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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) SOSIALITAS (Jurnal Ilmiah Pend. Sos-Ant) Paradigma Disease Prevention and Public Health Journal Jurnal Pendidikan Fisika Indonesia Jurnal Pendidikan Fisika Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Folia Medica Indonesiana MEDISAINS EDU-MAT: Jurnal Pendidikan Matematika LENTERA: Jurnal Ilmu Dakwah dan Komunikasi JURNAL INDRIA (Jurnal Ilmiah Pendidikan Prasekolah dan Sekolah Awal) Jurnal Komunikasi CARING (Center of Publication in Midwifery and Nursing) : Jurnal Publikasi Penelitian Kebidanan dan Keperawatan Media Ilmu Kesehatan JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmu Kedokteran dan Kesehatan Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Medika Malahayati JKPK (Jurnal Kimia dan Pendidikan Kimia) JFM (Jurnal Farmasi Malahayati) Jurnal Pengabdian Farmasi Malahayati (JPFM) Jurnal Review Pendidikan dan Pengajaran (JRPP) de Fermat : Jurnal Pendidikan Matematika JUXTA: Jurnal Ilmiah Mahasiswa Kedokteran Universitas Airlangga Jurnal Farmamedika (Pharmamedica Journal) Jurnal Kesehatan Madani Medika Dinasti International Journal of Education Management and Social Science Jurnal Borneo Saintek Jurnal Riset Mahasiswa Dakwah dan Komunikasi (JRMDK) Mandalika Mathematics and Educations Journal Nakhoda: Jurnal Ilmu Pemerintahan Jurnal Peduli Masyarakat Jurnal Manajemen Bisnis Indonesian Journal of Dental Medicine PAKAR Pendidikan Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Computer Networks, Architecture and High Performance Computing Community Empowerment Jurnal Manajemen Bisnis dan Keuangan International Journal of Business, Economics, and Social Development Operations Research: International Conference Series International Journal of Global Operations Research Al-Mashrof: Islamic Banking and Finance Jurnal Ilmiah Wahana Pendidikan International Journal of Engagement and Empowerment (IJE2) International Journal of Quantitative Research and Modeling Journal of Innovation in Community Empowerment (JICE) Historia JURNAL KESEHATAN SAMODRA ILMU MATHEMATIC EDUCATION AND APLICATION JOURNAL (META) PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) International Journal of Ethno-Sciences and Education Research Borobudur Communication Review Jurnal Pendidikan Transformatif Mitra Teras: Jurnal Terapan Pengabdian Masyarakat JURNAL HANDAYANI PGSD FIP UNIMED Jurnal Edumatic : Jurnal Pendidikan Matematika Healthy Behavior Journal MAXIMA CERATA Damhil: Jurnal Pengabdian kepada Masyarakat
Dwi Susanti
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Journal : International Journal of Quantitative Research and Modeling

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Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method Pamungkas, Abram Chandra Aji; Subartini, Betty; Susanti, Dwi
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v%vi%i.594

Abstract

Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.
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.
IDX30 Stock Portfolio Optimization Using Genetic Algorithm Based on Capital Asset Pricing Model Rahmadhisa, Nayra Pavita; Susanti, Dwi; Subartini, Betty
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.v6i2.981

Abstract

The stock market plays a vital role in supporting economic growth by serving as a primary channel for companies to raise capital and for investors to gain profits through long-term investments. In practice, one of the biggest challenges for investors is identifying which stocks are worth purchasing and how to allocate their funds optimally. One commonly used approach to evaluate stock feasibility is the Capital Asset Pricing Model (CAPM), which helps identify undervalued and overvalued stocks based on the relationship between systematic risk and expected return. Additionally, it is necessary to determine the optimal investment weight allocation. Therefore, this study combines the CAPM method for stock selection and Genetic Algorithm, a metaheuristic approach capable of finding optimal solutions in complex problems, to determine the optimal portfolio weight composition. The object of this study includes stocks listed in the IDX30 index during the period from February 2021 to November 2023. The results show that five stocks—ADRO, BBCA, BBNI, KLBF, and TLKM—are classified as undervalued according to the CAPM method and are recommended for inclusion in the optimal portfolio. Portfolio optimization using the Genetic Algorithm results in the following stock weight composition: ADRO 26.55%, BBCA 36.20%, BBNI 9.09%, KLBF 12.20%, and TLKM 15.96%, with a Sharpe Ratio of 4.043906. The expected return and risk of the optimal portfolio are 0.00067373 and 0.00012407, respectively.
Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method Pamungkas, Abram Chandra Aji; Subartini, Betty; Susanti, Dwi
International Journal of Quantitative Research and Modeling Vol. 5 No. 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v%vi%i.594

Abstract

Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.
Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia) Labitta, Kirana Fara; Susanti, Dwi; Sukono, Sukono
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.v5i1.603

