Early warning effect based on traditional financial indicators and the combination of MD&A text readability and similarity indicators.

Early warning effect based on traditional financial indicators and the combination of MD&A text readability and similarity indicators.</p

Flow chart of empirical analysis.

Nowadays, the international situation is severe and complex, and the structural issues within the Chinese economy are prominent. Consequently, the financial risks faced by Chinese listed companies continue to escalate. Hence, it is of great practical significance to furnish effective early warnings for financial crises in listed companies. In this paper, we first employ web crawler technology and natural language processing technology to assess four text-linguistic features in the Management Discussion and Analysis (MD&A) section of the annual financial reports of listed companies in China from 2011 to 2020. These features are text tone, forward-looking, readability and similarity. Subsequently, we combine these features with traditional financial indicators and explore thirteen mainstream machine learning models to comparatively analyze their effectiveness in predicting financial crises in listed companies. The empirical findings of this research reveal that MD&A text readability and similarity indicators contribute valuable incremental information to prediction models, whereas text tone and forward-looking indicators exhibit the opposite effect. The latter two indicators can be manipulated more effortlessly by management, as this study’s empirical findings indicate no evidence of their contributions in incremental informational value. In fact, the forward-looking indicator even introduces additional noise to the prediction. Finally, by comparing the early warning effects of thirteen machine learning models, it is found that RF, Bagging, CatBoost, GBDT, XGBoost and LightGBM models maintain stable and accurate sample recognition ability. In general, this paper constructs a more effective financial crisis early warning model by exploring the MD&A text-linguistic feature indicators, thereby offering a fresh research perspective for further investigations in this field.</div

Early warning effect based on traditional financial indicators and MD&A text tone indicator.

Early warning effect based on traditional financial indicators and MD&A text tone indicator.</p

Mean values and ranking of early warning evaluation indicators of 13 machine learning models based on seven different sets of input feature variables.

Mean values and ranking of early warning evaluation indicators of 13 machine learning models based on seven different sets of input feature variables.</p

The selection of traditional financial indicators and formulas.

The selection of traditional financial indicators and formulas.</p

Early warning effect based on traditional financial indicators and the combination of MD&A text tone, readability and similarity indicators.

Early warning effect based on traditional financial indicators and the combination of MD&A text tone, readability and similarity indicators.</p

Early warning effect based on traditional financial indicators.

Early warning effect based on traditional financial indicators.</p

Early warning effect based on traditional financial indicators and MD&A text similarity indicator.

Early warning effect based on traditional financial indicators and MD&A text similarity indicator.</p

Early warning effect based on traditional financial indicators and MD&A text forward-looking indicator.

Early warning effect based on traditional financial indicators and MD&A text forward-looking indicator.</p

Descriptive statistics of traditional financial indicators and text-linguistic feature indicators.

Descriptive statistics of traditional financial indicators and text-linguistic feature indicators.</p