Forecasting the Trends of Financial Ratios of Greek Construction Companies

Forecasting the Trends of Financial Ratios of Greek Construction Companies

Vol. 5, No. 1, January-June 2013 51

* Aristotle University of Thessaloniki, GR 54124, Thessaloniki, Greece, E-mail: [email protected]; [email protected]; [email protected]

FORECASTING THE TRENDS OF FINANCIAL RATIOS OFGREEK CONSTRUCTION COMPANIES

Alexandros P. Tsaousoglou*, Georgios N. Aretoulis** and Glykeria P. Kalfakakou***

Abstract: This study examines the influence of economic parameters on financial ratios of Greek construction companies.Financial indicators highlight the sustainability and viability of construction enterprises. This research analyzed the financialstatus of ten of the largest Greek construction companies, for the period 2004 - 2008. Construction economy and approaches formeasuring performance are examined and the legislative framework of the Greek enterprises is analyzed. The study assessesperformance through financial statements, in the light of international accounting standards. The research identified parameters(economic factors) that potentially affect the value of Greek construction companies’ financial ratios. Finally, prediction modelsfor these financial ratios are proposed based on multiple regression analysis. The resulting models are designed for each specificconstruction enterprise.Keywords: Financial Analysis and Ratios, Construction Companies, Statistical Analysis, Prediction Models

INTRODUCTION

Kim et al. (2011) suggest that the constructionindustry plays an important role in leading thenational economy and macroeconomic fluctuationssubstantially influence the construction business.Having that in mind, emphasis should be given tothe unique stochastic nature of financial markets(Ghosh and Majumbar, 2011). In addition,construction is one of the most informationdependent and knowledge intensive industries(Ahman and An, 2008; Sweis et al., 2011), whileknowledge – management also acts as acomplement to the business activities of theorganizations (An and Ahmad, 2010). Largeconstruction enterprises mainly have the legal formof a “Societe Anonym” (S.A.). An S.A. is obliged topublish a number of documents every year such asperiodical financial statements, balance sheets,income statements and statements of cash flows.Public financial statements provide to all involvedsuppliers, creditors, investors and lenders as wellas to the shareholders the necessary informationand a picture of the company’s financial assets andsustainability (Mentis, 2005; Needles & Powers,2007; Pappas, 1998; Sakellis 2005; Williams et al.,2008).

The company’s efficiency is related to the profitsearned and the capital spent, in a fiscal year. Oneof the main methods for assessing performancethrough financial statements is analyzing financialratios (Altman, 1968; Balatbat et al., 2010; Palepu etal., 1997; Therios, 2002). Financial Ratio (FR) is arelative magnitude of two selected numerical valuestaken from an enterprise’s financial statement. Themajor FR categories are five, namely: LiquidityRatios, Debt Ratios, Activity Ratios, ProfitabilityRatios and Market Ratios (Balatbat et al., 2010;Groppelli & Nikbakht, 2000; Kalfakakou, 2004).

The main aim of this research is to identifycorrelations among various company performanceindicators (such as FRs) with other economic andconstruction industry parameters which can be inturn used as predictors for the future viability, ofsuch a company. To achieve this goal, the researchfocused on ten construction companies, registeredin the Athens Stock Exchange market, for whichdata were available. The data originated from theperiod 2004-2008. This period coincides with theAthens’ post Olympic Games time period, wheneconomic activities recesses, as compared to the preOlympic Games period. In addition, 2004 is the yearof adopting the International Accounting Standards

Alexandros P. Tsaousoglou, Georgios N. Aretoulis and Glykeria P. Kalfakakou

52 I J D A I S © Serials Publications

and 2008 highlights the beginning of the deepeconomic crisis in Greece.

In the following sections, the researchmethodology is presented, including: datacollection, selection of representative indicators andformation of prediction models. Models are basedon multiple regression analysis. The analysis’results are presented and conclusions are discussedwith a view to further research.

LITERATURE REVIEW

Chan et al. (2005) emphasized that ratio analysisinvolves analyzing data from financial statementsprepared under generally acceptable accountingstandards.

Sudarsanam et al. (1995), tried to answerwhether the relationship between financial rationumerators and deflators (as Sales, Total Assets andNet Worth), proportionate or otherwise, is stableover time, in order to achieve company’s sizecontrol.

Serrano – Cinca (1996) developed a decisionsupport system for financial diagnosis. Theapproach is mainly based on neural networks andcomplemented multivariate statistical models andMultilayer Perceptron.

Bozsik and Kozma (2011) introduced anartificial Intelligence based classification systemwith the aim to separate financial ratios into twoclasses.

