Document Type : Original Article
Authors
1
Ph.D. Candidate, Department of Accounting, Ker.C., Islamic Azad University, Kermanshah, Iran.
2
Associate prof., Department of Accounting, Ker.C., Islamic Azad University, Kermanshah, Iran.
3
Associate prof., Department of Accounting, Ker.C., Islamic Azad University, Kermanshah, Iran
4
Associate prof., Department of Economics, Ker.C. Islamic Azad University, Kermanshah, Iran
10.30699/ijf.2026.569343.1569
Abstract
This research identifies the determinants of trading risk (conditional variance) in the Iranian stock market over 15 years (2008-2023) using a novel hybrid approach combining spatial econometrics and machine learning algorithms. The main objective is to evaluate the superiority of hybrid models over traditional methods and to identify the roles of macroeconomic, geopolitical, behavioral factors, and firm characteristics in shaping systematic risk. The sample includes 172 companies listed on the Tehran Stock Exchange with 30,960 monthly observations and 33 explanatory variables. The methodology was implemented in three stages: First, GARCH and EGARCH models were employed to extract conditional variance and confirm the leverage effect. Second, the Spatial Durbin Error Model (SDEM) was used to decompose direct, spatial spillover, and total effects of variables while controlling for cross-sectional dependence (Pesaran CD statistic = 87.45***) and spatial autocorrelation (Moran's I = 0.4567***). Third, machine learning algorithms, including Linear Regression, SVM, Random Forest, XGBoost, LSTM, and Transformer, were applied independently and in combination with SDEM outputs. The results demonstrated a clear performance hierarchy: Linear Regression (R² = 0.4123, RMSE = 0.0987), SVM (R² = 0.5987), Random Forest (R² = 0.6789), XGBoost as the best standalone model (R² = 0.7456, RMSE = 0.0534), and Ensemble (R² = 0.7523). Hybrid models showed significant superiority: SDEM + XGBoost (R² = 0.7823, RMSE = 0.0471; 11.80% error reduction compared to standalone XGBoost and 52.3% improvement over Linear Regression), and SDEM + Ensemble (R² = 0.7867, RMSE = 0.0467) achieved optimal performance. Time-series cross-validation (average test RMSE = 0.0492) and the Diebold-Mariano test (DM = 3.456*** against XGBoost) confirmed statistical superiority. From a substantive perspective, the exchange rate with a total effect of 0.2443*** and SHAP contribution of 18.34% was identified as the most important systematic risk factor, followed by sanction intensity (total effect = 0.1274***, SHAP = 14.23%), Altman Z-score (SHAP = 15.67%), total stock index (total effect = -0.1801***, SHAP = 12.89%), and investor sentiment (total effect = 0.1001***, SHAP = 11.45%). The findings demonstrate that hybrid spatial econometrics and machine learning models improve prediction accuracy by 12-15% through extracting complementary information. Geopolitical and behavioral factors, in addition to traditional macroeconomic variables, are systematically important. Spatial spillovers constitute 15-25% of total effects, which are ignored in traditional models. This research shows that the frontier of financial risk modeling lies in the synergistic integration of economic theory and machine learning.
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