Journal of Operational Risk

Risk.net

Preventing the unpleasant: fraudulent financial statement detection using financial ratios

Michail Pazarskis, Grigorios Lazos, Andreas G. Koutoupis and George Drogalas

  • The proposed model achieves accuracy in the prediction of fraudulent financial statements with an accuracy rate of over 78%.
  • The proposed model contains two ratios that could serve as “red flags” in an audit process: “collection period” and “gearing”.
  • The companies without falsification show better results in their financial ratios than those companies that present falsification in their financial statements.
  • The proposed model could be used as an effective tool to detect fraudulent financial statements by the banking system, internal and external auditors, and tax authorities.

The aim of this study is to investigate financial fraud in companies listed on the Athens Stock Exchange during the period 2008–18, in which a major economic crisis took place in Greece. Based on 30 financial indicators resulting from the analysis of financial statements, several statistical tests are applied to the primary sample and the control sample in order to create a model that uses the indicators as “forecasts” to detect possible fraud. The data used in the research were obtained from the financial statements of the listed companies, the reviews in the auditors’ reports and the available data and information from the reports of the Athens Stock Exchange. The proposed model is able to correctly classify the total sample with an accuracy of 78.4%. The results of the research show that the model works effectively in detecting fraudulent financial statements when the economy is operating in crisis conditions. By using financial ratios, this model signals red flags for the audit process, and it could be used as an effective tool by the banking system, internal and external auditors, tax authorities and other government authorities.

1 Introduction

One of the most important issues concerning the annual financial reports of companies is the inclusion of falsified data (Dunn 2004; Koumanakos et al 2008; Dimitropoulos and Asteriou 2009; Firth et al 2010; Omar et al 2017; Aboud and Robinson 2020). Falsification of financial statements (FFS) generally refers to the deliberate alteration of companies’ financial data that are registered, or should be registered, in those companies’ accounting books. More specifically, in FFS an overstatement of assets, sales and profits or an understatement of liabilities, projections, expenses and losses (or a combination of those two practices) is attempted in order to achieve the desired financial result, which will give fictitious value to the business (Spatacean 2012; Young 2020). These manipulations change the appearance of companies’ financial statements (Baralexis 2004; Churyk et al 2009; Zager et al 2016; Albizri et al 2019). In order to achieve FFS, several methods are used in the context of so-called creative or imaginative accounting (Zainudin and Hashim 2016; Wei et al 2017; Chimonaki et al 2019; Temponeras et al 2019). The consequences of such phenomena are very significant and have a decisive influence on those who are interested in the performance of companies, such as investors, creditors, regulators, company shareholders and consumers. In addition, as technology evolves rapidly, this type of fraud is becoming increasingly complex and more difficult to detect (Kanellopoulos 2002; Moisiadou et al 2012; Riad Shams et al 2020).

More specifically, while traditional detection methods, which are nonautomated and simple to apply, can be depended upon for some reliable evidence of falsification (Omoye and Eragbhe 2014; Kanapickiene and Grundiene 2015), these methods may not be suitable for the analysis of large volumes of data. For this reason, the auditing authorities and administrations of financial institutions are making significant efforts to develop and optimize automated methods based on statistical computing and artificial intelligence and to use many new technologies to detect manipulation (Kotsiantis et al 2006; Gaganis and Pasiouras 2007; Pazarskis et al 2017; Feess and Timofeyev 2020). Given the enormous importance of the timely detection of FFS, it is not surprising that there has been a large volume of research conducted on this subject in the last few decades (Kirkos et al 2005; Churyk et al 2009; Omar et al 2017; Lokanan et al 2019; Dimitrijevic et al 2020).