Yeh (2011) employed the partial adjustmentmodel with the generalized method of moments inorder to examine the adjustment behaviour ofcapital structure in the construction industry withinthe context of Taiwan during the period 1982 to2007.

Abidali and Harris (1995) estimated twoindicators, namely Z and A scores, in order topredict companies’ failure in construction industry.Similarly, McGurr et al. (1998) studied failureprediction models of retail firms, based on financialratios and cash flow information.

Chan et al. (2003) tried to monitor and assessthe financial health of contractors, during the Asianeconomic turmoil, by using the financial ratiostogether with one of Altman’s distress models.

Merwin (1942) cited in Chan et al. (2005)concluded that three ratios were very sensitivepredictors of discontinuance, up to as early as fourto five years in some instances. These ratios were:net working capital to total assets, current ratio andnet worth to total debt.

Using Factor Analysis in order to reduce andclassify data, Ocal et al. (2007), managed todetermine the financial indicators that can be usedto analyze the financial trend of the TurkishConstruction industry.

Niemann et al. (2008) based their models onfinancial ratios for rating prediction models formultinational corporations. Chen (2010) proposedan approach that employs financial andmacroeconomic indicators to forecast sales of largedevelopment and construction corporations.

Kehinde and Mosaku (2006) employ descriptivestatistical analysis and ratio analysis in order toinvestigate the assets structure of medium – sizedconstruction contracting firms in Nigeria.

Kim et al. (2011) used financial ratios ofconstruction firms directly for analysis, againstmacroeconomic fluctuations.

Su (2011) also uses quarterly financialstatements of listed construction companies inChina, to produce a rule extraction based model,which is a prediction tool for the hedging offinancial risks through the use of derivatives in theconstruction industry. The methodology is basedon financial ratio analysis of failed and solventcompanies. Balatbat et al. (2011) aimed to providesome evidence, using time series financial ratios andperformance indicators to assess the performanceof publicly listed construction companies inAustralia.

Tsolas (2011) described a new framework thatintegrates data envelopment analysis (DEA) andratio analysis, in order to evaluate performance interms of profitability and effectiveness, ofconstruction firms listed in the Athens Exchange.Javalagi and Bhushi (2011) employ a correlationstudy along with factor analysis and structuralequation modelling to identify a causal relationshipamong financial ratios and the strategicmanagement of productivity in Indian sugarindustries.



Shuang et al. (2011), proposed an early warningbankruptcy - possibility prediction model aboutChina’s construction companies. At the same time,Ng et al. (2011) used financial ratios and applied Z-score model as an approach to detect insolventcontractors.

Horta et al. (2012) used regression analysis andDEA to develop a quantitative approach to evaluatethe financial soundness of construction companies,and to identify the drivers that promoteperformance improvements and innovation.

Adler and Smith (2009) discuss the wayavailable managerial accounting processes facilitatethe appropriate costing of projects.Sundaramoorthi, Coult and Nguyen (2012)proposed classification and Regression Trees(CART) - a data mining method which is utilizedto extract patterns of the financial market dynamicsbased on a particular data set.

Brezigar-Masten and Masten (2012), proposeda bankruptcy prediction methodology and anapproach to selection of bankruptcy predictors forthe logit model.

Chen (2011a) applies swarm-inspiredoptimization techniques to establish a predictionsystem for bankruptcies in Taiwan’s electronicindustry.

Chen (2011b), compares some traditionalstatistical methods for predicting financial distressto some more ‘‘unconventional’’ methods, such asdecision tree classification, neural networks, andevolutionary computation techniques, using datacollected from Taiwan Stock Exchange Corporation(TSEC) listed companies.

Chen, et al. (2011), propose their bankruptcyprediction model based on an adaptive fuzzy k-nearest neighbour (FKNN) method, which is alsoutilized to choose the most discriminative features(financial ratios) for prediction.

Lin, et al. (2011) tries to discover potentiallyuseful financial features for better predictionaccuracy. They had applied data mining techniquesto identify five useful financial ratios, two of which,tax rates and continuous four quarterly EPS werepreviously unaware to the research community.

Chen (2012a) created a composite neuralnetwork based model with the aim to predict

financial distress of construction companies. Inaddition, Chen (2012b) proposed a hybridknowledge-sharing model, that suggest performingor not performing foreign construction investment,in China’s construction market.

Ramalho, et al. (2012), discusses the maineconometric assumptions and features of tobitmodels and the fractional regression model, toprovide a theoretical foundation for their use in theregression analysis of leverage ratios.

Most of the available research based on varioustools and approaches and utilizing financial ratiosfocuses on creating early warning systems in thecase of companies’ failure or bankruptcy predictionmodels. The current research aims at forecasting thevalue of specific critical financial ratios that couldin turn predict the viability of constructioncompanies and how government’s policy andchoices could contribute to that.

