Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (8): 2462-2467.DOI: 10.11772/j.issn.1001-9081.2018122588

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Semi-exponential gradient strategy and empirical analysis for online portfolio selection

WU Wanting1, ZHU Yan1, HUANG Dingjiang2   

  1. 1. School of Science, East China University of Science and Technology, Shanghai 200237, China;
    2. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2019-01-02 Revised:2019-03-12 Online:2019-08-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China — Guangdong United Foundation (U1711262).


吴婉婷1, 朱燕1, 黄定江2   

  1. 1. 华东理工大学 理学院, 上海 200237;
    2. 华东师范大学 数据科学与工程学院, 上海 200062
  • 通讯作者: 吴婉婷
  • 作者简介:吴婉婷(1994-),女,吉林吉林人,硕士研究生,主要研究方向:机器学习、在线学习;朱燕(1992-),女,安徽马鞍山人,硕士,主要研究方向:在线学习;黄定江(1981-),男,江西上饶人,教授,博士,主要研究方向:机器学习、人工智能。
  • 基金资助:

Abstract: Since the high frequency asset allocation adjustment of traditional portfolio strategies in each investment period results in high transaction costs and poor final returns, a Semi-Exponential Gradient portfolio (SEG) strategy based on machine learning and online learning was proposed. Firstly, the SEG strategy model was established by adjusting the portfolio only in the initial period of each segmentation of the investment period and not trading in the rest of the time, then a objective function was constructed by combining income and loss. Secondly, the closed-form solution of the portfolio iterative updating was solved by using the factor graph algorithm, and the theorem and its proof of the upper bound on the cumulative loss of assets accumulated were given, guaranteeing the return performance of the strategy theoretically. The experiments were performed on several datasets such as the New York Stock Exchange. Experimental results show that the proposed strategy can still maintain a high return even with the existence of transaction costs, confirming the insensitivity of this strategy to transaction costs.

Key words: machine learning, online learning, portfolio selection, semi-exponential gradient strategy, factor graph

摘要: 针对传统投资组合策略的高频资产配置调整产生高额交易成本从而导致最终收益不佳这一问题,提出基于机器学习与在线学习理论的半指数梯度投资组合(SEG)策略。该策略对投资期进行划分,通过控制投资期内的交易量来降低交易成本。首先,基于仅在每段分割的初始期调整投资组合而其余时间不进行交易这一投资方式来建立SEG策略模型,并结合收益损失构造目标函数;其次,利用因子图算法求解投资组合迭代更新的闭式解,并证明该策略累积资产收益的损失上界,从理论上保证算法的收益性能。在纽约交易所等多个数据集上进行的仿真实验表明,该策略在交易成本存在时仍然能够保持较高的收益,证实了该策略对于交易成本的不敏感性。

关键词: 机器学习, 在线学习, 投资组合选择, 半指数梯度策略, 因子图

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