财务困境预测模型与仿真研究

公司财务困境受到决策者、市场、经济和政策多种因素影响,针对传统预测方法预测精度低缺陷,提出了一种贝叶斯判别分析的财务困境预测方法。首先选用了反映公司财务状况的24个指标作判别因子,建立了公司财务困境的贝叶斯判别分析模型,然后采用85个上市公司的实际数据作为学习样本建立贝叶斯判别函数,以交差确认估计法对判别准则进行评价,以验证模型的有效性,最后利用判别函数对5个待评价公司进行预测,得到判别函数值,进行仿真。结果表明,采用贝叶斯判别分析模型提高了公司财务困境的预测精度,是一种有效的财务困境预测方法。     

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.     

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.     

基于新闻文本的上市公司财务困境组合预测模型

已有上市公司财务困境预测模型主要是基于结构化数据进行研究,为进一步提高上市公司财务困境预测模型准确率,本文将非结构化数据引入上市公司财务困境预测问题中,研究了基于新闻文本分类的上市公司财务困境预测模型,结合新闻文本信息和财务信息提出上市公司财务困境组合预测模型。本文首先将新闻数据进行预处理,然后基于新闻文本数据通过支持向量机（SVM）进行财务困境预测,同时基于财务数据通过Logistic模型进行财务困境预测,最后采用阈值表决集成策略整合两种模型的预测结果,实验结果证明了模型的有效性。     

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student’s level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.     

基于集成学习的上市公司高送转预测实证研究

我国证券市场中高送转题材股备受中小投资者的追捧,但市场中也存在着借高送转概念炒作的乱象,如何利用上市公司的财务数据挖掘真正有潜力的股票无疑具有重要意义。采用2?158家制造业上市公司7年的财务指标作为研究数据,利用采样、特征选择以及集成学习算法构建上市公司高送转预测模型并进行实证研究。结果显示：采样和特征选择方法均能有效提高集成预测模型的性能;相较于数据集中的冗余信息,数据不平衡问题对模型预测准确率的影响更显著;ADASYN+mRMR+XGBoost组合模型取得了最好的预测结果,高送转样本的分类准确率达到84.96%,建议投资者优先选用该组合模型对上市公司的高送转情况进行预测。     

Aggregating multiple classification results using Choquet integral for financial distress early warning

Financial distress prediction methods based on combination classifier become a rising trend in this field. This paper applies Choquet integral to ensemble single classifiers and proposes a Choquet integral-based combination classifier for financial distress early warning. Also, as the conditions between training and pattern recognition cannot be completely consistent, so this paper proposes an adaptive fuzzy measure by using the dynamic information in the single classifier pattern recognition results which is more reasonable than the static prior fuzzy density. Finally, a comparative analysis based on Chinese listed companies’ real data is conducted to verify prediction accuracy and stability of the combination classifier. The experiment results indicate that financial distress prediction using Choquet integral-based combination classifier has higher average accuracy and stability than single classifiers.     

Ratio Selection for Classification Models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.     

Novel feature selection methods to financial distress prediction

Financially distressed prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. One of the core problems to FDP is to design effective feature selection algorithms. In contrast to existing approaches, we propose an integrated approach to feature selection for the FDP problem that embeds expert knowledge with the wrapper method. The financial features are categorized into seven classes according to their financial semantics based on experts’ domain knowledge surveyed from literature. We then apply the wrapper method to search for “good” feature subsets consisting of top candidates from each feature class. For concept verification, we compare several scholars’ models as well as leading feature selection methods with the proposed method. Our empirical experiment indicates that the prediction model based on the feature set selected by the proposed method outperforms those models based on traditional feature selection methods in terms of prediction accuracy.     

Ensemble learning for data stream analysis: A survey

In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.     

