A multiperiod multiobjective portfolio selection model with fuzzy random returns for large scale securities data
Wu, Y; Li, C; Lu, Z; et al.Wang, J; Hu, Y
Date: 6 May 2020
Article
Journal
IEEE Transactions on Fuzzy Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
It is agreed that portfolio selection models are of
great importance for the financial market. In this article, a
constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the
trading restrictions and quantifies future security returns by
fuzzy random variables to ...
It is agreed that portfolio selection models are of
great importance for the financial market. In this article, a
constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the
trading restrictions and quantifies future security returns by
fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile, it considers terminal
wealth, conditional value at risk (CVaR), and skewness as
tricriteria for decision making. Obviously, the proposed model
is computationally challenging. This situation gets worse when
investors are interested in a larger financial market since the data
they need to analyze may constitute typical big data. Whereafter,
a novel intelligent hybrid algorithm is devised to solve the
presented model. In this algorithm, the uncertain objectives of
the model are approximated by a simulated annealing resilient
back propagation (SARPROP) neural network which is trained
on the data provided by fuzzy random simulation. An improved
imperialist competitive algorithm, named IFMOICA, is designed
to search the solution space. The intelligent hybrid algorithm
is compared with the one obtained by combining NSGA-II,
SARPROP neural network, and fuzzy random simulation. The
results demonstrate that the proposed algorithm significantly
outperforms the compared one not only in the running time
but also in the quality of obtained Pareto frontier. To improve
the computational efficiency and handle the large scale securities
data, the algorithm is parallelized using MPI. The conducted
experiments illustrate that the parallel algorithm is scalable and
can solve the model with the size of securities more than 400 in
an acceptable time.
Computer Science
Faculty of Environment, Science and Economy
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