A COMPARATIVE ANALYSIS OF THE ACCURACY OF ARBITRAGE PRICING THEORY ON SHARIA STOCKS IN THE INDONESIA STOCK EXCHANGE - Erna Garnia; Neli Andriani; Siti Riyyan Lisaumi; Hasna Inatsan Layiza; Deshinta Arrova Dewi (Universitas Muhammadiyah Prof DR HAMKA, 2026)
Research aims: This study examines and compares the predictive accuracy of the Arbitrage Pricing Theory (APT) model in forecasting Sharia-compliant stock returns listed in the Jakarta Islamic Index (JII) across two presidential periods: Susilo Bambang Yudhoyono (2004–2014) and Joko Widodo (2014–2024). It analyzes how macroeconomic factors influence Islamic stock performance under different political-economic regimes. Design/Methodology/Approach: This study employs quantitative panel data regression using Ordinary Least Squares (OLS) on 27 companies consistently listed in the JII from 2004 to 2021. Seven macroeconomic factors global index, macroeconomic conditions, world oil prices, China index, Arabia index, competitive resources, and inflation—are derived using Principal Component Analysis (PCA). Observations are classified into high-return and low-return groups. Difference-in-regression tests examine variations across periods, while Mean Absolute Deviation (MAD) evaluates prediction accuracy. Research findings: The results show that the seven macroeconomic factors jointly have a significant influence on Sharia stock returns. However, the significance and direction of individual factors vary across return groups and presidential periods. The APT model achieves the highest predictive accuracy during high-return periods under President Joko Widodo, indicated by the lowest MAD value of 0.489591. Theoretical Contribution/Originality: This study extends multifactor APT analysis across different political-economic regimes. Practitioners/Policy Implications: The findings provide insights for investors and policymakers in developing macroeconomic-sensitive investment strategies. Research Limitations/Implications: This study is limited to secondary data and a specific market context; future research should incorporate broader datasets and alternative models