Shariah Banking Stock Prices: Expected Return, Risk Level and Macroeconomics Conditions In Indonesia
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Abstract
The present study evaluates how Expected Return (ERT), Risk Level (RLV), alongside Macroeconomic Conditions (MCN) impact the Shariah Banking Stock Price (SBS). The scope focuses specifically on Islamic banking institutions actively listed on the Indonesia Stock Exchange (IDX) between October 2021 and August 2024. All shariah-compliant banks featured on the IDX constituted the target population. To select the final subjects, a purposive sampling approach was applied. The core inclusion criteria required these firms to maintain a consistent listing status and provide uninterrupted data throughout the designated timeframe. Consequently, four specific entities met these parameters: BRIS, BTPS, BANK, and PNBS. Given the 35-month timeline, the empirical testing processed a final dataset comprising 140 distinct observations. Analytical procedures relied on SPSS software to conduct descriptive statistics and classical assumption evaluations—specifically assessing normality, multicollinearity, heteroscedasticity, and autocorrelation. Following this, hypothesis verification was carried out via simultaneous (F-statistic) and partial (t-statistic) assessments, complemented by the coefficient of determination (R²). Empirical findings reveal that, collectively, the three independent components (ERT, RLV, and MCN) significantly drive SBS movements, evidenced by an F-value of 64.321 at a 0.000 significance threshold. When evaluated individually, only Macroeconomic Conditions (MCN) demonstrated a distinct, adverse effect on the stock prices (t-value = -8.290, p = 0.000). Conversely, neither Expected Return nor Risk Level showed any statistically meaningful impact on the dependent variable. The generated Adjusted R² stood at 0.741. This metric implies that the formulated predictors account for exactly 74.1% of the fluctuations observed in the shariah stock prices, while unidentified external factors outside this framework are responsible for the remaining 25.9% variance. Moving forward, subsequent investigations could benefit from extending the observational timeframe. Scholars are encouraged to incorporate alternative macroeconomic indicators—such as fluctuating interest rates or inflation metrics—and potentially broaden the sampling frame to encompass conventional banking or entirely different industries to yield more comprehensive insights.
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