influencing factors for predicting financial performance based on genetic algorithms

& ResearchPaper

Influencing Factors for PredictingFinancial Performance Based onGenetic Algorithms

Yanxia Jiang1*, Lida Xu2, Huacheng Wang1 and Hui Wang1

1School of Business, Renmin University of China, Beijing 100872, China2Department of Information Technology and Decision Science, Old Dominion University, Norfolk,VA 23529, USA

In this paper, considering the financial performance of China’s listed companies as thedependent variable, a computational intelligencemethod based on genetic algorithms anddiscriminant analysis is employed to screen variables that influence financial performanceand forecast the change of financial performance. Specifically, a new model based ongenetic algorithms is developed to screen factors that influence financial performance ofChinese listed companies. The empirical results show that variables selected by geneticalgorithms can predict financial performance well. Copyright # 2009 John Wiley &Sons, Ltd.

Keywords financial performance; influencing factors; genetic algorithms; ratio selection;systems thinking

INTRODUCTION

Business enterprises’ financial performance areconcerned by the shareholders, creditors andmanagement of the business organizations.Accurate prediction of a company’s financialperformance is of great significance. To predict acompany’s financial performance, it is importantto locate those factors that are influencingit. Public information on companies’ financialperformance mainly includes the data and

information such as balance sheets and divi-dends for specific companies. Among which, theinformation of financial statements includingbalance sheets, income statements, cash flowstatements is the main reference for investors topredict the financial performance of a particularcompany. With a large amount of data, how tochoose the factors that are most closely linkedto the financial performance is an interestingresearch topic (Liang, 2008). There are manypotential factors affecting the financial perform-ance of listed companies; however, it is notpractical to take all the factors into account. Thefinancial statements of the listed companiesusually contain a wealth of information that is

SystemsResearch andBehavioral ScienceSyst. Res.26, 661^673 (2009)Published online 9 March 2009 inWiley InterScience(www.interscience.wiley.com)DOI:10.1002/sres.967

*Correspondence to: Yanxia Jiang, School of Business, RenminUniversity of China, Beijing 100872, China.E-mail: [email protected]

Copyright # 2009 John Wiley & Sons, Ltd.

related to the company’s financial performance;in which, a variety of financial ratios playdifferent roles for interpreting the company’sfinancial performance. Such financial ratios areusually selected and used based on analysts’experience and preference to establish a model.

Beaver (Beaver, 1966) first used empiricalmethods to study corporate financial crisis.Beaver showed that ‘cash flow to total debt ratio’is the best variable to explain financial crisis, thenwould be the ratio of total debt to total assets, netincome to total assets, working capital to totalassets and current ratio. Altman (Altman, 1968)put forward the well-known z-score model.Altman used 22 financial ratios and multiplediscriminant analysis to select out five financialratios with best explanatory ability: workingcapital to total assets, retained earnings to totalassets, earnings before interest and taxes to totalassets, market value equity to book value oftotal debt and sales to total assets. Later Altman(Altman et al., 1977) proposed a model of zeta toimprove the z-score model. The results clearlyshowed that there were seven variables whichcould appropriately explain the enterprise bank-ruptcy prediction compared to the others: returnon assets, stability of earnings, debt service,cumulative profitability, liquidity, capitalizationand size.

The study of Ou (Ou, 1990) showed that thenon-profit data in financial annual report containinformation indicating under which directioncorporate profits change to in the following year.Ou first set up the candidate variables setcomposed of 61 independent variables by twosteps (the first step was to use single variablemodel Logit to select 13 variables being signifi-cant on level of 10%; the second step was touse multi-variable model Logit to estimate these13 variables, then kept those significant on thelevel of 10%). Finally, the last eight variableswere selected: percentage growth in the ‘inven-tory to total assets’ (GWINVN), percentagegrowth in the ‘net sales to total assets’(GWSALE), change in ‘dividends per share’relative to that of the previous year (CHGDPS),percentage growth in ‘depreciation expense’(GWDEP), percentage growth in the ‘capitalexpenditure to total assets’ ratio (GWCPX1),

GWCPX1 with a 1-year lag (GWCPX2), theaccounting rate of return (ROR), change inROR relative to the previous year’s ROR (DROR).Lam (Lam, 2004) selected 16 financial statementvariables based on previous studies in theforecast of financial performance: currentassets/current liabilities, net sales/total assets,net income/net sales, (long-term debtþ short-term debt)/total assets, total sources of fund/total uses of fund, research expense, pre-taxincome/net sales, current assets/common share-holders’ equity, common shares traded, capitalexpenditure, earnings per share (EPS), dividendper share, depreciation expense, tax deferral andinvestment credit, market capitalization andrelative strength index. Kiviluoto (Kiviluoto,1998) also draw on previous research in theforecast of the company’s financial performance,chose four financial indicators: operatingmargin,net income before depreciation and extraordi-nary items, net income before depreciation andextraordinary items of the previous year andequity ratio.