The global financial crisis of 2008 affected the Greek economy, in particular, with the result that Greece fell into a dire economic position for a long period. The problems in the Greek economy intensified in 2009, when the government could not borrow at reasonable interest rates from the capital markets to finance the current budget deficit and refinance the large public debt. In 2010 the European Commission considered there to be major problems in the Greek economy that were concealed by the submission of false data by the Greek government to its regulatory authorities. In order to deal with this difficult situation as efficiently as possible, the Greek economy joined the European Financial Stabilisation Mechanism created by the European Union, the European Central Bank and the International Monetary Fund. During the period of Greece’s accession to this mechanism, Greek companies faced complex financial problems as a result of the general macroeconomic environment. The basis of these problems was limited liquidity, which in many cases was the beginning of the contraction of economic activity and the consequent failure of many companies (Pantelidis et al 2014; Pazarskis et al 2017).

In this difficult economic environment, some of the companies resorted to various methods incompatible with generally accepted principles and methods for the preparation of financial statements – and ultimately to FFS – in an attempt to avoid the worst and to present an improved situation for their financial position and financial results (Spathis 2002; Dunn 2004; Liou 2008; Spatacean 2012; Karlos et al 2017; Borisova et al 2021). The present investigation identified Greek companies listed on the Athens Stock Exchange that used tricks associated with accounting fraud or deceived investors through the publication of data that did not correspond to their actual financial data. In these cases, the FFS involved the deliberate increase of reported expenses and costs and was mainly aimed at reducing profits and, consequently, the corresponding tax (Kanellopoulos 2002; Moisiadou et al 2012; Young 2020).

The aim of this study is to use financial ratios to investigate financial fraud for all Greek companies listed on the Athens Stock Exchange during the years 2008–18 of the financial crisis. Based on the sample data and the analysis of financial ratios, a model is developed to find the key factors related to FFS during the period of financial crisis. Our study’s contributions to the growing literature include its findings on fraudulent financial reporting, as well as examining a period of financial crisis and providing reflections on a recent experience in a small open economy that is a member state of the European Union. In addition, in this study financial ratios are presented that could be used as red flags in the audit process in a period of economic crisis. Therefore, this research contributes significantly to the existing literature in this field and could be appropriately used for the exercise of government policy by tax authorities and other governmental authorities.

The structure of the paper is as follows. Section 2 provides a review of the relevant literature. Section 3 describes the data set and methodology of the research. Section 5 presents the empirical results. Section 5 states the conclusions of the study.

2 Literature review

One of the first studies in this field was conducted by Kanellopoulos (2002), who used tax audit data and other financial variables to determine the characteristics and extent of companies’ financial fraud. Kanellopoulos’s research concluded that the economic sector in which a company is active is an important determinant of tax compliance. Kirkos et al (2005), using data mining techniques, identified companies with falsified financial statements and studied the factors associated with them.

The manipulation of accounting documents, the fragmentary recording of facts, transactions or other important information, and the intentional incorrect application of accounting principles are methods of falsifying financial information, according to Spatacean (2012). Overvaluation of assets is, according to Zager et al (2016), the most common technique used to falsify financial statements. Moisiadou et al (2012) found that the largest percentage of deliberate errors in the financial statements of Greek companies is related to the provisions concerning “doubtful receivables”, “retirement compensation”, “unaudited tax years” and “litigation cases”.

Koumanakos et al (2008) examined the relationship between the reports of certified auditors in Greece and different levels of discretionary earnings manipulation. According to Spathis (2002), FFS, which usually occurs through deliberately misleading revenue and expenditure management, can cause significant financial damage and have a significant impact on unions, customers and investors. FFS can be done in order to increase the price of shares, to take loans from banks or to distribute smaller dividends to shareholders (Ravisankar et al 2010). According to Habib et al (2013), the financial hardship faced by companies is a key incentive for manipulating financial results, which is practiced by managers of companies in difficulty to a much greater extent than their counterparts in healthy companies. Further, according to Baralexis (2004), small companies resort to income manipulation through creative accounting in order to devalue their profits, while large companies do so in order to increase them.

