财务预警外文文献
- 格式:pdf
- 大小:1.17 MB
- 文档页数:16
外文文献The Important Of Financial RiskSohnke M. Bartram Gregory W. Brown and Murat AtamerAbstract:This paper examines the determinants of equity price risk for a largesample of non-financial corporations in the United States from 1964 to 2008. Weestimate both structural and reduced form models to examine the endogenous natureof corporate financial characteristics such as total debt debt maturity cash holdingsand dividend policy. We find that the observed levels of equity price risk areexplained primarily by operating and asset characteristics such as firm age size assettangibility as well as operating cash flow levels and volatility. In contrast impliedmeasures of financial risk are generally low and more stable than debt-to-equity ratios.Our measures of financial risk have declined over the last 30 years even as measuresof equity volatility e.g. idiosyncratic risk have tended to increase. Consequentlydocumented trends in equity price risk are more than fully accounted for by trends inthe riskiness of firms’assets. Taken together the results suggest that the typical U.S.firm substantially reduces financial risk by carefully managing financial policies. As aresult residual financial risk now appears negligible relative to underlying economicrisk for a typical non-financial firm.Keywords:Capital structure;financial risk;risk management;corporate finance1 1.IntroductionThe financial crisis of 2008 has brought significant attention to the effects offinancial leverage. There is no doubt that the high levels o f debt financing by financialinstitutions and households significantly contributed to the crisis. Indeed evidenceindicates that excessive leverage orchestrated by major global banks e.g. through themortgage lending and collateralized debt obligations and the so-called “shadowbanking system”may be the underlying cause of the recent economic and financialdislocation. Less obvious is the role of financial leverage among nonfinancial firms.To date problems in the U.S. non-financial sector have been minor compared to thedistress in the financial sector despite the seizing of capital markets during the crisis.For example non-financial bankruptcies have been limited given that the economicdecline is the largest since the great depression of the 1930s. In fact bankruptcyfilings of non-financial firms have occurred mostly in U.S. industries e.g.automotive manufacturing newspapers and real estate that faced fundamentaleconomic pressures prior to the financial crisis. This surprising fact begs the question“How important is financial risk for non-financial firms”At the heart of this issue isthe uncertainty about the determinants of total firm risk as well as components of firmrisk.Recent academic research in both asset pricing and corporate finance hasrekindled an interest in analyzing equity price risk. A current strand of the assetpricing literature examines the finding of Campbell et al. 2001 thatfirm-specificidiosyncratic risk has tended to increase over the last 40 years. Other work suggeststhat idiosyncratic risk may be a priced risk factor see Goyal and Santa-Clara 2003among others. Also related to these studies is work by Pástor and V eronesi 2003showing how investor uncertainty about firm profitability is an important determinantof idiosyncratic risk and firm value. Other research has examined the role of equityvolatility in bond pricing e.g. Dichev 1998 Campbell Hilscher and Szilagyi2008.However much of the empirical work examining equity price risk takes the riskof assets as given or tries to explain the trend in idiosyncratic risk. In contrast thispaper takes a different tack in the investigation of equity price risk. First we seek tounderstand the determinants of equity price risk at the firm level by considering totalrisk as the product of risks inherent in the firms operations i.e. economic or businessrisks and risks associated with financing the firms operations i.e. financial risks.Second we attempt to assess the relative importance of economic and financial risksand the implications for financial policy.Early research by Modigliani and Miller 1958 suggests that financial policymay be largely irrelevant for firm value because investors can replicate manyfinancial decisions by the firm at a low cost i.e. via homemade leverage andwell-functioning capital markets should be able to distinguish between financial andeconomic distress. Nonetheless financial policies such as adding debt to the capitalstructure can magnify the risk of equity. In contrast recent research on corporate riskmanagement suggests that firms may also be able to reduce risks and increase valuewith financial policies such as hedging with financial derivatives. However thisresearch is often motivated by substantial deadweight costs associated with financialdistress or other market imperfections associated with financial leverage. Empiricalresearch provides conflicting accounts of how costly financial distress can be for atypical publicly traded firm.We attempt to directly address the roles of economic and financial risk byexamining determinants of total firm risk. In our analysis we utilize a large sample ofnon-financial firms in the United States.Our goal of identifying the most importantdeterminants of equity price risk volatility relies on viewing financial policy astransforming asset volatility into equity volatility via financial leverage. Thusthroughout the paper we consider financial leverage as the wedge between assetvolatility and equity volatility. For example in a static setting debt provides financialleverage that magnifies operating cash flow volatility. Because financial policy isdetermined by owners and managers we are careful to examine the effects of firms’asset and operating characteristics on financial policy. Specifically we examine avariety of characteristics suggested by previous research and as clearly as possibledistinguish between those associated of the company(i.e. factors determining economic risk) and those associated with financing the firm(i.e. factors determining financial risk).We then allow economic risk to be a determinant of financial policy in the structural framework of Leland and Toft(1996),or alternatively, in a reduced form model of financial leverage.An advantage of the structural model approach is that we are able to account for both the possibility of financial and operating implciations ofsome factors(e.g .dividends),as well as the endogenous nature of the bankruptcy decision and financial policy in general.Our proxy for firm risk is the volantility if common stock returns derived from calculating the standard deviation of daliy equity returns.Our proxies for econmic risk are designed to capture the essential charactersitics of the firm’s operations and assets that determine the cash flow generating process for the firm.For example,firm size and age provide measures of line of –business maturity; tangible assets(plant,property,and equipment)serve as a proxy for the ‘hardness’of a firm’s assets;capital expenditures measure captial intensity as well as growth potential.Operating profitability and operating profit volatility serve as measures of the timeliness and riskiness of cash flows.To understand how financial factors affect firm risk,we examine total debt,debt maturity,dividend payouts,and holdings of cash and short-term investments.The primary resuit or our analysis is surpriing:factors determining economic risk for a typical company exlain the vast majority of the varation in equity volatility.Correspondingly,measures of implied financial leverage are much lower than observed debt ratios. Specifically, in our sample covering 1964-2008 average actual net financial (market) leverage is 1.50 compared to our estimates of between 1.03 and 1.11 (depending on model specification and estimation technique).This suggests that firms may undertake other financial policise to manage financial risk and thus lower effective leverage to nearly negligible levels.These policies might include dynamically adjusting financial variables such as debt levels,debt maturity,or cash holdings (see,for example , Acharya,Almeida,and Campello,2007).In addition,many firms also utilize explicit financial risk management techniques such as the use of financial dervatives,contractual arrangements with investors (e.g. lines of credit,call provisions in debt contracts ,or contingencies in supplier contracts ),spcial purpose vehicles (SPVs),or other alternative risk transfer techniques.The effects of our ecnomic risk factors on equity volatility are generally highly statiscally significant, with predicted size and age of the firm.This is intuitive since large and mature firms typically have more stable lines of business,which shoule be reflected in the volatility. This suggests that companties with higher and more stable operating cash flows are less likely to go bankrupt, and therefore are potentially less risky .Among economic risk variables,the effects of firm size ,prfit volatility,and dividend policy on equity volatility stand out. Unlike some previous studies,our careful treatment of the endogeneity of financial policy co nfirms that leveage increases total firm risk. Otherwise,fiancial risk factors are not reliably to total risk.Given the large literature on financial policy , it is no surprise that financial variables are , at least in part , determined by the econmic risks frims take.However, some of the specific findings are unexpected. For example , in a simple model of capital structure ,dividend payouts should increase financial leverage since they represent an outflow of cash from the firm(i.e.,increase net debt ).We find that dividends are associated with lower risk. This suggests that paying dividends is not as much a product of financial policy as a characteristic of a firm’s operations(e.g.,a mature company with limited growth opportunities). We also estimate howsensitivities to different risk factors have changed over time.Our result indicate that most relations are fairly stable. One exception is firm age which prior to 1983 tends to be positively related to risk and has since been consisitently negatively related to risk.This is related to findings by Brown and Kapadoa (2007) that recent trends in idiosyncratic risk are related to stock listings by younger and riskier firms.。
企业财务风险防范外文文献<i>企业财务风险防范外文文献</i>从分析财务风险入手,阐述其含义、特征以及种类等内容;在此基础上对财务风险产生的原因进行深入细致的分析研究,分析总结出财务风险产生的内因和外因诸方面;从而提出树立风险意识,建立有效的风险防范机制;建立和完善财务管理系统,以适应财务管理环境变化;建立财务风险预警机制,加强财务危机管理;提高财务决策的科学化水平,防止因决策失误而产生的财务风险;通过防范内部制度,建立约束机制来控制和防范财务风险五个方面的财务风险防范措施以及自我保险、多元化风险控制、风险转移、风险回避、风险降低五种技术方法。
只有控制防范和化解企业财务风险,才能确保企业在激烈的市场竞争中立于不败之地。
财务风险的成因(一)外部原因1、国家政策的变化带来的融资风险。
一般而言,由于中小企业生产经营不稳定。
一国经济或金融政策的变化,都有可能对中小企业生产经营、市场环境和融资形式产生一定的影响。
从2007 年开始,我国加大了对宏观经济的调控力度,央行第四次提高存款准备金率,尤其是实行差额准备金制度使直接面向中小企业服务的中小商业银行信贷收紧,中小企业的资金供给首先受阻,融资风险徒增不少,中小企业也因无法得到急需资金而被迫停产或收缩经营规模。
2、银行融资渠道不流畅造成的融资风险。
企业资金来源无非是自有资金和对外融资两种方式。
在各种融资方式中,银行信贷又是重要的资金来源,但是银行在国家金融政策以及自身体制不健全等情况的影响下,普遍对中小企业贷款积极性不高,使其贷款难度加大,增加了企业的财务风险。
(二)内部原因1、盲目扩张投资规模。
有相当一部分的中小企业在条件不成熟的情况下,仅凭经验判断片面追求公司外延的扩大,忽略了公司内涵和核心竞争力,造成投资时资金的重大浪费。
2、投资决策失误。
对企业来说,正确的产业选择是生存发展的战略起点。
但一些企业在选择产业过程中,往往忽视了“产业选择是一个动态过程”的观念,不能敏锐地把握产业演变的趋势和方向。