Forecasting financial condition of Chinese listed companies based on support vector machine

Due to the radical changing and specialty of Chinese capital market, it is challenging to develop a powerful financial distress prediction model. In this paper, we first analyzed the feasibility of Chinese special-treated companies as distressed sample by using statistical methods. Then we developed a prediction model based on support vector machines (SVM) for an unmatched sample of Chinese high-tech manufacture companies. The grid-search technique using 10-fold cross-validation is used to find out the best parameter value of kernel function of SVM. The experiment results show that the proposed SVM model outperforms conventional statistical methods and back-propagation neural network. In general, SVM provides a robust model with high prediction accuracy for forecasting financial distress of Chinese listed companies. It is also suggested that Chinese special-treated event adopted as cut-off line has some effect on the prediction accuracy of the models.     

Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers

How to effectively predict financial distress is an important problem in corporate financial management. Though much attention has been paid to financial distress prediction methods based on single classifier, its limitation of uncertainty and benefit of multiple classifier combination for financial distress prediction has also been neglected. This paper puts forward a financial distress prediction method based on weighted majority voting combination of multiple classifiers. The framework of multiple classifier combination system, model of weighted majority voting combination, basic classifiers’ voting weight model and basic classifiers’ selection principles are discussed in detail. Empirical experiment with Chinese listed companies’ real world data indicates that this method can greatly improve the average prediction accuracy and stability, and it is more suitable for financial distress prediction than single classifiers.     

Traveling time prediction in scheduled transportation with journey segments

Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a ‘source/destination’ pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory, works well yet suffers from outliers. To overcome the outliers problem, we use Machine Learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.     

An agent-based simulation model of enterprises financial distress for the enterprise of different life cycle stage

This paper proposes a new agent-based simulation model to simulate the causes and processes of enterprise financial distress. The general framework of the model including four agents, which are enterprise, product, bank and macro environment is described. By investigating the different causes of financial distress in enterprise’s different life cycle stages, we implement the simulation model to four specific cases which belong to start-up, growth, mature and decline life cycle stages respectively. Then a comparative analysis between our simulation results and real situation in four proposed specific cases is conducted, demonstrating that the proposed simulation model is a promising tool for comprehensively analyzing the causes and processes of financial distress.     

Business health characterization: A hybrid regression and support vector machine analysis

Business health prediction is critical and challenging in today's volatile environment, thus demand going beyond classical business failure studies underpinned by rigidities, like paired sampling, a-priori predictors, rigid binary categorization, amongst others.In response, our paper proposes an investor-facing dynamic model for characterizing business health by using a mixed set of techniques, combining both classical and “expert system” methods. Data for constructing the model was obtained from 198 multinational manufacturing and service firms spread over 26 industrial sectors, through Wharton database.The novel 4-stage methodology developed combines a powerful stagewise regression for dynamic predictor selection, a linear regression for modelling expert ratings of firms’ stock value, an SVM model developed from unmatched sample of firms, and finally an SVM-probability model for continuous classification of business health. This hybrid methodology reports comparably higher classification and prediction accuracies (over 0.96 and ∼90%, respectively) and predictor extraction rate (∼96%). It can also objectively identify and constitute new unsought variables to explain and predict behaviour of business subjects.Among other results, such a volatile model build upon a stable methodology can influence business practitioners in a number of ways to monitor and improve financial health. Future research can concentrate on adding a time-variable to the financial model along with more sector-specificity.     

A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable failure prediction models for public industrial firms in Taiwan. Multiple discriminate analysis (MDA), logit, probit, and artificial neural networks (ANNs) methodology were employed to a dataset of matched sample of failed and non-failed Taiwan public industrial firms during 1998–2005. The final models are validated using within sample test and out-of-the-sample test, respectively. The results indicated that the probit, logit, and ANN models which used in this study achieve higher prediction accuracy and possess the ability of generalization. The probit model possesses the best and stable performance. However, if the data does not satisfy the assumptions of the statistical approach, then the ANN approach would demonstrate its advantage and achieve higher prediction accuracy. In addition, the models which used in this study achieve higher prediction accuracy and possess the ability of generalization than those of [Altman, Financial ratios—discriminant analysis and the prediction of corporate bankruptcy using capital market data, Journal of Finance 23 (4) (1968) 589–609, Ohlson, Financial ratios and the probability prediction of bankruptcy, Journal of Accounting Research 18 (1) (1980) 109–131, and Zmijewski, Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research 22 (1984) 59–82]. In summary, the models used in this study can be used to assist investors, creditors, managers, auditors, and regulatory agencies in Taiwan to predict the probability of business failure.     

