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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. [ABSTRACT FROM AUTHOR]

China's economy has shifted from the stage of high-speed growth to the stage of high-quality development. This paper selects panel data of 30 provinces, municipalities directly under the Central Government and autonomous regions (except Tibet) in mainland China with the time dimension of 2006–2016, selects indicators from five dimensions of innovation, coordination, greenness, openness and sharing, uses principal component analysis to construct a system of indicators of high-quality economic development, and uses the spatial Durbin model to empirically the impact of government investment behavior in the three industries on the regional economic high-quality development was studied. The results show that: first, there is a significant positive spatial correlation between China's economic quality development. Second, government investment in different industries has different effects on the quality development of the economy. Investment in the tertiary industry can boost employment, raise income level, promote economic quality development, and enhance sharing, coordination and innovation; investment in the secondary industry can enhance innovation, but because of the "crowding-out effect", it will reduce openness and sharing, and is not conducive to promoting economic quality development; investment in the primary industry can Investment in the primary industry can significantly improve coordination, but it will reduce innovation, greenness, openness and sharing, which will also have a negative impact on the overall high-quality economic development. Third, there is a significant spatial spillover effect of government investment behavior on regional economic quality development, and the economic quality development of this region will have an impact on neighboring regions. Finally, based on the research results, this paper puts forward countermeasure suggestions for promoting high-quality economic development. [ABSTRACT FROM AUTHOR]

This paper analyses the interaction between the novel coronavirus pandemic (COVID-19), unemployment rate, stock market, consumer confidence index (CCI), and economic policy uncertainty (EPU) index in China within a time-frequency framework. We compare the changes in economic indicators during the global financial crisis (GFC) and study the different impacts of the two events on China's economy. An unprecedented impact of COVID-19 shocks on the unemployment rate, CCI, EPU index, and stock market volatility over the low frequency bands is uncovered by applying the coherence wavelet method to China monthly data. The COVID-19 effect on the stock market volatility and the EPU index is substantially higher than on the unemployment rate and the CCI. On the contrary, the GFC's impact on the unemployment rate is much greater than that on the EPU index and CCI. Additionally, the impact of the GFC on the economy is more cyclical in the long-term, while the COVID-19 pandemic is a short-term shock with a relatively short oscillation cycle. This study concludes that the economic impact of COVID-19 will not spread into a financial crisis for China and believe that the COVID-19 pandemic is more of a health event than an economic crisis for Chinese economy. [ABSTRACT FROM AUTHOR]