外文翻译原文Title:FinancialRatiosandtheProbabilisticPredictionofBankruptcyMaterialSource:Author:JamesOhlson1 IntroductionThis paper presents some empirical results of a study predicting corporatefailureasevidencedbytheeventofbankruptcy.Therehavebeenafairnumberofprevious studies in this field ofresearch;the more notable published contributionsareBeaver(1966;1968a;1968b),Altman(1968;1973)andsoon.Twounpublishedpapersby WhiteandTurnbuli (1975a; 1975b)anda paperbySantomeroandVinso(1977)areofparticularinterestasthey appeartobethefirststudies whichlogicallyand systematically develop probabilistic estimates of failure. The presentstudy issimilar tothelatterstudies,in thatthemethodology is oneofmaximum likelihoodestimationoftheso-calledconditionallogitmodel.Thedatasetused inthisstudyis fromtheseventies(1970-76).Iknow ofonlythreecorporatefailure researchstudieswhichhave examined data fromthis period.Oneis a limited study by Altman andMcGough (1974])inwhich only failed firmsw ere dra w n from th e peri od 1 9 7 0 -7 3 a nd on l y o ne ty pe of cla ssi fica ti onerror(misclassificationoffailedfirms)wasanalyzed.Moyer(1977)consideredtheperiod1965-75,butthesampleofbankruptfirmswasunusually small(twenty-sevenfirms).Thethirdstudy,byAltman,Haldeman,and Narayanan (1977),which "updates"theoriginal Altman(1968)study,basically considersdata fromtheperiod1969 to1975.Their samplewasbasedonfifty-threefailedfirms andabout the same numberofnonfailed firms. In contrast, my study relies on observations from 105bankruptfirmsand2,058nonbankruptfirms.Althoughtheotherthreestudiesdifferfrom thepresentoneso farasmethodologyandobjectivesareconcerned,itis,nevertheless,interestingandusefultocomparetheirresul tswiththosepresentedinthispaper.AnotherdistinguishingfeatureofthepresentstudywhichIshouldstressisthat,contrarytoalmostallpr eviousstudies,thedatafor failed firms were notderivedfrom Moody's Manual. The data were obtained instead from 10-K financialstatementsasreported atthe time. This procedurehas oneimportantadvantage:thereportsindicateatwhatpointintimethey werereleasedtothepublic, andonecanth ere fore che ck w h ether the c om pa ny en tere d ba nk ru ptc y prior to or a fter the da te ofrelease.Previousstudieshavenotexplicitly consideredthistimingissue.2SomeCommentsRegarding Methodology and Data CollectionThefundamentalestimationproblemcan be reduced simply to the followingstatement:giventhatafirmbelongstosomeprespecified population, what is theprobabilitythatthefirmfailswithin some prespecifiedtime period?Noassumptionshavetobemade regarding prior probabilities ofbankruptcy and/or thedistributionofpredictors. These are the major advantages. The statistical significance of thedifferentpredictorsareobtainedfromasymptotic(large sample)theory.Clearly,muchcanbegainedbyimprovingthedatabase.Theevaluationofthepredictive classification power of a model should be more realistic, and, moreimportanthere,the same should apply for standard tests ofstatistical significance.Thisis nottosuggestthat itis importanttohave "superaccurate"data forpurposesofdeveloping(asopposedtoevaluating)a discriminatory device. Itmightwell bethat the predictive quality of any model is reasonably robust acrossa variety ofdatagatheringandestimatingprocedures.3CollectionofFinancialStatementDataThe next phase was one ofactually collecting financial data for the bankruptfirms.Theobjectivewastoobtainthreeyearsofdatapriortothedateofbankruptcy.Eachreporthadtoincludethebalancesheet,incomestatement,fundsstatement, andtheaccountants'report.Incasethelastavailableaccountants'reportexplicitlystatedthat the company had filed for bankruptcy, then a fourth reportwas collected. AllreportswereretrievedfromtheStanfordUniversity BusinessSchoolLibrary, whichhas an extensive microfilm file of 10-K reports. The relevant parts of the 10-Kreportswerephotocopiedandsubsequently coded. Some firms had to be deletedfromthesamplebecausenoreportwhatsoeverwasavailable, but these were few.Otherfirms,again very few, were deleted because they were corporate shells andhad nosales.Ontheotherhand,nofirmwasdeletedbecauseofitsyoung(exchange)a g e , a nd som e fi rm s ha d only o ne set o f reports.The final sample was made up of 105 bankrupt firms. I noted that whileeighteenofthe105firms (17percent)had accountants'reportswhich disclosed thatthecompany had entered bankruptcy, the fiscal year-end was prior to the date ofbankruptcy.These reports were deleted and reports from the previous fiscal yearw ere su bstitu ted.4A ProbabilisticModelofBankruptcyL et X , d e no te a v ec tor of pre di ctors for the i th observa tion; β be a v e ctorofunknown parameters,andletP(X, p)denotethe probability ofbankruptcy foranygiven X, a ndβ . P is some probability function, 0≤P≤1. The logarithm of thelikelihoodof any specific outcomes, as reflected by the binary sample space ofbankruptcy versusnonbankruptcy,L(β )=∑log(X,β )+∑log(1-P(X,β ))i s then g i v en by : w here S 1 , i s the ( i nd e x ) se t o f ba nk ru pt fi rm s a n d S 2 i s thesetof nonbankrupt firms. For any specified function P, the maximum likelihoodestimatesof β 1,β 2…,areobtainedbysolving:m a x l ( β)In the a bse nc e of a po si ti v e the ory of ba nk ru ptc y , there i s no ea sy solu ti ontotheproblemofselectinganappropriateclassoffunctionsP.Asapracticalmatter,allonecandoistochoose onthebasisofcomputationalandinterpretative simplicity.5RatiosandB asicResultsFor pu rpose s of the pre sent report, no a ttem pt w a s m a d e to d ev e l o p a ny ne worexotic"ratios.Thecriterionforchoosingamongdifferentpredictorswassimplicity.(1).SIZE = log(total assets/GNP price-level index). The index assumes a basev a lu e of 1 0 0 for 1 9 6 8 . T ota l a s sets a re a s re ported in d olla r s. T he i nd e x y ea r i s a s ofthe y ea r prior to the y ea r o f thebalancesheet date. The procedure assures a real-time implementation of themodel.Thelogtransformhasanimportantimplication.Supposetwofirms,AandB,havea balancesheetdateinthe sa m e y ea r, the n the si g n of P A — P B i s ind e pe nde nt of the p ri ce - l ev el i n de x.(Thiswillnotfollow unlessthelog transformisapplied.)Thelatteris,ofcourse,adesirable property.(2).TLTA=Totalliabilitiesdividedbytotalassets.(3).CLCA= Currentliabilitiesdividedby currentassets.(4).OENEG= Oneiftotalliabilitiesexceedstotalassets,zerootherwise.( 5 ) . NITA = N etinc om e di v i de d by tota l a ssets.(6).FUTL= Fundsprovidedbyoperationsdividedbytotalliabilities.Afteraseriesofresearch,this paperanalyzesthechoiceanddividing pointtwocategories,therelationshipbetweentheafrwithdividingpointnumericalincreases,thebankruptc ompanydiscriminant accuracy lower, with the bankruptcy of thecompany discriminant rate is higher and higher. B y 50% as dividing pointinbankruptcy,modeltothe bankrupt company year before the discriminant 99.37%,but for correctness criterion accuracy is bankrupt company, if by 6% 32.4% asdividingpoint(initssamples,bankruptcompanyproportionof105/(105+2058)=4.85%,accordingtotheinformationprovided6% weretheauthorsprovide themostcloseto4.85%a segmentationpoint), Thecorrespondingtothebankruptcompanydiscriminant 88.2% of the accuracy and correctness of the correspondingdiscriminantinsolventcorporationis80%.6ConclusionsTherearetwoconclusionswhich should berestated.First,thepredictivepowerofany model dependsuponwhentheinformation(financialreport)isassumedtobeavailable.Somepreviousstudieshaveno tbeencarefulinthis regard.Second, thepredi c tiv e po w ers of l i nea r tra nsform s of a v ec tor of ra ti os se e m to be robu st a cross(largesample)estimationprocedures. Hence, more than anything else, significantimprovementprobably requiresadditional predictors.译文应用财务比率预警财务危机资料来源: 作者:奥尔逊 1 引言本文通过对财务危机进行防范,但最终仍导致破产的公司实例,从实证上得到一些分析结果。
我国财务风险预警机制研究的国内外文献综述I国外研究现状在国外,Ross.Wcstcrfic1.d,Jordan(2014)对于筹资风险的相关课题中,指出筹资会对公司和股东造成院患和风险⑵。
AgnieszkaDziadosz(2015)指出在财务风险中,项目的有序性、选择性和协调性非常重要。
可以为企业的管理决策提供借鉴⑶。
AsmacBcrrada(2017)指出在公司项的决策中,财务风险的敏感性对于项目投资决策行作用I,1.oredanaCu1.trera(2016)建立应用1.Ogit模型,帮助中小企业项测破产风险通过对比样本的方式来进行-1.1..C1.cOfaS-SanChCZ(2016)提出了一种财务风险预警模型,经过各种论证分析,得出结论,模里很成功,特别是在信用和破产能力上网。
Finger等(2017)指出一种财务风险预警模式,是通过完善现金流量模型的手段。
这个系统比起以往更加的系统,让风险控制在企业更好的实行为增加企业抗风险能力,UmeShS.Mahtani.ChandraPrakashGarg(2018)运用了层次分析的方法,论证了相对重要性,为企业的风险的相关因析按照一定方式排序冏。
1.es1.ie(2014)将财务分析、风险控制、风险响应和风险筹资归类为财务风险控制网。
CaMOSo.Barbosa-Povoa(2015)以ENPV为目标,运用帕累托最优曲线,提出四种控制措施“叫Corne1.1.B(2017)注意到了年轻人采用金融服务时,社会互动在其中起到了很大的作用。
经过研究分析指出社交关系的影响因素并地位并不相同,下面一种因素很关键:长期的关系队医财务决策来说举足轻Ii1.此外,这种同伴影响只存在于有凝聚力的社会结构中。
这一证据与信任在金融决策中的重要作用是•致的。
当代理人考虑是否采用金融系统时,他们会面临风险,可能会更看重他们信任的代理人提供的信息.这些结果可以帮助解择面对面的社会接触对于财务决策的重要性U1.BakeN2018)认为,面对当今夏杂的经济环境,做好财务风险的防范工作对公司发展非常重要。
企业财务风险管理外文文献Enterprise Financial Risk Management: A Literature ReviewAbstractThe enterprise financial risk management (EFRM) is a crucial tool applied by modern enterprise to manage their financial exposure and control risks. EFRM systems have become increasingly complex with time and one must have a thorough knowledge of the different facets of enterprise finance in order to effectively use them. This literature review briefly reviews existing literature and provides current understanding of the EFRM systems. Key topics discussed include the need for EFRM and the various risk management frameworks, regulations, and tools. Additionally, recent research efforts on areas such as Enterprise Risk Management Systems (ERM) and financial forecasts are discussed.1. IntroductionRisk management is an important aspect of corporate management and is extensively applied in modern enterprises. With the emergence of globalization, uncertainties, and complexity in the global business environment, effective risk management is a necessity for all corporations. Enterprises must manage different types of risks associated with inadequate financial results, including liquidity issues, treasury and debt management, insolvency or bankruptcy, and others. Enterprise Financial Risk Management (EFRM) has become an increasingly important tool to manage the risks associated with corporate financial activities. The purpose of this review is to explorethe most recent advances and research in the field of EFRM to providea comprehensive understanding of the current state of the field.2. Need for EFRMFinancial risks are a major concern in the management of any business. Inadequate risk management can lead to financial losses and even bankruptcy. The EFRM system helps to alleviate the associated financial risks. Financial risks can arise from various sources, such as external environment changes, inadequate financial planning, and insufficient internal control systems. Therefore, enterprises should implement proper EFRM strategies to protect their financial health and minimize the associated risks.EFRM systems provide the enterprise with a comprehensive risk management framework, allowing them to identify and address any existing and potential financial risks. This risk management system also enables the enterprise to analyze the short-term and long-term effects of different management decisions and to plan and implement adequate responses. Furthermore, EFRM systems facilitate financial forecasting and help management to make informed decisions. Proper risk management reduces uncertainty and increases the enterprises’ profitability.3. EFRM Risk Management FrameworksThe first step in EFRM is to identify different financial risks. Risks can be divided into two broad categories, namely, market risks and operational risks. Market risks are the risks associated with different types of financial markets, such as foreign exchanges, stocks,commodities, and interest rates. On the other hand, operational risks are the risks associated with the operations of the enterprise. These risks involve internal factors such as personnel, policies, and procedures.Once the financial risks have been identified, the enterprise should develop a risk management strategy and goals that cover the different types of risks. Different risk management tools and techniques can be used to address these risks. These tools and techniques include the use of financial analysis, financial simulation, portfolio management, financial derivatives, and enterprise risk management systems (ERM). Additionally, regulations and compliance must be taken into account when devising a risk management framework.4. Regulations and ToolsAnother important aspect of EFRM is the application of regulations. The enterprise should ensure compliance with all applicable regulators and laws and should develop a comprehensive risk management system that adheres to all the relevant laws and regulations. Furthermore, enterprise risk management systems (ERM) have become increasingly important in the management of financial risks. ERM systems are computer-based systems that allow enterprises to manage their financial risks in an efficient and integrated manner. These systems provide support in forecasting, reporting, and decision-making.5. Recent Research EffortsOver the past few years, there has been an increasing number of research studies in the field of EFRM. Some of the recent research efforts include the development of models for financial forecasts, the assessment of ERM systems, the design of financial derivatives and structured products, and the application of artificial intelligence and machine learning in financial forecasting. Further research is needed to identify new techniques and approaches that can be used to improve the effectiveness of the EFRM systems.6.ConclusionIn conclusion, effective EFRM is essential for a successful enterprise due to the increasing complexity of the global business environment. Risk management tools, techniques, and regulations must be applied to address the different types of financial risks. Additionally, research efforts in the field of EFRM are continuously increasing, and it is important to keep up to date with the latest developments.。
国内外财务风险预警研究⽂献综述⼀、企业财务预警的基本含义 企业财务预警,即财务失败预警,是指借助企业提供的财务报表、经营⽅案及其他相关会计资料,⾏使财会、统计、⾦融、企业管理、市场营销理论,采⽤⽐率剖析、⽐较剖析、因素剖析及多种剖析⽅法,对企业的经营活动、财务活动等进⾏分析预测,以发现企业在经营管理活动中潜在的经营风险和财务风险,并在危机产⽣之前向企业经营者发出警告,督促企业管理当局采取有效措施,避免潜在的风险演变成损失,起到未⾬绸缪的作⽤。
⼆、国外专家学者对财务风险预警的研究 (⼀)单变量模型 1、1932年,Fitzpatrick利⽤单变量破产模型,选取19个样本运⽤单个财务⽐率进⾏预测,结果发现判别能⼒最⾼的是净利润/股东权益和股东权益/⽋债两个⽐率。
当时由于条件限制,主要的研究⽅法就是对正常企业和⾮正常企业进⾏财务⽐率⽐较和经验分析。
2、1966年,Bwaver利⽤30个财务⽐率进⾏研究,发现三个⽐率是有效的:债务保障率(现⾦流量/债务总额)、资产收益率(净收益/资产总额)、资产⽋债率(债务总额/资产总额),此中,债务保障率指标表现最好。
这可以看作是单变量模型的开创性研究,⽅法简单易⾏,可操作性强,在当时研究条件较差的情况下优势很⼤;其局限性也较明显,单⼀的财务⽐率不能全⾯反映客观事实,有可能在编制财务报表时存在粉饰某个指标的嫌疑,影响预测的有效性。
(⼆)多变量模型 1、Z计分模型。
20世纪六⼗年代,爱德华•阿尔曼对5个财务⽐率分别给出⼀定权数,计算其加权平均数值Z值: Z=1.2X1+1.4X2+3.3X3+0.6X4+1.0X5 此中:X1=营运资⾦/资产总额;X2=留存收益/资产总额;X3=息税前利润/资产总额;X4=股份市值/负债账⾯价值总额;X5=销售收⼊/资产总额。
⼀般地,Z值越低,企业越有可能产⽣破产。
若Z≥2.675,则表明企业的财务状况良好,发⽣破产的可能性较⼩;若Z≤1.81,则企业存在很⼤的破产危险。
财务与审计矣扌图內外财务风险额警檬塑询丈献探述□昆明李秀雷企业为了及时有效地识别和防范财务风险隐患,实现了持续健康发展的目标,建立财务风险预警模型。
然而,现有的财务风险预警模型往往效率低下,存在缺陷。
首先分析了国内外财务风险预警模型,其次分析了模型的局限性,最后对研究财务风险预警模型提出了合理化建议。
一、国外财务风险预警模型研究综述1.国外财务风险研究现状。
国外学者通常通过财务危机定义财务风险。
Beave需有这种看法:如果一家公司面临破产,或者存在未支付优先股息和无法偿还债务的现象,那么它可以被视为面临财务危机。
Ross等人指出了破产的四个内涵:技术、企业、会计、法律破产。
并认为从危机预防的角度来看,财务危机是指技术破产。
C.VanHome等财务风险的定义更为广泛,而财务风险由两个组成部分,即使用财务杠杆引发每股收益变动和失去偿付能力的风险。
