Portfolio optimization by using linear programing models based on genetic algorithm

Hidayat, Yuyun; Lesmana, Eman; Putra, Adam Sukma; Napitupulu, Herlina; Supian, Sudradjat

doi:10.1088/1757-899x/300/1/012001

“…Back testing is done to measure the performance of the M V aR i model used. Back testing is done using equations (14) and (15), then the calculation results as shown in Tabel 1 of the column QP S i . The amount of QP S i produced for each stock S 1 up to S 10 , indicates that the values are in the range [0,2], and tend to approach 0.…”

Section: Resultsmentioning

confidence: 99%

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Lesmana¹,

Susanti²,

Napitupulu³

et al. 2019

JMI

0

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Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

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“…Suppose P it and r it successively stated stocks prices and returns i (i = 1, ..., N and N the number of stocks analyzed), at the time t (t = 1, ..., T , T periode observasi data). Stocks return r it calculated using equation r it = ln(P it /P it−1 ) [14], [17]. Furthermore, the return data is analyzed in the following sections.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Sukono

¹

,

Lesmana

²

,

Susanti

³

et al. 2019

jmi

0

View full text Add to dashboard Cite

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

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“…e first computer evolution program in history [2,3] was mainly used to simulate tree groups. e theory that genetic algorithm can be used in the process of design improvement and optimization is proposed, and the theoretical framework of the whole design is explained [4,5]. His design system could be used to make cars and seats; the use of computeraided evolutionary design in the field of artistic innovation [6,7] opens a new door for evolutionary art.…”

Section: Introductionmentioning

confidence: 99%

Illustration Design Model with Clustering Optimization Genetic Algorithm

Liu

¹

,

Chen

²

,

Tian

³

2021

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For the application of the standard genetic algorithm in illustration art design, there are still problems such as low search efficiency and high complexity. This paper proposes an illustration art design model based on operator and clustering optimization genetic algorithm. First, during the operation of the genetic algorithm, the values of the crossover probability and the mutation probability are dynamically adjusted according to the characteristics of the population to improve the search efficiency of the algorithm, then the k-medoids algorithm is introduced to optimize the clustering of the genetic algorithm, and a cost function is used to carry out and evaluate the quality of clustering to optimize the complexity of the original algorithm. In addition, a multiobjective optimization genetic algorithm with complex constraints based on group classification is proposed. This algorithm focuses on the problem of group diversity and uses k-means cluster analysis operation to solve the problem of group diversity. The algorithm divides the entire group into four subgroups and assigns appropriate fitness values to reflect the optimal preservation strategy. A large number of computer simulation calculations show that the algorithm can obtain a widely distributed and uniform Pareto optimal solution, the evolution speed is fast, usually only a few iterations can achieve a good optimization effect, and finally the improved genetic algorithm is used to design the random illustration art. The example simulation shows that the improved algorithm proposed in this paper can achieve higher artistic and innovative illustration art design.

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Portfolio optimization by using linear programing models based on genetic algorithm

Cited by 10 publications

References 9 publications

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Illustration Design Model with Clustering Optimization Genetic Algorithm

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