RESEARCH METHODOLOGY

The main aim has been the production of prediction- mathematical models that forecast future financialindicators of large construction companies.Financial statements of the ten companiesbelonging to the highest class and registered in theAthens Stock Exchange, were obtained either fromthe enterprises themselves or from the Athens StockExchange. In addition the main economic andfinancial data and parameters pertaining to theGreek and European economy as well as to theGreek construction industry were collected.

Most of the variables originated from the Greekmicroeconomic and international macroeconomicenvironment and were obtained from EuropeanCommission Statistics (Eurostat) and the HellenicStatistical Service. Data includes, but is not limitedto: “Gross Domestic Product (GDP)”, “ConsumerPrice Index (CPI)”, “Material Cost Indicators”,“Number of Construction Firms”, “Number ofemployees (Constructions)”, “Personnel’sRemuneration (Constructions)”, “Social SecurityPayments (Constructions)”, Production Value(Constructions), Investments (Construction Sector),“Evolution of the Greek Monetary Unit”, “Turnoverfrom Civil Engineering Activities, BuildingActivities, Construction Activities”, “Euro-Super 95Consumption”, “Petroleum Products Prices”,



“Public Investments Program” (the PublicInvestment Program is the total of the co-financedprogrammes and projects; and projects financedpurely by (other) national resources), Number oftrucks in operation (private-public use), “AverageDeficit value, Total Liabilities, Net Profits and LongTerm Debt” of the construction sector perconstruction firm.

A database was designed, able to accommodateall necessary data. In addition new variables wereadded, to record a number of FRs and otherindicators suitable for the research purposes. Thefive years chosen for examination, are the cases inthe database. In addition, each ratio per eachcompany defines a variable, along with theparameters of the national and internationaleconomy. In total 246 different data variables wereincluded, all of continuous type.

Data processing and analysis was implementedusing the statistical package «SPSS PASW». ACorrelation Analysis (linear Correlation) wascarried out using Pearson Analysis, based on theassumption of the normal distribution of thevariables. This analysis presents which factorscorrelate with the construction companies’ financialratios values and to which point.

Then Multiple Regression Analysis was usedwith the aim to create Prediction Models associatingFRs to other economic parameters of theConstruction industry and the domestic andinternational economy. «Stepwise» method waschosen, in order to reject the variables with weak,or no correlation and keep these with the greatestassociation. In the case of the multiple regressionanalysis, FRs were selected as the dependentvariables while economic parameters served as theindependent variables. Dependent variables,include the value of each financial ratio per yearand company, the average value of each financialratio per company, and the average and medianvalue, of all the enterprises’ financial ratios per year

For confidentiality purposes the ten companiesfor which financial statements were obtained andexamined, appear in this research as Company 1,Company 2, etc. In the context of the currentresearch, two construction enterprises presentedstrong correlations and reliable regression models,namely the second and the fourth company.

“Company 2 “ have as a competitive advantagethe accumulated experience obtained through itsdynamic participation in the execution of the majorpublic infrastructure and building projects as wellas large private projects in Greece. This company isexpanding its strong presence in the local andinternational market. Additional locations ofactivities include: South Eastern Europe, MiddleEast, Italy, Russia. Its main activities are: building/infrastructure/ industrial projects, Mining /Quarrying Activities, Facility Management, ProjectManagement.

“Company 4” has been actively involved in theimplementation of a broad spectrum of public andprivate projects of considerable budget and complexknow-how, such as the construction of Railway andHighway networks, of high - quality officebuildings, hospitals, museums, resorts, hydro-electric plants, dams, harbours, industrial facilities,etc. Furthermore, “Company 4” has been involvedin large infrastructure projects in South EasternEurope and recently in the Middle East.

RESEARCH FINDINGS

The analysis with the SPSS PASW, identified thefollowing correlations and produced a number ofprediction models. In that framework, specificresults which are representative were chosen, thatcan infer useful conclusions and represent aremarkable gamut of cases. Thus, two companies’results are being presented in this paper,“Company’s 2" and “Company’s 4".

Correlation Analysis

Table 1 presents the Correlation Analysis’ resultsof Construction “Company 2” Current LiquidityRatio, Debt Ratio and Debt/Equity Ratio withselected economic parameters.

Debt Ratio was calculated by dividing total debtby total assets and indicates what proportion of debta company has relative to its assets. The measuregives an idea to the leverage of the company alongwith the potential risks the company faces in termsof its debt-load (Helfert, 2001).