Abstract

Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.
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. 
Prediction of Motor Vehicle Insurance Claims Using ARMA-GARCH and ARIMA-GARCH Models Maraya, Nisrina Salsabila; Susanti, Dwi; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.672

Abstract

Motorized vehicles are one of the means of transportation used by Indonesian people. As of 2021, the Central Statistics Agency (BPS) recorded the growth of motorized vehicles in Indonesia reaching 141,992,573 vehicles. Lack of control over the number of motorized vehicles results in losses for various parties, such as accidents, damage and other unwanted losses. The size of insurance claims has the potential to fluctuate, because it is influenced by several factors, such as policy changes, market conditions and economic conditions. This research aims to predict the size of motor vehicle insurance claims using the ARMA-GARCH model which is used to predict the size of vehicle insurance claims by dealing with non-stationarity and heteroscedasticity in time series data. Based on research, the best model obtained is the ARMA(3,3)-GARCH(1,0) model which produces nine significant parameters. Meanwhile, based on the MAPE value, it shows that the ARMA(3,3)-GARCH(1,0) model is quite accurate. The results of this research can be taken into consideration in predicting the size of insurance claims in the future.
Based Stock Valuation Analysis on Fuzzy Logic for Investment Selection (Case Study: PT. XL Axiata Tbk. and PT. Telkom Indonesia Tbk.) Audina, Maudy Afifah; Susanti, Dwi; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.673

Abstract

The stock value of a company fluctuates with capital market conditions, requiring investors to consider various factors for precise investment decisions. Stock valuation determines the fair price of a company's stock, guiding buying and selling transactions. This research uses Discounted Cash Flow (DCF), Price to Earnings (P/E), and Enterprise Value to EBITDA (EV/EBITDA) to ascertain fair stock prices, integrating results with Mamdani fuzzy logic to determine investment weights. The result of this research is that both EXCL and TLKM hold significant weight in the investment portfolio with TLKM has slightly higher stock weight than EXCL. This suggests TLKM offers more potential for profitable future investments. Investors can use these results in portfolio management for investment selection
Forecasting Indonesian Stock Index Using ARMA-GARCH Model Susanti, Dwi; Labitta, Kirana Fara; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.686

Abstract

The stock market is an institution that provides a facility for buying and selling stocks. Covid-19 is an issue that has affected the stock markets of many countries, including Indonesia. Due to the pandemic, the condition of stocks before and during Covid-19 is certainly different. Stocks can be measured using stock indices. To predict future stock conditions, it is necessary to forecast the stock index. This research aims to predict the Indonesian stock index in the before and during Covid-19 period, using ARMA-GARCH time series model. According to the results obtained for before Covid-19 data, the best predictive model is the ARMA(0,2)-GARCH(1,0), and for the data during Covid-19, it is ARMA(3,3)-GARCH(3,3). Since the MAE is close to zero, it indicates that the model is quite accurate. These findings can help investors make better investment decisions in the future.
Comparison of Projected Unit Credit, Entry Age Normal, and Individual Level Premium Methods in Calculation of Normal Retirement on PNS Pension Funds Putri S. R., Aulianda Anisa; Susanti, Dwi; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i2.692

