感知认知技术在金融风险预警中的应用研究

金融风险全球溢出效应与国内金融业态创新发展中的伴生风险相叠加，使得我国所面临的国际金融及内生性金融风险形势非常严峻。针对传统风险预警技术因缺乏有效、及时的关键因子导致实践中对金融风险预警难度极大的技术难题，本文重点总结了如何利用感知认知技术从海量非结构化信息提取有效、及时的金融风险预警关键因子，并在回顾现有金融风险预警模型研究现状的基础上，对相关技术难点和未来研究趋势进行总结和展望。本文研究内容可为我国研发自主可控的金融风险预警技术提供必要参考。     

基于神经网络优化组合的供水预警机制

在供水预警机制理论分析的基础上,集结多种单个模型所包含的信息,进行优化组合,提出在单一模型预警结果基础上的基于神经网络的优化组合预警机制,并用误差灰色模型修正预测结果。该模型具有良好的预测及预警效果,从而为供水预警提供一种新的方法和思路。     

油田异常井组合预警模型研究

由于油田生产数据具有高度的非线性和时变性,单一的预警模型无法达到在异常初期进行及时、准确预警的要求,为提高预警准确度,论文提出一种油田异常井组合预警模型——k-SVR预警模型.k-SVR预警模型是用k均值模型先对数据进行聚类,再用支持向量回归机模型对聚类后的数据进行边缘数据界定和处理,从而得到较为精确的预警信息,达到在异常初期进行预警的目的.通过组合预警模型对油田已有历史生产数据进行仿真实验,结果表明：相较于单一的预警模型,组合预警模型取得了较精确的预警结果,验证了模型的有效性.     

基于GIS的矿井自然灾害隐患识别、预警模型研究

以煤矿地理中间数据一体化管理的数据模型和数据结构为基础,文章提出了一种基于GIS的煤矿自然灾害隐患识别单体评判预警模型、区域评判预警模型以及两者综合的煤矿自然灾害隐患识别区域与单体联合评判预警模型,并详细研究了模型的数据要求和模型的实现流程等关键问题。     

基于预警雷达的远程攻击检测系统研究

提出一种新的预警雷达远程攻击检测系统的建立方法。通过计算雷达预警时间和相关数学模型,使得在受到远程攻击时,雷达检测系统缩短预警时间,并且通过建立预警时间模型、攻防双方模型进而建立远程攻击预警雷达模型,通过获取远程攻击相关数据,对远程攻击实现准确、快速检测。实验证明,提出的模型建立的方法可以准确获取远程攻击相关数据,对远程攻击可以及时和准确检测,获取了令人满意的效果。     

基于系统状态集合的攻击模型及其应用

为了评价系统的安全状况,对可能发生的攻击行为进行预警,提出了一种基于系统状态集合的攻击模型,使用系统状态的集合对系统的安全威胁进行抽象,并将攻击过程描述为系统状态集合的改变。同时还描述了一种利用此攻击模型进行攻击检测和预警的方法。基于该模型,实现了一个安全预警的原型系统。实验结果表明该系统能够有效检测攻击过程,并预测出系统可能达到的危险等级。     

基于城管热线数据的城管问题预警研究

城市管理中的违规、违法问题事件一直是困扰城管执法人员的一个痼疾,如何对这些城管问题进行有效预测预警,已成为转变城市管理模式,提高城市管理效率的重要问题。针对城管预测预警问题,本文应用时间序列与BP神经网络相结合的思路,通过指数平滑预测、主成分分析、综合评价警度、神经网络预警等设计了一种基于热线数据的城管问题预测预警理论方法和模型,并对城管问题中比较典型的六大类城管问题发生情况进行预测预警。实例分析表明,模型具有较好的预测预警效果,从而为城管问题预警研究提供了一种新的方法和思路,以期对城管问题预警提供参考。     

煤矿瓦斯异动预警系统的设计

针对煤矿安全监控系统因无瓦斯变化趋势分析功能,当瓦斯浓度处于较低水平时,即使瓦斯有异常变化也无法及时提醒的问题,设计了一种煤矿瓦斯异动预警系统。该系统采用文件方式和OPC方式接入安全监控系统及综合自动化系统的监测数据;结合矿井实际通风状况,采用固定门限模型、均方差预警模型和分时均方差模型等三种预警模型计算得出测点的预警参考值及预警状态;通过消息客户端、短信等多种方式发布预警信息。实际应用表明,该系统实现了对瓦斯灾害的有效预防,为保证井下作业人员的安全提供了参考依据。     

基于熵理论的企业危机预警模型研究

基于熵的最优化原理建立了一种新的企业危机预警模型.首先利用最小判别熵选取企业危机预警特征值;然后提出一种新的聚类算法--极大熵聚类算法,并对预测结果进行分类,判断企业的危机状态.该算法是硬C-均值算法的发展和推广.通过实例分析表明,该模型有效、可行,为企业危机预警提供了一条新的途径.     