Debt/Equity Ratio gives a measure of acompany’s financial leverage calculated bydividing its total liabilities by stockholders’equity. It indicates what proportion of equity



and debt the company is using to finance itsassets. A high debt/equity ratio generallymeans that a company has been aggressive infinancing its growth with debt. Generally a value

under 1 is preferred, but this depends on theindustry in which the company operates.According to Table 1, the following findings canbe observed:

Table 1“Company 2” Financial Ratios’ Correlation Analysis with Selected Parameters

Company 2 Year Material and Cost of Cost of New Resi- Consumer Evolution of Euro-superLabour Costs Cement- Machinery dential Price the Greek 95 Con-

Index ready Fuels- Buildings Index Monetary sumptionMixed electric Construc- Unit

Concrete Energy- tionwater Index

Current PearsonLiquidity Correlation -.991 -.992 -.993 -.965 -,992 -.986 .984 -.944Ratio Sig. (2- tailed) .001 .001 .001 .008 .001 .002 .002 .016Debit Ratio Pearson .941 .963 .964 .870 .963 .957 -.946 .828

CorrelationSig. (2- tailed) .003 .002 .002 .009 .002 .005 .005 .018

Debt/Equity Pearson Correlation .902 .934 .937 .816 .934 .925 -.909 .761Ratio Sig. (2- tailed) .036 .020 .019 .092 .020 .024 .387 .135

“Company 2” Current Liquidity Ratio, presentsmany correlations. Significantly high negativecorrelations are observed with the parameters“Machinery Fuels-Electric Energy-Water” and“Euro-Super 95 Consumption”. It is logical tosuggest that as the cost of equipment and materialincreases, the value of the “Current Liquidity Ratio”will decrease. The increase in the fuel consumptionalso reflects mainly an increase in employees andpersonnel transportations and in the expendituresof the construction company, and therefore adecrease in the ratio’s value. Also high negativecorrelations appear with the parameters “Year”,“Material and Labour Costs Index”, “Cost ofCement-Ready Mixed Concrete”, “New ResidentialBuildings Construction Index” and “ConsumerPrice Index”. As the years pass (increase) Greekeconomy is moving closer to the beginning of thefinancial crisis (approximately the year 2008), whichis reflected in the “Current Liquidity Ratio”. Thisis also true for two other indexes concerning thecost of material, labour and concrete. As their unitprice increases, the liquidity ratio logicallydecreases. In addition “Consumer Price Index” isanticipated to present a negative association withthe “Current Liquidity Ratio”. The growth of the“New Residential Buildings Construction Index”,over a limit means great stock of unsold buildings

and liquidity problems. This shows exactly thebeginning of real estate crisis, in Greece. At the sametime the parameter “Evolution of the GreekMonetary Unit” has positive correlation, whichmeans that as this parameter increases, similarly“Current Liquidity” Indicator increases.

“Company 2” Debt Ratio, presents manycorrelations and tendencies. High positivecorrelations appear with the parameters “Materialand Labour Costs Index”, “Cost of Cement-ReadyMixed Concrete” and “New Residential BuildingsConstruction Index”. These findings are inaccordance with what was expected. As the cost ofthe materials increases so does the “Debt Ratio”.

In the case of “New Residential BuildingsConstruction Index”, the first thought would be thattheir association should be negative. In realityhowever, the financial crisis has made the paymentsfor new buildings scarce and even the bankingsector is sceptic enough when it comes to providefunding for purchase of residential housing. As aresult the association among these variables ispositive.

Interesting negative trends appear withparameters “Year”, and “Consumer Price Index”.A good explanation about the decrease with ‘’Year’’,is that the specific company undertook many



Olympic Games (Athens 2004) construction projectsand received partially its payment from the GreekGovernment, in the following years. The parameter“Evolution of the Greek Monetary Unit” hasnegative correlation, unlike to Current Liquidityindicator.

“Company 2” Debt / Equity Ratio, presentspositive correlations with the parameters “Materialand Labour Costs Index”, “Cost of Cement-ReadyMixed Concrete”, “New Residential BuildingsConstruction Index” and “Consumer Price Index”.These associations are to be expected since anincrease in these indexes leads to an increase in debt.The parameter “Evolution of the Greek MonetaryUnit” has negative correlation with Debt/EquityRatio indicator.

Table 2 shows the Correlation Analysis resultsbetween Net Profit Margin Ratio of the Construction“Company 4” and selected parameters. This is a ratioof profitability, calculated as net profits (after taxes)divided by sales. Profit margin is very useful when

comparing companies in similar industries. A higherprofit margin, indicates a more profitable companythat has better control over its costs, compared to itscompetitors (Bull, 2008; Helfert, 2001).