Chen (Chen, 2000) used financial ratios incompanies’ annual reports to study the forecast-ing issues for the ST companies in the Chinesestock market. Chen selected six financial ratios asexplanatory variables: current ratio, total debt tototal assets, sales to total assets, net income ontotal assets, net income on equity and net incometo sales (not related to the cash flow ratios). Inforecasting financial distress of Chinese listedcompanies, Wu and Lu (Wu and Lu, 2001) firstlychose 21 financial indicators, then used stepwiseregression method to analyse and finally foundsix variableswithwealthy information: growth innet income, return on total assets, current ratio,long-term debt to equity, working capital to totalassets and sales to total assets. Wu and Zhang(Wu andZhang, 2005) found that industry factorsand the corporate size played a great role inaffecting the financial distress: cost of financialdistress became great when enterprise in finan-cial distress stood in a poor business environ-ment, and asset size of enterprises had a positiverelationship with financial distress cost. Zhanget al. (Zhang et al., 2006) selected 15 financialindicators to forecast EPS: equity per share,dividend pay-out ratio, dividend per share,

Copyright � 2009 JohnWiley & Sons,Ltd. Syst. Res.26, 661^673 (2009)DOI:10.1002/sres

662 Yanxia Jiang et al.

RESEARCHPAPER Syst. Res.

return on net assets, retained earnings ratio,current ratio, quick ratio, total debt to total assets,long-term debt to total assets, sales to accountsreceivable, sales to inventory, gross margin ratio,net income to sales, return on investment andreturn on equity.

From the above-mentioned studies, it can beseen that researchers have not yet reached anagreement on the understanding of factors affect-ing financial performance. Besides several import-ant ratios that were chosen with a relatively highpotential, such as current ratio, sales to accountsreceivable, net income on equity and time interestearned, the other selected ratios vary from study tostudy with a wide range. With so many variables,how to distinguish those factors that have anability to better explain financial performance is aninteresting research topic which may requireinnovative research methods.

A MODEL OF RATIO SELECTION

Applying Genetic Algorithms to RatioSelection

The role of ratio selection is to select variableswith wealthy information from a large numberof variables to establish a model. In theory,variables that are selected should be in line withtwo conditions: (1) the selected variables shouldbe highly related to the removed variables; (2) theselected variables should be unrelated to eachother. Condition (1) requires the subset withinformation of removed variables, while con-dition (2) asks for a subset of variables providingabundant information as much as possible, sothat the subset of variables could provide thelargest amount of information. To find outvariables subset in line with the two conditionsmentioned above, researchers have proposedmany methods. There are two leading methodsfor choosing variables in existing literature: one isto use the method of empirical analysis, and theother is the stepwise multiple regressionmethod,combining with univariate significance test oflinear models. However, there are defects inthose methods mentioned above (Lu et al., 2004):(1) since the number of variables is restricted as

empirical analysis method is used to selectvariables, it is difficult to make a study fullyreflect all of the financial information of a certaincompany; (2) it does not take enough consider-ation of nonlinear effects. Univariable signifi-cance tests and stepwise regression methods areonly applicable to linear models, and in occasionsin which variable level of interaction is notsignificant, the effect will not be satisfactory inthe model with complex interacting effects.

For nonlinear models, the variables selectionmethods greatly affect the quality of the model.Even in amodel that appears right, inappropriatecombination of variablesmay affect the reliabilityof the model and the accuracy of the prediction.From an accounting point of view on financialdata, it is not difficult to see that the majority offinancial indicators are scale variables, and ingeneral the relationship between scale variablesand a company’s financial condition is not asimple linear one. Therefore, exploring aneffective way for selecting independent variablesfor nonlinear models is an important task forestablishing a rational model and increasing theefficiency of forecasting. In this regard, thegenetic algorithm is a choice for choosing a rightsubset of variables. Genetic algorithms, is anewer form of artificial intelligence based oninductive learning technique (Xu, 1999, 2000,2006; Chaudhry et al., 2000; Li et al., 2007, 2008; Xuet al., 2008). The integration of genetic algorithmsand other techniques, such as neural networksand fuzzy logic, within the intelligent systems,has been the topic of interest to the researchers.A variety of theoretical and application researchpapers have been published in the literature.

