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International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 236 Documents
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Portfolio Optimization by Considering Return Predictions Using the ARIMA Method on Jakarta Islamic Index Sharia Stocks Millantika, Salwa Cendikia
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.v6i2.1012

Abstract

In investment decision-making, accurate return projections are an important component in maximizing profits while minimizing risk. This study aims to construct an optimal stock portfolio in the Jakarta Islamic Index (JII) sharia stock sector by considering return predictions using the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is used to forecast future stock returns based on historical data. The prediction results are then utilized as input for expected returns in the Mean-Variance portfolio optimization model developed by Markowitz. This model considers the trade-off between expected return and risk (variance), with the goal of forming an optimal portfolio. The portfolio is evaluated to compare the performance of the prediction-based portfolio with the historical return-based portfolio. This study is expected to contribute to data-driven quantitative investment strategies and statistical predictions. The results of this study indicate that the ARIMA model is effective in predicting stock returns, which in turn improves the efficiency of portfolio construction. The prediction-based portfolio yields a higher average weekly return of 0.87% compared to 0.65% from the historical-based portfolio. Furthermore, the risk level, measured by standard deviation, is slightly lower in the prediction-based portfolio (1.46%) than in the historical one (1.50%). This leads to a significant improvement in the Sharpe ratio, rising from 0.43 to 0.60. These findings demonstrate that integrating ARIMA-based predictions into the portfolio optimization process enhances overall performance by increasing return per unit of risk. Therefore, the use of forecasting models such as ARIMA in portfolio selection provides a valuable tool for investors seeking to make more informed, data-driven investment decisions—particularly within the context of sharia-compliant equity markets such as the Jakarta Islamic Index.
Portfolio Performance Analysis with Jensen's Alpha Using Single Index Model and CAPM on IDX30 Stocks Wahid, Alim Jaizul; Saputra, Jumadil
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.v6i2.1013

Abstract

This study aims to evaluate the formation of an optimal stock portfolio using the Capital Asset Pricing Model (CAPM) and Single Index Model (SIM) approaches, and to assess portfolio performance using Jensen's Alpha generated from stocks included in the IDX30 index during the period April 2024 to March 2025. This study uses a quantitative descriptive approach with a population of 30 IDX30 stocks. The methods applied include calculating stock returns and betas, as well as forming an optimal portfolio using the CAPM and SIM formulas. Portfolio performance is then measured by Jensen's Alpha. The results of the study show that based on CAPM, BRIS.JK and EMTK.JK stocks are worthy of being included in the optimal portfolio because they have a positive expected return and Jensen's Alpha that slightly outperforms the market. EMTK.JK also has a lower risk. Meanwhile, based on SIM, only BBCA.JK is included in the optimal portfolio because it meets the criteria for Excess Return to Beta (ERB) > cut-off rate (C^*), and shows a positive Jensen's Alpha. The conclusion of this study is that both models can identify superior performing stocks for the optimal portfolio in the period.
Implementation of Simulated Annealing Algorithm for Portfolio Optimization in Jakarta Islamic Index (JII) Stocks with Mean-VaR Riadi, Nadia Putri; Riaman, Riaman; 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.v6i2.1016

Abstract

One of the challenges for investors in the investment world is to manage the stock portfolio optimally. The main objective of portfolio optimization is to obtain maximum profit with a controlled level of risk. This study aims to find a portfolio combination that provides the best return with a more controllable risk than the conventional method, using Simulated Annealing. This research method applies the Mean-Value at Risk (Mean-VaR) approach in measuring portfolio performance and uses the application of the Simulated Annealing algorithm as an optimization method to determine the optimal investment weight on stocks in the Jakarta Islamic Index (JII), so as to obtain a portfolio with the best performance compared to a simple weighting strategy. The data used in this study is the daily closing price of stocks listed in the JII during the period January 3, 2022 - January 2, 2024. Based on the results and discussion, there are 7 stocks included in the formation of the optimal portfolio of JII index stocks, namely ADRO, ICBP, INKP, ITMG, MIKA, TPIA, and UNTR. The weight allocation of each stock generated by the Simulated Annealing method for the period is for ADRO shares 7,4177%; ICBP 1,7817%; INKP 7,3369%; ITMG 15,0006%; MIKA 2,5894%; TPIA 63,5506%; and UNTR 2,323%. The optimal portfolio of the Mean-VaR model with the Simulated Annealing method is generated when the risk tolerance is 0 (Ď„=0), with a return or return of 0,001923 and a VaR risk level of 0,029788. This approach is expected to be an alternative for investors in determining investment strategies based on Islamic stocks in Indonesia.
Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Nurdyah, Himda Anataya; Subartini, Betty; 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.v6i2.1017

Abstract

The importance of the food sector to Indonesia's economy makes it one of the most attractive sectors to consider in an investment portfolio. An optimal portfolio is the best choice for investors among various efficient portfolios, aiming to maximize returns while minimizing risk. Moreover, since investment is inherently associated with fluctuating stock prices, accurate forecasting is necessary to anticipate future stock movements. This study aims to accurately predict stock prices and construct an optimal portfolio consisting of five food sector stocks listed on the Indonesia Stock Exchange, namely DMND, ICBP, HOKI, INDF, and ULTJ. Stock price predictions are generated using the Holt-Winter method, which can identify seasonal patterns and trends from historical data. The predicted stock prices are then used to calculate returns, which serve as the basis for portfolio optimization using the Mean-Variance model. The results show that the Holt-Winter method successfully produces accurate stock price forecasts, with Mean Absolute Percentage Error (MAPE) values for all stocks below 10%. These forecasts are used to calculate returns in the portfolio optimization process. The optimal portfolio composition is determined with the following weight proportions: HOKI (4%), ICBP (18%), ULTJ (21%), DMND (26%), and INDF (30%). This portfolio yields an expected return of 0.0441% and a portfolio variance of 0.0063%, reflecting a balanced trade-off between potential return and risk.
Optimization Model in Transportation Based on Linear Programming Manuela, Angellyca Leoni; Harahap, Reivani Putri Berlinda; Yoefitri, Tina; Meizani, Nicko; Hidayana, Rizki Apriva
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.v6i2.1018

Abstract

This study discusses the development of optimization models in transportation costs and routes and resource distribution based on Linear programming using various methods. This study aims to improve logistics efficiency, maximize the utilization of transportation equipment, infrastructure, operations management, and minimize transportation costs. The methods used include data collection, data processing, and the application of mathematical models to determine the optimal route with iteration methods such as the Simplex Method or Simplex Algorithm (SIMPLEKS), Modified Distribution Method (MODI), Vogel's Approximation Method (VAM), North-West Corner Method, Least Cost Method, and Initial Cost Minimum Method (ICMM). This study successfully shows that this method is able to reduce the cost of reducing carbon emissions, significantly reduce shipping costs and increase the efficiency of goods distribution that can be applied to complex distribution systems, support efficiency, and sustainability of transportation management. Using Linear programming and transportation methods to reduce SME costs and produce more efficient costs and fast solutions. In general,optimizationThis supports economic development, efficiency and sustainability of transportation management.
Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
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.v6i2.1020

Abstract

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.

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