Construction Company 4 “Net Profit MarginRatio”, presents significant negative correlationwith the parameter “Cost of Glass Materials”. Highnegative correlations appear with the parameter“Cost of Cement-Ready Mixed Concrete”. Negativetendencies appear with the parameters “Cost ofWood Materials”, “Cost of Water Supplies-Heating-Sewerage Materials”. The identified correlations arealso anticipated. In the case where materials andequipment are increasing their unit price then theprofit margin of the construction company ispotentially decreased.

The results are discussed based on theassumption, that higher correlations appear when“Pearson Correlation” is closer to value 1 and atthe same time the “Significance” is less than 0.05(Field, 2009; Pallant, 2001).

Table 2“Company 4” Financial Ratio Correlation Analysis with Selected Parameters

Company 4 Cost of Cement- Cost of Wood Cost of Water Cost of Glassready Mixed Materials Supplies-heating- Materials

Concrete sewerage Materials

Net Profit Margin Ratio Pearson Correlation -.917 -.868 -.872 -.981Sig. (2- tailed) .028 .056 .054 .003

Multiple Regression Analysis and PredictionModels

Tables 3, 4 present the Regression Analysis’ results,of the depended variable “Company 2” “Current

Table 3“Company 2” Current Liquidity Ratio - Selected Parameters: Coefficients

Unstandardized StandardizedCoefficients Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) 9,872 ,125 78,742 ,000Cost of Natural Stone Material -,081 ,001 -1,000 -65,971 ,000

2 (Constant) 9,860 ,010 952,878 ,001Cost of Natural Stone Material -,081 ,000 -,999 -800,100 ,001Net Profits Average Amount 1,736E-5 ,000 ,021 17,140 ,037

3 (Constant) 10,411 ,000Cost of Natural Stone Material -,083 ,000 -1,031Net Profits Average Amount 2,451E-5 ,000 ,030Evolution of Greek Monetary Unit -,263 ,000 -,033

Liquidity Ratio” and certain independent variables.This is a very popular ratio, used to test a company’sliquidity, by deriving the proportion of currentassets available to cover current liabilities. The



concept behind this ratio is to ascertain whether acompany’s short-term assets (cash, cashequivalents, marketable securities, receivables andinventory) are readily available to pay off its short-term liabilities (notes payable, current portion ofterm debt, payables, accrued expenses and taxes)(Clauss, 2010). In theory, the higher the currentratio, the better. From the results it can be concludedthat:

The b-value of the first model (-0,081) presentsa negative relationship between “Current LiquidityRatio” and “Cost of Natural Stone Material”. Thismeans that there is a negative relationship betweenthe predictor and the outcome. The b-values revealto what degree each predictor affects the outcomeif the effects of all other predictors are held constant.

The values of significance are all less than .05,which means that the predictor is making asignificant contribution to the model. The valuesof t-statistics are great that means that thecontribution of the predictors to the models issignificant.

The sign of the b-values reveals the direction ofthe relationship between the predictor and theoutcome. Summarizing the results, it is obvious thatthe parameters “Cost of Natural Stone Material”and “Evolution of Greek Monetary Unit” have allnegative effect on the ratio value, while theparameter “Net Profits average Amount” has apositive relationship.

All three Models have perfect Correlation (R=1,Table 4). The linear functions that can predict themore possible value of this Ratio, based on availableinformation about the independent parameters are:

C.L.R.= 9,872 – 0,81 x (Cost of Natural StoneMaterial)

C.L.R.= 9,860 – 0,81 x (Cost of Natural Stone Material)+ 1,736E-5 x (Net Profits Average Amount)

C.L.R.= 10,411 – 0,083 x (Cost of Natural StoneMaterial) + 2,451E-5 x (Net Profits AverageAmount) - 0,263 x (Evolution of Greek MonetaryUnit)

The results are quite logical, judging by thecorrelations identified in the previous analysis. Thepresence of material costs and their increase maydecrease the value of the “liquidity” ratio. On theother hand an increase in the “average amount ofnet profits” should lead to an increase of theliquidity ratio.

In the column labeled R are the values of themultiple correlation coefficients between thepredictors and the outcome. The next column givesthe value of R square, which is a measure of howmuch of the variability of the outcome is accountedfor by the predictors.

For the first model its value is 1, which meansthat “Cost of Natural Stone Material” accounts foralmost 100% of the variation in the CurrentLiquidity ratio. The addition of the other twopredictors “Net Profits Average Amount” and“Evolution of Greek Monetary Unit” (model 2 andmodel 3), also provide models which explain 100%of the ratio variance.

The adjusted R square gives some idea of howwell the model generalizes and ideally its valueshould be the same or very close to, the value of Rsquare. In this case in the first model, the differencebetween the values is 1 – 0,999 = 0,001. Thisshrinkage means that if the model was derived fromthe population rather than the sample it wouldaccount for approximately 0,1% less variance in theoutcome.

