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All Journal International Journal of Quantitative Research and Modeling
Alim Jaizul Wahid
Research Collaboration Community
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Portfolio Performance Analysis with Jensen's Alpha Using Single Index Model and CAPM on IDX30 Stocks Alim Jaizul Wahid; Jumadil Saputra
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.v6i2.1013

Abstract

This study aims to evaluate the formation of an optimal stock portfolio using the Capital Asset Pricing Model (CAPM) and Single Index Model (SIM) approaches, and to assess portfolio performance using Jensen's Alpha generated from stocks included in the IDX30 index during the period April 2024 to March 2025. This study uses a quantitative descriptive approach with a population of 30 IDX30 stocks. The methods applied include calculating stock returns and betas, as well as forming an optimal portfolio using the CAPM and SIM formulas. Portfolio performance is then measured by Jensen's Alpha. The results of the study show that based on CAPM, BRIS.JK and EMTK.JK stocks are worthy of being included in the optimal portfolio because they have a positive expected return and Jensen's Alpha that slightly outperforms the market. EMTK.JK also has a lower risk. Meanwhile, based on SIM, only BBCA.JK is included in the optimal portfolio because it meets the criteria for Excess Return to Beta (ERB) > cut-off rate (C^*), and shows a positive Jensen's Alpha. The conclusion of this study is that both models can identify superior performing stocks for the optimal portfolio in the period.
Comparison of LSTM and ARIMA-GARCH Models in Predicting Stock Price Volatility of Islamic Banking in Indonesia Alim Jaizul Wahid; Rizki Apriva Hidayana; Nestia Lianingsih
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1327

Abstract

Stock price volatility is one of the most critical investment risk indicators, particularly in Islamic banking stocks that face the dual challenges of conventional capital market dynamics and compliance with Islamic principles. This study compares the predictive performance of two modeling approaches: Long Short-Term Memory (LSTM), a deep learning architecture based on Recurrent Neural Networks, and the classical econometric ARIMA-GARCH model, in predicting stock price volatility of four Islamic banking issuers in Indonesia, namely BRIS (Bank Syariah Indonesia), BSIM (Bank Sinarmas Syariah), PNBS (Bank Panin Dubai Syariah), and BTPNS (Bank BTPN Syariah), for the period 2019–2024. Daily closing price data was obtained from the Indonesia Stock Exchange (IDX). The ARIMA-GARCH model was built through the stages of identification, estimation, and Box-Jenkins diagnostic testing, while the LSTM model was optimized through hyperparameter tuning with a 60-day rolling window. Predictive performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the LSTM model consistently produces lower MAPE values than ARIMA-GARCH for all issuers studied, especially during periods of high volatility such as the COVID-19 pandemic (2020) and global interest rate instability (2022–2023). However, the ARIMA-GARCH model provides better interpretability and is more stable under calm market conditions. This research contributes to the literature on Sharia-based quantitative finance in Indonesia and provides practical implications for investors and risk managers.
The Impact of Macroeconomic Variables on Actuarial Premium Calculation: Evidence from Indonesian Life Insurance Using OLS Regression Alim Jaizul Wahid; Khandker Farid Uddin Ahmed; Renda Sandi Saputra
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1328

Abstract

Actuarial science requires accurate net premium calculations, yet traditional approaches often ignore macroeconomic dynamics such as inflation and interest rates. This study investigates the impact of macroeconomic variables specifically inflation rate and Bank Indonesia reference interest rate (BI Rate) on actuarial net premium calculations for term life insurance products in Indonesia. Using annual time-series data from 2005 to 2023, this study applies Ordinary Least Squares (OLS) regression to quantify the relationship between macroeconomic indicators and net premium values computed using the Indonesian Mortality Table (TMI-2019). The results indicate that inflation has a significant positive effect on net premium values, while the BI Rate exerts a significant negative effect, consistent with actuarial theory regarding the present-value discounting mechanism. The adjusted R-squared value of 0.847 confirms strong explanatory power of the model. These findings provide practical guidance for insurance companies and the Financial Services Authority (OJK) in setting premium reserves under dynamic macroeconomic conditions. This study contributes to the intersection of actuarial science and macroeconomic modeling, which remains underexplored in the Indonesian context.
Co-Authors Jumadil Saputra Khandker Farid Uddin Ahmed Nestia Lianingsih Renda Sandi Saputra Rizki Apriva Hidayana