2.财务风险预警模型研究现状。
①单变量判别模型。
Fitzpatrick是最早探索财务风险预警模型的学者之-O他以19家公司为样本,他建立一个单变量判别模型来探索财务风险预警问题,通过对破产和经营正常企业财务比率的对比分析,得出产权比率和净资产收益率两个指标对财务风险具有较高的预警精度。
芝加哥大学教授BeaverJF发了一个基于F i tzpatrick的单变量预警模型,以1954-1966年158家破产企业与正常企业的财务关系为研究对象,得出净利润/总资产指标和净现金流量/总负债指标在财务风险预测方面更为准确。
②多变量判别模型。
Altman是将多变量判别模型应用于财务风险预警领域研究的首位开拓者。
他提出的Z-Score模型是国外影响最大的多元线性判别模型。
从1946年至1965年期间66家有问题和经营中的公司中随机抽取一个样本,它从22个提供最佳预警的备选财务比率的范围内选择了5个,并建立了一个五变量判别模型来计算Z值,并根据Z值的大小确定公司破产或失败的概率。
本份文档包含:关于该选题的外文文献、文献综述一、外文文献Evaluating Enterprise Risk Management (ERM); Bahrain Financial Sectors as a CaseStudyAbstractEnterprise Risk Management (ERM) is a process used by firms to manage risks and seize opportunities related to the achievement of their objectives. ERM provides a proactive framework for risk management, which typically involves identifying particular events relevant to the organization's objectives, assessing them and magnitude of impact, determining a response strategy, and monitoring progress. This research measures the awareness of Bahrain financial sector of ERM and if companies maintain an effective ERM framework. The results show success since all companies are aware of ERM and have an effective ERM framework in place.Keywords: Enterprise Risk Management, Financial sectors, ERM framework1. Introduction1.1 OverviewThe pace of change and characteristics of the new economy are exposing organizations to take risks more than ever before. Therefore mastering these risks can be a real source of opportunity and challenge and a powerful way of sustaining a competitive edge. Especially for companies to sustain and survive in the long run where companies need an effective &continuous risk management. Risk influences every aspect of business as they say "Risk is a risk is a risk". Understanding the risks Bahraini Companies face and managing them appropriately will enhance their ability to make better decisions, deliver company's objectives and hence subsequently improve performance. It is also important to note that risk is categorized into: financial, operational, strategic, and reputation risk. Enterprise Risk Management is any significant event or circumstance, which could impact the achievement of business objectives, including strategic, operational, financial, and compliance risks. ERM helps create a comprehensive approach to anticipating, identifying, prioritizing,and managing material risks of the Company.1.2 Research ObjectiveThe objective of this research is to take a more strategic and consistent approach to managing risk across, Bahrain's financial sector through the introduction of an Enterprise Risk Management ("ERM") framework and associated activities assisting the protection and the creation of value. When looking back at the corporate scandals i.e. Enron &world com, financial crisis of 1997, 2009 misled the investors and resulted in investors loosing confident &dissatisfaction. The result of that crisis was not limited to the country of origin. However it spread globally due to globalization.Global marketplace = Increased risksThis means that global risks combined with rapidly evolving business conditions are prompting financial sector to turn to ERM. It is very important for Bahrain Financial sector to have ERM. Bahrain does international business with other, therefore it is effected by downturns and since no company can prevent an economic downturn, those who map out the steps they would take to respond to a downturn won't find themselves taking quick decisions which will ultimately affect the firm negatively.1.3 Research MethodologyA questionnaire will be constructing and publish on Google document targeting only Bahraini financial sector. SPSS application will be utilized to analyze results. In addition, the questionnaire will measure how important of ERM factors to establish a good ERM practice.1.4 Research ChallengesThe goal of any firm is to achieve its objectives and create value; therefore each company has value chain which is divided into key and support activities. The company must be successful in each and every process in order to deliver a good result and achieve competitive advantage. Each of the processes in the value chain might result in more than five risks. If firms were not able to identify and put appropriate controls then all firms will end up bankrupt, in crisis, investor's dissatisfaction, etc. This research focuses on the importance of addressing key riskswhich helps and organization understand accountability-who owns the risks and whether the risks are being properly monitored. Often, because companies are organized by function or geography and not by risk, the highest risks might not have designated risk owners or risk monitors. Risk Management is the responsibility of each and every staff. This research will allocate one full section which will be called "Challenges" where it will mention what kind of risk will the bank face of proper ERM framework was not in place.2. Literature ReviewWhen EJ Smith (1912) was asked if he has encountered any risks during his 40 years experience he said "cannot remember any serious risks. I have only once seen a ship in distress." However immediately after that SS Titanic sank. The accident demanded 1500 lives including that of Captain EJ Smith. This article highlights the importance of our subject which is Enterprise risk Management if (EJ Smith, 1912) though of some risks that could occur, then Titanic wouldn't have sunk. This article is too general and it has nothing to do with our ERM on financial sector in Bahrain, however it could help us understand that there are risks in each and every business, process, task, etc we do in our life and ERM should be considered whether it is a financial or non financial sector, however for the purpose of specificity, the research will focus only on the financial sector in Bahrain. This quote also shows that risks always exist and it has nothing to do with the current environment or crisis. In another article Ed O'Donnell, (2005) talked about the framework for ERM and he stated "The guidelines establish objectives for event identification and suggest general procedures for identifying events that represent business risks." Internal Audit is concerned about identifying the root cause of the risk however here in ERM it is about identifying the root cause of the root cause. The Author is right as he mentioned that ERM task is to identify the risks which can stop us from achieving our objectives. The author wants companies to be proactive in identifying risks.The project will also highlight the factors according to COSO framework and it will also highlight what type of risks the company is going to face if it didn't have ERM framework implemented. In Nocco, Brian W &Stulz, René M article publishedon (2006) He state That " ERM adds value by ensuring that all material risks are owned and risk - return tradeoffs carefully evaluated, by operating managers and employees throughout the firm. ".It is strongly believed that implementing ERM adds value to the firm if it was effective and the action points were implemented correctly. Also supports what (Ed O'Donnell, 2005) said in his article that ERM improves the performance of the firms which will ultimately add value to the firm "there is growing support for the general argument that organizations will improve their performance by employing the ERM concept". Rao, Ananth (2009) has conducted a case on private sector organization which uses ERM within strategic control process. (Rao, Ananth 2009) uses risk identification, risk assessment, value at risk as the quantitative risk assessment techniques. Rao recommends the implementation of COSO (2004) which focuses on the Internal controls, (Rao, Ananth 2009) research stated "This case study demonstrates the prudence and practicality of the recommendations of COSO (2004) framework and Turnbull report for integrating the management of risk and organizational performance in general as part of a coherent approach to corporate governance." .However Our case study will differ than the above research as we are going to focus on COSO frame work in financial institutions .this research will focus on all supply chain areas not only strategic.(Please refer to Challenges section in this research for more details). Arena, and Giovanni, (2010) stated that "ERM is the main form taken by firms' increasing efforts to organize uncertainty, which 'exploded' in the 1990s." This project supports Arena and Giovanni article's that ERM &Risk Management are important and that is because uncertainty always exist, we agree with this article somehow because all our plans are for the future and as we know the only thing we are sure about the future is that many things might change in future so we are not certain .It is impossible to have no risk however by preparing ourselves and implementing ERM we can minimize it.In the article above the researcher used longitudinal multiple case study however we are going to build a questionnaire to test if companies are aware with the concept of ERM and they are implementing effective ERM framework. We agree with the researcher as there is a strong link between risk management &business strategytherefore financial institutions should maintain Disaster recovery plan and business continuity plan when doing their business strategy which ensures backup of all information and which reduces the safety hazards such us fire (Umbrella insurance). He also used the longitudinal multiple case study to make readers understand more about ERM process. Mark S. Beasley, Richard Clune, and Dana R. Hermanson,(2005) stated that "there is little research on factors associated with the implementation of ERM. Research is needed to provide insights as to why some organizations are responding to changing risk profiles by embracing ERM and others are not."(Mark S. Beasley, Richard Clune, and Dana R. Hermanson 2005) and it focuses on US, however as a result of our research and discussions with our managers we were informed that Bahraini corporations and specifically financial sector are aware of the ERM concept. It came to our attention that Central Bank of Bahrain force institutions to have independent auditors, however ERM is still grey area to many banks. We also noticed that now ERM factors are clearly defined, and frameworks have been established.The following article supports the idea that if a company has established good risk management process then this will help it reduce the risk and therefore the cost of having consultants to check compliance against the Central Bank. We are going to test if banks in Bahrain maintain a compliance checklist against CBB rulebook and if there is adequate monitoring and follow up with this checklist .This will be tested as part of our questionnaire. Standard &Poor's Ratings Services( 2005 )"The HP Compliance Suite for Financial Institutions is a collection of HP products, market offerings, and services that help financial services firms reduce the cost of achieving regulatory compliance, improve risk management capabilities, and also reduce the cost of sustaining compliance. This paper explains how with the Enterprise Risk Management component of its compliance suite, HP can help organizations". Craig Faris (2010) states "Climbing out of a recession can be heavy going. At the same time, it can be a stimulating wake-up call." We agree that the recession was like a wakeup call for many financial institutions which didn't give risk management attention; in our introduction we mentioned briefly that because of the financial crisis and scandals(i.e. Enron &Worldcom) Bahrain's financial sector need to establish and implement ERM framework. In another article for Walker, Paul L and Shenkir, William G. (2008) his article highlights Enterprise Risk Management (ERM) practices that improve a company's ability to manage risks effectively. "The authors argue that ERM allows companies to proactively manage risk, clarify the organization's risk philosophy, and develop a risk strategy." Further, the article discusses how the ERM process forces companies to consider those events that might stand in the way of achieving corporate goals. Then companies can assess these risks and develop strategic plans. Discussion of contingency plans, measuring effectiveness, and communications strategies is also presented. Referring to Paul, shenkir &William (2008) article we mentioned that Bahrain financial sector need to establish &implement ERM framework, this article highlights one way to start implementing ERM which is establishing good internal controls which we will also consider it one of the ERM factors in later stages of this project.This article explains how we can implement ERM. We strongly agree with Walker as ERM is trying to imagine what could happen to the company in the worst scenario therefore the company should establish Internal controls for each and every department and those controls should be communicated to all employees &implemented. As we said we agree with the author when he said that "ERM allows companies to proactively manage risk"Cokins, Gary (2010) stated that "It notes that the four types of alternative risk categorization are market and price risk, credit risk and operational risk". This article talks about risk categorization .We disagree somehow with the researcher when he categorized risks into Market, Price, credit &Operational. As we would classify them to: Strategic, financial, Operational &Reputation. He could have done better job by listing price and credit under financial. In another article for Richard S.Warr &Donald P.Pagach, (2010) said in his article "We study the effect of adoption of enterprise risk management (ERM) principles on firms' long-term performance by examining how financial, asset and market characteristics change around the time of ERM adoption. Overall, our results fail to find support for the proposition that ERM is value creating, although further study is called for."