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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)
Publisher : Research Collaboration Community (RCC)

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

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.

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

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.

Actuarial Pension Fund Using the Projected Unit Credit (PUC) Method: Case Study at PT Taspen Cirebon Branch Office Amalia, Hana Safrina; Subartini, Betty; Sukono, Sukono
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.v5i3.745

The pension fund program is a program held by the government to ensure the welfare of Civil Servants (PNS) in retirement as old-age security. The pension program for civil servants is managed by a pension fund, PT Taspen (Persero). Actuarial calculations of pension funds need to be carried out to determine the amount of normal contributions and actuarial liabilities that must be paid by pension plan participants and companies. The actuarial calculation of pension funds used by PT Taspen in managing civil servant pension funds is the Accrued Benefit Cost which determines in advance the benefits that will be obtained by participants. The Projected Unit Credit (PUC) method is one part of the Accrued Benefit Cost. This study aims to determine normal contributions and actuarial liabilities using the Projected Unit Credit (PUC) method for civil servant pension program participants of PT Taspen (Persero) Cirebon Branch Office. The calculation results show that the PUC method provides a more accurate calculation of the estimated normal contributions and actuarial liabilities of the company. This study is expected to be a reference for other companies in managing employee pension funds using an actuarial approach.

Comparison of Stock Price Forecasting with ARIMA and Backpropagation Neural Network (Case Study: Telkom Indonesia) Carissa, Katherine Liora; Subartini, Betty; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.896

The growth of capital market investors in Indonesia is increasing every year. The most popular investment instrument is stocks. One of the stocks on the Indonesia Stock Exchange (IDX) is the Telkom Indonesia (TLKM). Through stock investment, investors can make a profit by utilizing stock prices in the market. However, stock price fluctuations are uncertain. Therefore, modeling is needed to be able to predict stock prices more accurately. The purpose of this study was to find an appropriate time series model and Neural Network model architecture, and to measure the accuracy of the two models in predicting future stock prices of TLKM. The study was conducted using the Autoregressive Integrated Moving Average (ARIMA) model and Backpropagation Neural Network (BPNN). For comparison, the Mean Absolute Percentage Error (MAPE) method was used. The data used in both models were the stock prices of Telkom Indonesia (TLKM) from September 1, 2023 to September 30, 2024. The result shows that the best ARIMA model, selected based on the least Akaike Information Criterion (AIC) value, is ARIMA(0,1,3) with a MAPE value of 1.20%. Meanwhile, the best BPNN model selected from the smallest testing Mean Squared Error (MSE) value, is BPNN(1,3,1) with a MAPE value of 1.17%. Among those two models, the BPNN model is more accurate because it has less MAPE value compared to the ARIMA one. The results of this research can be considered in forecasting TLKM stock price in the future.

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

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.

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

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.

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

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.

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

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

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.

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