All too often the motive for FFS is to show lower taxable incomes in order to minimize tax liabilities and evade taxation (Spathis 2002; Ravisankar et al 2010; Jan 2018). In Greece, especially after the beginning of 2009, when the contraction of the economy started to accelerate, there was an increase in the phenomenon of tax evasion that led to a reduction in tax compliance (Tagkalakis 2014). This necessitated the strengthening of the tax system’s mechanisms of enforcement, which was achieved by utilizing the appropriate techniques for detecting FFS (Repousis 2016).

However, FFS to show lower taxable incomes increases the cost of attracting new capital, while, conversely, the “beautification” of financial statements in order to attract capital incurs a higher tax burden. Therefore, if accounting income is linked to taxable income, then this trade-off acts as a safeguard against attempts to manipulate financial statements (Eilifsen et al 1999).

The main negative effects of FFS are reduced access to capital markets, falling stock prices, increased costs of raising funds and widening spreads. Of the companies that falsify financial statements, those located in highly developed regions suffer the most serious consequences (Firth et al 2010). Omoye and Eragbhe (2014) concluded that investors and liquidity are the main motivations for companies to falsify financial statements. Kotsiantis et al (2006) highlighted the importance of analyzing the financial ratios of companies publishing false financial statements.

The complexity and scope of portfolio management activities require the implementation of a strong internal control system to oversee financial reporting. Tsipouridou and Spathis (2014), studying the relationship between the opinion of auditors and the management of companies’ profits, found that when the control mechanisms are weak there is a high risk of a nontransparent audit process. Spatacean (2012), investigating the relationship between the effectiveness of internal control and the risk of fraud, found that the more effective the internal control over financial statements is, the smaller the magnitude of their falsification.

3 Research design

3.1 Sample selection

The reference period for the research covers the years 2008–18, starting from the year in which the economic crisis in Greece began. The sample under investigation consists of 23 companies listed on the Athens Stock Exchange for which, as recorded in the auditors’ reports, fraud (specifically, FFS) was detected. Of these falsifications, under the categories defined in the International Standard on Auditing (ISA 700), in 19 cases, auditors’ opinions were classed as “qualified”, while either “disclaimer of opinion” or “adverse opinion” were recorded for the remaining 4 cases (Tsipouridou and Spathis 2014; Pazarskis et al 2017).

Table 1: Classification of financial ratios. [P&L, profit or loss. EBIT, earnings before interest and taxes. EBITDA, earnings before interest, taxes, depreciation and amortization. ROCE, return on capital employed. ROA, return on assets. ROE, return on equity.]
(a) Company size ratios (for sample and control sample comparison)
Variable Ratio Ratio analysis
VAR_1 Operating revenue Net sales+other operating revenues
      =turnover
VAR_2 Total assets Total assets
VAR_3 P&L for period Profit and loss for period
      =net income
VAR_4 Shareholders’ funds Shareholders’ funds
VAR_5 Cashflow Cashflow
(b) Efficiency ratios
Variable Ratio Ratio analysis
VAR01 Profit margin Profit/sales
VAR02 Gross margin Gross profit/sales
VAR03 EBITDA margin Profit before interest, taxes, depreciation
      and amortization/sales
VAR04 P&L before tax Profit or loss before taxes
VAR05 ROCE using P&L before tax Profit or loss before taxes
      /(shareholders’ funds
       + reserves+long-term loans)
VAR06 ROCE using net income Net income
      /(total assets
       - short-term liabilities)
VAR07 ROA using P&L before tax Profit or loss before taxes/total assets
VAR08 ROE using P&L before tax Profit or loss before taxes
      /stakeholders’ equity
VAR09 EBIT margin Profit before interest and taxes/sales
VAR10 ROE using net income Net income/shareholders’ equity
VAR11 ROA using net income Net income/total assets
(c) Liquidity ratios
Variable Ratio Ratio analysis
VAR12 Current ratio Current assets/current liabilities
VAR13 Liquidity ratio (Current assets-stocks)
      /current liabilities
(d) Activity ratios
Variable Ratio Ratio analysis
VAR14 Net turnover of assets Sales/(shareholders’ funds
        + noncurrent liabilities)
VAR15 Collection period (Debtors/sales)×360
VAR16 Credit period (Creditors/sales)×360
VAR17 Stock turnover Net sales/stocks
VAR18 Cashflow/operating revenue Cashflow/operating revenue
VAR19 Enterprise value/EBITDA Enterprise value/EBITDA
VAR20 Export revenue Export revenue
    /operating revenue   /operating revenue
(e) Capital structure ratios
Variable Ratio Ratio analysis
VAR21 Solvency ratio (asset based) Shareholders’ funds/total assets
VAR22 Solvency ratio (liability based) Shareholders’ funds/total liabilities
VAR23 Gearing Long-term debt
      /shareholders’ funds
VAR24 Shareholders’ liquidity ratio Shareholders’ funds
      /(long-term liabilities
       + risk provisions and expenses)
VAR25 Interest cover EBIT/interest expenses

