【作者】 陈刚；

【导师】 王刚；

【作者基本信息】 合肥工业大学 ， 管理科学与工程， 2019， 硕士

【摘要】 随着我国金融行业改革的不断深化,以P2P网贷、众筹、第三方支付等为代表的互联网金融模式不断普及,促进了企业发展,提升了金融市场效率。然而,开放的互联网环境下,激烈的市场竞争和迅速变化的市场环境等因素使得金融市场主体面临更为严峻的来自金融风险的挑战。金融风险预测能够为金融市场主体提供有效、及时的预警信息和决策支持,从而在一定程度上帮助其避免或减少财务损失。因此,如何对互联网环境下的金融风险进行准确预测成为近年来学术界和产业界瞩目的焦点。目前,已经有大量研究者对金融风险进行了相关研究,并提出了一系列金融风险预测方法。然而,这些方法大多是基于单一数据源对金融风险进行预测,其预测准确率有限,且针对不同的预测场景,其预测效果也不一而足。近期,有少数研究者开始尝试利用多源数据进行金融风险预测,他们将获得的多源数据进行简单叠加后加以使用,而忽略了数据原本的多源、异构、相互冗余等特性,致使预测效果的提升并不明显。为此,本研究构建了基于多源异构数据自适应融合的金融风险预测方法,对来自不同数据源、不同数据分布的信息进行自适应融合,从而达到对预测信息充分利用、极大限度地提升预测效果的目的。首先,本研究在对金融风险预测的研究现状进行系统梳理和总结后,针对现有研究中存在的问题与不足,分别从面向传统的企业金融风险预测和面向个人的金融风险预测两个角度出发,构建了基于加权融合自适应随机子空间的金融风险预测方法WFAIB_RS以及基于两阶段自适应融合随机子空间的金融风险预测方法TSAIB_RS。接着,本研究利用抓取和收集的中国上市公司相关数据与中国P2P借贷市场的个人借贷相关数据分别对WFAIB_RS方法和TSAIB_RS方法进行了验证。实验结果表明,上述两方法在各自的金融风险预测应用场景中均取得了较好的预测效果,从而验证了其在金融风险预测中的可行性与有效性。一方面,本研究对金融风险预测问题的相关理论进行了系统梳理,并分析了来自不同信息源、不同结构、不同时间面板的数据对金融风险预测的影响,为利用多源异构数据进行金融风险预测提供了一定的理论基础;另一方面,本研究创新性地分别面向金融市场主体中的企业和个人提出了基于多源异构数据自适应融合的金融风险预测方法,为大数据时代多源异构数据在金融风险管理领域中的应用提供了指导,同时也为进一步突破现有金融风险预测效果提供了新的途径。更多还原

【Abstract】 With the deepening of the reform of China’s financial industry,the Internet financial model represented by P2 P lending,crowdsourcing,third-party payment and etc.has received increasing prevalence,which promotes enterprise’s development and financial market’s efficiency.In the meanwhile,factors hastened by the open Internet environment such as the fierce market competition and the fast-changing market environment have impelled financial market players to be confronted with severe challenges from financial risks.Financial risks prediction can provide such players with effective and prompt warning signals as well as decision support so that their financial losses can be avoided or reduced to some extents.Therefore,how to predict financial risks with respect to the Internet environment as accurate as possible has raised a common concern of both academic research and industry applications in recent years.At present,numerous researchers have studied on financial risks and proposed a series of feasible financial risks prediction methods.However,most of them are constructed based on a single data source,resulting in limited prediction accuracy.Besides that,the prediction performance achieved by these methods commonly varies drastically from different data scenes.Recently,some researchers begin to predict financial risks utilizing multi-source data,by which the improvements on prediction effects are somewhat not obvious.We attribute this to that they simply superpose the obtained data of multiple sources for use but neglecting the inherent data characteristic such as data’s different sources,heterogeneous structures,and redundancy.Hence,novel financial risks prediction methods are constructed in this study to conduct adapted integration on multi-source heterogeneous data,so as to make full use of predicion information and maximally improve the prediction performance.First,this study systematically summarizes the research with regard to financial risks prediction.In the light of their deficiencies,we construct a financial risks prediction method of weightfused adaptive integration-based Random Subspace(i.e.,WFAIB_RS)for listed companies and a financial risks prediction method of two steps adaptive integration-based Random Subspace(i.e.,TSAIB_RS)for individual borrowers from Peer-to-Peer(P2P)lending market,respectively.Then,we verify the proposed methods WFAI_RS and TSAIB_RS by applying them into the crawled and collected datasets with respect to Chinese listed company and individual borrowers from Chinese P2 P lending market respectively.The experimental results show that both of the two methods have achieved remarkable prediction performance in their respective application scenary,which demonstrates their feasibility and effectiveness for financial risks prediction.On the one hand,this study systematically combs the theories redarding financial risks prediction and thoroughly analyzes the impacts of different data sources,different data structures,and different data panels on financial risks prediction,which provides a theoretical basis for using multi-source heterogeneous data to predict financial risks.On the other hand,this study innovates to focus on financial risks of market’s players from perspectives of company-level and individual-level,and proposes novel financial risks prediction methods that enables multi-source heterogeneous data to be adaptively integrated.This,in fact,has provided a guidance for applying multi-source heterogeneous data into the field of financial risks manegement in the big data era,and has also provided new possibilities to further breakthrough the accuracy level of existing financial risks prediction.更多还原