Ratio Selection Model Based onGenetic Algorithms

Compared to other optimization algorithms,genetic algorithms has its own advantages:(1) genetic algorithms has fewer mathematicalrequirements for seeking optimized solutions,and it can deal with arbitrary forms of objectivefunction and constraints; (2) the ergodic propertyof genetic operators enables genetic algorithmsto do global search effectively; (3) genetic


Financial Performance Basedon Genetic Algorithms 663

Syst. Res. RESEARCHPAPER

algorithms can provide great flexibility to con-struct an independent heuristic for a variety ofspecial problem which ensures the effectivenessof algorithm. As already mentioned, the problemof determining independent variables in theforecast model can be viewed as an optimizationproblem, that is to choose the combination ofindependent variables which could generatebetter forecasting results. However, the basicgenetic algorithms have some obvious weak-nesses such as the slow pace of convergence, andconverging into local maximum points quiteearly (pre-mature). In order to overcome the ‘pre-mature’, we use the elitist selection strategies toimprove the genetic algorithms’ efficiency.

In the following, we introduce the design ofratio selectionmodel developed in this study thatis based on genetic algorithms coupling with theelitist strategy.

Encoding MethodTo apply genetic algorithms in the ratio selectionof classification model, we assume that there isan initial set of L variables. Subsets of variablesare represented as individuals competing forsurvival in a population in the genetic selectionalgorithm. The genetic code of each individualis stored in a chromosome, which is a string ofL binary genes, each gene associated to one ofthe variables available for selection. The geneswith value 1 indicate the variables which are tobe included in the classification model, whilevalue 0 indicating variables which are to be outof election. There are 2L individuals in thepopulation.

Selection of the Fitness FunctionSince the correct forecast rate is the objectivefunction that measures the performance of theclassification model, we choose the correctforecast rate to calculate the value of individualfitness. However, the results are probably notgood if we directly select the rate. Since thecorrect forecast rate is a number ranges from 0 to1, various individuals may have very smalldistinctions among their correct forecast ratesthat result in small differences between theadaptation values. For example, values of 10and 20 are much more clearly distinguished than

1010 and 1020. We apply the exponentialtransformation to define an individual’s fitnessfunction as

F ¼ expð�bð1� correctÞÞ

In this function, b> 0 is the scale transform-ation parameter, the probability of the bestindividual being picked in current populationchanges as b’s value changes. With regard to aspecific fitness function, the smaller the value ofb, the more probable of choosing those individ-uals with the maximum adaptation value. Thelarger the fitness function F value is, the better theindividual is.

Selection OperatorIn this study, the roulette wheel selection methodis adopted. It is the choice of roulette wheel thatmakes every individual in the population has theopportunity to be selected according to theirrelative adaptation value, and this choice is basedon probability. The advantage of the probability-based selection method is giving opportunity toan individual with low fitness value to be chosen,which could maintain the diversity of thepopulation. On the other hand, the individualswith high fitness values also might be eliminated.This is the disadvantage of this probability-basedselection method. In order to overcome theshortage, we have adopted the elitist selectionstrategy, in which crossover and mutation haveno influence on the individuals with great valuesof fitness, and the best individual in population isalways descended to the next generation uncon-ditionally. As previously shown, we could reachthe global optimum with convergence.

Crossover OperatorCrossover methods designed for binary codeinclude the single point crossover, multi-pointcrossover and uniform crossover. In this study,we have chosen the model of single pointcrossover.

Mutation OperatorSince we have adopted the binary code, themutation operator becomes relatively simple,




which takes the reverse value of the genetic valueexisting in some genetic positions of the selectedindividuals in a certain degree of probability pm(known as the mutation probability), that meansto make 0 for 1, 1 for 0.

Parameter SettingIn the micro-genetic strategy of genetic algor-ithms, population size, the probability of cross-over, mutation probability, scale transformationof fitness value are the basic forms. For variousparameters combination, Grefenstette (Grefenst-ette, 1986) made a large number of experimentalcalculation and comparative analysis to examinethe performance of genetic algorithms.

(1) Population size (P): The size of the popu-lation generally ranges from 10 to 160. In thisstudy, we make P¼ 100.

(2) Crossover rate (Pc): In order to obtain theoptimal parameter settings, in this study weadopt the experimental method to make Pc

three values: 0.3, 0.6, 0.9.(3) Mutation rate (Pm): In this study we take

three values for Pm: 0.005, 0.01, 0.05.(4) Fitness function parameter (b): For the form

of fitness function, we adopt the exponentialtransformation form which includes scaletransformation parameter b. In this study, wehave taken the approach of experimentalvalue selection, and set three options: 0.01,0.1, 1.

