopalkrishnan, V. (2011). CORN: Correlation-driven nonparametric learning approach for portfolio selection. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):1–29.

    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.

    Li, B., Hoi, S. C., Zhao, P., and Gopalkrishnan, V. (2013). Confidence weighted mean reversion strategy for online portfolio selection. ACM Transactions on Knowledge Dis- covery from Data (TKDD), 7(1):1–38.

    Li, B., Zhao, P., Hoi, S. C., and Gopalkrishnan, V. (2012). PAMR: Passive aggressive mean reversion strategy for portfolio selection. Machine learning, 87:221–258.

    Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91.

    Vovk, V. and Watkins, C. (1998). Universal portfolio selection. In Proceedings of the
    eleventh annual conference on Computational learning theory, pages 12–23.
    描述: 碩士
    國立政治大學
    統計學系
    110354028
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110354028
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

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