类金融企业的异质性及其对防范民间金融风险的启示

Heterogeneity in Quasi-Finance Firms:An Empirical Study Basing on Big Data Technology to Prevent Risks in Informal Finance Activities

由于金融的代表性主体(representative agent)的传统假设不符合类金融企业的发展特点,且国内外在防控民间金融风险领域缺乏微观层面研究,因此,传统的风险分析框架难以满足当前民间金融风险防范的需要。为此,本文探索性地利用大数据技术,汇总企业股权结构、投资行为、关联企业分布、网络舆情、消费者投诉、行政和司法处罚等多维信息,总结非法集资活动的经验规律,建立了类金融企业风险的分析框架;并利用某地6万多家类金融企业的公开数据研究发现,从事非法集资企业与从事正常民间借贷活动的企业可以被合理区分,且具有一定的稳定性,不会发生短期变化。本文研究是国内外这一领域创新性的学术探索,有望产生积极的社会效益。

Basing on the representative agent assumption, there is no effective surveillance and risk control measures worldwide on informal finance activities. With Big Data Technology, the paper innovatively organized a set of proxy variables of the related quasi-finance firms, basing on information about share structure, investment activities, public relations, etc. Empirical study with data of about 60000 quasi-finance firms shows that the model could differentiate illegal financing activities from general ones with highly acceptable reliability. Furthermore, we also found that the characteristics of illegal financing activities are stably shown in our model, with very low short-term volatility. This research is a forerunner in related area and expected to contribute much to the healthy development of informal finance