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. It decreased the portfolio loss during the crisis. By evaluating portfolio value on the COVID crisis for long investment, replacing CAPM with SCAPM in the BL model can transform the asset proportion. The results illustrate the empirical study which can be implemented for the Shariah -compliant stock market in Indonesia. Hence, this study manifested that BL-SCAPM outperformed the reference portfolio. Despite a decline in portfolio value before and during the outbreak, the reference portfolio losses were higher than those of BL-SCAPM. After the COVID-19 outbreak was officially declared in January 2020, the performance of BL-SCAPM was still above the BL. The equal benefit was procured from both portfolios in July and August 2019.
The impact of the Sharpe ratio of BL-SCAPM was more significant than the reference portfolio. The result presents that the portfolio performance of BL-SCAPM outperformed the MV and BL-CAPM. Furthermore, the portfolio performance of BL-SCAPM was compared with two reference portfolios, the mean-variance method and BL-CAPM. This proposed model was implemented in Indonesia using monthly returns from the Jakarta Islamic Index (JII) list collected from February 2014 to June 2019. The Sharia-compliant asset pricing model (SCAPM) with the inflation rate was regarded as the new starting point in the BL model. 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. 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.
The authors proposed modifying the Black–Litterman (BL) model adapted to the Sharia principle. This research aims to demonstrate portfolio modeling, which leads to Sharia compliance in encountering crises because of COVID-19. Originality/value-This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information. 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. 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. 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. To train the neural network, this paper employs the three DL techniques, namely Levenberg-Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG). 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. 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.
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