Investment Portfolio Selection Model Based on Typical Transaction Cost
Author(s) -
Jinchuan Wang
Publication year - 2016
Publication title -
revista tecnica de la facultad de ingenieria universidad del zulia
Language(s) - English
Resource type - Journals
eISSN - 2477-9377
pISSN - 0254-0770
DOI - 10.21311/001.39.4.47
Subject(s) - transaction cost , portfolio , selection (genetic algorithm) , investment (military) , investment portfolio , database transaction , business , computer science , economics , microeconomics , finance , database , artificial intelligence , political science , politics , law
This paper attempts to build an investment portfolio optimization model based on the typical transaction cost. First, shortages of current investment portfolio models are analyzed. By analyzing the changing process of the transaction cost in practical investments, the author introduces the non-concave and non-convex transaction cost function and the mean-variance model, and adopts the investment risk value as the objective value and the comprehensive earnings value as the restriction under the prerequisite of no short selling. The rate of return on investment, the rate of investment dividends and the typical transaction cost function are taken into consideration to build the investment portfolio optimization model. At last, the improved PSO algorithm is employed to get solutions. Based on cases of numerical value, the influence of different transaction cost functions and model parameters on the investment portfolio is analyzed. Calculation results suggest that: the typical transaction function can achieve an efficient investment portfolio and increase the market investment efficiency. The weight coefficient, , of objective risks, does not influence the objective investment portfolio results, but just influence the risk value, f . The judgment of the risk value is not influenced.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom