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All Journal Media Statistika Matematika: Jurnal Teori dan Terapan CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Cendekia : Jurnal Pendidikan Matematika International Journal of Supply Chain Management Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Kumawula: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat International Journal of Business, Economics, and Social Development International Journal of Research in Community Services Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Herlina Napitupulu
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Journal : International Journal of Quantitative Research and Modeling

 1 
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Febrianty, Popy; Napitupulu, Herlina; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1002

Abstract

Stock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311.
Application of Mathematical Model in Bioeconomic Analysis of Skipjack Fish in Pelabuhanratu, Sukabumi Regency, Jawa Barat Nurkasyifah, Fathimah Syifa; Supriatna, Asep K.; 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.v5i1.598

Abstract

Presently, sustainability has emerged as a crucial and compelling concern across diverse sectors, evolving into a long-term agenda championed by the United Nations through the implementation of the Sustainable Development Goals (SDGs). Within the SDGs, particularly under point 14 addressing life below water, emphasis is placed on ensuring sustainability in aquatic ecosystems, encompassing the fisheries sector. The concept of Maximum Sustainable Yield (MSY) holds significance in the bioeconomic analysis of fisheries, influencing decision-making processes aimed at preserving sustainability. Regrettably, several studies have identified inaccuracies in the determination of MSY, leading to instances of overfishing in various regions. Conversely, it is imperative to give due attention to Maximum Economic Yield (MEY) to ensure that economic considerations remain integral to decision-making processes. Consequently, a more comprehensive and detailed bioeconomic analysis, incorporating mathematical models, becomes essential. Among these models, the logistic growth rate model and the Gompertz growth rate model stand out as significant contributors. 
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. 
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Febrianty, Popy; Napitupulu, Herlina; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1002

Abstract

Stock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311.
Co-Authors Adi Suripto, Adi Agus Santoso Aisyah, Ranti Rivani Albert Raja Harungguan Alit Kartiwa Alit Kartiwa Ariesandy, Sena Asep K. Supriatna Asep K. Supriatna Asep Kuswandi Supriatna Balqis, Viona Prisyella Betty Subartini Diah Chaerani Dwi Purnomo Dwi Susanti Dwi Susanti Edi Kurniadi Edi Kurniadi Edi Kurniadi Ema Carnia Eman Lesmana Erwin Harahap Ewen Hokijuliandy Fasa, Rayyan Al Muddatstsir Fauziyah, Wida Nurul Febrianty, Popy Firdaniza Firdaniza Firdaus, Hamidah 'Alina Firosi, Valeska Isma Ghazali, Puspa Liza Hadiana, Asep Id Helma Syifa Izzadiana Hidayana, Rizki Apriva Ida Widianingsih Ira Sumiati Ismail Bin Mohd Jeane R. M. D. P Chantique Julita Nahar, Julita Kusumaningtyas, Valentina Adimurti Melina Melina Melina Melina, Melina Michael Lim Michelle Selina Buntara Muhammad Deni Johansyah Muhammad Helambang Prakasa Yudha Muhammad Ribhan Hadiyan Nabilla, Ulya Norizan Mohamed Novitasari, Ela Nurkasyifah, Fathimah Syifa Nursanti Anggriani Nurul Gusriani Nurul Gusriani, Nurul Rahmadini, Nurhaliza Riaman Riaman Ridwan Pandiya Saprilian Hidayat Saputra, Jumadil Satyaputra, Ida Bagus Wira Krishna Siti Aizal Yasni Ellena Sudrajat Supian Suhaimi, Nurnisaa binti Abdullah Sukono . Supian, Sudradjat Supian, Sudrajat Sutisna, Sarah Syahla, Raynita Valentina Adimurti Kusumaningtyas Valerie ​Valerie Valerie ​Valerie Viona Prisyella Balqis Wida Nurul Fauziyah Yosza Dasril Yudha, Muhammad Helambang Prakasa Yulison Herry Chrisnanto Yuyun Hidayat