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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Spatial Distribution Measurement of the Implicit Debt Risks of China's Local Governments and the Space Choice of Venture Capital: Industry First or Capital First WANG Liang, LI Zi-lian, JIN Zhen-zhong Contemporary Finance & Economics    2020, 0 (12): 50-62.   Abstract （302）            Knowledge map    Save Based on the micro data of venture capital from 2010 to 2017, it is found that the venture capital of various industries in China presents a significant spatial imbalance, among which the service industry is more obvious than the manufacturing industry. Taking the provinces as the spatial units to study the above phenomena, this paper focuses on the discussion of the influencing effect of the two key elements of industry and capital on the space selection preference of venture capital, and measures the effect degree of different factors. The findings show that both industrial agglomeration and venture capital agglomeration have enhanced the possibility of the relevant industries in the region to obtain venture capital, and the promotion effect of capital agglomeration at the venture capital center is more significant. The results of further research by industry show that the spatial choice of venture capital in manufacturing industry has a stronger preference to industry than to capital, while the spatial choice of venture capital in service industry has a stronger preference to capital than to industry. Among the service industry, the life service, production service and public service are more attracted by capital than by industry, while the venture capital in the science and technology service is relatively less affected by the two elements. Therefore, during the process of encouraging innovation and entrepreneurship, it is necessary not only to promote the development of industrial agglomeration, but also pay more attention to the cultivation of venture capital and other financial factors. Reference | Related Articles | Metrics | Comments（0） Select Identification of Default Risks of Online Loan Borrowers:From the Perspective of Language Features in Loan Description WANG San-xing, YUE Lei-lei, WEN Yan-bing, XIONG Ge Contemporary Finance & Economics    2020, 0 (12): 63-74.   Abstract （326）            Knowledge map    Save From the perspective of language features in loan description, this paper makes use of the loan data of online loan platform Company A in China from 2010 to 2018 to study how to effectively identify the default risks of online loan borrowers. The results show that such features as the degree of difficulty in reading the loan description language, the degree of positive language emotion and the length of the loan description language are positively correlated with the default risks of online loan borrowers, while the specific degree of the loan description language is negatively correlated with the default risks of online loan borrowers. The more difficult to read the loan description language is, the more positive the language emotion is, the longer the language length is, and the less specific the language is, the higher the default risks will be. According to the relationship between the characteristics of the loan description language and the default risks of the online loan borrowers, the online loan platforms can effectively identify and reduce the default risks of the online loan borrowers by optimizing the risk-assessment system, improving the risk-control model, and enriching the risk-control strategies. Reference | Related Articles | Metrics | Comments（0）