The three models exhibit very high values for Rwhich in almost all cases is equal to 1. In the case ofmodel 1, the R value (correlation) is also very highas it reaches 0,999. Regarding the value of R square,which presents the percentage of variance that themodel predicts, it could be seen that the first modeland the second one predict 100% of the variance.For the third model the value could not be defined.

Tables 5, 6 present the Regression Analysis’results of the depended variable, “Company 2”Return on Investment (ROI) Ratio. This ratioevaluates the efficiency of an investment and is avery popular metric because of its versatility and

Table 4“Company 2” Current Liquidity Ratio - Selected

Parameters: Model SummaryModel R R Square Adjusted R Std. Error of

Square the Estimate1 1,000a 1,000 ,999 ,0093482 1,000b 1,000 1,000 ,0007703 1,000c 1,000



simplicity. That is, if an investment does not have apositive ROI, or if there are other opportunities witha higher ROI, then the investment should be not beundertaken. The following are the analysis results:

The b-value of the first model (-0,991) presentsa negative relationship between “Return onInvestment” ratio and “Production Value”. Thevalues of significance are all less than .05, whichmeans that the predictor is making a significantcontribution to the model. The values of t-statisticsare adequate that means that the contribution of thepredictors to the models is significant. Regarding thesign of the b-values it could be noted that theparameters “Production Value” and “ConstructionPersonnel Cost” have all negative effect on the ratiovalue, while the parameter “Program for PublicInvestments” has a positive relationship.

Two out of three Models have perfectCorrelation (R=1, Table 6). The linear functions thatcan predict the more possible value of this Ratio,based on available information about the involvedparameters are:

R.O.I.= 74,926 – 3,558E-6 x (Production Value)+ 0,001 x (Program for Public Investments)

R.O.I. = 75,013 – 3,393E-6 x (Production Value)+ 0,001 x (Program for Public Investments) – 5,917E-7 x (Construction Personnel Cost)

One of the highlights is the positive presenceof the “Program for Public Investments”. As morecapital is invested in the realization of constructionprojects more value is produced for the constructioncompanies. In general, this could act as a positivedevelopment for the whole Greek economy. At thesame time the “Construction Personnel Cost” has anegative contribution in the R.O.I. ratio. This is alsosomething to be expected because as the cost ofpersonnel increases, then the R.O.I. ratio decreases.

R square for the first model has a value of 0,982,which means that “Production Value” parameteraccounts for 98,2% of the variation in R.O.I. ratio.However, when the other two predictors areincluded as well (model 2 and model 3), this valueincreases in 1 or 100%, in the R.O.I. ratio predictionmodel. In the case of the adjusted R square of thefirst model, the difference between the values is0,982 – 0,973 = 0,009 (about 0,9%). This shrinkage isalmost negligible. Again, the three models exhibitvery high values for R which in two cases models 2and 3 are equal to 1. In the case of the first model,the R value (correlation) is also very high as itreaches 0,991. Regarding the value of R square,which presents the percentage of variance that themodel predicts, it could be seen that the first modelpredicts 97,3% of the variance and the second model100% of the variance. For the third model the valuecould not be defined.

Tables 7, 8 demonstrate the RegressionAnalysis’ results, of the depended variable

Table 5“Company 2” Return on Investment (ROI) Ratio - Selected Parameters: Coefficients



1 (Constant) 72,987 4,897 14,905 ,004Production Value -2,786E-6 ,000 -,991 -10,469 ,009

2 (Constant) 74,926 ,326 229,534 ,003Production Value -3,558E-6 ,000 -1,265 -91,238 ,007Program for Public Investments ,001 ,000 ,305 22,010 ,029

3 (Constant) 75,013 ,000Production Value -3,393E-6 ,000 -1,207Program for Public Investments ,001 ,000 ,261Construction Personnel Cost -5,917E-7 ,000 -,028

Table 6“Company 2” Return on Investment (ROI) Ratio -

Selected Parameters: Model Summary

Model R R Square Adjusted R Std. Error ofSquare the Estimate

1 ,991 a ,982 ,973 ,6594922 1,000b 1,000 1,000 ,0423313 1,000c 1,000



“Company 2” Interest Coverage Ratio. This ratiois the quotient between earnings before interest& taxes (EBIT) of one period and interestexpenses of the same period. It determines thecapability of the enterprise to pay off theinterest and for the shareholders is a ratioabout the short-term economic subsistence. Aninterest coverage ratio below 1 indicates thecompany is not generating sufficient revenues tosatisfy interest expenses. The analysis’ results arethe following:

The b-value of the first model (0,008) presents apositive relationship between “Interest CoverageRatio” and “Other National Resources for PublicInvestments”. There is a positive relationshipbetween the predictor and the outcome.

