Portfolio selection based on a nonlinear neural network: An application on the Istanbul Stock Exchange (ISE30)

Year 2019, Volume: 68 Issue: 2, 1709 - 1723, 01.08.2019

İlgım Yaman Türkan Erbay Dalkılıç

https://doi.org/10.31801/cfsuasmas.443670

Abstract

Heuristic techniques have used frequently in portfolio
optimization problem. However, almost none of these techniques used a neural
network to allocate the proportion of stocks. The main goal of portfolio
optimization problem is minimizing the risk of portfolio while maximizing the
expected return of the portfolio. This study tackles a neural network in order to
solve the portfolio optimization problem. The data set is the daily price of
Istanbul Stock Exchange-30 (ISE-30) from May 2015 to May 2017.  This study uses Markowitz’s Mean-Variance
model. Indeed, the portfolio optimization model is quadratic programming (QP)
problem. Therefore, many heuristic methods were used to solve portfolio
optimization method such as particle swarm optimization, ant colony
optimization etc. In fact, these methods do not satisfy stock markets demands
in the financial world. This study proposed a nonlinear neural network to solve
the portfolio optimization problem. 

Keywords

Portfolio optimization, Nonlinear neural network, ISE-30

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