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The study evaluates the effectiveness of financial indicators in financial risk prediction and develops a framework using financial and textual data. It emphasises the importance of both data types in risk assessment and prioritises liquidity and industry specific metrics. The analysis of the existing literature affirmed the significance of both data types in risk assessment. The findings of the study revealed a strong correlation between financial and textual indicators. The selection of deep learning was based on its adeptness in handling diverse unstructured data, justifying its application. This innovative methodology enhances financial risk prediction and supports strategic decision-making.
Huang et al. (Tue,) studied this question.