华东师范大学学报（自然科学版） ›› 2020, Vol. 2020 ›› Issue (6): 115-128.doi: 10.3969/j.issn.1000-5641.201921020

• 计算机科学 • 上一篇    下一篇

基于二次平滑-灰色预测的在线投资组合选择

刘晓玉1, 黄定江1,2   

  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
  • 收稿日期:2019-08-27 发布日期:2020-12-01
  • 通讯作者: 黄定江, 男, 研究员, 博士生导师, 研究领域为机器学习与人工智能. E-mail: djhuang@dase.ecnu.edu.cn E-mail:djhuang@dase.ecnu.edu.cn
  • 基金资助:
    国家自然科学基金(11501204, U1711262)

Online portfolio selection based on quadratic smooth-gray prediction

LIU Xiaoyu1, HUANG Dingjiang1,2   

  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:2019-08-27 Published:2020-12-01

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摘要： 在线投资组合是近年来计算金融领域热门的研究课题. 目前已有的策略, 对股票价格的预测效果并不十分理想, 而对股价的准确预测对投资组合方式有重要的指导意义. 考虑到股价的滞后性及其分布的复杂性, 首次利用股价中的二阶信息, 提出了DMAR (DMA (Double Moving Average) Reversion)、DEAR (DEA (Double Exponential Average) Reversion)、GMR (GM Reversion)、DA-GMR (DA-GM Reversion) 4种投资组合策略: 分别通过二次移动平均法、二次指数滑动预测法、灰色预测法, 对下一期的价格数据进行了预测、集成学习; 将二次平滑预测和灰色预测的结果进行了优化, 得到了下一期的预测价格; 再利用被动攻击(Passive-Aggressive, PA)算法更新投资组合, 最终得到了4种投资组合策略, 并在真实的金融市场的数据集中验证了策略的有效性. 结果表明, 与已有的算法相比, 在NYSE (O)、NYSE (N)、DJIA和MSCI 这4个真实的金融市场的数据集上, 所提出的4种投资组合策略都达到了较高的累计收益.

关键词: 在线学习, 投资组合选择, 二次平滑预测

Abstract: In recent years, online portfolios have been a popular topic of research in computational finance. The existing strategies used to forecast stock prices is not ideal, and accurate prediction of stock prices is important for evaluating investment portfolios. Considering the lag in stock prices and the complexity of their distribution, this paper makes use of second-order information in stock prices, for the first time, and proposes four strategies, namely DMAR(DMA(double moving average) reversion), DEAR(DEA(double exponential average) reversion), GMR(GM reversion), and DA-GMR(DA-GM reversion). The second-order moving average method, the second exponential sliding prediction method, and the gray prediction method are used to predict price data for the next period; integrated learning optimizes the results of second-order smoothing prediction and the gray prediction is used to obtain the predicted price. Next, we use PA(passive-aggressive) algorithms to update the portfolio, and we arrive at four portfolio strategies. We verify the effectiveness of these strategies using real data from the financial market. The results show that compared with existing algorithms, the four strategies proposed in this paper achieved higher cumulative returns on datasets for NYSE(O), NYSE(N), DJIA, and MSCI.

Key words: online learning, portfolio selection, quadratic smooth prediction

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