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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Supply Chain Management International Journal of Business, Economics, and Social Development Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research
Riaman Riaman, Riaman
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

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Analysis of the French Five Factors Fama Model on Excess Return of Stocks Listed on IDXBUMN20 for the Period 2020-2023 Putri, Linda Damayanti; 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.v6i2.966

Abstract

Excess return is the difference between the rate of return earned on an investment and the rate of risk-free return in a given period. This shows how much return is received because they are willing to take risks in investing. This study aims to analyze the Fama French Five Factor model on the excess return of stocks listed in IDXBUMN20 2020-2023 period. The factors in the model are market factors, size factors, book to market ratio, profitability, and investment. The population in this study amounted to 20 companies registered in the IDXBUMN20 index, the sample selection in this study used the purposive sampling method and a sample of 12 companies was obtained. The data used in the study are close price, number of shares outstanding, Bank Indonesia (BI) interest rate, and company financial statements. The analysis method used was the Common Effect Model (CEM) panel data regression analysis. Based on hypothesis testing, market factors were obtained which only had an effect on excess returns. This factor shows the influence of the ups and downs of market performance on the price of a stock.
Investment Portfolio Optimization Using Genetic Algorithm on Infrastructure Sector Stocks Based on the Single Index Model Bayyinah, Ayyinah Nur; 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.v6i2.977

Abstract

Investment is a strategic step in managing assets to gain profits in the future by allocating some funds in the present. However, behind the promising potential returns, investment also contains risks that cannot be ignored. One way to reduce the level of risk in investing is to implement a portfolio diversification strategy, which is to form an optimal portfolio by allocating investments to various stocks. This study aims to identify the stocks that form the optimal portfolio, determine the optimal weight of each stock, and calculate the expected return and risk of the portfolio. The portfolio optimization process is carried out using Genetic Algorithm, with the calculation of expected return and risk using the Single Index Model (SIM) approach. The data used includes data on stocks in the infrastructure sector for the period July 1, 2023 to June 30, 2024. The results showed that there were six stocks selected in forming the optimal portfolio with the weight of each stock: PGEO 15.0023%, ISAT 32.1522%, GMFI 4.7822%, EXCL 15.3236%, JSMR 29.7379, and OASA 3.0018%. This optimal portfolio provides an expected return of 0.1167% with a portfolio risk of 0.0152%.
Investment Portfolio Optimization Using Ant Colony Optimization (ACO) Based on Fama-French Three Factor Model on IDX High Dividend 20 Stocks Maharani, Asthie Zaskia; Susanti, Dwi; Riaman, Riaman
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.v6i2.978

Abstract

Stock investment is one of the investment options that provides both profit and risk for investors. In an effort to maximize profits and minimize risks, investors need an optimal portfolio. The optimal portfolio is a portfolio selected from a collection of efficient portfolios. To form an optimal portfolio, this study combines the Fama-French Three Factor Model (FF3FM) for stock selection and Ant Colony Optimization (ACO) for stock weight optimization in the portfolio. FF3FM considers more factors resulting in more comprehensive stock selection than other methods. While ACO has the ability to explore the solution space widely and efficiently, minimizing the risk of getting stuck on a local solution. The performance of the optimal portfolio is measured using the Sharpe Ratio which considers total risk, thus providing an overview of overall investment efficiency. The research object used is quarterly stock data on IDX High Dividend 20 from the Indonesia Stock Exchange (IDX) for the period 2020-2023. Of the 20 stocks, 12 stocks were selected that were consistently included in the index during the 2020-2023 period. By selecting stocks using the FF3FM method, 10 efficient stocks were selected, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, ITMG, PTBA, TLKM, and UNTR. Portfolio optimization using ACO produces a portfolio return of 0.0473 and a risk of 0.0257 with the weight of each ADRO stock of 6.90%, BBCA of 17.24%, BBNI of 10.34%, BBRI of 27.59%, INDF of 3.45%, ITMG of 27.59%, TLKM of 3.45%, and UNTR of 3.45%. The results showed that the integration of FF3FM and ACO was able to form a portfolio with optimal performance with a Sharpe Ratio value of 1.41868, which means that the portfolio return is greater than the portfolio risk.
Portofolio Optimization of Mean-Variance Model Using Tabu Search Algorithm with Cardinality Constraints Ma’mur, Lutfi Praditia; 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.v6i2.1010