基于数据包络和数据挖掘的财务危机预测模型研究

以往的企业财务危机预测研究只能预测企业是否具有财务危机,无法预测企业财务危机的程度,这是由于在界定企业财务危机时,只依据了企业是否为ST企业的分类方式。鉴于此,通过数据包络分析法,近一步细化了企业财务危机的分类,再使用关联规则算法筛选出重要的预测变量,最后使用决策树技术构建企业财务危机预测模型,并对分类的有效性和预测的准确率进行了验证。实证结果表明,基于数据包络和数据挖掘的财务危机预测模型既能保持较高的准确率,又能预测企业财务危机的程度,使得预测结果更具有参考价值。     

Predicting financial distress of the South Korean manufacturing industries

The financial distress forecasting has long been of great interest both to scholars and practitioners. The financial distress forecasting is basically a dichotomous decision, either being financial distress or not. Most statistical and artificial intelligence methods estimate the probability of financial distress, and if this probability is greater than the cutoff value, then the prediction is to be financial distress. To improve the accuracy of the financial distress prediction, this paper first analyzed the yearly financial data of 1888 manufacturing corporations collected by the Korea Credit Guarantee Fund (KODIT). Then we developed a financial distress prediction model based on radial basis function support vector machines (RSVM). We compare the classification accuracy performance between our RSVM and artificial intelligence techniques, and suggest a better financial distress predicting model to help a chief finance officer or a board of directors make better decision in a corporate financial distress. The experiments demonstrate that RSVM always outperforms other models in the performance of corporate financial distress predicting, and hence we can predict future financial distress more correctly than any other models. This enhancement in predictability of future financial distress can significantly contribute to the correct valuation of a company, and hence those people from investors to financial managers to any decision makers of a company can make use of RSVM for the better financing and investing decision making which can lead to higher profits and firm values eventually.     

基于UML的模具企业动态联盟框架设计

单个模具企业设计与生产能力不足问题的解决途径是建立动态联盟,而动态联盟建立过程的关键是系统模型.因此,采用UML统一建模语言设计了一个模具企业动态联盟框架模型.系统结构为分层结构;系统功能包括基本信息管理、加盟企业信息管理、生产信息管理、分析控制管理.系统的静态模型使用静态结构图、组件图和用例图描述,系统的动态模型使用序列图、活动图等描述,数据库模型使用类图描述,并给出了模具企业动态联盟各成员与市场机遇目标的依赖关系.     

Efficiently processing deterministic approximate aggregation query on massive data

Financial distress prediction is very important to financial institutions who must be able to make critical decisions regarding customer loans. Bankruptcy prediction and credit scoring are the two main aspects considered in financial distress prediction. To assist in this determination, thereby lowering the risk borne by the financial institution, it is necessary to develop effective prediction models for prediction of the likelihood of bankruptcy and estimation of credit risk. A number of financial distress prediction models have been constructed, which utilize various machine learning techniques, such as single classifiers and classifier ensembles, but improving the prediction accuracy is the major research issue. In addition, aside from improving the prediction accuracy, there have been very few studies that specifically consider lowering the Type I error. In practice, Type I errors need to receive careful consideration during model construction because they can affect the cost to the financial institution. In this study, we introduce a classifier ensemble approach designed to reduce the misclassification cost. The outputs produced by multiple classifiers are combined by utilizing the unanimous voting (UV) method to find the final prediction result. Experimental results obtained based on four relevant datasets show that our UV ensemble approach outperforms the baseline single classifiers and classifier ensembles. Specifically, the UV ensemble not only provides relatively good prediction accuracy and minimizes Type I/II errors, but also produces the smallest misclassification cost.