(5) Generations (G): In order to obtain higheraccuracy, we let genetic algorithms withvarious parameters combination iterate from0 to 200, that means to take 200 values for G.Although it would increase the load and timefor computation, we could see the changes ofthe algorithm’s optimal solutions in differentmaximum evolutionary generations, which ishelpful for us to choose the global optimum.

Since the outcome of the genetic algorithm isdependent on the initial population and is alsoaffected by random factors (roulette selection,mutation and crossover), several runs may beperformed. The best result in terms of the fitnessmeasure is then kept.

EMPIRICAL ANALYSIS AND RESULTS

On the basis of the theoretical analysis and thedescription of the methods in the last twosections, in this section, the set of variables isintroduced first, then the samples are analysed,and software Matlab7.1 is used to do empiricaltests on the ratio selection that was put forwardin the previous section. In order to studydifferences of industries, this section also testdata in different industry sectors.

Initial Set of Variables

With respect to the choice of dependent vari-ables, we selected the most commonly usedindicator for investors–EPS to measure corporatefinancial performance. EPS is the ratio ofcorporate net income divided by the number ofissuing common stock. It evaluates the return oninvestment from the view of fundamental equityshare which more directly shows equity invest-ment returns, and is also the important referenceto determine the stock price. As a result, thedependent variable of our financial performanceprediction model is the change in primary EPS: ifthe EPS increase next year, then take þ1; if theEPS decline in next year, take �1. In order toeliminate the firm-specific trend, we take theadjusted changes of EPS as dependent variables,the dependent variable of sample i in the t year isdefined as EPSitþ1 � EPSit � driftitþ1, in which,driftitþ 1 is estimated as the mean EPS changeover the 4 years prior to year tþ 1.

With respect to independent variables, forfactors influencing the company’s financialperformance, we not only take the company’scondition into account, but also take the macro-economic variables into the consideration inorder to establish the set of initial variables.Micro factors were selected partially based onprevious studies (Ou and Penman, 1989). As theChinese financial reporting system for infor-mation disclosure is not perfect compared toother developed capital markets, for someaccounting indicators that were used in previousresearch, we were not able to obtain data such as




research and development expense, advertisingexpense and fund information. In addition, thedata on ratios of dividend per share, cashdividends, long-term debts, etc., are imprecisefor some companies. In order to ensure thesample size, these indicators had to be given up.After screening, we finally chose 50 accountingindicators as shown in Table 1. In selectingmacroeconomic indicators, we mainly refer tothe studies of Lam (Lam, 2004) and take con-sideration of Chinese macroeconomic data avail-ability, and finally pick 17 indicators that mightaffect EPS for Chinese listed companies (shownin Table 2).

Samples

The information on annual financial statements isobtained from the CSMAR (China Stock Market& Accounting Research) database developed bythe Center of Chinese Accounting and FinanceResearch at the Hong Kong Polytechnic Univer-sity andGTA Information Technology Company.Companies selected are in general industryissuing A shares in Shenzhen Stock Exchangeand Shanghai Stock Exchange. The data rangesfrom 1996 to 2005 (statement information oflisted companies is fragmentary before 1996, forexample, data related to cash flow is absent).There are also B shares and H shares in Chinesestock market, but they take very little marketshare, and their liquidities are quite weak. Asa result, we selected A-share companies forsampling purpose. In addition, many financialcompanies are characterized by preferring highrisk and high return, their assets structures aresignificantly different from that of those compa-nies in general industry. Since the number ofcompanies in financial industry is relativelysmall, this study takes general industry compa-nies for sampling purpose. Macro data arecollected from the Chinese Statistical Yearbook2006, which is on the People’s Republic ofChina National Bureau of Statistics Website(http://www.stats.gov.cn/tjsj/ndsj/). Based onthe above selection criteria, a data set with 4492samples is obtained (Table 3).

Table 1 Accounting variables

Variable number Accounting variablea

1 Current ratio2 %D in 13 Quick ratio4 %D in 35 Days sales in accounts receivable6 %D in 57 Inventory turnover8 %D in 79 Inventory/total assets10 %D in 911 %D in inventory12 %D in sales13 %D in depreciation14 Depreciation/plant assets15 %D in 1516 Return on opening equity17 %Din (capital expenditure/

total assets)18 17, 1-year lag19 Debt–equity ratio20 %D in 1921 LT debt to equity22 Equity to fixed assets23 %D in 2224 Times interest earned25 %D in 2426 Sales/total assets27 %D in 2628 Return on total assets29 Return on closing equity30 Gross margin ratio31 %D in 3032 Operation profit (before

depreciation) to sales33 %D in 3234 Pre-tax income to sales35 %D in 3436 Net profit margin37 %D in 3638 Sales to total cash39 Sales to accounts receivable40 Sales to inventory41 %D in 4042 Sales to fixed assets43 %D in total assets44 Cash flow to total debt45 Working capital/total assets46 %D in 45

