Bankruptcy theory development and classification via genetic programming

Bankruptcy is a highly significant worldwide problem with high social costs. Traditional bankruptcy risk models have been criticized for falling short with respect to bankruptcy theory building due to either modeling assumptions or model complexity.Genetic programming minimizes the amount of a priori structure that is associated with traditional functional forms and statistical selection procedures, but still produces easily understandable and implementable models. Genetic programming was used to analyze 28 potential bankruptcy variables found to be significant in multiple prior research studies, including 10 fraud risk factors. Data was taken from a sample of 422 bankrupt and non-bankrupt Norwegian companies for the period 1993–1998. Six variables were determined to be significant.A genetic programming model was developed for the six variables from an expanded sample of 1136 bankrupt and non-bankrupt Norwegian companies. The model was 81% accurate on a validation sample, slightly better than prior genetic programming research on US public companies, and statistically significantly better than the 77% accuracy of a traditional logit model developed using the same variables and data. The most significant variable in the final model was the prior auditor opinion, thus validating the information value of the auditor’s report. The model provides insight into the complex interaction of bankruptcy related factors, especially the effect of company size. The results suggest that accounting information, including the auditor’s evaluation of it, is more important for larger than smaller firms. It also suggests that for small firms the most important information is liquidity and non-accounting information.The genetic programming model relationships developed in this study also support prior bankruptcy research, including the finding that company size decreases bankruptcy risk when profits are positive. It also confirms that very high profit levels are associated with increased bankruptcy risk even for large companies an association that may be reflecting the potential for management to be “Cooking the Books”.     

A preference disaggregation decision support system for financial classification problems

Classification is one of the most extensively studied problems in the fields of multivariate statistical analysis, operations research and artificial intelligence. Decisions involving a classification of the alternative solutions are of major interest in finance, since several financial decision problems are best studied by classifying a set of alternative solutions (firms, loan applications, investment projects, etc.) in predefined classes. This paper proposes an alternative approach to the classical statistical methodologies that have been extensively used for the study of financial classification problems. The proposed methodology combines the preference disaggregation approach (a multicriteria decision aid method) with decision support systems. More specifically, the FINancial CLASsification (FINCLAS) multicriteria decision support system is presented. The system incorporates a plethora of financial modeling tools, along with powerful preference disaggregation methods that lead to the development of additive utility models for the classification of the considered alternatives into predefined classes. An application in credit granting is used to illustrate the capabilities of the system.     

基于多元分析的股票评价方法

对上市公司钢铁板块2004年财务报表中的八个主要指标应用多元分析法进行了总体评价,得出上市公司的业绩主要受三个具有一定含义的因子的影响,并对上市公司各因子的得分情况及综合得分情况给出了相应分析.     

Credit risk assessment using a multicriteria hierarchical discrimination approach: A comparative analysis

Corporate credit risk assessment decisions involve two major issues: the determination of the probability of default and the estimation of potential future benefits and losses for credit granting. The former issue is addressed by classifying the firms seeking credit into homogeneous groups representing different levels of credit risk. Classification/discrimination procedures commonly employed for such purposes include statistical and econometric techniques. This paper explores the performance of the M.H.DIS method (Multi-group Hierarchical DIScrimination), an alternative approach that originates from multicriteria decision aid (MCDA). The method is used to develop a credit risk assessment model using a large sample of firms derived from the loan portfolio of a leading Greek commercial bank. A total of 1411 firms are considered in both training and holdout samples using financial information through the period 1994–1997. A comparison with discriminant analysis (DA), logit analysis (LA) and probit analysis (PA) is also conducted to investigate the relative performance of the M.H.DIS method as opposed to traditional tools used for credit risk assessment.     

我国上市公司审计收费影响因素的实证分析

本文首次选取我国第一支统一指数沪深300的指标股2005年年报披露的审计费用作为研究对象,应用多元线性回归的统计方法,对我国上市公司审计收费影响因素进行实证研究。研究表明:企业资产规模、纳入合并报表的子公司数、会计师事务所为"国际四大"显著影响审计收费;而存货和应收账款占总资产的比例、资产负债率、净资产收益率、审计意见类型、独立董事的数量、公司所在地等对审计收费不存在显著的影响。最后提出有针对性的政策建议。     

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies.     

基于面板logit模型的上市公司财务困境预测

目前关于财务困境预测的研究大多是局限于截面数据的静态计量和统计模型,忽视了公司的财务状况是不断变化的事实.为了揭示公司财务状况的变化过程,利用面板数据建立了panel logit概率模型.研究结果表明,panel logit模型在预测准确度方面优于普通的logit模型.     

