摘要
基于过去信息对股价进行预测是无统计假设下在线投资组合的关键问题之一。为了减小市场异常值或白噪声的影响,本文采用多期历史价格信息预测下一期的股价,并结合指数平滑法和L_(1)-中位数估计法构造组合预测模型。在股价预测值的基础上,以期望收益最大化作为目标进行决策。为减少交易费用,在优化目标函数中加入一个惩罚项来调节投资比例的变动幅度,通过求解得到一种新的在线投资组合选择策略。理论分析结果证明,本文所提出的策略与最优定常再调整策略的平均对数收益率渐近相同。数值分析结果进一步表明:该文提出的策略在国内外多个数据集上的表现均优于其他经典的泛证券投资组合策略,能够承受一定范围内的交易费用,并且对其他参数的选择不敏感。
Online portfolio selection is regarded as an important research issue in the field of quantitative finance,which often aims to maximize returns or risk-adjusted returns.Mean-variance model,a classic portfolio model,assumes that the returns on assets obey a certain probability distribution,which characterizes the return and risk by calculating the mean value and covariance matrix of the portfolio,respectively.However,it is difficult to accurately obtain the future return or return distribution of assets,and the only information that can be accurately grasped is historical price data.Therefore,some scholars try to use only historical information to construct portfolio strategy,so they pay more and more attention to online portfolio selection problem.The so-called“online”means that when making decisions in the current period,the updated investment proportion only depends on the historical data obtained up to the beginning of the current investment,and the cycle is carried out until the end of the whole investment.Stock price prediction based on past information is one of the key problems of online portfolio selection without statistical assumption.In this paper,historical price data are used to predict the stock prices,and then a new online portfolio selection strategy is constructed.In the first part of this paper,we design a new online portfolio selection strategy based on the predicted stock prices with the goal of maximizing expected returns.First of all,in order to minimize the influence of market outliers or white noise,we adopt multiperiod historical price information to predict the stock prices for the next period.Secondly,in order to reduce the prediction bias caused by a single prediction model,the exponential smoothing method and L_(1)-median estimation method are combined to construct a combination forecasting model.Then,the stock estimator can be obtained based on the above-mentioned combination forecasting model.Finally,a new online portfolio selection strategy named Combination Forecasting for Exp
作者
林虹
张永
杨兴雨
黎嘉豪
LIN Hong;ZHANG Yong;YANG Xingyu;LI Jiahao(School of Management,Guangdong University of Technology,Guangzhou 510520,China)
出处
《管理工程学报》
CSSCI
CSCD
北大核心
2023年第5期130-141,共12页
Journal of Industrial Engineering and Engineering Management
基金
教育部人文社会科学研究基金资助项目(21YJA630117、18YJA630132)
广东省哲学社会科学规划项目(GD19CGL06)。
关键词
在线投资组合
泛证券投资组合
组合股价预测
最优定常再调整策略
Online portfolio selection
Universal portfolios
Combination forecasting price
Best constant rebalanced portfolios