(Continues)




Modelling

In order to select the factors affecting financialperformance significantly, we use genetic algor-ithms to select variables. As the genetic algor-ithms work based on the value of the fitnessfunction, genetic algorithms alone cannot accom-plish the task of ratio selection. As a result,combining with particular algorithms for fitnessfunction is required. In this research, the fore-casting result of a particular set of independentvariables is used tomeasure the fitness value, andthe genetic algorithms are combined with certainpredictive functions to complete the task. Thisinvolves the choice of forecasting function. A

combined model of genetic algorithms anddiscriminant analysis method is used to completethe task of the variable selection. Discriminantanalysis is one of the major research methodsfor classification purpose. As the discriminantanalysis offers a high precision in forecasting andis easy to use, it has been applied to many fieldsincluding health care and financial sectors. In thisstudy, discriminant analysis method has beencombined with genetic algorithms.

Parameter SettingsIn the previous sectionwe have discussed that forobtaining the optimal model, we adopt differentcombinations of parameters in the actual exper-iment. The parameter setting of genetic algor-ithms is shown in Table 4. Discriminant analysismethod has special functions in Matlab 7.1 anddifferent types of discriminant functions areavailable.

Global ModelThe samples for year 2000–2003 are used for thetraining set, and the samples for year 2004–2005for the test set. The experimental results show

Table 2 Macroeconomic variables

Variablenumber

Accounting variable

51 Fiscal budget/gross domestic product52 %D in 5153 Government spending/gross

domestic product54 %D in 5355 Money supply 156 %D in 5557 Money supply 258 %D in 5759 Short-term interest rate60 Long-term interest rate61 Consumer price index (1985¼ 100)62 Consumer price index (last year¼ 100)63 Trade balance/gross domestic product64 %D in 6365 Current account balance/gross

domestic product66 %D in 6567 Effective exchange rate

Table 3 Samples for analysis

Year Samples

2000 5462001 6632002 7472003 8532004 8942005 789Total 4492

Table 1. (Continued)

Variable number Accounting variablea

47 Operating income/total assets48 %D in 4749 %D in working capital50 Net income over cash flows

aD means the change. In its calculation, we took the absolutevalue, if the denominator is zero, we removed the sample.

Table 4 Parameter settings for genetic algorithms

Item Value

Population size P 100Generations G 1–200Crossover rate Pc 0.3/0.6/0.9Mutation rate Pm 0.005/0.01/0.05Fitness function parameter b 0.01/0.1/1Crossover method UniformSelection method Roulette wheel




that, when generation N¼ 100, crossover ratePc¼ 0.6, mutation rate Pm¼ 0.01, fitness functionparameter b¼ 0.01 and the discriminant functionis linear, the forecast accuracy of the model forthe test data reaches the highest. The algorithmreaches the optimal solution after the algorithmiterates 188 times. In the optimal solution, theforecast accuracy for the test data is 62.39 percent, and the accuracy for the training data is64.26 per cent. The relationship between thealgorithm’s generations and the accuracy of theclassification model is shown in Figure 1, andthe results of genetic algorithms with differentparameters are shown in Table 5.

The empirical results show that the macrofactors have no impact on the direction of EPS forthe following year. When genetic algorithmsachieves optimal solution (forecast accuracy oftest data is the highest), the set of variables thathave significant influence on the company’sfinancial performance is {2, 5, 6, 7, 8, 10, 11, 12,13, 16, 18, 19, 22, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35,39, 42, 43, 44, 45, 47, 49}. That is, factors affectingEPS significantly in A-shares of Chinese listedcompanies in general industry include thefollowing: per cent growth in the ‘current ratio’,days sales in accounts receivable, per cent growthin the ‘days sales in accounts receivable’,inventory turnover, per cent growth in the‘inventory turnover’, per cent growth in the‘inventory/total assets’, per cent growth ininventory, per cent growth in sales, per cent

growth in depreciation, return on openingequity, 1-year lag of the per cent growth in‘capital expenditure/total assets’, debt equityratio, equity to fixed assets, times interest earned,per cent growth in the ‘times interest earned’,sales/total assets, per cent growth in the ‘sales/total assets’, return on total assets, per centgrowth in the ‘gross margin ratio’, operationprofit (before depreciation) to sales, per centgrowth in the ‘operation profit (before deprecia-tion) to sales’, pre-tax income to sales, per centgrowth in the ‘pre-tax income to sales’, sales toaccounts receivable, sales to fixed assets, per centgrowth in total assets, cash flow to total debt,working capital/total assets, operating income/total assets and per cent growth in workingcapital.

