基于有限关注的P2P借贷市场监管模式:以百度为例

P2P借贷相对于传统金融中介具有向下整合闲散资金并降低交易成本的优势,在"互联网+"和"大众创业万众创新"的政策背景下能够有效倒逼传统金融中介改革,为创新企业提供资金.但目前行业仍存在严重的监管缺失问题,而银监会等政府部门监管缺乏法律授权,监管行为缺乏积极性.因此,提出了以《互联网信息搜索服务管理规定》为契机,通过监管百度对P2P的推广业务进而规范行业发展的思路,并论证了这种方法的有效性.百度搜索量对P2P用户投资行为的相关性分析表明,监管百度推广业务能有效阻碍问题平台获得用户,进而减少问题平台的产生及影响.     

基于改进TOPSIS法的P2P网贷平台运营效率评价北大核心CSCD

近年来,P2P网络借贷发展迅猛,吸引了来自金融、经济、管理等诸多领域研究人员越来越多的关注.如何结合运营数据对P2P网贷平台效率进行综合评价,这对P2P网贷平台的运营管理和投资者的投资决策有着十分重要的影响,目前关于这方面的研究相对欠缺.鉴于此,文章提出数据驱动赋权的改进TOSPSIS法对P2P网贷平台效率进行综合评价.首先,针对TOPSIS法存在的主观权重问题,提出数据驱动赋权的数学模型;其次,利用教与学优化算法确定最优权重,以最大化赋权前后数据的一致性和权重的客观性;最后,结合网贷之家的运营数据应用改进TOPSIS法对100家P2P网贷平台效率进行综合评价.结果表明,基于改进TOPSIS法的评价结果和网贷之家的评价结果具有较高的一致性.     

文本信息在P2P借贷平台违约评价中的作用——基于Multinomial Lasso-logistic模型

选取Lending Club 2007年1月至2016年3月的交易数据,运用Multinomial Lasso-logistic模型得到影响平台违约的关键因素并预测了违约概率.结果表明,出借人实际借款的总额、借款利率等因素对违约有显著的影响,此外与以往研究不同的是,发现由借款人提供的借款描述和借款标题等文本信息与违约之间显著负相关,说明当借款人提供更多的文本信息,将表现出相对较低的违约率.研究结论补充了现有文献的不足,对P2P平台的监管和投资者的决策提供了借鉴意义.     

野蛮生长的P2P江湖

自2007年国内首家P2P平台“拍拍贷”成立起，P2P网贷这一源自西方发达国家的“舶来品”在中国市场上展现出了强悍的繁殖能力。短短7年内，无论是P2P平台的数量、存量资金、还是整体的交易量、投资者数量，都发生了翻天覆地的变化。尤其是2014年以来，P2P行业更是火爆异常，越来越多的精英、草根、巨贾、骗子开始涌入这个正在“井喷”的市场。     

P2P网贷平台风险预测研究

随着大数据和机器学习的流行,其在破产预测和风险预测领域逐渐崭露头角。本文运用爬虫技术得到885家网贷平台的16815条数据,通过因子分析及模型验证挖掘出了若干能较好评估P2P平台风险的因子。然后本文通过选取的指标体系建立了Logistics回归、支持向量机、BP神经网络、LightGBM等单模型以及融合模型进行学习训练,所建立的融合模型在测试集中得到最高的准确率,说明本文所建的融合模型能较好地评估网贷平台的风险。本文还选取决策树绘图以及对特征进行重要性排名,选取出了对识别问题平台有重要作用的十项特征。这对投资者选取安全平台进行投资或者监管者选取重点平台进行监管有很好的借鉴意义。     

基于BP神经网络的P2P网贷借款人信用评估

评估借款人信用是P2P网贷公司控制风险的重要步骤,对于网贷公司的正常运行有着极其重要的意义。论文参考商业银行信用指标体系并根据P2P网贷自身特点,建立了P2P网贷借款人的信用评估指标体系。根据建立的指标体系构建相应的BP神经网络模型,并利用一步正切法进行优化。然后选取具有代表性的P2P网贷平台的相关数据,对该模型进行训练和仿真,证明了该模型对P2P网贷平台的风险控制起到一定的作用。     

基于改进TOPSIS法的P2P网贷平台运营效率评价

近年来,P2P网络借贷发展迅猛,吸引了来自金融、经济、管理等诸多领域研究人员越来越多的关注.如何结合运营数据对P2P网贷平台效率进行综合评价,这对P2P网贷平台的运营管理和投资者的投资决策有着十分重要的影响,目前关于这方面的研究相对欠缺.鉴于此,文章提出数据驱动赋权的改进TOSPSIS法对P2P网贷平台效率进行综合评价.首先,针对TOPSIS法存在的主观权重问题,提出数据驱动赋权的数学模型;其次,利用教与学优化算法确定最优权重,以最大化赋权前后数据的一致性和权重的客观性;最后,结合网贷之家的运营数据应用改进TOPSIS法对100家P2P网贷平台效率进行综合评价.结果表明,基于改进TOPSIS法的评价结果和网贷之家的评价结果具有较高的一致性.     