( Richard S.Warr &Donald P. Pagach, 2010) failto support that ERM is value creating, we mentioned earlier that implementing ERM does not only mitigate the risks, however it also adds value .During our detailed testing in Bahrain financial sector we are either going to agree with this article or disagree. McAliney, Peter J (2009) said "providing readers the operational considerations to implement this program within their organization to enhance performance improvement. At the individual initiative level, readers will recognize elements used in developing retrospective return on investments (ROIs) for learning programs. " We agree with (McAliney, Peter J, 2009) in teaching and communicating ERM concept with the line executive as all employees in the company must be aware of what possible risk might take place to better appreciate the internal controls which will be established by the Management in later stages. We are going to test how whether financial sector in Bahrain have well established internal controls while doing their business. If Management doesn't communicate such issues with employees then they won't understand the reason for policies &procedure changes, etc. We also agree that by performing good and effective ERM the ROI's will improve as we believe that ERM adds value to all business processes. In another SHABUDIN, Ebrahim, DREW, John O. &PEROTTI, William L. (2007), said that" noted that risk management is not just about mitigation but also about optimization. "We think that this article will help us in writing the project as they segregated risk into other classification which is quantitative &qualitative risk. We might need to through light on the difference between quantitative &qualitative as part of the introduction. We also agree with the researcher when he mentioned that it is not about mitigation but optimization however we would also like to add that ERM is also about "value adding" to the corporations.While Ohio State University, (2006) stated that "we explain how enterprise risk management creates value for shareholders", this article highlights the role of ERM in creating value, and as we learnt in our finance courses that the goal of any firm is to increase the value of stockholders. This article also draws attention on the risk appetite which we will explain later as one of the ERM factors. David L. Olson, (2010), stated that "This paper demonstrates support to risk management through validation of predictive scorecards for a large bank. The bank developed a model toassess account creditworthiness". This article supports our project. It writes about financial sector (Bank), it talks about scorecards which we are going to consider it as one of the ERM factors which falls under Internal Controls. This article also supports benchmarking and evaluating actual performance against competitors which will also be tested in our questionnaire.3. Research MethodologyThis project was conducted by analyzing results of distributed questionnaires about Enterprise Risk Management (ERM) in Bahrain Financial Sector. A questionnaire was published on the web and sent to banks in Bahrain and specifically to risk management and internal audit department. The questionnaire will cover the following areas being: (1) general questions, (2) questions relating to risk awareness and (3) questions relating to ERM factors. This research is trying to identify the extent to which good enterprise risk management (ERM) practices are being implemented and communicated throughout the banks. In addition, we are going to test how important the ERM factors established against a good ERM practice.HypothesisIn order to state the hypothesis we need to identify successful factors defining ERM.As defined by KPMG (one of the big four Auditing firms) the following are the factors of successful ERM:Referring to the above table we can identify the following independent factors which must be in place for an effective and successful ERM:According to the above factors we are going to design a questionnaire tailored for Bahrain corporations (financial sector), published on Google. Results will be analyzed and hence conclude findings.4. ChallengesIf firms don't implement ERM all value chain activities will be subject to risk. As previously mentioned; risk can be divided into strategic/financial/operational. Each of the activities whether its support or primary it is subject to risks. If we ignore deploying clear ERM framework we might end up with financial losses (likebankruptcy/operational risk reputation.5. Result analysisWe have identified 8 factors however due to time constraints we will only choose the 4 most important factors according to KPMG one of the big 4 auditing firms and specialized in ERM.A questionnaire was tailored based on the above mentioned 8 factors and this questionnaire was conducted in 2010 &it was distributed to financial sector in Bahrain; only 33 questionnaires .The results were organized in tables and graphs which facilitate our analysis and help the reader better understand.SPSS was used for the purpose of analyzing the results. SPSS test results were conducted on 4 independent factors &the dependent factor (ERM) using 2 questions for each factor.6. DiscussionsAfter analyzing the results we conclude that F test results show that there is no relationship between risk assessment &ERM, communication &ERM, monitoring &ERM, but there is a relationship between control &ERM.In addition, almost all the respondents answer the questions positively either strongly agree or somewhat agree, and these factors that we used for the analysis were essential factors in COSO framework, so we used T test analysis to examine the three factors separately, and the results for each factor conclude a positive results which mean that all Bahrain financial firms consider risk assessment, control, monitoring and communication while implementing ERM.After reviewing all the published literature reviews written about Enterprise Risk Management .We think that this subject was not fully consumed by researchers, as researchers didn't touch all the areas or in some cases they did but very briefly.This research is more comprehensive as it includes ERM from the very first point, then it will walk easier through the different factors in COSO framework.As we mentioned in the result analysis that financial sector in Bahrain are aware of the importance of ERM however companies can't perform it by themselves and use other professional firms to have effective ERM in place and we can say the reason forthat is due to unavailability of enough sources and explanation of ERM framework.This project agrees with Walker, Paul L and .Shenkir, William G. Research published on (Mar2008) as ERM is trying to imagine what could happen to the company in the worst scenario therefore the company should establish Internal controls for each and every department and those controls should be communicated to all employees &implemented. However our project results disagree somehow with the researcher when he categorized risks into Market, Price, credit &Operational. As we would classify them to: Strategic, financial, Operational &Reputation. He could have done better job by listing price and credit under financial.7. ConclusionThe result of the project concludes that Bahrain financial Corporations are aware of the Concept of ERM and its factors and that awareness can be traced to the fact that companies appreciate that risks are things that might face us in our day to day activities and all companies regardless of its capital and how experienced is their employees are subject to different types of risks. The project conclude that companies are aware of the different classifications of risks and that there is no one standard classification of risk as mentioned in the literature review section Article number 11 where better classification of risk could be put in place.Bahraini Financial Institutions are lead by the Central Bank of Bahrain rules and since having Effective ERM framework is not insisted in the rulebook as the need for independent internal auditors, the corporations will not consider it as priority .As we understand that companies are more concerned with compliance with the CBB rule book .As explained earlier although companies are not required to have ERM process by the Central Bank of Bahrain, it seems that many companies are looking forward to have contracts signed with Professional firms to conduct ERM studies.Keeping in mind that professional firms charge quite a high fee for conducting ERM studies, however as a result of the questionnaire we can say that companies would not matter to pay any amount as long as they can ensure that they are on the safe side by having ERM.文献出处:Jalal, A., AlBayati, F. S., & AlBuainain, N. R. Evaluating Enterprise Risk Management (ERM); Bahrain Financial Sectors as a Case Study [J] International Business Research, 2015, 4(3), 83-92.二、文献综述民营企业财务风险文献综述摘要民营企业在我国的发展进程中具有十分主要的地位,民营企业为我国的经济创造了巨大的价值,对我国的发展产生了很大的推动作用。
文献信息:文献标题:Financial Risk Disclosure: Evidence from Albanian and Italian Companies(财务风险披露:阿尔巴尼亚和意大利公司的证据)国外作者:Grazia Dicuonzo, Antonio Fusco, Vittorio Dell’Atti文献出处:《KnE Social Sciences》,2017,1(2):182-196字数统计:英文4819单词,2734字符;中文15451汉字外文文献:Financial Risk Disclosure:Evidence from Albanian and Italian Companies Abstract In recent years standard setters, regulators and professional bodies worldwide have shown an increased interest in risk reporting. This has reflected the fallacy of the fina ncial reporting model to communicate a company’s risk profile, the recent scandals and the financial crisis. The European Union, the International Accounting Standards Board (IASB) and other national standard setters have introduced specific requirements in order to impose companies to highlight the principal financial risks and uncertainties that they face. The idea is that high-quality risk disclosure help investors and other market participants in their decision-making process, by providing a better understanding of the risk exposures and risk management practices of companies.Previous studies show large heterogeneity in risk reporting within individual countries and identify size as key determinant of risk disclosure. A few researches propose a cross-country investigation of risk reporting and to date there is a lack of evidence about companies operating in Southern Europe, especially in the Balkans.The aim of this study is twofold. First, we fill this gap by analyzing risk reporting regulations in Albania and in Italy to examine the different requirements. Second, we examine risk information disclosed by a sample of 12 Albanian companies and 12Italian companies within their annual reports, using content analysis. Due to small sample size we offer preliminary findings about financial risk disclosure. The results show that on average Albanian companies disclose less information on financial risk than Italian companies. Different explanations can be given for this evidence: i) risk disclosure regulation is less incisive in Albania, because it is limited to inform investors about the relevance of financial instruments and the terms and conditions of loans; ii) Albanian companies have fewer incentives to disclose risk information than Italian companies.Keywords: Financial risk disclosure, risk reporting, risk disclosure, content analysis, cross-country investigation1. IntroductionIn recent years risk reporting has received increasing attention by standard setters, regulators and professional bodies worldwide. Since 2007, listed companies must report the exposure, the objectives and the processes for managing financial risks (IFRS 7). Similarly, the Financial Stability Board (FSB) has developed new guidelines to improve risk reporting. This interest reflects the fallacy of the financial reporting model to communicate company’s risk profile, the recent scandals and the 2007-2009 financial crisis.The objective of this study is to provide further empirical evidence about the financial risk reporting practices in Albania and in Italy and key factors that influence risk disclosure decisions.The extant literature focuses on: i) the level of compliance with ad hoc standard about market risk; ii) the impact of risk disclosure on