To complete the analysis, a control sample consisting of 23 companies without FFS was selected for comparison. The selection criteria for the control sample were the following: the companies belonged to the same sectors as the companies that falsified their financial statements, and their total assets, turnover and number of employees were of comparable size to the companies with falsified statements. These data were obtained from data published by the Athens Stock Exchange. The specific factors used in this research have been used in many other studies in the relevant scientific literature (see, for example, Spathis 2002; Omoye and Eragbhe 2014; Kanapickiene and Grundiene 2015; Zainudin and Hashim 2016). The accounting measures, or variables, used to compare the financial statements of both the control sample and the sample of companies with falsified statements are operating revenue, total assets, profit and loss (P&L) for the period, shareholders’ funds and cashflow (see VAR_1 to VAR_5 in Table 1).

3.2 Ratios used as quantitative variables

As financial ratios provide useful information about the falsifications of the listed companies (Dunn 2004; Kotsiantis et al 2006; Koumanakos et al 2008; Tsipouridou and Spathis 2014; Kanapickiene and Grundiene 2015), in the present study the processing of the sample and the examination of the financial statements was performed using appropriate ratios. Table 1 shows all the financial ratios that have been used and analyzed.

The companies’ financial statements, the financial data and the auditors’ reports were taken from the website of the Athens Stock Exchange,11 1 URL: http://www.athexgroup.gr/. which provides relevant financial data. Any additional necessary data were obtained from the database of the library of the International Hellenic University.

3.3 Methodology

To investigate the relationship between the two samples, ie, fraudulent companies and trustworthy companies, the average of the 30 ratios used was calculated. The Student t test was used to compare the means of the ratios of the two independent variables, for the whole 10-year period. For the statistical processing of the data, SPSS software was used (IBM Corp. 2017). Comparisons of the means of the ratios of VAR_1, …, VAR_5 show the relationship between the selected sample and the appropriate control sample, as well as the differences in individual ratios between the samples. Further, comparisons of the average of the remaining 25 ratios (VAR01,…,VAR25) indicate any significant differences in the mean values of proportions of the two samples. Manipulation of companies’ financial statements is probably related to the emergence of high statistical significance for these ratios.

In addition, the statistical method of logistic regression analysis (Demaris 1992; Menard 2002) was used to detect FFS. This study seeks to find out which factors significantly affect companies with FFS (Spathis 2002; Pazarskis et al 2017) by analyzing a data set that includes FFS and non-FFS data. Thus, the following logit model was formulated to identify FFS-related ratios using the financial ratios listed in Table 1:

  E(y)=exp(β0+β1x1+β2x2++βnxn)1+exp(β0+β1x1+β2x2++βnxn),  

where

  • y=1 if an FFS firm is chosen, and y=0 if a non-FFS firm is chosen;

  • E(y)=p(FFS firm is chosen)=Π, where Π denotes the probability that y=1;

  • β0 is the intercept term;

  • β1,β2,,βn are the regression coefficients of the independent variables; and

  • x1,x2,,xn are the independent variables.

