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This study empirically examined the impact of financing innovation on technological innovation efficiency of select internet companies, that were affiliated with China between 2008 and 2017. Analysis was based on their patent and annual report data and used multiple input-output SFA model, system GMM, and panel fixed-effect model. The results are as follows. (1) There is significant variation in overall technological innovation efficiency of listed companies in the internet industry, and there is a downward trend. The technological innovation efficiency of business that use financing innovation methods is higher than those that do not. (2) The number of patents and intangible capital investment of internet businesses increase obviously every year, but there is no corresponding increase in the efficiency of technological innovation, and little intangible capital investment of non-financing innovation businesses. Thus, determining how to effectively improve the overall quality of patents and the efficiency of intangible capital investment is essential to improve the efficiency of technological innovation for Chinese internet businesses. (3) There is a term mismatch in the investment and financing of internet businesses in China. The financing structure between the financing innovation and non-financing innovation businesses has different impacts on the efficiency of technological innovation. And nowadays, more financing channels are short-term debt financing channels which invest in projects to improve the efficiency of technological innovation due to the pressure of debt repayment and the need to protect shareholders’ interests. (4) In the panel regression, the coefficients of Icd and Roa are significantly negative, suggesting that the investment efficiency of internet businesses needs to be improved.

This study empirically examined the impact of financing innovation on technological innovation efficiency of select internet companies, that were affiliated with China between 2008 and 2017. Analysis was based on their patent and annual report data and used multiple input-output SFA model, system GMM, and panel fixed-effect model. The results are as follows. (1) There is significant variation in overall technological innovation efficiency of listed companies in the internet industry, and there is a downward trend. The technological innovation efficiency of business that use financing innovation methods is higher than those that do not. (2) The number of patents and intangible capital investment of internet businesses increase obviously every year, but there is no corresponding increase in the efficiency of technological innovation, and little intangible capital investment of non-financing innovation businesses. Thus, determining how to effectively improve the overall quality of patents and the efficiency of intangible capital investment is essential to improve the efficiency of technological innovation for Chinese internet businesses. (3) There is a term mismatch in the investment and financing of internet businesses in China. The financing structure between the financing innovation and non-financing innovation businesses has different impacts on the efficiency of technological innovation. And nowadays, more financing channels are short-term debt financing channels which invest in projects to improve the efficiency of technological innovation due to the pressure of debt repayment and the need to protect shareholders’ interests. (4) In the panel regression, the coefficients of Icd and Roa are significantly negative, suggesting that the investment efficiency of internet businesses needs to be improved.

The super efficiency DEA model and panel Tobit model were used in this study to conduct empirical research on innovation performance and its influencing factors in expansive companies based on patent and annual report data for A-shares, dilated listed companies in China from 2009 to 2015. Our results suggest that innovation performance in Chinese listed companies is generally stagnating at a low level, but scores for computer, communications equipment, electrical machinery, chemical, and pharmaceutical industries are high. There are significant differences in innovation performance between internal and external expansion companies. The internal expansion scale shows a significant negative correlation with innovation performance, while there is a “U” shaped nonlinear relationship between external expansion and innovation performance; the turning point appears when the external expansion scale is 0.2, that is, it is significantly negative to innovation performance below 0.2 (and vice versa). Firm age, firm size, executive pay, average age of executives, and depreciation have a negative impact on innovation performance, while equity concentration, capital intensity, and financial leverage have a positive impact on innovation performance.

With the restrictions of equity financing of Chinese listed companies, debt dimensions are increasing, and the issue of corporate financial structure and financing constraints influence on capital misallocation has become an important practical problem which Chinese listed companies face. This paper is concerned with a model about capital misallocation and its influencing factors of integrated financing, capital operation, and investment performance. We take 7096 observations of 646 Chinese listed companies during fiscal years 2003 to 2014 for A-shares on the Shanghai and Shenzhen stock exchange, for instance, to empirically test the microscopic influencing factors of capital misallocation under different external financing dependence. The study illustrates the following: (1) in descriptive statistics of different industries capital misallocation, more than half of firms experience the circumstance of capital misallocation; (2) although Chinese listed companies are faced with financing constraints, capital market inefficiency, and other issues, most companies still depend on external financing; (3) the main factors that affect capital misallocation of the listed companies are financial liquidity and financial pledgeability; (4) the firms with high innovation abilities generally have stronger profitability, superior financial liquidity, and better financial pledgeability, thus reducing corporate capital misallocation; (5) the Chinese listed companies with large-scale assets and strong profitability easily obtain bank loans and equity financing, while violating the principle of assets matching.

Purpose: In a macroeconomic environment with the non-tradable shares reform, financial crisis, tax reform and monetary policy, to examine the dynamic characteristics and factors of the debt maturity structure, this research tends to offer an empirical analysis about Chinese listed companies in different industries. Design/methodology/approach: Learned from Leary (2009), Voutsinas and Werner (2011), this study designs a model of debt maturity structure with an unbalanced panel data set. Consists of 1352 Chinese listed companies with 8124 observations during the period of 2003-2011, the sample passed Hausman test, and the findings support the fixed effects model. Findings: Besides the factors that have been confirmed by previous researches, debt maturity structure is also sensitive to other factors, such as economic expectations, monetary policy, financial restrictions and changes in tax rates. Research limitations/implications: There are still many cases, which affect the debt maturity structure, are worth of further exploring, for instance, the impact of lagged monetary policy, the determinants of short-term debt ratio and the cost of operating. Practical implications: From the macro point of view, research in this area enables the government to introduce more suitable policies that direct and promote the development of the bond market. From the micro point of view, it spurs corporations to choose proper finance structure. Firms can learn from the research to adopt the efficient method and term of financing as well as debt structure. Originality/value: In some way, conclusions of this paper contribute to the study of dynamic characteristics and factors of debt maturity structure in Chinese listed companies.

In this paper , basing on the measurement of the innovations and the default risk indicators, we take default risk as the induced variable, and take R&DR, innovations, corporate size, system risk and asset-liability ratio as the dependent variable, to empirical analysis the correlation among the listed company's default risk ,innovation performance and other variables. By the empirical analysis of 241 listed companies, we find that the main factors which affect the DR consisting of Constant, firm size, Lev, R&DR, region, year, and R&DR. And there are problems of over-investment in R&D existed before the financial crisis. The regression coefficient of innovation performance is negative before the financial crisis, but it is positive after the financial crisis, which is consistent with the conclusions of many abroad literatures.