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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Kubik Jurnal Matematika: MANTIK Teorema: Teori dan Riset Matematika International Journal of Supply Chain Management Eksakta : Berkala Ilmiah Bidang MIPA FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Kumawula: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika KUBIK: Jurnal Publikasi Ilmiah Matematika 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 Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Mathematical Sciences and Applications Journal
Betty Subartini
Departemen Matematika, FMIPA, Universitas Padjadjaran
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sianturi, Sri Novi Elizabeth; 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.v6i2.1001

Abstract

Stock mutual funds gained popularity among the public as an investment alternative due to the convenience they offer, especially for beginner investors who have limited time and investment knowledge. Compared to money market and bond mutual funds, these mutual funds offer higher potential returns but also come with higher risks due to value fluctuations, so forecasting stock mutual fund prices is essential to minimize losses. Since stock mutual fund prices is time series data, this research employs two forecasting models such as Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR). The objective of this research is to determine the best-performing model between ARIMA and NNAR, and compare their forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The data used consists of daily closing prices of stock mutual funds from March 1, 2022, to March 31, 2025, with the criteria that the selected issuers have been operating for more than five years. The results of this research show that the best ARIMA and NNAR for the RNCN are ARIMA([1],1,0) and NNAR(2,2); for TRAM are ARIMA(0,1,[1]) and NNAR(4,1); for SCHRP are ARIMA(0,1,[1]) and NNAR(4,2); for MICB are ARIMA([1],1,0) and NNAR(2,2); and for BNPP are ARIMA([1],1,0) and NNAR(5,1). The MAPE values in the same order are 6.83% and 5.49%; 6.53% and 5.75%; 8.57% and 7.10%; 8.39% and 8.75%; 8.51% and 7.30%. Based on the comparison, NNAR outperformed ARIMA in four out of five mutual funds, with lower MAPE values and also marked by the ARIMA model tend to produce stable or unchanging values over the long term. The results of this research are expected to assist investors in consederating by choosing NNAR model, both in the short and long term, to obtain better stock mutual fund price forecasts.
Stock Portfolio Optimization of IDX30 using Agglomerative Hierarchical Clustering and Ant Colony Optimization Algorithm Firdaus, Muhammad Rayhan; Subartini, Betty; Sukono, Sukono
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.394

Abstract

The stock market offers high profit opportunities but also entails significant risks, making portfolio optimization essential to help investors manage risk and maximize returns. This study aims to cluster IDX30 stocks to form a more diversified portfolio, determine the optimal stock weights, and evaluate portfolio performance. The method employed is Agglomerative Hierarchical Clustering (AHC) with Ward linkage for clustering stocks based on financial ratios, with the silhouette score used to evaluate cluster quality. Subsequently, the Ant Colony Optimization (ACO) algorithm is applied to optimize stock weights in the portfolio based on the clustering results. The findings indicate that the best portfolio is obtained in clusters 5 and 6, with a maximum fitness value of 0.064555 and a portfolio return of 0.000814. Portfolio performance evaluation using the Sharpe ratio yields a value of 0.044767 for both clusters, indicating that the resulting portfolios are efficient. This research is expected to contribute to the development of more accurate and practical data-driven investment strategies for investors.
Optimal Stock Portfolio Analysis using Mean-Value at Risk (Mean-VaR) under Arbitrage Pricing Theory (APT) Banowati, Puspa Dwi Ayu; Subartini, Betty; 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.v5i1.584

