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All Journal International Journal of Business, Economics, and Social Development Operations Research: International Conference Series International Journal of Quantitative Research and Modeling
Syahla, Raynita
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences

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Determination of Micro-Insurance Premium Price for Fisheries Using Poisson-Exponential Aggregate Distribution Approach Fadhilah, Dila Nur; Syahla, Raynita; Azahra, Astrid Sulistya
Operations Research: International Conference Series Vol. 5 No. 2 (2024): Operations Research International Conference Series (ORICS), June 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i2.315

Abstract

Engaging in pond aquaculture is currently an attractive option amid the high demand for fish in the market. Entrepreneurial opportunities in the pond fish farming sector are increasingly open, although the risk of crop failure remains, both due to weather factors and the livestock process. Crop failure can have a significant financial impact on pond aquaculture cultivators. Therefore, it is necessary to have special insurance to protect against financial losses due to risks that can occur, namely Fisheries Micro Insurance. Microinsurance is a type of insurance product that is specifically designed for low-income people, offering simple, accessible, economically priced features and administration, and a fast compensation settlement process. The focus of this research is to calculate premium prices by applying an aggregate risk model approach. The data used is the number of incidents and the amount of losses due to crop failure in shrimp pond cultivation in Pandeglang Regency in the period of January 1, 2019-January 1, 2021. Data on the number of events follows the Poisson distribution, while data on the magnitude of losses follows the Exponential distribution. Furthermore, the Maximum Likelihood Estimation (MLE) method is used to calculate the parameter estimation. The average and variance estimates of the aggregate risk are used to determine the amount of premiums. The result of the premium selection in this study is IDR 42,005,600. The amount of premium reflects the collective premium resulting from the calculation based on the standard deviation principle.
The Comparison of Investment Portfolio Optimization Result of Mean-Variance Model Using Lagrange Multiplier and Genetic Algorithm Syahla, Raynita; Susanti, Dwi; Napitupulu, Herlina
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.611

Abstract

Investment portfolio optimization is carried out to find the optimal combination of each stock with the aim of maximizing returns while minimizing risk by diversification. However, the problem is how much proportion of funds should be invested in order to obtain the minimum risk. One approach that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The purpose of this study is to compare the results of the Mean-Variance model investment portfolio optimization using Lagrange Multiplier method and Genetic Algorithm. The data used are stocks that are members of the LQ45 index for the period February 2020-July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL stocks. The optimal portfolio generated by the Lagrange Multiplier method has a risk of 0.000606 and a return of 0.000726. Meanwhile, using the Genetic Algorithm resulted in a risk of 0.000455 and a return of 0.000471. Thus, the Genetic Algorithm method is more suitable for investors who prioritize lower risk. Meanwhile, the Lagrange Multiplier method produces a relatively higher risk, making it less suitable for investors who expect a small risk. 
Optimization of Investment Portfolio Mean-Variance Model Using Genetic Algorithm Syahla, Raynita; Susanti, Dwi; Napitupulu, Herlina
International Journal of Business, Economics, and Social Development Vol. 5 No. 2 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i2.654

Abstract

The optimization of investment portfolio is aimed at finding the optimal combination of each stock with the goal of maximizing returns while minimizing risk through diversification. However, the question is how much funds should be invested to achieve the minimum risk. One of the approaches that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The aim of this research is to determine the weights of the optimal portfolio components with the minimum risk. The data used consists of stocks included in the LQ45 index for the period from February 2020 to July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL. The allocated weights for each stock are ADRO 9.896%, AKRA 32.049%, BBCA 30.749%, CPIN 13.949%, and EXCL 13.357%. The optimal portfolio generated by the Genetic Algorithm method has a risk of 0.000472 and an expected return of 0.000492.
Co-Authors Azahra, Astrid Sulistya Dwi Susanti Fadhilah, Dila Nur Herlina Napitupulu