decision making; iii) the determinants of mandatory financial risk reporting practices. A few of prior researches focused on cross-country investigation, but they are limited to U.S., Canadian, UK and German settings. This study contributes to fill the gaps by exploring the differences between Albanian and Italian financial risk reporting. This paper proceeds as follows. Section 2 goes on to describe risk disclosure regulations. Section 3 reviews the literature related torisk reporting practices. Section 4 provides details onresearch design. Section 5 describes the main findings, while Section 6 details the conclusions.2. Albanian and Italian Risk Disclosure RegulationsThe increased importance of risk information has led financial accounting bodies and national legislators to enhance and improve risk reporting requirements. In the last decade a gradual transition from voluntary risk disclosure to mandatory risk disclosure has been observed, in response to request of investors and users of financial statements. This change concerned the majority of European countries even if it is adopted in different ways and times.In this paragraph we examine the regulatory developments about risk reporting in Albania and in Italy. To understand the differences on mandatory risk disclosure we offer a preliminary brief overview about the two accounting systems.AlbaniaThe first step of Albania for the development of an accounting framework right after the starting of the transition period is represented by the issue of Law No. 7661 “On accounting”, entered into force the 1st January 1993. This law establishes the rules, the principles and the procedures to draw up the Financial Statement of all companies in Albania.The financial report recommended by Law No. 7661 consists in Balance Sheet, Comprehensive Income Statement and Summary Notes and all of them must be considered and drafted as a unique and inseparable element. At the beginning no specific format was required but only a minimum of elements of financial report indicated by the law. Even if the law provided for a chart of accounts, the Minister of Finance enabled operators to use the Annual Fiscal format report.It can be stated that the introduction of this first accounting law was forced by the opening to the market economy and the starting of the first private entrepreneurship and it was still influenced by the old accounting practices.The framework produced few transparency and it was inconsistent with Albanian Company Law. In order to enhance foreign investment and to respond to EuropeanUnion struggles to increase the accounting harmonization, Albanian government issued Law No. 9228 “on Accounting”in 2004, which is still into force.The Law of 2004 also identifies the National Council of Accounting (NCA) as an independent public professional body with legal entity, which is first of all required to develop national accounting standards.The main impact on Albanian accounting framework is the introduction and approval of National Accounting Standards (NAS, or SKK in Albanian language) by the NCA. They were written taking IAS/IFRS as example and result compliant with them. They entered into force in 2008 and the first financial reports written consistently with the new standards have been issued in 2009.Even if the introduction of Standards compliant with IAS/IFRS improved Albanian accounting practices, Albanian financial reports are still considered to be on a poor quality level. The only sector which issues a higher level of reports is the Banking sector: the reason is the affiliation of Albanian banks to European groups which force to adopt a standard accounting practice. Moreover, the affiliation to foreign groups produces the import of higher qualified staff than average Albanian accountants because of a longer accounting tradition. Furthermore, Bank of Albania carries out an important work in the improvement of accounting level. Poor quality of other sectors’firm mainly derives from the absence of information request from third parties, the lack of interest of the Authority to ask for “best practices”and the lack of experienced administrative staff. As a demonstration of the poor quality of financial reports, banks don’t consider Annual Reports so important to investigate loan applicant’s financial conditions.Regarding risk disclosure, the only requirements by NAS are included in Standard No. 3, which in paragraph 37 states that entities must indicate policies used in evaluating financial instruments and other information regarding financial instruments in order to improve financial statement’s comprehension. Paragraph 39, in the end, requires that entities must disclose all the information which enables users to evaluate financial instrument in place relevance and their characteristics.Albanian framework doesn’t require more disclosure for financial risk and the Authority did not prepare any best practice regarding this particular aspect of accounting.ItalyIn Italy we can identify three stages that have characterized the regulatory changes in risk reporting. Before 2005 disclosure was fundamentally voluntary because firm had discretion to choose which information regarding risks had to be communicated. It was generically required to describe the trend of the management, which could consist also in a risk disclosure. A study run in the period 2000-2003 shows high variability in risk disclosure policies, in respect of industry and firm size. The main factor was the absence of regulation regarding risk disclosure.The second stage (2005-2007) shows an increase in financial risk mandatory disclosure requirements.Through Legislative Decree 394/2003 the Italian system adopted Directive 2001/65/EC regarding fair value of financial instrument evaluation.In order to illustrate the new requirements, Italian standard setter (Organismo Italiano di Contabilità, OIC) issued Standard No. 3 “Information about financial instruments to be disclosed in Supplementary Notes and Management Report”in March 2006. Through this document, the standard setter clarifies fair value evaluation and gives exemplifications regarding derivatives’evaluation. OIC 3, like IFRS 7, divides risk into the following categories: market risk, credit risk, liquidity risk and other price risk.From the above, it is evident that in the period taken into consideration financial risk disclosure became mandatory while non-financial risk disclosure remained voluntary.The third stage started in 2007 with the amendment of article 2428 Civil Code by the Legislative Decree 32/2007. This Decree has been adopted as mandatory content of Direc tive 2003/51/EC, also known as “Accounts Modernization Directive”. The new regulations state that Management Report must present “an accurate, balanced and exhausting analysis of the firm’s financial situation and management trends andresults” (…) “and al so a description of main risks and uncertainties to which the firm is exposed”. It is also specified that this analysis must be “consistent with entity and complexity of firm’s business” and include “the necessary financial indicators to understand the financial situation of the company, its trend and its results and, if necessary, non-financial indicators relevant to the specific business, human resources and environment”. Therefore the legal framework now consists not only in financial risk mandatory disclosure but also in cogent system which involves all risk categories that could affect the firm.3. Literature ReviewThe growth of the risk disclosure’s demand from the financial market represents an incentive for academics and practitioners’ associations to investigate risk reporting. Starting from the nineties, the literature examined the need of information to improve risk disclosure quality. It has been revealed that through the analysis of firm risk communication’s best practices it is possible to ass ess the relevance of this kind of corporate disclosure.Literature contribution consists in the analysis of risk factors communicated by the companies. The present work pays attention to the empirical researches which showed the risk disclosure’s policies and the limits of annual report disclosure in the absence of a common legal framework. In order to evaluate informative contents of risk disclosure, some Authors observed the nature (qualitative or quantitative), the dimension (financial or non-financial), the timeframe (past, present or prospective) and the impact (positive or negative) of each information released and communicated to the market. This analysis allows to assess both the existing reporting model and the variability of the information disclosed by firms. Currently, many works investigate the generic risk disclosure practiceswhile some investigate financial risk disclosure or particular categories of financial risks.In conclusion, the results of the different works made at international level show that risk disclosure practices are still inadequate. Most of the information disclosed is qualitative and generic and it turns to be un-useful for financial statements users, whoprefer information regarding future events’impact on the firm’s economy instead of what happened in the past. Some surveys made on investors and analysts sample show a limited appreciation about the risk reporting practices, so the improvement of risk disclosure’s model is necessary (CFA Institute).Given the regulatory background and the gaps in empirical previous studies on financial risk disclosure in Southern Europe, our paper aims at providing a preliminary analysis on risk reporting practices in Albania and in Italy. Our expectations are that Italian companies disclose more information about financial risk than Albanian companies.4. Research Design4.1. Sample SelectionTo conduct our research, we analyse annual financial reports of Albanian companies and we compare them with Italian companies’ financial reports. Because of the lack of an organic list of entities operating in Albania and the difficulties to find financial statements useful for our research, we use a small size sample, constructed as following.In the beginning, the sample consisted in 70 Albanian entities, selected among affiliated to Italian-Albanian Chamber of Commerce. Their financial reports have been collected from the National Registration Centre, in which the entities are obliged to deposit, among other documents, their annual reports. From the initial 70 entities sample, we excluded: i) 19 associations or tax and legal services; ii) 12 companies with 2014 financial reports missing; iii) 2 financial companies and other 2 IAS/IFRS adopters; and iv) 23 companies with narrative information missing. Therefore, the sample of Albanian companies consists in 12 non-financial companies using NAS (SKK in Albanian language).After the selection of Albanian companies, a symmetrical sample of Italian companies using national accounting standards (OIC) has been constructed. We chose Italian firms considering the same industry and the similar size of the companies from Albania.The final sample is composed of 12 Albanian firms using NAS and 12 Italian firms using OIC.4.2. Method of analysisTo examine and classify financial risks disclosure within the Albanian and Italian annual reports we use content analysis. This approach has been widely adopted in previous studies on narrative disclosure. Content analysis is defined as “a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use”. According to the extant literature, a single coder performed the content analysis to ensure reliability. Financial risk information is examined in the supplementary notes and in the management reports. We consider the sentence as recording unit and we classify risk information in these seven categories:1.financial risk management;2.credit risk;3.liquidity risk;4.price risk;5.interest rate risk;6.currencyrisk;7.other financial risk (as a residual category).This analysis captures three attributes of each sentence:1.time orientation: past, present or no-time specific, forward-looking;2.type: quantitative, qualitative;3.nature: good news, bad news, neutral news.5. Findings and DiscussionIn this section we examine how Albanian and Italian companies disclose relevant information about their financial risk exposure. A total of 44 sentences were identified within the Albanian sample, whereas we found a total of 124 sentences within the Italian sample. As shown in table 6, Albanian companies disclose on average 4 sentences about financial risk, while Italian companies disclose 10 sentences. This evidence con-firms our expectations about the predominance of financial riskdisclosure in Italy. Our explanation for this difference is that Italian companies have more incentives to disclose risk information.As regards risk categories, we can observe that Albanian firms disclose mostly other financial risk (75%). Examining their annual reports, we find that these risks arise mainly from tax regulation or litigation problems, factors that can affect negatively the financial position. Information about financial risk management (5%), credit risk (2%), liquidity risk (2%) and currency risk (5%) appears less important for Albanian firms. Some information is provided to users of financial statements regarding interest rate risk (11%).Italian companies disclose mainly information about credit risk (32%) and liquidity r isk (21%), in line with users’ expectations, as revealed by a survey (CFA Institute, 2011). The other risk categoriesare disclosed on average from 10% (price risk) to 13% (interest rate risk).One possible explanation for these differences is probably linked to the regulatory environment and accounting standards. Italy has a more pervasive legislation about financial risk disclosure, similar to IFRS 7, than Albania.6. ConclusionsThis paper is a first financial risk disclosure study that compares two Southern Europe countries. Based on a content analysis of annual reports of a matched-sample of 24 firms from Albania and Italy, we provide evidence on the individual-country and the cross-country levels.We find a prevalence of present (or no-time specific) and of qualitative risk disclosure. Forward-looking information is missing in Italian reports, while Albanian firms provide some details on the future, especially about the possible impact of tax regulation. Descriptive cross-country statistics suggest heterogeneity in risk disclosure quantity. Italian firms provide more risk disclosure than Albanian firms. This finding is consistent with more strict regulation imposed by Legislative Decree 32/2007.Our study is subject to limitations. Firstly, we examine a limited sample size of Albanian and Italian companies due to a difficulty to find published Albanian annualreports. Secondly, we analyse only the quantity of disclosure and we do not provide evidence on the quality of disclosure.中文译文:财务风险披露:阿尔巴尼亚和意大利公司的证据摘要近年来,全球标准制定者、监管机构和专业机构对风险报告的兴趣日益增强。
