CART-based selection of bankruptcy predictors for the logit model

Balance-sheet data offer a potentially large number of candidate predictors of corporate financial failure. In this paper we provide a novel predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logit model. We show that a simple logit model with dummy variables created in accordance with the nodes of estimated classification tree outperforms both standard logit model with step-wise-selected financial ratios, and CART itself. On a population of Slovenian companies our method achieves remarkable rates of precision in out-of-sample bankruptcy prediction. Our selection method thus represents an efficient way of introducing non-linear effects of predictor variables on the default probability in standard single-index models like logit. These findings are robust to choice-based sampling of estimation samples.     

Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria

A Bayesian method for estimation of a hazard term structure is presented in a functional data analysis framework. The hazard terms structure is designed to include the effects of changes in economic conditions, as well as trends in stock prices and accounting variables from financial statements. The hazard function contains time-varying parameters that are modelled using splines. To estimate the model parameters, a Markov-chain Monte Carlo sampling algorithm is developed. The Bayesian predictive information criterion is employed to assess the default predictive power of the estimated model. The method is then applied to a Japanese firm’s default data listed on the Japanese Stock Exchange. The results demonstrate that the proposed method performs well.     

A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines

The detection of fraudulent financial statements (FFS) is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. Although nonfinancial ratios are generally acknowledged as the key factor contributing to the FFS of a corporation, they are usually excluded from early detection models. The objective of this study is to increase the accuracy of FFS detection by integrating the rough set theory (RST) and support vector machines (SVM) approaches, while adopting both financial and nonfinancial ratios as predictive variables. The results showed that the proposed hybrid approach (RST+SVM) has the best classification rate as well as the lowest occurrence of Types I and II errors, and that nonfinancial ratios are indeed valuable information in FFS detection.     

Predicting corporate financial distress based on integration of decision tree classification and logistic regression

Lately, stock and derivative securities markets continuously and rapidly evolve in the world. As quick market developments, enterprise operating status will be disclosed periodically on financial statement. Unfortunately, if executives of firms intentionally dress financial statements up, it will not be observed any financial distress possibility in the short or long run. Recently, there were occurred many financial crises in the international marketing, such as Enron, Kmart, Global Crossing, WorldCom and Lehman Brothers events. How these financial events affect world’s business, especially for the financial service industry or investors has been public’s concern. To improve the accuracy of the financial distress prediction model, this paper referred to the operating rules of the Taiwan Stock Exchange Corporation (TSEC) and collected 100 listed companies as the initial samples. Moreover, the empirical experiment with a total of 37 ratios which composed of financial and other non-financial ratios and used principle component analysis (PCA) to extract suitable variables. The decision tree (DT) classification methods (C5.0, CART, and CHAID) and logistic regression (LR) techniques were used to implement the financial distress prediction model. Finally, the experiments acquired a satisfying result, which testifies for the possibility and validity of our proposed methods for the financial distress prediction of listed companies.This paper makes four critical contributions: (1) the more PCA we used, the less accuracy we obtained by the DT classification approach. However, the LR approach has no significant impact with PCA; (2) the closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain in DT classification approach, with an 97.01% correct percentage for 2 seasons prior to the occurrence of financial distress; (3) our empirical results show that PCA increases the error of classifying companies that are in a financial crisis as normal companies; and (4) the DT classification approach obtains better prediction accuracy than the LR approach in short run (less one year). On the contrary, the LR approach gets better prediction accuracy in long run (above one and half year). Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential financial distress of a company in short run.     

The consumer loan default predicting model – An application of DEA–DA and neural network

In this paper we construct the consumer loan default predicting model through conducting the empirical analysis on the customers of unsecured consumer loan from a certain financial institution in Taiwan, and adopt the borrower’s demographic variables and money attitude as the real-timeaneous discriminant information. Furthermore, we construct respectively through four predicting methods, such as DA, LR, NN and DEA–DA, to compare the suitability of these four mentioned methods. The results show that DEA–DA and NN are possessed better predicting capability and they are the optimal predicting model that this study longing for. In addition, this study showed that the default loan predicting model will be possessed higher level of predicting capability after added money attitude.     

Measuring firm performance using financial ratios: A decision tree approach

Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.     

An evolution-based approach with modularized evaluations to forecast financial distress

Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.     