基于灰度混合核AR-SVM的预警模型及应用

为提高市场偏好预警分析算法的有效性,提出一种基于灰色混合核AR-SVM模型的新方法。使用支持向量机(SVM)算法来构建财务市场风险预警分析模型,该模型存在非极端风险和极端风险两种情况。采用SVM算法找到基于训练集的最优分类过程。SVM模型容易出现极端风险预警,因此由改进的灰色模型处理市场偏好预测问题的错误市场偏好数据。采用混合核函数对SVM算法进行改进,实现样本数据,提高自回归模型的预测性能。SVM算法可以用于提高市场预警分析的准确性。实验结果表明,该方法可以很好地分析市场偏好数据。     

Analysis of early warning of corporate financial risk via deep learning artificial neural network

To forecast the financial crisis of manufacturing corporations more accurately, a risk warning model of corporate finance is constructed based on back propagation (BP) neural network to forecast the financial crisis. Firstly, based on the principle of index selection, the forecast indexes are selected and the index system of financial risk early warning is constructed. Then the index system is optimized by factor analysis. Finally, the BP neural network algorithm model is adopted to forecast the financial crisis of 200 manufacturing corporations in 2018 and 2019, and the forecasting results are compared with the traditional method. The results show that the prediction accuracy of the enterprise financial risk early warning model based on the BP neural network for 2018 is above 85%, and the prediction accuracy for 2019 is above 95%, or even 100%. Through comparison with other traditional methods, the prediction accuracy of the BP neural network in 2018 (above 88%) is higher than that of other algorithms (below 87%). In 2019, the prediction accuracy of BP neural network (above 90%) is higher than other algorithms (less than 88%). The accuracy of the proposed financial risk warning model is 95%, and the accuracy is at least 2% higher than traditional method, which prove that the risk early warning model constructed in this study can accurately forecast the financial crisis of the corporation. This study is of important reference value for the establishment of efficient financial crisis forecasting model under deep learning.     

Financial early warning system model and data mining application for risk detection

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.     

基于Web Services的金融风险预警系统研究

WebServices是一种面向服务的体系结构,其优势在于跨平台互操作性和软件复用。金融风险预警系统是一个供金融决策部门使用的综合的决策支持系统。文中对WebServices的体系结构、关键技术进行了探讨,提出了基于WebServices的金融风险预警系统框架结构,证实了WebServices为该系统提供了强大的技术支撑。     

基于Web Services的金融风险预警系统研究

Web Services是一种面向服务的体系结构,其优势在于跨平台互操作性和软件复用。金融风险预警系统是一个供金融决策部门使用的综合的决策支持系统。文中对Web Services的体系结构、关键技术进行了探讨,提出了基于Web Services的金融风险预警系统框架结构,证实了Web Services为该系统提供了强大的技术支撑。     

基于SOM-PNN的信贷风险预警模型研究

信贷对现代市场经济非常重要，同时又会带来风险．以人工智能的思想为指导，将SOM与PNN网络相结合．提出并建立了一种基于SOM-PNN的信贷风险预警模型；结合统计理论方法对输入样本进行预处理，解决了网络训练中样本选用的问题；并利用因素分析方法对预警结果进行了解释．实验表明，利用该模型在得到可视化预测结果的同时，还可得到较高的预警精度．     

A study on the market instability index and risk warning levels in early warning system for economic crisis

Early warning system (EWS) can be treated as a pattern recognition problem since the distinctive feature of economic crisis makes it possible to distinguish critical and normal economic situations using a pattern classifier. Although the most works in EWS are mainly focused on training and pattern classifier, little attention has been paid to the effective indices or feature variables that allow closer look and analysis about the current instability nature of the economic crisis. This paper proposes to utilize market instability index (MII) and stepwise risk warning levels that can diagnose the current instability of the stock market to foretell how the current stock market will proceed in advance. This approach allows the proper policy actions to be taken for the possible financial crisis according to different risk warning levels of instability. Through empirical examples with Korean stock market and Greece stock market, the proposed method demonstrates its potential usefulness in an early warning system.     

Is grey relational analysis superior to the conventional techniques in predicting financial crisis?

This study proposes a new approach for analyzing the credit risks of banking industry based on the modeling of grey relational analysis (GRA). In order to construct a financial crisis warning system for banking industry, a GRA approach is developed and applied to the real data set with 111 samples. The results of the current model are compared to those of traditional ones, logistic regression and back-propagation neural network. The results illustrate that in the prediction of financially crisis as well as financially sound banks, the proposed GRA model demonstrates better prediction accuracy than the conventional ones. The results also imply that the financial data set one year before the crisis leads to the best accuracy. It is helpful for the establishment of early warning models of financial crisis for banks. The current results show that the proposed GRA provides a novel approach in handling financial crisis warning tasks.     

基于文本挖掘的上市公司财务风险预警研究

上市公司年报中的描述性文本信息是上市公司信息披露的重要组成部分,通过对上市公司信息披露文本的挖掘与分析可以提高对其财务风险的预测能力.基于BERT(bidirectional encoder representations from transformer)模型与自编码器(autoencoder,AE),提出了BERT...     

Early warning of enterprise decline in a life cycle using neural networks and rough set theory

Early warning of whether an enterprise will fall into decline stage in a near future is a new problem aroused by the enterprise life cycle theory and financial risk management. This paper presents an approach by use of back propagation neural networks and rough set theory in order to give an early warning whether enterprises will fall into a decline stage. Through attribute reduction by rough set, the influence of noise data and redundant data are eliminated when training the networks. Our models obtained favorable accuracy, especially in predicting whether enterprises will fall into decline or not.     

不平衡样本下的金融市场极端风险预警研究

为了提高金融市场极端风险识别及预警能力,采用沪深300指数作为研究数据,通过少数类样本过采样算法（SMOTE）解决样本不均衡问题,利用因子分析提取特征,通过粒子群（PSO）优化的最小二乘支持向量机（LSSVM）算法构建（SMOTE-PSO-LSSVM）预测模型。使用SMOTE-PSO-LSSVM模型对2007-2010年沪深300指标样本进行预测,样本含极端风险样本193条,模型成功识别风险样本154条,识别准确率达到了83.1%。研究结果表明SMOTE-PSO-LSSVM模型对金融风险数据识别能力较强,能够较为精准地识别风险样本,且求解速度快运行效率高,比传统BP网络和支持向量机等方法性能更优秀。该研究结论对金融市场的风险识别、市场趋势把控、股市交易管制以及投资者决策具有一定意义。