BUILDING AN OPTIMAL STOCK PORTFOLIO WITH MEAN- SEMI VARIANCE MODEL BY USING GENETIC ALGORITHM

Grahandika, Ganis (2019) BUILDING AN OPTIMAL STOCK PORTFOLIO WITH MEAN- SEMI VARIANCE MODEL BY USING GENETIC ALGORITHM. Undergraduate thesis, UNDIP.

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Abstract

Genetic algorithm is a metaheuristic search algorithm based on the mechanism of natural selection and genetic operations to get a solution. The solution obtained from the genetic algorithm is determined by the operator and the parameters used. One of the problems that can be solved using genetic algorithms is the formation of an optimal stock portfolio. In the formation of stock portfolios, the portfolio model is also used, the mean semi variance, to calculate the level of profit and loss level that exists in the period May 2017 - December 2018. The shares used are LQ 45 shares with each share grouped in a certain group of shares totaling 8. The 8 stocks are then searched for the optimal weight of each share by using a genetic algorithm. The conventional method which will be used as the basis for choosing the optimal stock portfolio formation decision is by manual calculation using a lingo application with a mean semi variance risk model. From the calculation results, it is found that solving the problem of the formation of an optimal stock portfolio using genetic algorithms provides optimal solution results. The results obtained are by using a crossover operator in the form of extended intermediate crossover, mutation using random mutation, and selection using selection elistism. From these operators, optimal results were obtained with a population of 100, generation of 275, and a crossover rate of 0.6 mutation rate of 0.4. Keywords: Genetic Algorithms, Stock Portfolios, Mean Semi Variance, Optimal, Stocks.

Item Type:Thesis (Undergraduate)
Subjects:Q Science > QA Mathematics
Divisions:Faculty of Science and Mathematics > Department of Mathematics
ID Code:84235
Deposited By:Admin Departemen Matematika
Deposited On:12 Jun 2022 12:34
Last Modified:12 Jun 2022 12:34

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