Deciding the financial health of dot-coms using rough sets

We conducted an empirical investigation of dot-coms from a financial perspective. Data from the financial statements of 240 such businesses was used to compute financial ratios and the rough sets technique was used to evaluate whether the financial ratios could predict financial health of them based on available data. The most predictive financial ratios were identified and interesting rules concerning the financial ratios and financial health of dot-coms were discovered. It was shown that rough sets performed a satisfactory job of predicting financial health and were more suitable for detecting unhealthy dot-coms than healthy ones.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

考虑社会网络关系的P2P借贷项目违约风险预测

针对P2P（Peer to Peer）借贷项目违约风险预测中财务信息不完全或质量较低、预测准确率不高等问题,提出了一种考虑平台社会网络关系的P2P借贷项目违约风险预测的方法。通过对P2P借贷平台社会网络相关信息进行分析,从社会资本的结构维度、关系维度和认知维度发掘其中具有风险预测价值的关键特征,即社会网络风险特征,并将这些特征作为预测指标用于违约风险预测,依据多种非线性预测方法分别构建基于传统财务指标预测模型和引入社会网络风险特征后的混合指标预测模型,并对模型的预测结果进行了对比分析。实验结果表明,P2P借贷社会网络关系中蕴含着与借贷项目违约风险显著相关的特征,通过对这些特征进行有效挖掘并将其合理引入P2P借贷项目违约风险预测模型,有助于提高借贷项目违约风险预测效果,为投资者的投资风险规避及P2P借贷市场风险管理提供支持。     

Wavelets Analysis on Structural Model for Default Prediction

In recent years, to improve predictive ability of corporate defaults has become an important problem. In this paper, regarding on characteristics of listed companies, we sampled 100 companies according to industry types, constructed wavelet structural model, experimented with wavelet decomposition proceeds to get low frequency and high frequency sequence, built the prediction model for both sequences, and then using the prediction of future returns to reconstruct predictive returns, thus avoiding accumulated prediction process with earnings volatility of time series model, therefore enhanced the precision of default prediction. Finally we compared wavelet structural model with time series structural model based on the predictive default distance of China’s listed companies.     

Assisting accountants in developing financial statements

The development of financial statements is often a difficult and time consuming task, especially for less experienced accountants. We have built a knowledge-based system to aid in developing the financial statements for corporate firms. The usability of the system for different categories of accountants has been tested. We describe the system and the test conducted. The results of the test show that the system is of considerable help for accountants.     

分组密码算法SMS4的暴力破解及模拟实现

加密算法的安全性在很大程度上取决于暴力破解的不可行性。暴力破解加密算法是密码学研究的一个重要方向。该文采用分布式计算方法,设计了暴力破解SMS4加密算法的软件。在局域网内对SMS4算法的暴力破解做了模拟实现,并对软件的性能进行了测试。最后对软件及SMS4算法的暴力破解结果进行了分析,并指明了下一步的工作方向。     

Using partial least squares and support vector machines for bankruptcy prediction

The evaluation of corporate financial distress has attracted significant global attention as a result of the increasing number of worldwide corporate failures. There is an immediate and compelling need for more effective financial distress prediction models. This paper presents a novel method to predict bankruptcy. The proposed method combines the partial least squares (PLS) based feature selection with support vector machine (SVM) for information fusion. PLS can successfully identify the complex nonlinearity and correlations among the financial indicators. The experimental results demonstrate its superior predictive ability. On the one hand, the proposed model can select the most relevant financial indicators to predict bankruptcy and at the same time identify the role of each variable in the prediction process. On the other hand, the proposed model’s high levels of prediction accuracy can translate into benefits to financial organizations through such activities as credit approval, and loan portfolio and security management.     

Are we modelling the right thing? The impact of incorrect problem specification in credit scoring

Classification and regression models are widely used by mainstream credit granting institutions to assess the risk of customer default. In practice, the objectives used to derive model parameters and the business objectives used to assess models differ. Models parameters are determined by minimising some function or error or by maximising likelihood, but performance is assessed using global measures such as the GINI coefficient, or the misclassification rate at a specific point in the score distribution. This paper seeks to determine the impact on performance that results from having different objectives for model construction and model assessment. To do this a genetic algorithm (GA) is utilized to generate linear scoring models that directly optimise business measures of interest. The performance of the GA models is then compared to those constructed using logistic and linear regression. Empirical results show that all models perform similarly well, suggesting that modelling and business objectives are well aligned.     

