广东工业大学学报 ›› 2018, Vol. 35 ›› Issue (03): 61-66.doi: 10.12052/gdutxb.170163

• 综合研究 • 上一篇    下一篇

基于移动窗口的适应性在线投资组合策略

杨兴雨, 何锦安, 沈健华   

  1. 广东工业大学 管理学院, 广东 广州 510520
  • 收稿日期:2017-12-05 出版日期:2018-05-09 发布日期:2018-04-26
  • 作者简介:杨兴雨(1981-),男,副教授,博士,主要研究方向为金融工程与在线金融决策.
  • 基金资助:
    国家自然科学基金资助项目（71301029）；广东省高等学校优秀青年教师培养计划（Yq2013060）；广东省哲学社会科学2016年度学科共建项目（17ZS0103）

An Adaptive Online Portfolio Strategy Based on Moving Window

Yang Xing-yu, He Jin-an, Shen Jian-hua   

  1. School of Management, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2017-12-05 Online:2018-05-09 Published:2018-04-26

HTML

PDF (PC)

2747

摘要： 在线投资组合选择问题是当前量化投资领域一个重要的研究问题.为了避免剧烈波动的股票市场中过去较长时间的股价数据对当前的投资决策产生干扰，基于移动窗口设计在线投资组合策略.首先利用近期股价数据，计算所有定常再调整策略的近期表现并对其进行排序；根据其排序构造权重，对所有定常再调整策略进行加权平均，提出了基于移动窗口的策略；进一步采用适应性学习的方法选择移动窗口的长度，提出了适应性学习的策略.采用实际股价数据对提出的策略进行了实证分析，结果表明它们具有较好的性能.

关键词: 投资组合, 移动窗口, 适应性策略, 在线算法, 实证分析

Abstract: Online portfolio selection is an important research problem in the field of quantitative investment. To avoid the interference with current investment decisions caused by the stock price data far from now in the intensely fluctuating stock market, online portfolio strategies based on moving window are designed. Using the recent stock price data, the recent performances of all constant rebalanced portfolios are computed and ranked. An online portfolio strategy based on moving window is designed by weighted averaging all constant rebalanced portfolios. Further using adaptive learning method to select the length of the moving window, the adaptive learning strategy is put forward. Empirical analyses are made on the proposed strategies using the real stock price data. The results show that they have better performance.

Key words: portfolio, moving window, adaptive strategy, online algorithm, empirical analysis

中图分类号: 

  • F830.59
[1] MARKOWITZ H M. Portfolio selection[J]. Journal of Finance, 1952, 7(1):77-91.
[2] 王伟, 刘巍. 不确定收益率下投资组合的可拓评价及变换[J]. 广东工业大学学报, 2012, 29(1):83-87.WANG W, LIU W. Extension evaluation and transformation of the stock under uncertain profit rates[J]. Journal of Guangdong University of Technology, 2012, 29(1):83-87.
[3] BASAK S, CHABAKAURI G. Dynamic mean-variance asset allocation[J]. Review of Financial Studies, 2010, 23(8):2970-3016.
[4] HUANG X X, QIAO L. A risk index model for multi-period uncertain portfolio selection[J]. Information Sciences, 2012, 217(25):108-116.
[5] COVER T M. Universal portfolios[J]. Mathematical Finance, 1991, 1(1):1-29.
[6] SINGER Y. Switching portfolios[J]. International Journal of Neural Systems, 1997, 8(4):445-455.
[7] AGARWAL A, HAZAN E, KALE S, et al. Algorithms for portfolio management based on the Newton method[C]//Proceedings of International Conference on Machine Learning. Pittsburgh:ACM, 2006:9-16.
[8] HELMBOLD D, SCHAPIR R, SINGER Y, et al. On-line portfolio selection using multiplicative updates[J]. Mathematical Finance, 1998, 8(4):325-347.
[9] 张卫国, 张永, 徐维军, 等. 基于线性学习函数的泛证券投资组合策略[J]. 系统工程理论与实践, 2012, 32(8):1647-1654.ZHANG W G, ZHANG Y, XU W J, et al. Universal portfolio based on on-line learning of linear function[J]. Systems Engineering-Theory & Practice, 2012, 32(8):1647-1654.
[10] 张永, 张卫国, 徐维军, 等. 集成有限个专家意见的在线投资组合策略[J]. 系统工程理论与实践, 2015, 35(1):57-66.ZHANG Y, ZHANG W G, XU W J, et al. Online portfolio selection strategy by aggregating finite expert advices[J]. Systems Engineering-Theory & Practice, 2015, 35(1):57-66.
[11] 刘善存, 邱菀华, 汪寿阳. 带交易费用的泛证券组合投资策略[J]. 系统工程理论与实践, 2003, 23(1):22-25.LIU S C, QIU W H, Wang S Y. Universal portfolio selection with transaction Costs[J]. Systems Engineering-Theory & Practice, 2003, 23(1):22-25.
[12] BORODIN A, EL-YANIV R, GOGAN V. Can we learn to beat the best stock[J]. The Journal of Artificial Intelligence Research, 2004, 21:579-594.
[13] LI B, ZHAO P, HOI S C H, et al. PAMR:Passive aggressive mean reversion strategy for portfolio selection[J]. Machine Learning, 2012, 87(2):221-258.
[14] LI B, HOI S C H, SAHOO D, et al. Moving average reversion strategy for on-line portfolio selection[J]. Artificial Intelligence, 2015, 222(1):104-123.
[15] O'SULLIVAN P, EDELMAN D. Adaptive universal portfolios[J]. The European Journal of Finance, 2015, 21(4):337-351.
[1] 陈思豆, 黄卓铨, 杨兴雨. 考虑限制性卖空的多期模糊投资组合优化模型[J]. 广东工业大学学报, 2021, 38(02): 39-47.
[2] 杨兴雨, 刘伟龙, 井明月, 张永. 基于模糊收益率的分散化投资组合调整策略[J]. 广东工业大学学报, 2020, 37(05): 13-21.
[3] 何锦安, 王蓓, 林家星. 基于活跃专家意见的在线投资组合策略[J]. 广东工业大学学报, 2020, 37(04): 59-64.
[4] 刘冬宁, 武小亮, 卢明健, 卢明俊. 广告关键字群组角色组合投资预测研究[J]. 广东工业大学学报, 2018, 35(03): 54-60.
[5] 王伟1,刘巍2. 不确定收益率下投资组合的可拓评价及变换[J]. 广东工业大学学报, 2012, 29(1): 83-87.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发