Abstract

Stock investment is increasingly attractive to Indonesians, especially through the IDX30 index, which is known to have high liquidity and solid company fundamentals. In forming an optimal stock portfolio, investors are faced with the challenge of maximizing return and minimizing risk simultaneously. An optimal portfolio is defined as a combination of assets that provides the highest expected return at a certain level of risk, or the lowest risk for the expected level of return. This study aims to form an optimal portfolio on the IDX30 index by considering cardinality constraints, which limit the maximum number of stocks in the portfolio. From 30 IDX30 stocks, 20 stocks were selected based on consistency of existence during the period February 1, 2023 to January 31, 2025. Next, 8 stocks that have positive expected return values are selected, and from these 8, 4 efficient stocks are selected using cardinality constraints. Selection is done with the Tabu Search algorithm, a memory-based metaheuristic optimization method used to find the best solution by avoiding previously explored solutions. The portfolio is formed using the Mean-Variance model, resulting in an allocation of BMRI (30,02%), PTBA (35,18%), INDF (2,48%), and BRPT (32,32%), with an expected return of 0,00207 and a variance of 0,001587.
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 (τ=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.
Calculation of Rice Farming Insurance Premium Price in Magelang City Based on Rainfall Index with Black-Scholes Method Raharjanti, Amalia; Riaman, Riaman; Sukono, Sukono
International Journal of Business, Economics, and Social Development Vol. 5 No. 1 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i1.581

Abstract

Indonesia is a country with two seasons, the rainy season and the dry season. Unstable rainfall can affect rice production and may cause crop failure. The amount of rice production in Indonesia, one of which is in Magelang City, is quite large, so the losses that may be experienced are quite significant. Therefore, a way to reduce the impact of losses experienced by farmers is needed, one of which is through the rice farming insurance program. The purpose of this study is to determine the premium price of rice farming insurance based on rainfall index based on the exit value and trigger value in each growing season. Insurance using the rainfall index can provide protection to farmers due to too little rainfall or too much rainfall. Too much rainfall can cause damage to rice plants resulting in crop failure. The premium calculation method uses the Black-Scholes principle, while the exit value and trigger value are determined by the Historical Burn Analysis method. The result of this study is to obtain various trigger values and exit values as well as premiums that must be paid by farmers in each normal, high, and low (dry) rainfall condition. This value determines the premium price obtained for normal rainfall which is IDR 735,739.66 to IDR 871,698.64, for high rainfall the premium price obtained is IDR 1,404,184.75 to IDR 1,643,307.75, and for low rainfall (dry season) it is IDR 5,541,806.10 to IDR 6,689,629.88. 
Investment Portfolio Optimization Using Black-Litterman Model in Smart Carbon Economy Transition Kahar, Ramadhina Hardiva; Riaman, Riaman; Sukono, Sukono
International Journal of Business, Economics, and Social Development Vol. 5 No. 1 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i1.582

Abstract

An optimal investment portfolio needs to be formed before an investor invests because it can help investors determine which financial instruments are suitable to choose in order to get the maximum return or profit and the minimum level of risk. In the current situation, where there is an economic transition to a smart carbon economy or low carbon economy, it is necessary to form the optimal portfolio of stocks to facilitate investors in making investments. The purpose of this study is to form the optimal investment portfolio using the Black-Litterman model in a smart carbon economy. The data used is stock data from 24 companies listed on the LQ45 Low Carbon Leaders index for the period 2022-2023. Based on the research results, the Black-Litterman model generates the optimal portfolio with a 0.1% expected return. Thus, the optimal portfolio results with the Black-Litterman model are estimated to generate a profit of 0.1% for smart carbon stock data listed on the LQ45 Low Carbon Leaders index for the 2022-2023 period.
Determination of Optimal Stock Portfolio Return by Single Index Model (Case Study on Banking Sector Stocks in Indonesia) Rahmawati, Septi; Susanti, Dwi; Riaman, Riaman
International Journal of Business, Economics, and Social Development Vol. 5 No. 1 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i1.585

