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All Journal International Journal of Quantitative Research and Modeling
Millantika, Salwa Cendikia
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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The Effect of Double Date Discounts on Sales Levels In E-Commerce Shopee (Case Study on Students of Padjadjaran University in Jatinangor) Wahid, Alim Jaizul; Millantika, Salwa Cendikia; Supriatna, Asep Kuswandi
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.835

Abstract

This study aims to analyze the impact of double date sale discounts on sales levels on the Shopee e-commerce platform, focusing on students from Universitas Padjadjaran in Jatinangor, who are primarily from the millennial and Generation Z cohorts. The method used is simple linear regression, linking discount variables to sales. Additionally, the study conducts classical assumption testing to ensure the models validity and sensitivity analysis to assess the effect of parameter changes on the predicted outcomes. The results show that double date sale discounts significantly influence sales, with the double date sale coefficient (eta_1) being highly sensitive to changes. The regression model yields a low MSE, indicating good prediction accuracy. While changes in the intercept (eta_0) also affect the predictions, the impact is smaller compared to changes in the double date sale coefficient.
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.
The Effect of Double Date Discounts on Sales Levels In E-Commerce Shopee (Case Study on Students of Padjadjaran University in Jatinangor) Wahid, Alim Jaizul; Millantika, Salwa Cendikia; Supriatna, Asep Kuswandi
International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.835

Abstract

This study aims to analyze the impact of double date sale discounts on sales levels on the Shopee e-commerce platform, focusing on students from Universitas Padjadjaran in Jatinangor, who are primarily from the millennial and Generation Z cohorts. The method used is simple linear regression, linking discount variables to sales. Additionally, the study conducts classical assumption testing to ensure the models validity and sensitivity analysis to assess the effect of parameter changes on the predicted outcomes. The results show that double date sale discounts significantly influence sales, with the double date sale coefficient (eta_1) being highly sensitive to changes. The regression model yields a low MSE, indicating good prediction accuracy. While changes in the intercept (eta_0) also affect the predictions, the impact is smaller compared to changes in the double date sale coefficient.
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.
Co-Authors Asep Kuswandi Supriatna Wahid, Alim Jaizul