A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP

Corporate scandals such as those at Tyco, Enron, and WorldCom have caused the decline of public trust in accounting and reporting practices. In response, US passed the Sarbanes Oxley Law (Sarbanes) in 2002, the most important corporate governance law since securities laws in 1930s. Section 302 of Sarbanes mandates corporations to ensure accurate financial disclosure and to take greater financial reporting and control responsibilities. Management is required to make an annual assertion regarding the design and effectiveness of company’s internal controls. Consequently, many internal auditing resources are stretched. The objective of this case study is to develop a multi-criteria decision making aid that can identify the most critical businesses units within a corporation. Using the aid, we can use efficiently and effectively the internal auditing resources. Internal audits determine if the accounting processes and systems are working as intended. It focuses on the reliability of the accounting data and evaluates business through financial, operational, and compliance review. It assesses the risk of asset loss, studies business processes, and identifies opportunities to improve efficiency and effectiveness. This study explores the potential of applying data envelopment analysis (DEA) and analytic hierarchy process (AHP) to determine the business units that need audit. Compared with conventional methods, the proposed combined model incorporates a much wider range of quantitative and qualitative criteria, and provides a more detailed and thorough study. The proposed evaluation framework is comprehensive and flexible and it shows great potential for internal audit prioritization and resource allocation.     

A multicriteria approach for rating the credit risk of financial institutions

Within the new bank regulatory context, the assessment of the credit risk of financial institutions is an important issue for supervising authorities and investors. This study explores the possibility of a developing risk assessment model for financial institutions using a multicriteria classification method. The analysis is based on publicly available financial data for UK firms. The results indicate that the proposed multicriteria methodology provides promising results compared to well known statistical methods.     

Fuzzy set based models and methods of multicriteria group decision making

In the paper, the term consensus scheme is utilized to denote a dynamic and iterative process where the experts involved discuss a multicriteria decision problem. This discussion process is conducted by a human or artificial moderator, with the purpose of minimizing the discrepancy between the individual opinions.During the process of decision making, each expert involved must provide preference information. The information format and the circumstances where it must be given play a critical role in the decision process. This paper analyses a generic consensus scheme, which considers many different preference input formats, several possible interventions of the moderator, as well as admitting several stop conditions for interrupting the discussion process. In addition, a new consensus scheme is proposed with the intention of eliminating some difficulties met when the traditional consensus schemes are utilized in real applications. It preserves the experts’ integrity through the intervention of an external person, to supervise and mediate the conflicting situations. The human moderator is supposed to interfere in the discussion process by adjusting some parameters of the mathematical model or by inviting an expert to update his opinion. The usefulness of this consensus scheme is demonstrated by its use to solve a multicriteria group decision problem, generated applying the Balanced Scorecard methodology for enterprise strategy planning. In the illustrating problem, the experts are allowed to give their preferences in different input formats. But the information provided is made uniform on the basis of fuzzy preference relations through the use of adequate transformation functions, before being analyzed. The advantage of using fuzzy set theory for solving multiperson multicriteria decision problems lies in the fact that it can provide the flexibility needed to adequately deal with the uncertain factors intrinsic to such problems.     

Fuzzy set based models and methods of multicriteria group decision making

In the paper, the term consensus scheme is utilized to denote a dynamic and iterative process where the experts involved discuss a multicriteria decision problem. This discussion process is conducted by a human or artificial moderator, with the purpose of minimizing the discrepancy between the individual opinions.During the process of decision making, each expert involved must provide preference information. The information format and the circumstances where it must be given play a critical role in the decision process. This paper analyses a generic consensus scheme, which considers many different preference input formats, several possible interventions of the moderator, as well as admitting several stop conditions for interrupting the discussion process. In addition, a new consensus scheme is proposed with the intention of eliminating some difficulties met when the traditional consensus schemes are utilized in real applications. It preserves the experts’ integrity through the intervention of an external person, to supervise and mediate the conflicting situations. The human moderator is supposed to interfere in the discussion process by adjusting some parameters of the mathematical model or by inviting an expert to update his opinion. The usefulness of this consensus scheme is demonstrated by its use to solve a multicriteria group decision problem, generated applying the Balanced Scorecard methodology for enterprise strategy planning. In the illustrating problem, the experts are allowed to give their preferences in different input formats. But the information provided is made uniform on the basis of fuzzy preference relations through the use of adequate transformation functions, before being analyzed. The advantage of using fuzzy set theory for solving multiperson multicriteria decision problems lies in the fact that it can provide the flexibility needed to adequately deal with the uncertain factors intrinsic to such problems.     

Handling multicriteria preferences in cluster analysis

In the framework of multicriteria decision aid, a lot of interest has been devoted to sorting problems, in which the set of categories is pre-defined. Besides, preference oriented multicriteria clustering has received little attention. Usual geometric and related metrics are not well suited for this problem. Here, we propose a clustering method based on a valued indifference relation inspired by outranking methods. We suggest a method (based on comparing cluster centers and an average net flow score of clusters) to build a complete ranking of the set of clusters, that is, a way of defining a set of ordered categories for sorting purposes. The new approach performs very well in some examples.     

