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Constructing a CMBS Default Risk Sentiment Index Using Financial Text Embeddings and Evaluating Its Predictive Effectiveness
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Abstract
In the commercial real estate finance system, the timely identification of CMBS default risk is of critical importance for maintaining financial stability and preventing systemic risk. With advances in information technology and declining data acquisition costs, unstructured financial text data have accumulated rapidly, and textual information exhibits advantages that traditional structured data cannot easily replace in reflecting changes in market sentiment, the evolution of risk perceptions, and potential default signals. Focusing on financial text embedding techniques, this study systematically characterizes risk sentiment features related to CMBS defaults and, based on large-scale financial text data, constructs a CMBS default risk sentiment index that reflects market risk expectations. The index is then incorporated into a default risk prediction framework and evaluated using multiple econometric models to empirically examine its role in explaining CMBS default behavior and enhancing default risk predictive performance. The results show that risk sentiment information extracted through financial text embeddings effectively complements traditional structured indicators based on asset characteristics, tranche structure, and macroeconomic variables, exhibiting sig-nificant predictive advantages across various model specifications while maintaining strong stability in out-of-sample forecasts. Further robustness and extension analyses confirm the reliability and generalizability of the findings. This study provides a new quantitative perspective for the forward-looking identification of CMBS default risk and offers valuable insights into the application of financial text analysis methods in structured credit risk research and macroprudential regulatory practice.
Keywords
Text embedding; Default risk; Sentiment index; Risk prediction
References
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How to cite this paper
Constructing a CMBS Default Risk Sentiment Index Using Financial Text Embeddings and Evaluating Its Predictive Effectiveness
How to cite this paper: Jingzhi Yin. (2026). Constructing a CMBS Default Risk Sentiment Index Using Financial Text Embeddings and Evaluating Its Predictive Effectiveness. Engineering Advances, 6(1), 50-54.
DOI: http://dx.doi.org/10.26855/ea.2026.03.011
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