Predicting Shariah Stock Market Indices with Machine Learning: A Cross-Country Case Study
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ABSTRACT
Stock prices are influenced by numerous factors, including policy adjustments, economic conditions, and international developments. Consequently, forecasting stock price trends accurately has posed a significant challenge for economists to study. The Islamic financial industry experiences fewer shocks compared to the traditional financial sector, allowing investors to anticipate the performance of Islamic indices. This study aims to predict the Islamic stock market indices in six countries, including Indonesia, Thailand, Malaysia, Pakistan, the United Arab Emirates, and Qatar, using the Autoregressive Integrated Moving Average (ARIMA) model. Monthly data from 2013 to 2023 sourced from investing.com and Yahoo Finance are analyzed using R machine learning. The objective of this study is to provide accurate predictions for the next 25 months and offer insights into potential price movements. Overall, this research also sheds light on the dynamics of the Islamic market in Indonesia, Thailand, Malaysia, Pakistan, the United Arab Emirates, and Qatar, which adhere to the Efficient Market Hypothesis (EMH) due to the predictability of index prices by historical data.
Keywords: forecating, R-Studio, ARIMA, Islamic Stock Market, Machine Learning, R-Programming
ABSTRAK
Harga saham dipengaruhi oleh banyak faktor, termasuk penyesuaian kebijakan, kondisi ekonomi, dan perkembangan internasional. Oleh karena itu, memprediksi tren harga saham dengan akurat telah menjadi tantangan signifikan bagi para ekonom untuk mempelajarinya. Industri keuangan Islam mengalami lebih sedikit goncangan dibandingkan dengan sektor keuangan tradisional, yang memungkinkan investor untuk memperkirakan kinerja indeks Islam. Studi ini bertujuan untuk memprediksi indeks pasar saham Islam di enam negara, termasuk Indonesia, Thailand, Malaysia, Pakistan, Uni Emirat Arab, dan Qatar, menggunakan model Autoregressive Integrated Moving Average (ARIMA). Data bulanan dari tahun 2013 hingga 2023 yang berasal dari investing.com dan Yahoo Finance dianalisis menggunakan pembelajaran mesin R. Tujuan dari studi ini adalah untuk memberikan prediksi yang akurat untuk 25 bulan mendatang dan menawarkan wawasan tentang pergerakan harga yang potensial. Secara keseluruhan, penelitian ini juga memberikan cahaya tentang dinamika pasar Islam di Indonesia, Thailand, Malaysia, Pakistan, Uni Emirat Arab, dan Qatar, yang mengikuti Hipotesis Pasar Efisien (EMH) karena dapat diprediksi oleh data historis..
Kata Kunci: Prediksi, R-Studio, ARIMA, Indeks Pasar Modal Syariah, Machine Learning, R-Programming
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