论文标题
在大规模所有权网络中确定可口的公司
Identifying discreditable firms in a large-scale ownership network
论文作者
论文摘要
违反有关食品安全,生产安全,质量标准和环境保护的法律和法规,或贷款,担保和承诺合同所带来的负面后果,可能会导致公司的运营和信贷风险。上述非法或破坏信任活动的活动被共同称为可耻的活动,而具有含糊的活动的公司被称为可口的公司。确定可口的公司对于投资吸引力,银行贷款,股票投资,供应商选择,寻求工作等具有重要意义。在本文中,我们收集了约1.13亿个中国公司的注册记录,并建立了一个拥有约600万个节点的所有权网络,每个节点都是一家公司,其公司至少投资了一家公司,或者已经由至少一家公司进行了投资。鉴于其一名投资者或投资者是可耻的事实,对可耻活动的公开记录的分析表现出强烈的网络效应,即企业可口的可能性高于平均可能性。相比之下,为了成为一家可耻的公司的风险,投资者的影响力比投资者平均更高。公司对周围公司的影响随着拓扑距离的增加而衰落,类似于众所周知的“三个分离程度”现象。除了传播疾病,观点和人类行为之外,还可以将发现的可否认活动相关性被视为网络效应的代表性示例。最后,我们表明,网络效应的利用在很大程度上提高了算法的准确性以识别可耻的公司。
Violations of laws and regulations about food safety, production safety, quality standard and environmental protection, or negative consequences from loan, guarantee and pledge contracts, may result in operating and credit risks of firms. The above illegal or trust-breaking activities are collectively called discreditable activities, and firms with discreditable activities are named as discreditable firms. Identification of discreditable firms is of great significance for investment attraction, bank lending, equity investment, supplier selection, job seeking, and so on. In this paper, we collect registration records of about 113 million Chinese firms and construct an ownership network with about 6 million nodes, where each node is a firm who has invested at least one firm or has been invested by at least one firm. Analysis of publicly available records of discreditable activities show strong network effect, namely the probability of a firm to be discreditable is remarkably higher than the average probability given the fact that one of its investors or investees is discreditable. In comparison, for the risk of being a discreditable firm, an investee has higher impact than an investor in average. The impact of a firm on surrounding firms decays along with the increasing topological distance, analogous to the well-known "three degrees of separation" phenomenon. The uncovered correlation of discreditable activities can be considered as a representative example of network effect, in addition to the propagation of diseases, opinions and human behaviors. Lastly, we show that the utilization of the network effect largely improves the accuracy of the algorithm to identify discreditable firms.