Retno Subekti, Abdurakhman and Dedi Rosadi
This research aims to demonstrate portfolio modeling, which leads to Sharia compliance in encountering crises because of COVID-19. The authors proposed modifying the…
Abstract
Purpose
This research aims to demonstrate portfolio modeling, which leads to Sharia compliance in encountering crises because of COVID-19. The authors proposed modifying the Black–Litterman (BL) model adapted to the Sharia principle. The implementation of BL on Shariah-compliant stock data with capital asset pricing model (CAPM) requires adjustment because of the interest rate in the calculation. Thus, the objective of this study is to develop and evaluate the modified BL for Shariah-compliant stock portfolios in the financial crisis caused by the COVID-19 pandemic.
Design/methodology/approach
The Sharia-compliant asset pricing model (SCAPM) with the inflation rate was regarded as the new starting point in the BL model. This proposed model was implemented in Indonesia using monthly returns from the Jakarta Islamic Index (JII) list collected from February 2014 to June 2019. Furthermore, the portfolio performance of BL-SCAPM was compared with two reference portfolios, the mean-variance method and BL-CAPM.
Findings
The result presents that the portfolio performance of BL-SCAPM outperformed the MV and BL-CAPM. The impact of the Sharpe ratio of BL-SCAPM was more significant than the reference portfolio. The equal benefit was procured from both portfolios in July and August 2019. After the COVID-19 outbreak was officially declared in January 2020, the performance of BL-SCAPM was still above the BL. Despite a decline in portfolio value before and during the outbreak, the reference portfolio losses were higher than those of BL-SCAPM. Hence, this study manifested that BL-SCAPM outperformed the reference portfolio.
Practical implications
The results illustrate the empirical study which can be implemented for the Shariah-compliant stock market in Indonesia. By evaluating portfolio value on the COVID crisis for long investment, replacing CAPM with SCAPM in the BL model can transform the asset proportion. It decreased the portfolio loss during the crisis. Future research can be developed more from the open problems in this implementation to deliver the portfolio model into the Shariah framework with varied SCAPM in BL.
Originality/value
The attention to BL studies on portfolio building with Sharia-compliant stocks is rarely focused on the Islamic perspective. Hence, the novelty of this research is the idea of modifying the BL model with a Shariah starting point. More generally, this research enriches Shariah financial literacy regarding the stock market and, specifically, its implementation in the Indonesian stock market.
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Keywords
Mohammed Ayoub Ledhem and Warda Moussaoui
This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric…
Abstract
Purpose
This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.
Design/methodology/approach
This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.
Findings
The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.
Practical implications
This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.
Originality/value
This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.
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The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric…
Abstract
Purpose
The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.
Design/methodology/approach
This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).
Findings
The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.
Practical implications
This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.
Originality/value
This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.