Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (6): 115-128.doi: 10.3969/j.issn.1000-5641.201921020

• Computer Science • Previous Articles     Next Articles

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

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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