Abstract

The optimal portfolio is a portfolio chosen by investors from the many options available in the collection of efficient portfolios. To get the optimal proportion, which is the maximum return and minimum risk, it is necessary to analyze the stocks to be selected in the investment model. The research objective is to determine the optimal return, risk, and proportion for each banking stock portfolio in Indonesia in the period February - July 2023. The method used is the Single Index Model. The process of determining the optimal proportion of stocks with the Single Index Model requires stock and market return data as the main basis for applying this method. This study involves the formation of an optimal portfolio of daily closing prices of 46 banking stocks.  As a result of this research, there are 5 optimal stocks that meet the criteria for optimal portfolio formation with each fund proportion of 21.43% (BNII), 13.52% (BDMN), 35.02% (BBRI), 23.69% (BTPN), and 6.34% (BBCA).  Expected return from optimal stocks is 0.152% and the risk that will be borne by investors is 0.0011% per day.
Mean-Variance Portfolio Optimization with Lot Size Constraints in Energy Stocks: A Monte CarloApproach Vimelia, Willen; Riaman, Riaman; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32159

Abstract

Stock investment requires portfolio optimization strategies that maximize returns and consider risks and practical constraints, such as target lot sizes. These constraints are crucial to ensuring the realistic implementation of portfolios in compliance with market regulations, particularly in Indonesia, where 1 lot equals 100 shares. However, existing research on the Mean-Variance model and Monte Carlo simulation has rarely incorporated target lot constraints, limiting the applicability of these models in real-world scenarios. To bridge this gap, this study conducts a systematic literature review (SLR) on portfolio optimization in Indonesia's energy sector stocks, focusing on the Mean-Variance model, risk aversion, Monte Carlo simulation, and target lot constraints. The PRISMA framework guides this SLR, with bibliometric analysis performed using RStudio. A rigorous selection process from Scopus and ScienceDirect databases yielded 13 relevant articles for in-depth analysis creates a more practical and effective approach to portfolio management. This advancement enables investors to achieve balanced portfolios that are both theoretically robust and feasible in practice. The study contributes significantly to optimizing investment strategies for Indonesia’s energy sector and opens avenues for further research into practical portfolio optimization methods.
Optimization Modeling of Investment Portfolios Using The Mean-VaR Method with Target Return and ARIMA-GARCH Yasmin, Arla Aglia; Riaman, Riaman; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.30042

Abstract

This research develops a portfolio optimization model using the Mean-Value at Risk (Mean-VaR) approach with a target return constraint, addressing the gap in models that specific return objectives. The ARIMA-GARCH model is utilized to predict stock returns and volatility, offering precise inputs for optimization. By applying the Lagrange method and Kuhn-Tucker conditions, the model determines optimal portfolio weights that balance risk and return. Using data from infrastructure stocks on the Indonesia Stock Exchange (January 2019-September 2024), the model’s effectiveness is validated through numerical simulations. The results illustrate efficient frontiers for target returns of 5x10^-6, 0.001, and 0.0019, revealing that higher return targets proportionally increase risk. ARIMA-GACRH’s advantage lies in its ability to capture both mean and variance dynamics, ensuring reliable volatility estimates for informed decision-making. This study contributes to portfolio optimization literature by emphasizing target return constraints and demonstrating the practical utility of volatility modeling. The findings provide a robust framework for investors to align portfolios with financial goals and risk tolerance. Future work could explore broader market contexts or integrated additional constraints for enhanced applicability.
Co-Authors Adib, Andhita Zahira Ariyanti, Devi Bayyinah, Ayyinah Nur Betty Subartini Dewi Ratnasari Dianti, Estu Putri Dwi Susanti Ghazali, Puspa Liza Hasbullah, Soeryana Hidayana, Rizki Apriva Hukama, Atha Iin Irianingsih Kahar, Ramadhina Hardiva Kankan Parmikanti Laksito, Grida Saktian Maharani, Asthie Zaskia Ma’mur, Lutfi Praditia Moisino, Misel Lindi Noriszura Ismail Novianti, Saqila Pramudhita, Annisa Pryimak, Evgen Putri S. R., Aulianda Anisa Putri, Aulya Putri, Linda Damayanti Raharjanti, Amalia RAHMAWATI, SEPTI Ramdhania, Tya Shafa Reisnanda, Aldino Riadi, Nadia Putri Silvia Wijaya Subartiny, Betty Sukono Sukono Supian, Sudradjat Susanto, Sunarta Sya’imaa.HS, Audrey Ariij Tiswaya, Waway Ulfatmi, Ristifani Vimelia, Willen Wahid, Alim Jaizul Widyani, Azizah Rini Yasmin, Arla Aglia Yeremia Herry Parulian, Yeremia Herry Zahra, Ami Emelia Putri Zakirah, Khalilah Razanah