Ensemble learning algorithms show good forecasting performances for financial distress in many studies. Despite considering the feature selection and feature importance procedures, most overlook imbalanced data handling. This study proposes the Easyensemble method based on undersampling and combines it with ensemble learning models to predict financial distress. The results show that Easyensemble sampling presents better forecasting performance than SMOTE sampling. We subsequently conduct Permutation Importance (PIMP), Recursive Feature Elimination (RFE), and partial dependence plots, and the experimental results show that the feature selection procedure can effectively reduce the number of indicators without affecting the prediction accuracy, improve the prediction efficiency as well as save processing time. In addition, the indicators from profitability, cash flow, solvency, and structural ratios are essential in predicting financial distress.

This study investigates the relationship between stock liquidity and firm innovation for publicly traded growing small and medium-sized enterprises (SMEs) in China using both innovation input and output. We collected samples of 785 SMEs from China’s Shenzhen Growth Enterprises Market without the financial industry from 2010 to 2020. The empirical findings demonstrate a significant positive relationship between stock liquidity and both innovation input, as measured by R&D investments, and innovation output, as proxied by patenting activities. A series of robustness tests demonstrate the reliability of our results. Increased liquidity enhances SMEs' innovation mainly by alleviating financial constraints, whereas the mediating effect of mergers and acquisitions (M&A) is not apparent at the firm level. Furthermore, the inhibitory effect of blockholder ownership on firm innovation is weak. Further analysis reveals that this favorable impact can last for at least four years, with manufacturing SMEs benefiting the most. Our study shows that the innovation abilities of SMEs can be enhanced by improving stock liquidity, which is mainly driven by tackling financial constraints.

The digital economy has reshaped the structure and operation of the labor market through profound technological changes, exerting systematic impacts on the quality of flexible employment. Based on five consecutive periods of data from the China Family Panel Survey (CFPS) between 2014 and 2022, a multidimensional, flexible employment quality indicator system is constructed to empirically examine the effects, heterogeneity, and mechanisms of the digital economy on flexible employment quality. This study shows that the digital economy has significantly improved the overall quality of flexible employment. Specifically, male, low-skilled, young, and flexible workers with a low dependency ratio on the northwest side of the Hu-Huan-Yong Line benefit more significantly. Mechanism testing results indicate that industrial structure optimization, human capital accumulation, and improved matching efficiency are important intermediary pathways through which the digital economy enhances the quality of flexible employment. The conclusion indicates that amplifying the positive empowerment effect of the digital economy on the quality of flexible employment, implementing targeted policies, and activating three indirect transmission pathways—industrial structure, human capital, and supply–demand matching efficiency—are key measures to enhance the quality of flexible employment.