On the construction of mutual fund portfolios: A multicriteria methodology and an application to the Greek market of equity mutual funds

The evaluation of the performance of mutual funds (MFs) has been a very interesting research topic not only for researchers, but also for managers of financial, banking and investment institutions. In this paper, an integrated methodological framework for the evaluation of MF performance is proposed. The proposed methodology is based on the combination of discrete and continuous multicriteria decision aid (MCDA) methods for MFs selection and composition. In the first stage of the analysis the UTADIS MCDA method is employed in order to develop mutual fund's performance models supporting the selection of a small set of MFs, which will compose the final portfolios. In the second stage, a goal programming model is employed to determine the proportion of the selected MFs in the final portfolios. The methodology is applied on data of Greek MFs over the period 1999–2001 with encouraging results.     

外卖食品安全问题的演化博弈分析

运用演化博弈论的方法对外卖商家之间,订餐平台监管部门和外卖商家之间相互作用时策略选择行为进行分析.结果表明:当外卖商家生产不合格的外卖收益大于生产合格外卖的收益、订餐平台监督部门对生产不合格外卖的商家处罚力度过轻或对外卖合格性的检测成本过高时,不合格外卖生产必然发生.要确保外卖商家生产合格外卖,就必须对生产不合格外卖的商家进行严惩,降低生产不合格外卖的预期收益.同时,要加强订餐平台建设,降低监管成本,保证监管部门严格执法.     

Modelling banking sector stability with multicriteria approaches

Banking crises can be damaging for the economy, and as the recent experience has shown, nowadays they can spread rapidly across the globe with contagious effects. Therefore, the assessment of the stability of a county’s banking sector is important for regulators, depositors, investors and the general public. In the present study, we propose the development of classification models that assign the banking sectors of various countries in three classes, labelled “low stability”, “medium stability”, and “high stability”. The models are developed using three multicriteria decision aid techniques, which are well-suited to ordinal classification problems. We use a sample of 114 banking sectors (i.e., countries), and a set of criteria that includes indicators of the macroeconomic, institutional and regulatory environment, as well as basic characteristics of the banking and financial sector. The models are developed and tested using a tenfold cross-validation approach and they are benchmarked against models developed with discriminant analysis and logistic regression.     

Multicriteria classification models for the identification of targets and acquirers in the Asian banking sector

The purpose of the present study is the development of classification models for the identification of acquirers and targets in the Asian banking sector. We use a sample of 52 targets and 47 acquirers that were involved in acquisitions in 9 Asian banking markets during 1998–2004 and match them by country and time with an equal number of non-involved banks. The models are developed and validated through a tenfold cross-validation approach using two multicriteria decision aid techniques. For comparison purposes we also develop models through discriminant analysis. The results indicate that the multicriteria decision aid models are more efficient that the ones developed through discriminant analysis. Furthermore, in all the cases the models are more efficient in distinguishing between acquirers and non-involved banks than between targets and non-involved banks. Finally, the models with a binary outcome achieve higher accuracies than the ones which simultaneously distinguish between acquirers, targets and non-involved banks.     

A multicriteria DSS for stock evaluation using fundamental analysis

The paper describes a multicriteria decision support system which aims at presenting an evaluation of the Athens Stock Exchange (ASE) stocks, on the basis of fundamental analysis. The system evaluates the stocks based on the method of fundamental analysis ratios, which is the most appropriate evaluation approach regarding investment decisions within a long term horizon. In addition to quantitative data deriving from fundamental analysis, the system uses qualitative data as well, in order to improve the reliability of the evaluation. The system introduced in this paper, utilises multicriteria analysis methodologies in order to rank the stocks by placing the best stock first and the worst last. Stock evaluation considers the specific characteristics of the potential investor, as well as his attitude towards undertaken risk. The final output of the system is four stock rankings which respond to four different criteria groups, depending on the type of accounting plan each listed company belongs to. The system incorporates a large volume of relevant information and operates in ‘real world conditions’ since its data are constantly updated. Finally, the system is intended for both institutional and private investors.     

中国股市上市公司财务的统计分析

本文从财务年报出发 ,分析沪深两地上市公司财务的基本统计特征 ,并讨论上市公司财务分析的多元统计方法     

基于成本粘性的盈利预测及其精度检验

本文以2007-2009年沪深2652家A股上市公司为样本,验证了成本粘性的存在性,并以2008年的相关财务数据为基础构建盈利预测模型,对2009年的公司盈利进行预测,通过与当年各公司的实际利润相比较,发现基于成本粘性的盈利预测模型具有更高的预测能力,其预测精度显著高于其他常用预测模型,表明将诸如成本习性等管理会计的工具引入财务会计研究领域具有重大意义,可以提高会计信息的决策相关性。