Abstract

Every individual’s desire for a prosperous old age lead to the need for a pension fund program to ensure the welfare of every employee in their old age. The calculation of pension fund in this study was carried out using the Projected Unit Credit, Entry Age Normal and Individual Level Premium methods. This study aimed to determine the value of normal cost and actuarial liability using Projected Unit Credit method, Entry Age Normal method, and Individual Level Premium. Then the best method was determined based on the comparison results of the normal cost value and the actuarial liability value obtained using the three methods. The data used in this study is secondary data from PT Taspen (Persero) KCU Bandung. The results showed that the best method among the three methods studied was the Projected Unit Credit method because it produced the highest total normal cost with the lowest actuarial liability value each year.
Co-Authors A. Handjoko Permana Abdul Rahman Abi Suwito Abul Hasani, Rofi Adhy Firdaus Afif, Farid Agung Sosiawan Agustin, Rekha Yulia Ahmad Muhammad Fuadi, Ahmad Muhammad Ainul Fitri, Zaida Airohmah, Yesi Alisa, Nur Alkhoroni, Panji Andayani, Nyoman Etika Andayani, Sri Astutik Andi Taufiq Umar Andiyani, Intan Andriani, Cindy anggi prantika, Sindi Anjan, Aulia Aprila, Nur Amalia Apriliani, Nanda Putri Ardiansyah, Galang Aristina, Renny Astuti, Wella Audina, Maudy Afifah Ayu, Diana Puspita Az Zahra, Dita Aulia Badi’ah, Atik Barus, Febriana Krisdayanti Betty Subartini Budhi Mustika, I Made Budiawan , Muhammad Ardi Budiawan, Muhammad Ardi Christantie Effendy Dari, Tri Ulan Dewi Chusniasih Dewi Silaban, Eni Priska Dewi, Tiara Nilawati Dian Kristiana Diandra Rizki Isrohmaniar Dias Utami, Khristin Dwihantoro, Prihatin ekawati Elsyana, Vida Endah Meilana Endah Puji Astuti, Endah Puji Enjelia, Lisa Ernawati Ernawati Ervana, Lina Esmar Budi Fadila, Vivin Dwi Farha, Saniya Fatah, Rizki Amelia Femei Purnamasari, Femei Fira, Dilli Salsa Firmansyah, Frenza Fairuz Firmanul Catur Wibowo Fitriana, Deva Rizkia Fitriani, Dinda Ismi Florentina Sustini Fredinan Yulianda Gatra, Anggel Alba Ghufron, David Maulana Gunawan Santoso Hadi Nasbey Halim, Nurfadhlina Binti Abdul Haliza, Nur Hamid, Hariaty Handayani, Afifa Dwi Haris Suhendar Hasan, Ferisa Nuriasyifa Hasyim, Diah Mukminatul Haya, Aqella Fadya Herlina Napitupulu Hermansyah Hermansyah Hermansyah Hidayanto, Agung Ridwan Hutapea, Yohana Vita Melodi Hutasoit, Masta Ida Nursanti Ikramina, Mahreshaibati Bilqis Inah Indirayani, Triana Khairi, M. Ihsan Khairiah Khairiah Khira, Nafisa Khumaira, Aimena Salma Kunarfian, Krisna Artur Labitta, Kirana Fara Latifah Susilowati Linda Yanti Linda Yanti Lintang Muliawanti Lubis, Rita Mahdalena Lukita, Salsabila Arum Lutfiyati, Afi Mahaputra, A. V. Maharani, Asthie Zaskia Maraya, Nisrina Salsabila Marcellia, Selvi Marpaung, Maynisa Naomi Matondang, Khairani Maulana, Slamet Miftahorrozi, Miftahorrozi Muh Iqbal Muhammad Halim, Muhammad Muslichah Muslichah Nelly Astuti Ningsih, Jayanti Eka Sari Ningsih, Mulati Nofita, Nofita Nugraha, Dwita Safira Nuradiyah, Fanny Nurhasanah, Gina Nursamira, Nursamira Octavia, Selvia Ola, Sania Latek Pamungkas, Abram Chandra Aji Pardede, Ester Perangin angin, Martianus Pramita, Anggi Primadiamanti, Annisa Pudji Lestari Purnamaningsih, Nur ’Aini Purwono Hendradi Puspamaniar, Vania Ayu Putri Amalia Putri S. R., Aulianda Anisa Putri Utami, Desi Putri Utami, Desi Putri, Melita Regina Putri, Ria Desta R, Nurmala Rahadi, Fabiyan Rahma, Indah Eliza Rahmadhisa, Nayra Pavita Rahmatullah, Ivan RAHMAWATI, SEPTI Rahmayanti, Arini Rai Saputri, Gusti Ayu Ramadhana, Ramadhana Ramadhani, Herlina Ramadhani, Muhammad Ramadhanta, Sury Adellia Raufa, Raufa Reni Merta Kusuma Reny Wijayanti Restinah, Anjar Retno Asih Setyoningrum Retno Mawarti, Retno Revanti, Anggun Triana Riaman Riaman, Riaman Rosdiana, Anggita Cahya RR. Ella Evrita Hestiandari Rusdiani, Nurtina Irsad S, Septi Nurhaliza Sabrina, Putri Marsha Safitri, Nita Dwi Safitri, Rahmawati Sagita, Putri Dwi Sagitawening, Hardhini Saksana, Joned C. SANI SUSANTI Saputra, Jumadil Satiti, Ainur Rafiqah Setyabudi, Rendy Siagian, Irma Siburian, Claranita Silferia, Silferia Silvia Ari Agustina Simamora, Rustam Effendy Sinaga, Primawati Siti Fatimah Soetadi Waskito SRI SETIYARINI, SRI Suatin, Ricka Milla Sugiono, Muhamad Suhandoyo, Tri Sukono . Sukono, Sokono Sunarsih, Eka Suranto Suranto Surantoro Surantoro Suratno Surtini, Surtini Susanti, Yuli Eri Susanto Susanto Suwarno Suwarno Syahara, Tjut Afrieda Syahla, Raynita Syarifah, Indi Mazaya Teguh Budi Prayitno Tutik Tutik Ulfa, Ade Maria Upik Rahma Fitri, Upik Rahma Verrany, Maria Jatu Vina Serevina Wachyuni, Mhella Nia Wahyuni, Anis Wan Asrida Warih Angesti P, Warih Angesti Widia Rahayu, Setia Widyawati, Eka Windayani, Wulan Tri Yanita Trisetiyaningsih, Yanita Yati, Dwi Yulistianingsih, Desti Yundari, Yundari Yusticia, Ica Zahrani, Andieta