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We conjecture that locally government-led bank consolidation may negatively affect the SMEs’ credit availability in China because the breakdown of privately benefit-based relationships among small business borrowers, loan officers, and senior managers could reduce loan officers’ incentives to use their soft information. Empirical results show that this is indeed the case.

We investigate the relationship between chief executive officers’ (CEOs) early-life exposure to marketization transition and the research and development (R&D) intensity of Chinese small and medium-sized enterprises (SMEs). We find that there is a significant positive relationship between the early-life transition experiences of CEOs and the R&D intensity of SMEs. CEOs who have experienced higher stages of marketization transition are significantly more likely to engage in proactive innovation activities. The means of the potential R&D intensity distributions are increasing in the treatment level, although this increasing trend is not always significant. Overall, the results confirm the view that experiencing a transition in early-life significantly facilitates CEOs’ engagement in innovation activities.

This article proposes a hybrid intelligent system that predicts five-category risk grades by feeding past financial performance data into rough set approach and neural network. The attributes are reduced with no information loss through rough set approach, and then this reduced information is used to develop classification rules and train Elman neural network. For the experiment, the financial data of 896 firms are selected to predict risk grades. The effectiveness of our methodology is experimentally verified by comparing traditional logistic model with our hybrid approach.