Dynamic Optimization of Investment Portfolio under Liquidity with Taylor Extension of Value function

Document Type : Research Paper

Authors

1 Faculty of Economics and Accounting, Kish International Branch, Kish, Iran

2 Department of Accounting, Faculty member, South Tehanr Branch, Islamic Azad University, Tehran, Iran

3 Department of Financial management, shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

4 Department of Accounting, East Tehran Branch, Islamic Azad University, Tehran, Iran

10.22075/ijnaa.2020.4597

Abstract

Portfolio management is portfolio management created on behalf of the investor by the financial assets to ensure maximum efficiency within the risk rate and duration set by the investors. The most important goal in creating and managing managed portfolios to achieve maximum efficiency is to reduce the risk. This is equivalent to selecting the optimal portfolio from the portfolio of possible portfolios, which is called portfolio selection problem. The dynamic portfolio optimization model solves the complexities caused by the effects of various factors on the problem by focusing step by step on various factors and then combining the results of these investigations. The main issue in this research is the use of a new tool for selecting investment portfolios in view of the lack of high liquidity or low liquidity of firms and portfolio selection models. The statistical sample is considered for 27 active enterprises in the real of time from the beginning of March to 2014. The results show that the use of asset liquidity index to optimize portfolio using two Taylor series expansion methods has created a significant difference in the weight, yield and risk of portfolio compared to the Markowitz model. Also, the results of calculating the trainer criterion showed that the optimization model obtained from the expansion of the Taylor series of value function has a higher performance than the portfolios obtained from the Taylor process.

Keywords

International Journal of Nonlinear Analysis and Applications
Volume 11, Special Issue
Winter and Spring 2020
Pages 231-248

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