Financial risks and its early-warning system1,the description of financial risksBeaver(1996) defines financial risk as:bankruptcy, defaulting cumulative preference share and debt. He thinks that,the deterioration of an enterprise depends on the following factors : ①payment at maturity; ②the cash fllow of the enterprise;③profit rate to net worth; ④the capacity of obtaining capital inflow. Also Altman(1968) defines financial risk as a listed company that has been defined to be bankrupt by law. The lack of payment ability which was caused by the technological failure in found management . Generally these troubles are temporary and less important ,and are usually able to remendy , through various methods such as asking for a compromise through consultation ,and put off the the due to pay debts .In a word , if the problems are permanent , the enterprise may have to face the bankruptal liquidation ; however , if the problems are temporary ,the company may have a chance to get out of the tight spot, and turn a loss into a pofit !2,the relationship between financial risks and its early-warning system. Financial risks is not only associated with the company's survival and development , but also the interests of the investors,and the creditors. With the intensive development of China's market economy and the rapid development of the capital market, listed companies are threatened by financial risks. Which means that without a precaution system the corporate may get into a tight spot. As a matter of fact , it has become a great concern comprhesively by investors ,creditors, and some monitoring institutions of securities market .The early-warning system is available to for governors to knows more about the causes for financial crisis, so as to prevent financial risks . This early-warning system seems to operate after the symbol of financial risks . On contrary it has already listed all the possibilities of reasons for financial crisis and its monitoring data .It is a precaution system ,which is able to prevent and overcome the risk effectively when it is yet to come .3,to review a few of the methods for financial risks' early-warning system . There is a great range of methods for financial risks' early-warning ,including univarlate , Muiriple Discriminant Analysis (MDA), Logistic ,Probit and ANN , which are majorly used . Here I'd like to introduce two of them .First, UnivarlateUnivarlate a forecasting model ,which decides if an enterprise is faced with bankruptcy or not , based on one specific financial index. However ,univarlatehas two significant shortcomings: Firstly, if only one financial index was set to judge , it will easily be decorated by the manager once it was revealed . Secondly, there will be controversies if more than one indexes were set to judge the financial state of a company. Univarlate is comparetively a simple way of early-warning for finance , it can be easily learnt and used , but it is less sensitive. In the prediction over last year Univarlate is of lower accuracy comparing to Muiriple Discriminant Analysis . On the other hand , when it comes to the prediction over the last 2 to 3 years , it can be of a great ability to forecast, which revealed that somefinancial crisis early began with some financial indexes' deterioration .Second, Muiriple Discriminant Analysis (MDA)Muiriple Discriminant Analysis (MDA),is also called Z Score .It is aimed at working out the variable which differs the most between two groups and dispers the least among the couple of group, through a series of statistical classification techniques,so as to transform the variable identifier into a categorical variable with the least loss of information . In this way, a multi-head linear equation is acquired which can highly improve the predicting accuracy . MDA forms an easy and understandable multi-head linear equation which provide a very high capacity in application. MDA is of high accuracy ,however there are some shortcoming as below: 1, it needs a quite large amount of work. 2, In the prediction over one year, MDA can be quite effective and accurate . But in the prediction over more than one year (generally 2 to 3 years) it won't be so predictable as it dose in the former situation,sometimes even worse than the Univarlate. 3, MDAsticks strictly to a hypothesis which is hardly full filled in reality ,in this way the Muiriple Discriminant Analysis (MDA) is greatly limited when putting into use.4,Financial early warning system⑴a brief introduction of Financial early warning systemFinancial early warning system of listed company is a monitoring system, on the basis of enterprise information, by means of financial report forms, operation plans and others financial data of listed company, with the aid of financial analysis methods, such as proportional analysis, mathematical model, distinguishes financial crisis according to signal come from early warning index and lift a warning to the listed company interest related aspects.Financial early warning system arises from America in the very beginning. Overseas security market have been developing for quite a long time, with not only a pretty high frequency of studies of listed company financial early warning, but also a rather mature development. Some research workers have developed many financial early warning models, among which some even become time-tested business softwares, in the light of financial feature of financial crisis company. Overseas success stories indicate, financial early warning system of listed company can correctly forecast and prevent financial crisis for listed company, monitor the quality of listed company and the risk of security market for government manage departments, protect normal rights and interests of investor and creditor, reduce shock of security market and promote the benign development of listed company, which is helpful as regards the security both the economy and society.⑵the necessity of a early-warning system for financial risksWith the acceleration of international economic integration and the establishment and improvement of China's socialist market economy system, companies face more intense competitive environment. In the fierce market competition, corporate financial distress can happen at any time, or even bankruptcy. Therefore, the establishment of a viable financial early warning system to guard against financial risk has important practical significance.Financial management,which is the management of capital flow,is an important activity of the enterprise. Through the history of domestic and international businessdevelopment, corporate management can be seen from the financial crisis。
Financial early warning system model anddata mining application for risk detectionAli Serhan Koyuncugil a ,⇑,Nermin Ozgulbas ba Capital Markets Board of Turkey,Research Department,Eskisehir Yolu 18.km.,Ankara,TurkeybBaskent University,School of Health Sciences,Department of Healthcare Management,Eskisehir Yolu 20.km.,Ankara,Turkeya r t i c l e i n f o Keywords:CHAIDData miningEarly warning systems Financial risk Financial distress SMEsa b s t r a c tOne 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 finan-cial 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 7853SMEs based on Turkish Central Bank (TCB)2007data.By using EWS model,31risk profiles,15risk indicators,2early warning signals,and 4financial road maps has been determined for financial risk mitigation.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionAll enterprises especially SMEs need to think about global dimensions of their business earlier than ever.Especially in devel-oping countries,in addition to the administrative insufficiencies,competition,economical conditions,the permanent threat towards SMEs from globalization,and financial crisis have caused distress and affect firms’performance.SMEs are defined as enterprises in the non-financial business economy (NACE,Nomenclature statistique des activités économi-ques dans la Communautéeuropéenne (Statistical classification of economic activities in the European Community))that employ less than 250persons.The complements of SMEs –enterprises that em-ploy 250or more persons –are large scale enterprises (LSEs).With-in the SME sector,the following size-classes are distinguished: Micro enterprises,employing less than 10persons.Small enterprises,employing at least 10but less than 50persons.Medium-sized enterprises that employ between 50and 250persons.This definition is used for statistical reasons.In the European definition of SMEs two additional criteria are added:annual turn-over should be less than 50million €,and balance sheet total should be less than 43million €(Commission Recommendation,2003/361/EC).SMEs play a significant role in all economies and are the key generators of employment and income,and drivers of innovation and growth.Access to financing is the most significant challenges for the creation,survival and growth of SMEs,especially innova-tive ones.The problem is strongly exacerbated by the financial and economic crisis as SMEs have suffered a double shock:a drastic drop in demand for goods and services and a tightening in credit terms,which are severely affecting their cash flows (OECD,2009).As a result,all these factors throw SMEs in financial distress.The failure of a business is an event which can produce substan-tial losses to all parties like creditors,investors,auditors,financial institutions,stockholders,employees,and customers,and it undoubtedly reflects the economics of the countries concerned.When a business with financial problems is not able to pay its financial obligations,the business may be driven into the situation of becoming a non-performing loan business and,finally,if the problems cannot be solved,the business may become bankrupt and forced to close down.Those business failures inevitably influ-ence all businesses as a whole.Direct and indirect bankruptcy costs are incurred which include the expenses of either liquidating or an attempting to reorganize businesses,accounting fees,legal fees and other professional service costs and the disaster broadens to other businesses and the economics of the countries involved (Ross,Westerfield,&Jordan,2008;Terdpaopong,2008;Warner,1977).The awareness of factors that contribute to making a business successful is important;it is also applicable for all the related par-ties to have an understanding of financial performance and bank-ruptcy.It is also important for a financial manager of successful firms to know their firm’s possible actions that should be taken when their customers,or suppliers,go into bankruptcy.Similarly,firms should be aware of their own status,of when and where they should take necessary actions in response to their financial0957-4174/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.eswa.2011.12.021Corresponding author.Tel.:+905326657084;fax:+903122466670.E-mail address:askoyuncugil@ (A.S.Koyuncugil).problems,as soon as possible rather than when the problems are beyond their control and reach a crisis.Therefore,to bring out thefinancial distress risk factors into open as early warning signals have a vital importance for SMEs as all enterprises.There is no specific method for total prevention for afinancial crisis of enterprises.The important point is to set the factors that