Abstract

Investing in Sharia-compliant stocks is one of the rapidly growing investment options, making it a potential choice for investors' portfolios. Therefore, investors need to understand how to select an optimal composition of stocks in their portfolio. This research aims to calculate the expected return on Sharia-compliant stocks and determine the optimal portfolio. The data used in this study includes stocks within the Indonesian Sharia Stock Index (ISSI) in the energy and mining sectors from November 1, 2022, to October 30, 2023. The analytical models employed are the Arbitrage Pricing Theory (APT) and Mean-Value at Risk (Mean-VaR). Based on the research findings, seven stocks form the composition of the optimal stock portfolio. These stocks are AKRA, ANTM, PGAS, INCO, INDY, PTBA, and MDKA, with weights of 20.54%, 19.58%, 19.02%, 14.24%, 10.97%, 8.00%, and 7.66%, respectively. The expected return for the investor is 0.13% per day, with a corresponding risk of 0.23%.
Portfolio Analysis Using the Markowitz Model with Stock Lot Constraints and Target Returns or Without Target Returns Asri Rula Hanifah; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 3 No. 4 (2022): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i4.358

Abstract

Stock investment activities are inseparable from returns and risk, so an investor needs expertise to minimize investment risk. One way is by forming an optimal portfolio. The purpose of this research is to determine the number of stock lots in the optimal portfolio. This research analyzes the closing prices of stocks during the research period with the criteria of stocks being listed on the IDX30 index consecutively for 20 periods and belonging to the large cap group (the stock market capitalization exceeds $10 billion). Then the number of stock lots is calculated using the Markowitz model with stock lot constraints and target returns or without target returns. From the selected stocks, an optimal portfolio is formed using Microsoft Excel. Based on the research results, a combination of an optimal portfolio with a target return is ASII: 5, BBCA: 10, BBNI: 23, BBRI: 1, BMRI: 23, TLKM: 93, UNVR: 12, where the risk is 0,000149 and the target expected return is 0,00155. Meanwhile, the optimal portfolio without a target return is ASII: 8, BBCA: 7, BBNI: 32, BBRI: 40, BMRI: 9, TLKM: 62, UNVR: 17, where a risk is 0,000147 and the expected return is 0,00148. This research can be used as a consideration for investors in determining investment portfolios.
Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Himda Anataya Nurdyah; Betty Subartini; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025): International Journal of Quantitative Research and Modeling
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.
Co-Authors - Riaman Agus Santoso Agus Supriatna Agus Supriatna Aisyah Nurul Aini Alfan Azkiya Robbani Amalia, Hana Safrina Annisa Aprillia Annisa Nur Falah Annisa Nur Falah, Annisa Nur Apipah Jahira, Juwita Ary Robayani Asep K Supriatna Asep K Supriatna, Asep K Asri Rula Hanifah Azimah Lisan Robbani Banowati, Puspa Dwi Ayu Budi N. Ruchajana Budi N. Ruchajana, Budi N. Carissa, Katherine Liora Diah Chaerani Dian Nuryani Dini Aulia Dwi Susanti Eddy Djauhari Edi Kurniadi Edi Kurniadi Elis Hertini Endang Rusyaman Endang Soeryana Hasbullah Felfin Ulfah Annisa Firdaniza Firdaniza Firdaus, Muhammad Rayhan Ghazali, Puspa Liza Herlina Napitupulu Himda Anataya Nurdyah Jeane R. M. D. P Chantique Jumadil Saputra Kankan Parmikanti Muhammad Iqbal Al-Banna Ismail Nahda Nabiilah Novitasari, Ela Nurdyah, Himda Anataya Nursanti Anggriani Nurul Fadilah Nurul Gusriani Olivia Anjanette Pamungkas, Abram Chandra Aji Putri Adhira Novalia Putri Chaerunnisa Febryanti Putri, Sherina Anugerah R Wulantini R Wulantini, R Rahmadhisa, Nayra Pavita Rahman, Rezki Aulia Riaman . Riaman Riaman Risdawati Risdawati Sampath, Sivaperumal Saputra, Jumadil Sianturi, Sri Novi Elizabeth Sisilia Sylviani Sonia . Sukono Sukono Tampubolon, Carlos Naek Tua Tegar Ridwansyah Yeremia Herry Parulian Yeremia Herry Parulian, Yeremia Herry Zahra, Ami Emilia Putri