Hybrid Bayesian and Machine Learning for Profitability Prediction in LQ45 Firms - Junita Silele; Maylen Kambuaya; Hesty Theresia Salle; Ulfah Muslimin; Annisa Fitriah Mudassir; Bill Pangayow (AIBPM Publisher, 2025)
Understanding the drivers of firm profitability is essential for companies in emerging markets, where structural uncertainties and competitive pressures often shape financial performance. This study investigates how four core financial ratios: current ratio (CR), debt-to-asset ratio (DAR), net profit margin (NPM), and total asset turnover (TATO), influence return on assets (ROA) among LQ45 firms listed on the Indonesia Stock Exchange (IDX) from 2020 to 2023. Using 80 firm-year observations, the analysis applies a hybrid approach combining Bayesian regression, which captures parameter uncertainty, with XGBoost-SHAP to enhance interpretability and detect nonlinear patterns. The Bayesian posterior estimates indicate that TATO has the strongest effect on profitability (? = 0.072, 95% CI: 0.041–0.101), while DAR shows a moderate positive influence (? = 0.018, 95% CI: 0.005–0.032). NPM demonstrates a weaker but still positive contribution (? = 0.009, 95% CI: 0.001–0.018), whereas CR exhibits a non-significant effect (? = –0.002, 95% CI: –0.010–0.006). Numerically, a 10% increase in TATO corresponds to an estimated 0.7% rise in ROA, underscoring the central role of operational efficiency in shaping firm profitability. These findings reinforce the Resource-Based View and Trade-Off Theory and highlight the value of hybrid analytical frameworks for improving the interpretability and robustness of profitability models in emerging markets.