不完全信息博弈视角下的互联网借贷行为研究——以P2P网络贷款为例

P2P网络贷款的兴起为个人和小微企业带来了诸多的融资便利.但由于P2P网络贷款市场上存在着信息不对称的问题,导致市场上出现了诸多乱象.本文针对P2P行业信息不对称的现状,以P2P网络贷款平台和借款者作为参与者,构建了不完全信息博弈模型.该模型以实现P2P市场上的帕累托最优为研究目的,并分别探讨了在混合均衡和准分离均衡两种不同情况下,各自达到帕累托最优状态所需的市场条件,以及相关影响因素,并据此提出了相应的政策建议.     

P2P网络借贷市场利率主导权偏离程度的研究——基于双边随机前沿模型

近年来P2P网络借贷作为一种典型的互联网金融模式获得了跳跃式的发展,由于借贷双方信息不对称,导致我国P2P网贷市场利率普遍偏高。本文利用双边随机前沿分析(SFA)方法对我国P2P网贷市场借贷双方利率主导权力进行测算,并对借贷双方的主导权力对贷款利率的影响效应进行定量分析,同时对借款者个体特征对借贷双方利率主导权力的影响进行比较分析。实证结果表明,出借方拥有明显的主导权力,随着学历、年龄、收入、信用等级的增高,借款人地位将有所改善。     

基于SVM~(K-Means)的非均衡P2P网贷平台风险预测研究

P2P网贷平台的高速发展,降低了小微企业的借贷成本,提高了投资者的收益与效率,较好地满足了小微企业的融资需求.然而,现阶段中国的P2P网贷平台在发展过程中也暴露出大量的风险问题,不仅使投资者财富遭受损失,也严重危害了P2P行业的健康发展.因此,对P2P网贷平台进行早期风险预测,在风险问题未发生之前对投资者进行风险预警并为投资者提供投资辅助决策是目前学术界广受关注的一个热点研究问题.针对真实P2P网贷平台数据的类别分布非均衡性问题,文章提出了一种基于K-Means聚类和支持向量机(support vector machine,SVM)的非均衡分类方法SVM~(K-Means)用以预测P2P网贷平台风险.通过网贷之家真实数据并以经典的逻辑回归(logistic regression)、支持向量机以及神经网络(back propagation neural network)为基准方法进行的比较试验表明,文章提出的SVMK-Means方法能够更加准确地在早期进行P2P网贷平台风险预测.     

Sharing Resources in a P2P Network with Artificial Ants

As networks are growing up, more and more information becomes available every day. Despite the presence of software enabling communications and content sharing, they are not always shared among people inside networks. We present here an architecture aimed at helping people to share information items and find collaborators inside an organization. It is part of our PIAF framework, an intelligent agent system used to develop recommender and personalization software. The main contribution of this paper is the introduction of principles of stigmergy and artificial ants to model data flows in a social network.      

基于ZigBee技术的黄河河道冰情多点监测系统设计

针对黄河河道冰情的多点监测问题,提出了一种基于ZigBee技术的河道冰层厚度多点监测系统的设计方案,简要介绍了ZigBee技术的主要特点,冰层厚度传感器结构与检测原理,提出了基于ZigBee的黄河河道冰情多点监测系统结构,讨论了监测系统硬件与软件的设计思路,利用系统可以实现黄河河道局部区域内多点冰情远程无人连续自动监测.     

P2P保险的道德风险问题研究

伴随信息产业的快速发展和互联网保险的广泛运用,近几年来国际上出现了一种新型保险模式――P2P保险(Peer to Peer Insurance)。该模式基于互联网,通过聚集若干风险类型相似的投保人组建风险共担互助小组,风险互助小组成员间一般是亲戚、朋友等熟人关系。P2P保险形成的相互监督机制和声誉机制,可以较好地解决传统保险市场中存在的道德风险问题。本文利用经济学中的比较静态分析方法,研究了P2P保险的道德风险问题,并从理论上证明了在P2P保险模式下,投保人会倾向于更加努力防范风险以降低出险概率,道德风险问题可以得到有效缓解。     

Cognitive and self-selective routing for sensor networks

New approaches to Quality-of-Service (QoS) routing in wireless sensor networks which use different forms of learning are the subject of this paper. The Cognitive Packet Network (CPN) algorithm uses smart packets for path discovery, together with reinforcement learning and neural networks, while Self-Selective Routing (SSR) is based on the “Ant Colony” paradigm which emulates the pheromone-based technique which ants use to mark paths and communicate information about paths between different insects of the same colony (Koenig et al. in Ann Math Artif Intell 31(1–4): 41–76, 2001). In this paper, we present first experimental results on a network test-bed to evaluate CPN’s ability to discover paths having the shortest delay, or shortest length. Then, we present small test-bed experiments and large-scale network simulations to evaluate the effectiveness of the SSR algorithm. Finally, the two approaches are compared with respect to their ability to adapt as network conditions change over time.     

Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times.     

基于熵权灰色关联TOPSIS法的涉农P2P网贷项目优选研究

随着"互联网+农业"的逐步深入,涉农P2P网贷将成为缓解农民借款难的有效途径,建立涉农P2P网贷项目优选模型对保障资金借出者的资金安全和收益具有很好的现实意义.根据ECIRM模型从5个维度建立衡量信用风险及还款能力的涉农P2P网贷项目评价指标体系,在资金借出者根据自己的要求对借款项目初步筛选的基础上,建立网贷项目优选模型,利用熵权灰色关联TOPSIS法对网贷项目进行优选排序,为出资者决策提供参考依据.     

A framework for mapping the RFID-enabled process redesign in a simulation model

Empowered by the possibility to automatically identify unique product instances, the Radio Frequency Identification (RFID) technology is expected to revolutionize the supply chain processes. However, in view of the numerous possible ways that RFID can be implemented within the supply chain, the issue of supporting the design choices based on a credible assessment between the current (as-is) and the future (to-be) processes has become a matter of considerable concern and debate for both practitioners and academics alike. To design RFID implementations in the supply chain using a robust dynamic analysis, we resort to discrete event simulation. As a result, this paper conceptualizes the ‘RFID-enabled process redesign’ and proposes a framework regarding all possible types of RFID effects when integrating the technology within the supply chain processes. The research design was based on the empirical evidence through three case studies combined with the development of simulation models and on theoretical constructs regarding the information technology (IT)-enabled process redesign.     

A robust P2P streaming architecture and its application to a high quality live-video service

This paper explores the design of a P2P architecture that sends real-time video over the Internet. The aim is to provide good quality levels in a highly dynamic P2P topology, where the frequent connections/disconnections of the nodes makes it difficult to offer the Quality-of-Experience (QoE) needed by the client.We study a multi-source streaming approach where the stream is decomposed into several flows sent by different peers to each client including some level of redundancy, in order to cope with the fluctuations in network connectivity. We employ the recently proposed PSQA technology for evaluating automatically the perceived quality at the client side. We introduce a mathematical programming model to maximize the global expected QoE of the network (evaluated using PSQA), selecting a P2P connection scheme which enhances topology robustness. In addition, we provide an approximated algorithm to solve the proposed model, and we apply it to solve a case study based on real life data.     

A mobile wireless body area network platform

This paper aims to propose a system architecture for a mobile health-monitoring platform based on a wireless body area network (WBAN). We detail the WBAN features from either hardware and software point of view. The system architecture of this platform is three-tier system. Each tier is detailed. We have designed a flowchart of a use of the WBANs to illustrate the functioning of such platforms. We show the use of this platform in a wide area to detect and to track disease movement in the case of epidemic situation. Indeed, tracking epidemic disease is a very challenging issue. The success of such process could help medical administration to stop diseases quicker than usual. In this study, WBANs deployed over volunteers who agree to carry a light wireless sensor network. Sensors over the body will monitor some health parameters (temperature, pressure, etc) and will run some light classification algorithms to help disease diagnosis. Finally, the WBAN will send aggregated data about the disease to some base stations which collect the results. Our platform will run an on-line disease tracking program and to detect some information about how the disease is propagated.     

Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks

In this paper, we consider the duty scheduling of sensor activities in wireless sensor networks to maximize the lifetime. We address full target coverage problems contemplating sensors used for sensing data and transmit it to the base station through multi-hop communication as well as sensors used only for communication purposes. Subsets of sensors (also called covers) are generated. Those covers are able to satisfy the coverage requirements as well as the connection to the base station. Thus, maximum lifetime can be obtained by identifying the optimal covers and allocate them an operation time. The problem is solved through a column generation approach decomposed in a master problem used to allocate the optimal time interval during which covers are used and in a pricing subproblem used to identify the covers leading to maximum lifetime. Additionally, Branch-and-Cut based on Benders’ decomposition and constraint programming approaches are used to solve the pricing subproblem. The approach is tested on randomly generated instances. The computational results demonstrate the efficiency of the proposed approach to solve the maximum network lifetime problem in wireless sensor networks with up to 500 sensors.