A hybrid knowledge-sharing model for corporate foreign investment in China’s construction market

The research proposes a hybrid knowledge-sharing model, which integrates the concepts of the self-organizing feature map optimization, fuzzy logic control, and hyper-rectangular composite neural networks, to provide 32 rules that suggest performing or not performing foreign construction investment. The database is derived from 520 quarterly financial reports of all listed construction companies in Taiwan that have now or in the past five years made foreign investment in China’s construction industry. The input variables are set to all 25 financial ratios assessable in public, reducing to 11 ratios after feature deduction using t-test. The model yields a high successful classification rate of 90.6% and generates 14 and 18 rules for Taiwan construction companies performing or not performing foreign investment in China, respectively. The valuable rules give user a closer look at what is the appropriate corporate financial status, what knowledge can be shared from the interpretations of the rules, and the impact by investment on corporate finance.     

Data Mining techniques for the detection of fraudulent financial statements

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances.     

逾期风险预测的宽度和深度学习

逾期风险控制是信用贷款服务的关键业务环节,直接影响放贷企业的收益率和坏账率。随着移动互联网的发展,信贷类金融服务已经惠及普罗大众,逾期风控也从以往依赖规则的人工判断,转为利用大量客户数据构建的信贷模型,以预测客户的逾期概率。相关模型包括传统的机器学习模型和深度学习模型,前者可解释性强、预测能力较弱;后者预测能力强、可解释性较差,且容易发生过拟合。因此,如何融合传统机器学习模型和深度学习模型,一直是信贷数据建模的研究热点。受到推荐系统中宽度和深度学习模型的启发,信贷模型首先可以使用传统机器学习来捕捉结构化数据的特征,同时使用深度学习来捕捉非结构化数据的特征,然后合并两部分学习得到的特征,将其经过线性变换后,最后得到预测的客户的逾期概率。所提模型中和了传统机器学习模型和深度学习模型的优点。实验结果表明,其具有更强的预测客户逾期概率的能力。     

Visualization and dynamic evaluation model of corporate financial structure with self-organizing map and support vector regression

Prediction of financial bankruptcy has been a focus of considerable attention among both practitioners and researchers. However, most research in this area has ignored the non-stationary nature of corporate financial structures. Specifically, financial structures do not always present consistent statistical tests at each point of time, resulting in dynamic relationships between financial structures and their predictors. This characteristic of financial bankruptcy presents a significant challenge for any single artificial prediction technique. Therefore, this paper will propose a multi-phased and dynamic evaluation model of the corporate financial structure integrating both the self-organizing map (SOM) and support vector regression (SVR) techniques. In the 1st phase, the inputs to the SOM are financial indicators derived from listed companies’ public financial statements adopting the principle component analysis (PCA) to extract useful indicators with a strong influence that each year determines the company's position on the SOM. In addition, we used the SOM to visualize and cluster each corporate in the 2D map. We also investigated each cluster and classified them into healthy and bankrupt-prone ones based on their regions in visualizing the 2D map. In the 2nd phase, we drew the trajectory for the healthy and the bankrupt-prone companies for consecutive years in a 2D map. Therefore, several visualized and dynamic patterns of corporate behavior could be recognized. In the 3rd phase, we used the SVR method to forecast the future trend for corporate financial structure. In addition, this research also compared the hybrid SOM–SVR architecture with single SOM, SVR, and Learning Vector Quantization (LVQ) algorithms. The results showed that the proposed methodology outperformed the other methods in both prediction accuracy and ease of use.     

On sovereign credit migration: A study of alternative estimators and rating dynamics

Different estimators of rating transition matrices have been proposed in the literature but their behaviour has been studied mainly in the context of corporate ratings. The finite-sample bias and variability of three sovereign credit migration estimators is investigated through bootstrap simulations. These are a discrete multinomial estimator and two continuous-time hazard rate methods, one of which neglects time heterogeneity in the rating process whereas the other accounts for it. Panel logit models and spectral analysis are utilized to study the properties of the rating process. The sample consists of Moody's ratings 1981-2004 for 72 industrialized and emerging economies. Hazard rate estimators yield more accurate default probabilities. The time homogeneity assumption leads to underestimating the default probability and greater migration risk is inferred upon relaxing it. There is evidence of duration dependence and downgrade momentum effects in the rating process. These findings have important implications for economic and regulatory capital allocation and for the pricing of credit sensitive instruments.