Industry ModelAccording to industry classification standard forChinese listed companies, we divide the samplesinto 13 categories correspondingly (in this studywe only study general industry samples, finan-cial companies are not included). The number ofsamples of various industries for each year can beseen in Table 6. As shown in Table 6, listedcompanies of manufacturing industry containthe largest number of samples. The samplesizes of mining, construction, as well as mediaindustry are small, and their total number ofsamples for 6 years are only 40, 56, 36,respectively.

In view of that the sample sizes of the threeindustries are too small, their model results maybe lack of representativeness. It is decided notincorporate these to other categories (the threeindustries have obvious distinction with othercompanies). These three industries have beendisregarded and it is also decided not to build thesub-industry model for these three industries.We use the parameters of the best global geneticalgorithm (N¼ 100, Pc¼ 0.6, Pm¼ 0.01, b¼ 0.01,discriminant function is linear) to build modelsrespectively for the remainder nine industries.Experimental results show that factors influen-cing the companies’ financial performance ineach industry are different. Variables that havesignificant impact on the company’s performancefor industries are given in Table 7.

0 20 40 60 80 100 120 140 160 180 2000.61

0.615

0.62

0.625

0.63

0.635

0.64

0.645

0.65

Generations

Acc

urac

y

X: 188Y: 0.6426

X: 188Y: 0.6239

training datatest data

Figure 1 Accuracy reached through genetic algorithms forvarious generations




DISCUSSION

Appropriateness of Genetic Algorithms toRatio Selection

The empirical results show that the geneticalgorithms technique developed in this study isable to do a good job in ratio selection and thevariables selected could predict financial per-formance efficiently. For A-shares of Chineselisted companies in general industry, the mainfactors impacting on the financial performanceare: per cent growth in the ‘current ratio’, dayssales in accounts receivable, per cent growth inthe ‘days sales in accounts receivable’, inventoryturnover, per cent growth in the ‘inventory

Table 5 Prediction performance of various parameters in genetic algorithms

Parameter Prediction performance forthe training data (%)

Prediction performance forthe test data (%)

Pc Pm b G

0.3 0.005 0.01 95 64.15 60.150.3 0.005 0.1 56 63.77 61.250.3 0.005 1.0 1 63.39 59.530.3 0.01 0.01 3 63.51 59.530.3 0.01 0.1 12 63.64 59.290.3 0.01 1.0 50 64.19 59.900.3 0.05 0.01 1 63.39 59.530.3 0.05 0.1 7 63.68 59.900.3 0.05 1.0 4 63.60 59.530.6 0.005 0.01 104 64.02 60.520.6 0.005 0.1 1 63.47 59.780.6 0.005 1.0 33 63.77 61.010.6 0.01 0.01 1 63.81 61.500.6 0.01 0.1 72 64.15 60.760.6 0.01 1.0 2 63.56 60.520.6 0.05 0.01 26 64.10 61.380.6 0.05 0.1 1 63.39 59.530.6 0.05 1.0 11 63.85 60.890.9 0.005 0.01 1 63.43 61.130.9 0.005 0.1 2 63.51 59.660.9 0.005 1.0 32 64.02 61.010.9 0.01 0.01 67 64.36 60.150.9 0.01 0.1 24 63.64 60.760.9 0.01 1.0 177 64.23 60.270.9 0.05 0.01 1 63.39 59.530.9 0.05 0.1 13 63.94 59.900.9 0.05 1.0 1 63.39 59.53

Table 6 Samples for each industry

Industry Samples

Agriculture 91Mining 40Manufacturing 2486Utilities 190Construction 56Transportation 135IT 267Wholesale and retail 331Real estate 217Social services 144Media 36Conglomerates 499Total 4492




