Usefulness of support vector machine to develop an early warning system for financial crisis

Oh et al., 2006a, Oh et al., 2006b proposed a classification approach for building an early warning system (EWS) against potential financial crises. This EWS classification approach has been developed mainly for monitoring daily financial market against its abnormal movement and is based on the newly-developed crisis hypothesis that financial crisis is often self-fulfilling because of herding behavior of the investors. This article extends the EWS classification approach to the traditional-type crisis, i.e., the financial crisis is an outcome of the long-term deterioration of the economic fundamentals. It is shown that support vector machine (SVM) is an efficient classifier in such case.     

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.     

An early warning system for detection of financial crisis using financial market volatility

Abstract: This study proposes an early warning system (EWS) for detection of financial crisis with a daily financial condition indicator (DFCI) designed to monitor the financial markets and provide warning signals. The proposed EWS differs from other commonly used EWSs in two aspects: (i) it is based on dynamic daily movements of the financial markets; and (ii) it is established as a pattern classifier, which identifies predefined unstable states in terms of financial market volatility. Indeed it issues warning signals on a daily basis by judging whether the financial market has entered a predefined unstable state or not. The major strength of a DFCI is that it can issue timely warning signals while other conventional EWSs must wait for the next round input of monthly or quarterly information. Construction of a DFCI consists of two steps where machine learning algorithms are expected to play a significant role, i.e. (i) establishing sub-DFCIs on various daily financial variables by an artificial neural network, and (ii) integrating the sub-DFCIs into an integrated DFCI by a genetic algorithm. The DFCI for the Korean financial market is built as an empirical case study.     

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

Since the collapse or failure of a bank could trigger an adverse financial repercussion and generate negative impacts, it is desirable to have an early warning system (EWS) that identifies potential bank failures or high-risk banks through the traits of financial distress. This research is aimed to construct a novel fuzzy neural CMAC as an alternative to analyze bank solvency, in which a nature inspiration motivated from the famous Chinese ancient Ying–Yang philosophy is introduced to find the optimal fuzzy sets, and truth value restriction (TVR) inference scheme is employed to derive the truth-values of the rule weights. The proposed model functions as an early warning system and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. Our experiments are conducted on a benchmark dataset of a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed – bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the proposed Ying–Yang FCMAC network as a bank failure classification and early warning system is very encouraging.     

金融风险预警的MPSO-FNN模型构建与应用

提出了一种改进型粒子群算法,并结合神经网络与模糊逻辑系统建立金融风险预警模型。将模型应用于信贷风险预警研究,仿真实例的结果表明,该模型所获得的预测准确性更高,是处理金融风险这类复杂经济系统预警问题的一种有效方法。     

Financial distress early warning based on group decision making

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts’ experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts’ knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.     

数据挖掘技术在财务实时控制系统中的应用研究

通过对数据挖掘技术在财务管理方面应用现状和财务实时控制应用状况的介绍,提出构建一个适应现代信息技术,并与企业管理环境相协调的基于数据挖掘技术财务实时控制系统框架。并分析数据挖掘技术在资金调度优化、成本实时控制、财务风险动态预警子系统中的具体应用。本研究充分利用数据挖掘技术,提高了财务实时控制的实用性,为企业构建财务实时控制系统提供帮助,为数据挖掘技术在财务实时控制方面的应用提供了一个参考。     

财务困境预警的二阶段模型构建与应用

模糊神经网络汇集神经网络和模糊逻辑的优点,能有效避免神经网络的“黑箱”操作,但存在“维数爆炸”现象。将粗糙集和模糊神经网络有机集成,构建财务困境预警的二阶段模型：第一阶段利用粗糙集知识约简对数据集降维消冗,提取最优指标集;第二阶段以最优指标集设计基于模糊神经网络的财务困境预警模型。该模型融合粗糙集和模糊神经网络的特点,能提高网络结构的精练性、启发性和透明性。应用实例的结果表明该模型能有效克服“维数灾难”,避免数据噪声引起的模型过度适应,提高模型预测准确性。     

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.     

Usefulness of artificial neural networks for early warning system of economic crisis

During the 1990s, the economic crises in many parts of the world have sparked a need in building early warning system (EWS) which produces signal for possible crisis, and accordingly various EWSs have been established. In this paper, we focus on an interesting issue: ‘How to train EWS?’ To study this, various aspects of the training data (i.e. the past crisis related data) will be discussed and then several data mining classifiers including artificial neural networks (ANN) will be probed as a training tool for EWS. To emphasize empirical side of the problem, EWS for Korean economy is to be constructed. Our investigation suggests that ANN may be quite competitive in building EWS over other data mining classifiers.