Thus, the model is presented as

  FFS=β0+β1(VAR01)+β2(VAR02)++β25(VAR25)+e,  

where

  FFS={1if an FFS firm is chosen,0otherwise.  

4 Results

Table 2: Comparison results (t-tests) for characteristics of FFS and non-FFS companies. [***, ** and * denote that the change of the mean is significantly different from zero at significance levels of 0.01, 0.05 and 0.10, respectively, as calculated by comparing the average of two independent subassemblies (two independent sample mean t-tests) at ratios of the sample. More specifically, for the three significance levels above, the classification levels with respect to the p-value are the following: ***p<0.01 as strong evidence against H0, **0.01p<0.05 as moderate evidence against H0, *0.05p<0.10 as minimal evidence against H0 and 0.10p as no real evidence against H0.]
      Standard      
  Mean deviation      
           
Variable FFS non-FFS FFS non-FFS 𝒕-value 𝒑-value 95% confidence interval
VAR_1 -221 914 -341 225 1 073 888 1 645 231 -0.34 0.734 0 (-818 692; 580 071)
VAR_2 -600 011 -230 023 2 726 097 0 652 858 -0.68 0.504 0 (-751 496; 1 491 472)
VAR_3 0-15 396 00-8 916 0042 627 0021 950 -0.75 0.459 00(-23 977; 11 017)
VAR_4 -180 763 -102 813 0 980 630 0 325 877 -0.39 0.700 0 (-332 138; 488 038)
VAR_5 00-3 459 00-5 260 0056 840 0013 810 -0.76 0.451 00(-14 679; 32 117)

The initial processing of the survey data used five accounting measures, or variables, (VAR_1 to VAR_5) to compare the characteristics of the sample and the control sample (see Table 2). The results show that the five variables are not significantly affected. Therefore, the sample and the control sample do not differ significantly in these five selected accounting measures. Consequently, there is no relationship between the selected sample and the control sample.

The 25 ratios belonging to the four main categories of ratios (namely, profitability, liquidity, capital structure and cashflows) were examined using statistical methods. From the statistical analysis involving the comparison of the average values it emerged that 9 out of the 25 ratios are significantly affected (see Table 3). In particular, the ratios VAR01, VAR07 and VAR11, which evaluate the efficiency of companies, are the variables that were most significantly affected, since the companies without falsification show better results in their financial statements. The ratios VAR12 and VAR13, which determine the liquidity of companies, are also strongly related to the increased likelihood of FFS. In these ratios too, companies without falsification show better results. In addition, the VAR15 activity ratio and the VAR21, VAR23 and VAR25 capital structure ratios indicate a significant likelihood of FFS, which is a result of the fact that companies in the FFS sample perform worse than companies without FFS.