turnover’, per cent growth in the ‘inventory/totalassets’, per cent growth in inventory, per centgrowth in sales, per cent growth in depreciation,return on opening equity, 1-year lag of the percent growth in ‘capital expenditure/total assets’,debt equity ratio, equity to fixed assets, timesinterest earned, per cent growth in the ‘timesinterest earned’, sales/total assets, per centgrowth in the ‘sales/total assets’, return on totalassets, per cent growth in the ‘grossmargin ratio’,operation profit (before depreciation) to sales,per cent growth in the ‘operation profit (beforedepreciation) to sales’, pre-tax income to sales,per cent growth in the ‘pre-tax income to sales’,sales to accounts receivable, sales to fixed assets,per cent growth in total assets, cash flow to totaldebt, working capital/total assets, operatingincome/total assets and per cent growth inworking capital. In these factors, there are incomestatement variables, balance sheet variables andcash flow variables (cash flow to total debt).Different from the existing studies (Wu and Lu,2001) that mostly focus on different indicatorsof income statements, our results show that thebalance sheet factors play an important role inforecasting the financial performance of listedcompanies (about 50%). The results also showthat they are financial indicators that mainlyaffect the company’s financial performance, andmacroeconomic factors do not affect that much.This further illustrates the usefulness of theinformation of the financial statements, verifiesthe value theory in stock market (the financial

statements contain enough information fordetermining the value of listed companies).

As China’s economic development level is nothigh, and it belongs to the emerging capitalmarket, the characteristics of listed companiesare different from that of developed capitalmarkets. Our empirical studies have also provedthis. Using Logit and stepwise regression model,Ou and Penman (Ou and Penman, 1989) madeout the set of 28 financial indicators to forecastthe changes of earnings. Comparing the factorswe obtained in this study with the results of thestudy of Ou and Penman, it can be seen thatthere are some variables being selected inboth studies: per cent growth in the ‘currentratio’, per cent growth in the ‘inventory turn-over’, per cent growth in the ‘inventory/totalassets’, per cent growth in inventory, per centgrowth in sales, per cent growth in depreciation,1-year lag of the per cent growth in ‘capitalexpenditure/total assets’, debt-equity ratio, percent growth in the ‘sales/total assets’, return ontotal assets, per cent growth in the ‘pre-taxincome to sales’, cash flow to total debt, workingcapital/total assets, operating income/totalassets. Among these common financial variables,the balance sheet ratios are in the majority,followed by ratios of income statement, cash flowratios are at last. This not only shows that thefactors in the income statement impact thefinancial performance of listed companies, butalso that balance sheet factors also play a veryimportant role. On the other hand, it also shows

Table 7 Chosen variables for each industry

Industry Variable numbera

General model {2,5,6,7,8,10,11,12,13,16,18,19,22,24,25, 26,27,28,31,32,33,34,35,39,42,43,44,45,47,49}Agriculture {2,3,4,6,9,12,13,14,16,17,18,21,22,23,24,27,28,30,32,33,34, 36,37,38,39,41,43,46,47,48,49,50}Manufacturing {2,5,6,8,11,14,16,21,23,25,27,31,33,34,35,37,38,39,40,41,45,47,48,49,50}Utilities {2,3,7,8,9,14,15,16,17,18,20,25,29,30,31,32,34,37,39,40,41,42, 43,46,47,48}Transportation {1,2,3,5,7,9,12,14,17,18,19,21,22,23,25,29,30,33,34,36,38,40,41,42,43,44,45,46,48,50}IT {1,2,4,7,9,11,12,13,15,18,20,26,29,32,33,35,38,39,41,43,45,46, 48,50}Wholesale and retail {4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,21,22,23,24,27,28, 29,31,33,34,37,39,41,42,43,44,46,47}Real estate {1,2,3,7,9,10,14,15,16,17,18,19,21,22,23,25,26,28, 30,31,32,35, 36,39,43,44,45,47,49}Social services {2,3,4,8,10,11,13,14,15,17,18,19,20,21,22,23,24,26,28,31,32, 35,37,38,39,40,41,42,43,47,50}Conglomerates {1,3,4,5,11,13,14,15,16,17,18,19,21,23,24,25,26,27,28,29,30,31,32,36,38,40,43,44,49,50}

aCorrelation of the variable number and specific ratio can be found in Table 1.




that Chinese balance sheet meets the standard, isin line with international practice, and corre-sponds with the development history of China’sfinancial statements system (the introduction ofour balance sheet was first, followed by incomestatement, cash flow statement was the final).

The ratio selectionmethod based on the geneticalgorithms in this study is of generality.Whenweare unable to determine in advance the degree ofthe impact of certain indicators on outputs,applying this method can avoid the subjectiveintervention on ratio selection. China’s capitalmarket belongs to the socialist market economicsystem with some special characteristics; there-fore, foreign experiences are not quite matchingwith it.

