MODELLING AND FORECASTING SHARĪʿAH COMPLIANT STOCKS

Purpose — This research aims to forecast the volatility and future stock prices of Sharīʿah-compliant stocks of a sample of Islamic banks in Indonesia.

Design/Methodology/Approach — Modelling and forecasting of the stock prices were performed using the AutoRegressive Integrated Moving Average (ARIMA) and Exponential Moving Average (EMA) models. Daily data were retrieved via Yahoo Finance from 1 February 2021 to 30 September 2024. This study focused on the stocks of three Islamic banks listed on the Indonesian Stock Exchange (IDX), namely Bank Syariah Indonesia (BRIS), Bank Panin Dubai Syariah (PNBS), and Bank BTPN Syariah (BTPS). The models were employed to predict stock prices in the short term.

Findings — The accuracy of the EMA model was 99.73 per cent, whereas that of the ARIMA was 87.83 per cent. The results show that the stock price prediction is reasonably accurate, and the error might be due to stochastic macroeconomic conditions.

Originality/Value — Previous studies focused on modelling and forecasting stock prices using the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model. This research expands on prior work by incorporating the ARIMA and EMA models to forecast stock prices and evaluate their performance under different market conditions. This study makes a unique contribution by concentrating on Indonesia, the country with the largest Muslim population and a distinctive economic and financial landscape. Furthermore, stock prices are utilised to conduct a robustness test, ensuring the reliability and validity of the findings.

Research Limitations/Implications — The research uses ARIMA and EMA models to predict Sharīʿah stock prices only in Indonesia. Future research should explore alternative econometric models or machine learning techniques to enhance prediction accuracy. Additionally, expanding the scope to include multiple countries or different instruments beyond stock prices, such as bonds or cryptocurrencies, could provide deeper insights into the robustness of these models across various markets.

Practical Implications — The EMA model can help investors gain confidence in trading decisions. This study proves it is a viable investment strategy during economic uncertainty, demonstrating a lower forecasting error than the ARIMA model.