Almost all the values of significance are all lessthan .05 (except the first one), which means thatthe predictor is making a significant contributionto the model. The values of t-statistics are great thatmeans that the contribution of the predictors to themodels is significant. The first model presentssignificance equal to 0,095 (> 0,05) and the value of“t” is very small. As a result, the most reliablemodels are 2 and 3 where the second model hasvery good values as far as significance is concernedand the “t” values are significant. It should be notedthat the second and third model apart from theparameter “Other National Resources for PublicInvestments”, incorporate “Cost of Glass Material”

and “Cost of Door-Window Material”. Theirrelationship with the “Interest Coverage” Ratio isnegative, which could be expected. As the cost ofmaterials rises the ratio is decreasing.

There is a linear association among this Ratioand the parameters “Other National Resources forPublic Investments” (Public Investment Programis the total of the co-financed programmes andprojects; and projects financed purely by (other)national resources), “Cost of Glass Material” and“Cost of Door-Window Material”. Two of threeModels have perfect Correlation (R=1). The linearfunctions that can predict the more possible valueof this Ratio, based on available information aboutthese parameters are:

I.C.R. = 24,745 + 0,006 x (Other National Resourcesfor Public Investments) – 0,336 (Cost of GlassMaterial)

I.C.R. = 24,774 + 0,006 x (Other National Resourcesfor Public Investments) – 0,302 (Cost of GlassMaterial) – 0,034 (Cost of Door-Window Material)

The positive relation of the “Other NationalResources for Public Investments” to this ratio isobvious, as more capital being invested, strengthensEBIT (earnings before interest & taxes). On the otherhand the negative relation of the “Cost of GlassMaterial” and “Cost of Door-Window Material” tothis ratio, is expected because EBIT is beingweakened.

Table 7“Company 4” Interest Coverage Ratio - Selected Parameters: Coefficients



1 (Constant) -18,351 6,098 -3,009 ,095Other National Resources for ,008 ,002 ,954 4,480 ,046Public Investments

2 (Constant) 24,745 ,342 72,297 ,009Other National Resources for Public ,006 ,000 ,679 214,026 ,003InvestmentsCost of Glass Material -,336 ,003 -,407 -128,409 ,005

3 (Constant) 24,774 ,000Other National Resources for Public ,006 ,000 ,680InvestmentsCost of Glass Material -,302 ,000 -,367Cost of Door-window Material -,034 ,000 -,040



Table 8“Company 4” Interest Coverage Ratio - Selected

Parameters: Model Summary

Model R R Square Adjusted R Std. Error ofSquare the Estimate

1 ,954a ,909 ,864 3,1882052 1,000b 1,000 1,000 ,0351 1 23 1,000c 1,000

According to table 8, considering the firstmodel, the R square value is 0,909, which meansthat “Other National Resources for PublicInvestments” accounts for 90,9% of the variation inI.C.R. ratio. However, when the other twopredictors are included as well (model 2 and model3), this value increases in 1 or 100%, of the variancein the I.C.R. ratio.

The adjusted R square gives some idea of howwell the model generalizes and ideally its valueshould be the same, or very close to, the value of Rsquare. In this case in the first model, the differencebetween the values is 0,909 – 0,864 = 0,045 (about4,5%). This shrinkage means that if the model werederived from the population rather than the sampleit would account for approximately 4,5% lessvariance in the outcome. The three models exhibitvery high values for R which in two cases models 2and 3 is equal to 1.

In the case of model 1, the R value (correlation)is also very high as it reaches 0,954. Regarding thevalue of R square, which presents the percentageof variance that the model predicts, it could be seenthat the first model predicts 86,4% of the varianceand the second model 100% of the variance. For thethird model the value could not be defined.

CONCLUSIONS AND FUTURE WORK

The current research predicts the development ofcertain ratios’ values. These forecasting modelscould provide an alarm mechanism when a ratioor combination of ratios approach critical valuesand thus provide signs regarding the company’sviability or in order to set ideal goals for betterprofitability. One of the research goals was toexamine whether financial ratios of constructioncompanies are affected by specific economicparameters. Indeed, the correlation analysis as wellas the regression analysis indicated that there is a

significant relationship among important financialratios of major Greek construction companies andmain economic parameters of the National andInternational economy. Research findings haveshown that for certain construction companies, thevalues of the following ratios “Current Liquidity”,“Return on Investment (ROI)”, “Interest CoverageRatio”, are influenced and thus could be predictedby specific economic factors. The parameters thatfunction as basis for the predictions include: “PublicInvestments Program”, “Construction PersonnelCost”, “Net Profits Average Amount (ConstructionSector)”, “Evolution of Greek Monetary Unit”,“Other National Resources for Public Investments”,“Production Value” (in Construction branch), “Costof Natural Stone Material”, “Cost of Glass Material”and “Cost of Door-Window Material”.