Difference in Industry Models

Since enterprises are different in market struc-ture, growth capacity, correlation of macro-economic cycle and stages of industry’s lifecycle, their performance are not the same. Guptahas explored industry’s effects on financialstructure for numerous manufacturing enter-prises in the US (Gupta, 1969), and King foundcompany’s share-price behaviour has the signifi-cant industry feature (King, 1966). The industry’sconditions and developing trends provide pre-mise for analysing the changes and the prospectsof products, technology, process, markets of anindividual enterprise. Only with an understand-ing of these pre-conditions, researchers can makethe right evaluation of the company’s perform-ance. In performance forecasting, we should firstestimate the risks and advantages of the industry,then determine whether the corporate makeuse of its advantage and take measures to offsetthe risk. As a result, in order to accuratelyevaluate the company’s performance, we haveto study the conditions of industry which thecompany is at.

Through establishing models for nine indus-tries, we find that the independent variables ofmodel, i.e. the factors affecting financial per-formance, are different. The macro factorshave no effect to all industries and overallmodels, the main factors affecting the financial

performance of China’s listed companies arefinancial indicators. Although the financialindicators that significantly affect performanceare different across industry, and they scatterover a wide range (the 50 financial indicators areall involved), it is not difficult to find by carefulcomparison that the financial indicators’ appear-ing frequencies are different. Financial indicatorswith high appearing frequency show that thesefactors are relatively more important, they notonly have impacts on the financial performanceof overall model significantly, but also affect thefinancial performance of different industries toa significant degree, and we should pay moreattention to these variables. For example, theappearing frequency of variable 18 is ninetimes (90%); appearing frequency of variable 2,variable 39 and variable 43 is the same,eight times (80%); appearing frequency of vari-able 14, variable 16, variable 21, variable 31,variable 32, variable 41 and variable 47 is 7 (70%).Besides, the number of financial indicatorsappearing six times (60%) is relatively larger,they are the following variables: variable 3,variable 9, variable 11, variable 13, variable 15,variable 17, variable 19, variable 22, variable 23,variable 28, variable 34, variable 38, variable 50(correlation of the variable number and specificratio can be found in Tables 1 and 2).

In addition, we can see that, compared withother industries, the number of factors affectingfinancial performance of the manufacturing,utilities and IT industry sectors is relativelysmall. A possible reason is that the overallcharacteristics of these industries are explicit,so some financial indicators become less import-ant. The fewer the factors significantly affect thefinancial performance, the more advantages existfor the management to manage the company andthe investors to evaluate the stock price (Li et al.,2005).

Although the indicators affecting the financialperformance of different industry are overlappedto some degree, they are not exactly the same,and there are some differences among themwhich confirm the industry diversity theory.Furthermore, our findings are consistent withexisting literature. Trigueros (Trigueros, 1999)divided listed companies of US into 10 industries




according to the first number of StandardIndustrial Code, established model respectivelyto forecast companies’ financial performance.The results show that factors affecting financialperformance of each industry are different. Fromthe discussion above, it can be seen that similar toforeign markets, industry differences do exist inChina’s capital market.

CONCLUSION

The financial performance of listed companies isan issue that every stakeholder concern aboutvery much. Managers of companies try everymeans to improve the company’s financialperformance, and hope to maintain good trendsin the future. Investors also pay close attention tocompanies’ financial performance and makeinvestment decision basing on this. To forecastand improve companies’ financial performance,we must first understand what factors affect thefinancial outcomes. China has its own specialeconomic background and capital markets, there-fore, how to select potential factors affectingcompanies’ financial performance significantly isin urgent need. In this paper, we propose to usegenetic algorithms to select the variable subset.Using genetic algorithms can avoid the dis-advantages of pre-selection method, it wouldconvert ratio selection problem into a search forproblem’s optimal solution, in which subsets ofvariables are represented as individuals compet-ing for survival in a population. During eachgeneration, the genetic algorithms improve thestructures in its current population by perform-ing selection, crossover and mutation. After anumber of generations, the genetic algorithmsconverge and obtain the optimal solution, that isthe best set of variables. In this paper, we use dataof China’s listed companies to conduct empiricalresearch, the results show that the variablesselected by genetic algorithms could forecastfinancial performance well.

In order to analyse whether there are industrialdifferences in the factors affecting financialperformance, we separately construct modelsfor each industry. The empirical results show thatthe macro factors have no influence on every

industry and the overall model, financial ratiosare the major factors affecting the financialperformance of China’s listed companies.Although financial indicators of various indus-tries are different, scattering over a wide range(the 50 financial indicators are all involved), wecan see by careful comparison the financialindicators’ appearing frequencies are different;therefore, we should pay more attention to thosevariables with high appearing frequencies (suchas per cent growth of the current ratio, 1-year lagof the per cent growth in ‘capital expenditure/total assets’, sales to accounts receivable, per centgrowth in total assets).

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influencing factors for predicting financial performance based on genetic algorithms

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