cause the condition with calmness,to take corrective precautions for a long term,to make aflexible emergency plan towards the potential future crisis.The aim of this paper is to present an EWS model based on data mining.EWS model was developed for SMEs to detect risk profiles, risk indicators and early warning signs.Chi-Square Automatic Interaction Detector(CHAID)Decision Tree Algorithm was in the study as a data mining method.Remaining of this paper is orga-nized as follows:Section2presents definition of EWS.Section3 contains data mining model for risk detection and early warning system.Implementation of data mining for risk detection and early warning signals is presented in Section4.Concluding remarks and strategies were suggested in Section5.2.Financial early warning systemsAn early warning system(EWS)is a system which is using for predicting the success level,probable anomalies and is reducing crisis risk of cases,affairs transactions,systems,phenomena,firms and people.Furthermore,their current situations and probable risks can be identified quantitatively(Ozgulbas&Koyuncugil, 2010).Financial EWS is a monitoring and reporting system that alerts for the probability of problems,risks and opportunities be-fore they affect thefinancial statements offirms.EWSs are used for detectingfinancial performance,financial risk and potential bankruptcies.EWSs give a chance to management to take advan-tage of opportunities to avoid or mitigate potential problems. Nearly,all of thefinancial EWSs are based onfinancial statements. Balance sheets and income tables are the data sources that reflect thefinancial truth for early warning systems.In essence,the early warning system is afinancial analysis technique,and it identifies the achievement analysis of enterprise due to its industry with the help offinancial ratios.The efforts towards the separation of distressed enterprises started with the z-score that are based on the usage of ratios by Beaver(1966)for single and multiple discriminant analysis of Alt-man in1968.The examples of other important studies that used multi variable statistical models,are given by Deakin(1972),Alt-man,Haldeman,and Narayanan(1977),Taffler and Tisshaw (1977)with the usage of multiple discriminant model;are also given by Zmijewski(1984),Zavgren(1985),Jones(1987),Panta-lone and Platt(1987),with the usage of logit and probit models; are at the same time given by Meyer and Pifer(1970)with the usage of multiple regression model.Beside the business distressed studies,researchers focused on monitoring ongoing situations to detect sudden changes or unexpected risk factors of enterprises. These attempts made important the early warning systems for research.Some of previous studies conducted in SMEs,banks, insurers,i.e.,and their research methods are presented below.Brockett and Cooper(1990)developed an EWS by using neural network method.The model was developed with24variables firstly,and then the numbers of variables were decreased to8. These variables were equities,capitalization ratio,return on assets, turnover of assets,account receivables to equities,changing of loses,and debt to current assets.Lee and Urrutia(1996)compared the models of logit,hazard, neural networks and discriminant for developing an early warning system.They found different indicators or signs for each model. Also they determined that forecast power of all models were same.Barniv and Hathorn(1997)developed an early warning model based on logistic regression by evaluating the studies of Triesch-mann and Pinches(1973),Ambrose and Seward(1998),and Barniv and McDonald(1992)in insurancefirms.Laitinen and Chong(1999)presented a model for predicting cri-ses in small businesses using early-warning signals.Study summa-rized the results of two separate studies carried out in Finland(with 72%response)and the UK(26%)on the decision process of corpo-rate analysts(Finland)and bank managers(UK)in predicting the failure of small and medium-sized enterprises(SMEs).Both studies consisted of seven main headings and over40sub-headings of pos-sible factors leading to failure.Weighted averages were used for both studies to show the importance of these factors.There were significant similarities in the results of the two studies.Manage-ment incompetence was regarded as the most important factor,fol-lowed by deficiencies in the accounting system and attitude towards customers.However,low accounting staff morale was con-sidered a very important factor in Finland but not in the UK.Yang,Ling,Hai,and Jing(2001)used artificial neural networks (ANN)for detectingfinancial risk of banks as an early warning, and tested the method.Salas and Saurina(2002)compared the determinants of prob-lem loans of Spanish commercial and savings banks in the period 1985–1997,taking into account both macroeconomic and individ-ual bank level variables.The GDP growth rate,firms,and family indebtedness,rapid past credit or branch expansion,inefficiency, portfolio composition,size,net interest margin,capital ratio,and market power are variables that explain credit risk.Thefindings raised important bank supervisory policy issues:the use of bank level variables as early warning indicators,the advantages of bank mergers from different regions,and the role of banking competi-tion and ownership in determining credit risk.Edison(2003)developed an operational early warning system (EWS)that can detectfinancial crises.The system monitored sev-eral indicators that tend to exhibit an unusual behavior in the peri-ods preceding a crisis.When an indicator exceeded(or falls below) a threshold,then it was said to issue a‘‘signal’’that a currency cri-sis may occur within a given period.The model was tested in1997/ 1998crises,but several weaknesses to the approach were identi-fied.The paper also evaluated how this system can be applied to an individual country.The results suggested that an early warning system should be thought of as a useful diagnostic tool.El-Shazly(2003)investigated the predictive power of an empir-ical model for an early warning system of currency crises.EWS em-ployed qualitative response models within a signals framework that monitors the behavior of key economic variables and issues a warning when their values exceed certain critical levels.Author conducted a case study in Egypt.Results showed that this model, and in particular the extreme value model,captured to a good ex-tent the turbulence in the foreign exchange market and the onset of crises.Jacobs and Kuper(2004)presented an EWS for six countries in Asia.Financial crises were distinguished in three types;currency crises,banking crises,and debt crises.The significance of the indi-cator groups was tested in a multivariate logit model on a panel of six Asian countries for the period1970–2001.Author founded that some currency crises dating schemes outperform others by using EWS.Berg,Borensztein,and Pattillo(2004),developed early warning system models of currency crisis for Mexican and Asian crisis. Since the beginning of1999,IMF staff has been systematically tracking,on an ongoing basis,various models developed in-house and by private institutions,as part of its broader forward-looking vulnerability assessment.This study examined in detail at the per-formance of these models in practice.The forecasts of the in-house model were statistically and economically significant predictors ofA.S.Koyuncugil,N.Ozgulbas/Expert Systems with Applications39(2012)6238–62536239actual crises.On the whole,the short-horizon private sector mod-els examined performed poorly out of sample,despite stellar in-sample performance.Brockett,Golden,Jang,and Yang(2006)examined the effect of statistical model and neural network methods to detectfinancially troubled life insurers.They considered two neural network meth-ods;back-propagation(BP),and learning vector quantization (LVQ),two statistical methods;multiple discriminant analysis, and logistic regression analysis.The results showed that BP and LQV outperform the traditional statistical approaches.Abumustafa(2006)detected early warning signs for predicting currency crises in Egypt,Jordan and Turkey.The study proposed real exchange rate,exports,imports,trade balance/gross domestic product(GDP),foreign liabilities/foreign assets,domestic real interest rate,world oil prices,and government consumption/GDP as indicators to predict currency risk.The results showed that all crises were predictable,and EWSs should use for detecting crises.Kyong,Tae,Chiho,and Suk(2006)presented the construction process of a dailyfinancial condition indicator(DFCI),which can be used as an early warning signal using neural networks and non-linear programming.The procedure of DFCI construction was com-pleted by integrating three sub-DFCIs,based on eachfinancial variable,into thefinal DFCI.The study,then examined the predict-ability of alarm zone for thefinancial crisis forecasting in Korea.Katz(2006)proposed to use EWS and early warning signs. Study listed often common warning signs and the best ways to solve the problems.These are:payroll taxes,sales tax,and other fiduciary obligations;communications with executive manage-ment and company leaders;accounts receivable;customers and product profitability;accounts payable;inventory,management; for capital-intensive or manufacturing operations;and checks as an indicator of problems.Koyuncugil and Ozgulbas(2007a)aim to develop d afinancial early warning model for the SMEs listed in Istanbul Stock Exchange (ISE)in Turkey by using data mining.Authors conducted another study(2007b)and detected early warning signs forfinancial risk.A data mining method,Chi-Square Automatic Interaction Detector (CHAID)Decision Tree Algorithm,was used in the study forfinan-cial profiling and detecting signs.The study covered697SMEs listed in ISE between2000and2005.As a result of the study,the covered SMEs listed in ISE were categorized into19financial pro-files and it was determined that430of them had poorfinancial performance,in other words61.69%.According to the profiles of SMEs infinancial distress,return on equity(ROE)will be afinancial early warning signal for SMEs listed in ISE.Koyuncugil and Ozgulbas(2008a)emphasized the affect and importance of operational risk infinancial distressed of SMEs, beside thefinancial risk.Authors developed an early warning mod-el that qualitative(operational)and quantitative(financial)data of SMEs taken into consideration.During the formation of system;an easy to understand,easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background was targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor.This model was designed by data mining.Koyuncugil and Ozgulbas(2009a)developed afinancial early warning model that detected operational risk factors for hedging financial risk.For this purpose study used CHAID(Chi-Square Auto-matic Interaction Detector)Decision Trees.The study covered 6.185firms in Organized Industrial Region of Ankara in2008.It was found thatfirms should emphasize the educational back-ground of managers,status of managers,annual turnover,operat-ing length offirms,makers offinancial strategies,expenditure of energy,knowledge about BASEL-II,quality standards,and usage of credit as operational risk factors for hedging operational risk and raisingfinancial performance.Koyuncugil and Ozgulbas(2009b)to develop an intelligent financial early warning system model based on operational and financial risk factors by using data mining for SMEs in Turkey.This model was aimed to not remain in theoretical structure,be practi-cable for SME,and available for the utilization of SMEs managers. According to model,financial data of Turkish SMEs was obtained by means offinancial analyses of balance sheets and income state-ments through Turkish Central Bank.Operational data couldn’t be access by balance sheets and income statements was collected by a field study from SMEs.Next step of model was analyzed the financial and operational data by data mining and detecting early warning signs.Davis and Karim(2008a)successful predicted a majority of banking crises in emerging markets and advanced countries in 1970–2003.Karim also,suggested that logit was the most appro-priate approach for global EWS and signal extraction for country specific EWS.Davis and Karim(2008b)searched to assess whether early warning systems based on the logit and binomial tree approaches on the UK and US economies could have helped to warn about the crisis.The study suggested that a broadening of approaches of macro prudential analysis was appropriate for early warning.It can be seen that risk detection oriented early warning systems have a very large implementation domain from the pic-ture given above.Furthermore,last generation Business Intelli-gence approach data mining accelerated the accuracy of those systems.Operational logic of early warning systems is based on finding unexpected and extraordinary behaviors in subject area. On the other hand,data mining is the way of uncover previously unknown,useful and valuable knowledge,patterns,relations from big amount of data via sophisticated evolutionary algorithms of classical techniques such