Analysis of portfolio optimization with lot of stocks amount constraint: case study index LQ45

Chin, Liem; Chendra, Erwinna; Sukmana, Agus

doi:10.1088/1757-899x/300/1/012004

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…In this work, the portfolio optimization problem represents this real-world optimization problem, especially in the financial sector. A portfolio can be defined as a collection of investments owned by a person or institution [31]. These investments can be stocks, bonds, and any other assets.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Guided Pelican Algorithm

2022

IJIES

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This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA's performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.

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Section: Simulation and Resultsmentioning

confidence: 99%

Guided Pelican Algorithm

2022

IJIES

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“…In this work, the proposed algorithm is challenged to tackle the portfolio optimization problem. A portfolio is a set of valuable and productive assets that is owned by individual or institutions [26]. This asset can be property, stock, bond, gold, and so on.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Portfolio represents the wealth of the entity. As a portfolio, an individual or institution should distribute its asset into several options [26]. The objective of this arrangement is to protect its value in the context of maximizing the profit and avoiding the lost.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Hybrid Pelican Komodo Algorithm

Kusuma¹,

Dinimaharawati²

2022

IJACSA

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In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed. This algorithm is developed by hybridizing two shortcoming metaheuristic algorithms: the Pelican Optimization Algorithm (POA) and Komodo Mlipir Algorithm (KMA). Through hybridization, the proposed algorithm is designed to adapt the advantages of both POA and KMA. Several improvisations regarding this proposed algorithm are as follows. First, this proposed algorithm replaces the randomized target with the preferred target in the first phase. Second, four possible movements are selected stochastically in the first phase. Third, in the second phase, the proposed algorithm replaces the agent's current location with the problem space width to control the local problem space. This proposed algorithm is then challenged to tackle theoretical and real-world optimization problems. The result shows that the proposed algorithm is better than grey wolf optimizer (GWO), marine predator algorithm (MPA), KMA, and POA in solving 14, 12, 14, and 18 functions. Meanwhile, the proposed algorithm creates 109%, 46%, 47%, and 1% better total capital gain rather than GWO, MPA, KMA, and POA, respectively in solving the portfolio optimization problem.

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“…The line which we call CML (Capital Market Line) intersects with the efficient frontier. According to [1,[3][4][5], the followings are the formulas to calculate the Markowitz model.…”

Section: Methodsmentioning

confidence: 99%

Best Portfolio Choice by Using Markowitz Model

Chen¹

2022

Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

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Our paper practically implements the idea that people can have the best portfolio choice by Markowitz Model. Using a recent 20 years of historical daily total return data for ten stocks from S&P 500, one equity index (S&P 500) and a proxy for risk-free rate (1-month Fed Funds rate), we use Excel to arrange those data and categorize them with Solver Table . Then, to reduce the non-Gaussian effects, we aggregate the daily data into the monthly observations based on those monthly observations. Finally, we calculate all proper optimization inputs for the full Markowitz Model ("MM").

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Analysis of portfolio optimization with lot of stocks amount constraint: case study index LQ45

Cited by 8 publications

References 1 publication

Guided Pelican Algorithm

Guided Pelican Algorithm

Hybrid Pelican Komodo Algorithm

Best Portfolio Choice by Using Markowitz Model

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