Multiperiod Mean Absolute Deviation Uncertain Portfolio Selection

被引:6
|
作者
Zhang, Peng [1 ]
机构
[1] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Uncertain Variable; Multiperiod Uncertain Portfolio Selection; Uncertain Measure; Mean Absolute Deviation; The Forward Dynamic Programming Method;
D O I
10.7232/iems.2016.15.1.063
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.
引用
收藏
页码:63 / 76
页数:14
相关论文
共 50 条
  • [41] Multiperiod portfolio selection on a minimax rule
    Yu, M
    Wang, SY
    Lai, KK
    Chao, X
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2005, 12 (04): : 565 - 587
  • [42] A hybrid intelligent method for mean-absolute downside deviation dynamic portfolio
    Ma, Xiaoxian
    Zhao, Qingzhen
    Liu, Qiang
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2006, 13E : 1492 - 1496
  • [43] Mean-absolute deviation portfolio optimization model under transaction costs
    Konno, H
    Wijayanayake, A
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN, 1999, 42 (04) : 422 - 435
  • [44] Mean-risk model for portfolio selection with uncertain returns
    Li, Wei
    Qian, Weiyi
    Yin, Mingqiang
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ECONOMY, MANAGEMENT AND EDUCATION TECHNOLOGY, 2015, 29 : 369 - 373
  • [45] Mean-risk model for portfolio selection with uncertain returns
    Li, Wei
    Qian, Weiyi
    Yin, Mingqiang
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND ENGINEERING INNOVATION, 2015, 12 : 1764 - 1767
  • [46] Time-Consistent Strategies for a Multiperiod Mean-Variance Portfolio Selection Problem
    Wu, Huiling
    JOURNAL OF APPLIED MATHEMATICS, 2013,
  • [47] A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection
    Pahade, Jagdish Kumar
    Jha, Manoj
    INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2022, 24 (04) : 1958 - 1973
  • [48] A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection
    Jagdish Kumar Pahade
    Manoj Jha
    International Journal of Fuzzy Systems, 2022, 24 : 1958 - 1973
  • [49] A fuzzy mean semi-absolute deviation-semi-variance-proportional entropy portfolio selection model with transaction costs
    Meng, Xiaolian
    Shan, Yue
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 8673 - 8678
  • [50] Portfolio optimization using robust mean absolute deviation model: Wasserstein metric approach
    Hosseini-Nodeh, Zohreh
    Khanjani-Shiraz, Rashed
    Pardalos, Panos M.
    FINANCE RESEARCH LETTERS, 2023, 54