as statistics,pattern recognition,artificial intelligence,machine learning.The definitions of EWS and data mining given lead an interesting similarity.An EWS developed for SMEs must design according to the needs of SMEs managers.Therefore,system must be easy to understand and easy to use,must design according tofinancial and operational risk factors(as banks and BASEL II requirements),and must be intelligence for using update data.3.Data mining model for risk detection and early warning systemThe identification of the risk factors by clarifying the relation-ship between the variables defines the discovery of knowledge. Automatic and estimation oriented information discovery process coincides the definition of data mining.Data mining is the process of sorting through large amounts of data and picking out relevant information.Frawley,Piatetsky-Shapiro,and Matheus(1992)has been described data mining as‘‘the nontrivial extraction of impli-cit,previously unknown,and potentially useful information from data’’.Also,Hand,Mannila,and Smyth(2001)described data min-ing as‘‘the science of extracting useful information from large data sets or databases’’.Data mining,the extraction of hidden predictive information from large databases,is a powerful new technology with great potential to help companies focus on the most impor-tant information in their data warehouses.Data mining tools pre-dict future trends and behaviors,allowing businesses to make proactive,knowledge-driven decisions.The automated,prospec-tive analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.Data mining tools can answer business questions that traditionally were too time consuming to resolve.They scour databases for hidden patterns,finding predictive information that experts may miss because it lies outside their expectations(Thear-ling,2004).6240 A.S.Koyuncugil,N.Ozgulbas/Expert Systems with Applications39(2012)6238–6253Koyuncugil and Ozgulbas(2010)described the data mining as ‘‘collection of evolved statistical analysis,machine learning and pattern recognition methods via intelligent algorithms which are using for automated uncovering and extraction process of hidden predictional information,patterns,relations,similarities or dissim-ilarities in(huge)data’’.Data mining is used by business intelligence organizations,and financial analysts to get information from the large data sets.Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data,looking for patterns that can aid decision making(Monk&Wagner,2006). Today,data mining technology integrated measurement of differ-ent kinds of is moving into focus to measure and hedging risk.Data mining techniques have been successfully applied like fraud detec-tion and bankruptcy prediction by Tam and Kiang(1992),Lee,Han, and Kwon(1996),Kumar,Krovi,and Rajagopalan(1997),strategic decision-making by Nazem and Shin(1999)andfinancial perfor-mance by Eklund,Back,Vanharanta,and Visa(2003),Hoppszallern (2003),Derby(2003),Chang,Chang,Lin,and Kao(2003),Kloptch-enko et al.(2004),Magnusson,Arppe,Eklund,and Back(2005). Also,some earlier studies of Koyuncugil and Ozgulbas(2006a, 2006b,2006c,2007a,2007b,2008a,2008b,2009a,2009b),Ozgul-bas and Koyuncugil(2006,2009,2010)conducted onfinancial per-formance,financial risk and operational risk of Small and Medium Enterprises(SMEs)and hospitals by data mining.Fayyad,Piatetsky-Shapiro,and Symth(1996),proposed main steps of DM:Retrieving the data from a large database.Selecting the relevant subset to work with.Deciding on the appropriate sampling system,cleaning the data and dealing with missingfields and records.Applying the appropriate transformations,dimensionality reduction,and projections.Fitting models to the preprocessed data.Data mining techniques can yield the benefits of automation on existing software and hardware platforms,and can be imple-mented on new systems as existing platforms are upgraded and new products developed.When data mining tools are imple-mented on high performance parallel processing systems,they can analyze massive databases in minutes.The most commonly used techniques in data mining are(Koyuncugil,2006;Thearling, 2004):Artificial neural networks:Non-linear predictive models that learn through training and resemble biological neural networks in structure.Decision trees:Tree-shaped structures that represent sets of decisions.These decisions generate rules for the classification of a dataset.Specific Decision Tree methods include Classifica-tion and Regression Trees(CART)and Chi Square Automatic Interaction Detection(CHAID).Genetic algorithms:Optimization techniques that use process such as genetic combination,mutation,and natural selection in a design based on the concepts of evolution.Nearest neighbor method:A technique that classifies each record in a dataset based on a combination of the classes of the k record(s)most similar to it in a historical dataset.Some-times called the k-nearest neighbor technique.Rule induction:The extraction of useful if-then rules from data based on statistical significance.Decision trees are tree-shaped structures that represent sets of decisions.The Decision Tree approach can generate rules for the classification of a data set.Specific Decision Tree methods include Classification and Regression Trees(CART)and Chi Square Auto-matic Interaction Detection(CHAID).CART and CHAID are Decision Tree techniques used for classification of a data set.They provide a set of rules that can be applied to a new(unclassified)data set to predict which records will have a given outcome.CART typically requires less data preparation than CHAID(Lee&Siau,2001).During the developing EWS;an easy to understand,easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identifica-tion of effect level of every factor.Because of this reason,data min-ing is the ideal method forfinancial early warning system.Developing an EWS for SMEs focused segmentation methods. The main approach in analysis is discovering different risk levels and identifying the factors affectedfinancial performance.By means of Chi-Square metrics CHAID is able to separately segment the groups classified in terms of level of relations.Therefore,leaves of the tree have not binary branches but as much branches as the number of different variables in the data.So,it was deemed conve-nient to use CHAID algorithm method in the study.CHAID modeling is an exploratory data analysis method used to study the relationships between a dependent measure and a large series of possible predictor variables those themselves may inter-act.The dependent measure may be a qualitative(nominal or ordi-nal)one or a quantitative indicator.For qualitative variables,a series of chi-square analyses are conducted between the depen-dent and predictor variables.For quantitative variables,analysis of variance methods are used where intervals(splits)are deter-mined optimally for the independent variables so as to maximize the ability to explain a dependent measure in terms of variance components(Thearling,2004).Model of EWSThe model of EWS based on data mining and dataflow diagram of the EWS is shown in Fig.1.The steps of the EWS are:Step I:Preparation of data collection.Step II:Implementation of DMmethod.A.S.Koyuncugil,N.Ozgulbas/Expert Systems with Applications39(2012)6238–62536241Step III:Determination of risk profiles.Step IV:Identification for current situation of SMEs from risk profiles and early warning signs.Step V:Description of roadmaps for SMEs.The details of the EWS are given below:Step I:Preparation of data collectionFinancial data that are gained from balance sheets:Items of bal-ance sheets will be entered asfinancial data and will be used to calculatefinancial indicators of system:Calculation offinancial indicators like in Table1.Reduction of repeating variables in different indicators to solve the problem of collinearity/multicollinearity.Imputation of missing data.Solution of outlier and extreme value problem.Step II:Implementation of DM methodIn the scope of the methods of data miningLogistic regression.Discriminant analysis.Cluster analysis.Hierarchical cluster analysis.Self Organizing Maps(SOM),Classification and Regression Trees(C&RT).Chi-Square Automatic Interaction Detector(CHAID).can be the principal methods,in addition to this several classi-fication/segmentation methods can be mentioned.However,dur-ing the preparation of an early warning system for SMEs,one of the basic objectives is to help SME administrators and decision makers,who does not havefinancial expertise,knowledge of data mining and analytic perspective,to reach easy to understand,easy to interpret,and easy to apply results about the risk condition of their enterprises.Therefore,Decision Tree algorithms that are one of the segmentation methods can be used because of their easy to understand and easy to apply visualization.Although,several Decision Tree algorithms have widespread usage today,CHAID is separated from other Decision Tree algorithms because of the number of the branches that are produced by CHAID.Other Deci-sion Tree algorithms are branched in binary,but CHAID manifests all the different structures in data with its multi-branched charac-teristic.Hence,the method of Chi-Square Automatic Interaction Detector(CHAID)is used in the scope of this study.Assume that X1,X2,...,X NÀ1,X N denote discrete or continuous independent(predictor)variables and Y denotes dependent vari-able as target variable where X12[a1,b1],X22[a2,b2],...,X N 2[a N,b N]and Y2{Poor,Good}.While‘Poor’shows poorfinancial performance in red1bar and‘Good’shows goodfinancial perfor-mance in green bar.CHAID Decision Tree is given in Fig.2.In Fig.2we can see that,Only3variables of N have a statistically significant relation-ship with the target Y,X1has most statistically significant relation with target Y, X2has statistically significant relation with X1where, X16b11.X3has statistically significant relation with X1where b11<X16b12.Step III:Determination of risk profilesCHAID algorithm organizes Chi-square independency test among the target variable and predictor variables,starts from branching the variable which has the strongest relationship and arranges sta-tistically significant variables on the branches of the tree due to the strength of the relationship.An example of a CHAID Decision Tree is seen in Fig.2.As it is observed from Fig.2,CHAID has multi-branches,while other Decision Trees are branched in binary.Thus, all of the important relationships in data can be investigated until the subtle details.In essence,the study identifies all the different risk profiles.Here the term risk means the risk that is caused because of thefinancial failures of enterprises.Fig.2shows that there are six risk profiles:Profile B1shows thatThere are n11samples where X16b11and X26b21%m111has poor financial performance,%m211has goodfinancial performance.Profile B2shows thatThere are n12samples where X16b11and X2>b21%m112has poor financial performance,%m212has goodfinancial performanceProfile C1shows thatThere are n21samples where b11<X16b12and X36b31%m121has poorfinancial performance,%m221has goodfinancial performance. Profile C2shows thatThere are n21samples where b11<X16b12and X3>b31%m122has poorfinancial performance,%m222has goodfinancial performance. Profile D shows thatThere are n3samples where b12<X16b13%m13has poorfinancial performance,%m23has goodfinancial performance.Profile E shows thatThere are n4samples where X1>b13%m14has poorfinancial perfor-mance,%m24has goodfinancial performance.If all of the profiles are investigated separately,Profile B1shows that if anyfirm’s variables X1and X2have values where X16b11and X26b21,poorfinancial performance rate or in another words risk rate of thefirm will be R B1=m111.Profile B2shows that if anyfirm’s variables X1and X2have values where X16b11and X2>b21,poorfinancial performance rate or in another words risk rate of thefirm will be R B2=m112.Profile C1shows that if anyfirm’s variables X1and X3have values where b11<X16b12and X36b31poorfinancial performance rate or in another words risk rate of thefirm will be R C1=m121.Profile C2shows that if anyfirm’s variables X1and X3have values where b11<X16b12and X3>b31poorfinancial performance rate or in another words risk rate of thefirm will be R C2=m122.Profile D shows that if anyfirm’s variable X1have values where b12<X16b13poorfinancial performance rate or in another words risk rate of thefirm will be R D=m13.Profile E shows that if anyfirm’s variable X1have values whereTable1Financial indicators.Financial variablesCurrent ratioQuick ratio(liquidity ratio)Absolute liquidityInventories to current assetsCurrent liabilities to total assetsDebt ratioCurrent liabilities to total liabilitiesLong term liabilities to total liabilities Equity to assets ratioCurrent assets turnover rateFixed assets turnover rateDays in accounts receivablesInventories turnover rateAssets turnover rateEquity turnover rateProfit marginReturn on equity Return on assets1For interpretation of color in Fig.2,the reader is referred to the web version of this article.6242 A.S.Koyuncugil,N.Ozgulbas/Expert Systems with Applications39(2012)6238–6253。