Furthermore, the presented models identifiedthe contribution of each parameter to the ratio underconsideration. So the research could be focused onthe assessment of these independent economicpredictors. Also, it is interesting that, “Cost ofCement-Ready Mixed Concrete” is the parameterwhich is common among all correlations. This resultis expected, as the majority of construction projectsuse great amounts of this material and its products.

These findings enable the prediction of futurefinancial ratios and thus a prediction of the potentialevolution of construction enterprises, for alternativescenarios with respect to the Greek economy andmost specifically the construction industry inGreece.

The best customer of the Greek constructionenterprises is the public sector. Therefore, factorswith a negative effect on the financial status of theconstruction enterprises are the lack of a constantstrategic plan for the infrastructure development,the legislation, which follows the changes in thegovernment profile and a tax system which is alsounder continuous revision. All these events createa very unstable economic – financial environmentwhich surely does not support the well being of theconstruction sector. Therefore, greater financialstability and robustness for the constructioncompanies is required by stabilizing the legalframework, governance system and planningprocesses and the national strategic plan fordevelopment.



Considering the parameters of the economy, itshould be noted that some of them, such as: “PublicInvestments Program” and “Other NationalResources for Public Investments” are parameterswhich are defined directly by the political agendaof the Government. There are also some economicparameters, such as “Petroleum Products Prices”,“Consumer Price Index (CPI)”, “Social SecurityPayments (Construction)” which are controlledindirectly by the Government choices (taxes, valueadded tax and other legislative issues).

This translates in a capability of the governmentto influence radically the development of aconstruction company or the whole constructionsector and their viability and profit margin.Realizing the association among financialparameters and construction enterprises’ ratios,could help the economy rise again by properlymanaging the factors that control ratios. So theseprediction models and their philosophy couldprove a useful tool for designing the policy of thegovernment towards development. Even more itcould help foreign firms and hedge funds invest inGreece, based on robust construction companies.

Indeed up till now the major “customer” ofconstruction projects for the public infrastructurewas the Greek government, so it was anticipatedthat the funding originating from national and othernational resources would play a crucial role in thestructure of the construction industry’s ratios. Asthe financial crisis deepens and the public spendingis limited, at the same time constructioninfrastructure projects (public projects) are haltedeither in the tendering – procurement stage orduring construction. Thus, there was a need foralternative funding of public projects and that hasbeen partially achieved by PPPs. So, it would bewise to examine also the correlation of PPPs fundingwith the ratio development of constructioncompanies.

Another aspect which should be taken intoconsideration is the fact that some companies havedeveloped activities in foreign countries. The latterindicates that the financial factors are influencedby both domestic and foreign financialdevelopments.

The mean number of construction companieslisted in the highest class of the registry for public

projects is about ten. Therefore, the competitionamong these companies is fierce and theopportunities of jointly bidding for a project is alsoa frequent occurrence. In this context, it is worthmentioning that these two companies had jointventures in undertaking extensive publicinfrastructure projects. This fact could explain theirdependence on similar factors – predictors and theiranalogous financial behaviour. Another, veryinteresting common phenomenon, especially in thelarge construction companies, that usually formgroups of companies, is the creation of enterprisesfocused on real estate and asset management. Thiswas probably necessitated due to the large numberof unsold residential building projects.

In general, the research results appear quitelogical. The influence of the various economicparameters has the anticipated effect on the ratiosunder examination (Tsaousoglou et al., 2011).

Limitations of the current research are firstly thebarriers set by economic events, legislation,accounting principles, procedures and methods.The implementation of the new accountinginformation system set on the 2004, and theeconomic – financial crisis which has risen on 2008are two events-milestones that significantly limitthe sample of enterprises. Possible solutions couldinvolve transformation of the financial statementsbefore 2004 (year of introduction of IAS) from theGreek Accounting Standard to the correspondinginternational one.

It should also be noted that the correlations andthe regression models - equations describing ratiotendencies are unique for each company. The mainreason is the unique profile of each constructionenterprise, competing for each public project.According to the Greek legislation framework, eachconstruction company involved in publicconstruction projects deals with a certain numberof project types and up to a certain project budget.The highest the class of a construction enterprise,the greater the budget of the project the firm couldpursue.

The future work will be focusing on the analysisof financial ratios based on more historical data andat the same time alternative approaches forproducing prediction models, such as the neuralnetworks.



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Forecasting the Trends of Financial Ratios of Greek Construction Companies

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