A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm

•In this work, Genetic Algorithm and Nonlinear Neural network are hybridized.•Mixed Integer Quadratic Optimization problem is solved by the hybridized approach.•The proposed hybrid approach overcomes the challenges of NP-hard problems.•Information Ratio, Sharpe Ratio, Sortino Ratio, Average Return c...

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Bibliographic Details
Published in:Expert systems with applications Vol. 169; p. 114517
Main Authors: Yaman, Ilgım, Erbay Dalkılıç, Türkan
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.05.2021
Elsevier BV
Subjects:
Cardinality constraint
Constraint modelling
Exact solutions
Genetic algorithms
Heuristic methods
ISE-30
Mixed integer
Neural networks
Nonlinear neural network
Optimization
Portfolio optimization
Quadratic programming
Stock exchanges
Portfolio optimization
ISE-30
Nonlinear neural network
Cardinality constraint
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•In this work, Genetic Algorithm and Nonlinear Neural network are hybridized.•Mixed Integer Quadratic Optimization problem is solved by the hybridized approach.•The proposed hybrid approach overcomes the challenges of NP-hard problems.•Information Ratio, Sharpe Ratio, Sortino Ratio, Average Return calculated.•Istanbul Stock Exchange 30 datasets is used. Portfolio Optimization (PO) is an extremely popular method that selects the best portfolio for an investor. When the classic methods fail to find an exact solution, heuristic techniques are designed to find an approximate solution. In the literature, such techniques have often been used in the portfolio selection problem. However, hardly any of these methods utilize a neural network to apportion the extent of stocks. The basic objective of portfolio optimization is to minimize the risk of the portfolio while maximizing the expected return of the portfolio. In reality, the Standard Portfolio Optimization Model does not fulfill stock market requirements in a money related world. Indeed, this problem cannot limit the amount of stock in the portfolio. On the other hand, the precise number of stocks are taken into account by the Cardinality Constraint Portfolio Optimization model, which is the Mixed-Integer Quadratic Programming problem. While minimizing the risk of the portfolio, limiting the expected return and the number of stocks is the main subject of this study. In this study, a hybrid approach is proposed, based on the Nonlinear Neural Network and the Genetic Algorithm to solve the Cardinality Constraint Portfolio Optimization Model. To investigate the effectiveness of the proposed hybrid approach, the Istanbul Stock Exchange (ISE-301) data is used. The ISE-30 data1 consists of daily prices, from May 2015 to May 2017. The ISE-30 data1 from May 2017 to July 2018 is used as out-of-sample. To clarify the effectiveness proposed, the method was applied to publicly data sets which are used for numerous different portfolio selection strategies in many articles. The proposed hybrid approach to the cardinality constraint portfolio optimization problem has more viable outcomes than current strategies.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.114517