Table 3: Comparison results (t-tests) for ratios from FFS and non-FFS companies. [***, ** and * denote the rejection of the null hypothesis at significance levels of 0.01, 0.05 and 0.1, respectively.]
      Standard      
  Mean deviation      
          95% confidence
Variable FFS non-FFS FFS non-FFS 𝒕-value 𝒑-value interval
VAR01 000-23.5 000-7.3 00022.5 00022.1 -2.55 0.014** (-28.99; -3.39)
VAR02 000-10.2 -0027.4 00034.2 00034.1 -1.94 0.057* (-34.91; 0.52)
VAR03 000-11.7 000-3.9 00023.5 00025.4 -1.11 0.273 (-21.93; 6.35)
VAR04 -12 456 -6 348 32 249 15 622 -0.92 0.366 (-19 630; 7415)
VAR05 000-32.3 000-9.1 00061.8 00043.6 -1.19 0.252 (-64.7; 18.3)
VAR06 000-25.4 000-8.4 00042.6 00036.3 -1.23 0.237 (-46.5; 12.3)
VAR07 000-12.4 000-4.7 00013.6 00012.2 -2.27 0.027** (-14.45; -0.89)
VAR08 00-153 000-2 00 262 00 110 -1.70 0.110 (-279.9; 31.6)
VAR09 000-18.5 00-11.3 00023.0 00025.7 -1.06 0.295 (-20.85; 6.48)
VAR10 000-78 00-31 00 119 00 111 -1.31 0.200 (-119.9; 26.1)
VAR11 000-15.8 000-6.2 00013.0 00012.0 -3.07 0.003*** (-15.89; -3.36)
VAR12 0000-0.578 -0001.54 00000.503 00001.14 -4.44 0.000*** (-1.402; -0.526)
VAR13 0000-0.426 -0001.086 00000.393 00000.995 -3.51 0.001*** (-1.040; -0.281)
VAR14 0000-1.49 -0006.0 00002.06 00025.5 -0.96 0.343 (-14.10; 5.06)
VAR15 00-293 -0 157 00 253 00 184 -2.28 0.027** (15.9; 254.7)
VAR16 00-188 -0 141 00 192 00 253 -0.80 0.429 (-71.2; 165.0)
VAR17 0000-6.6 -0007.46 00013.5 00007.90 -0.28 0.781 (-7.20; 5.45)
VAR18 000-17.2 00-10.4 00025.7 00025.5 -0.86 0.395 (-22.68; 9.18)
VAR19 000-30.0 -0018.7 00041.7 00026.6 -0.61 0.565 (-33.9; 56.4)
VAR20 000-19.1 -0015.8 00033.8 00028.8 -0.40 0.690 (-13.18; 19.75)
VAR21 0000-1.3 -0034.3 00043.2 00039.4 -3.13 0.003*** (-58.6; -12.7)
VAR22 000-24.3 -0039.2 00022.4 00038.4 -1.21 0.239 (-40.4; 10.7)
VAR23 00-302 -0096 00 264 00 140 -2.63 0.019** (39.3; 373.8)
VAR24 0000-2.6 -0000.5 00029.8 00019.5 -0.32 0.749 (-11.29; 15.57)
VAR25 0000-4.9 -0001.36 00010.4 00007.89 -2.21 0.035** (-12.14; -0.46)

Given that the analysis showed that 9 of the 25 ratios are statistically significant, it is presumed that these ratios could reveal the manipulation of financial statements (see, for example, Tsipouridou and Spathis 2014). Specifically, it turned out that the averages of these financial ratios are much higher in companies without FFS. Profitability, which refers to the efficiency of companies, seems to be a strong incentive to falsify financial statements. Therefore, the evaluation of the effectiveness of financial ratios could contribute to the detection of fraud. VAR11, which measures the return on invested capital, is statistically significant (p<0.01) and is closely related to the increased likelihood of FFS (Spathis 2002; Kirkos et al 2005; Tsipouridou and Spathis 2014; Omeye and Eragbhe 2014). Further, the VAR07 ratio, which is also used to determine the effectiveness of invested capital, taking into account pretax results, is statistically significant (p<0.05) and provides strong indications of FFS. The ratio of the profit margin (VAR01) is also significantly related to the probability of fraud (p<0.05). In addition, the VAR15 collection period activity ratio appears to be significantly affected (p<0.05).

The determination of the short-term financial position of the companies and their ability to fulfill their current obligations is reflected in the ratios VAR12 and VAR13, which show a significant correlation (p<0.01) with the probability of FFS (Omeye and Eragbhe 2014). In addition, the long-term ability of companies to meet their obligations and the protection provided to investors, as expressed by the VAR21 ratio (p<0.01), as well as the VAR23 leverage ratio (Spathis 2002; Kirkos et al 2005; Kanapickiene and Grundiene 2015) and the VAR25 interest coverage ratio (Omeye and Eragbhe 2014), which both took a value of p<0.05, seem to be directly related to the occurrence of FFS, given that the means of the ratios for companies’ without falsification are obviously better.

Since univariate tests provide valuable information about a large number of variables in a sample, in the present study it was decided that they should be used. Each possible case of FFS presents peculiarities and many variables that are not important in a univariate test are likely to be useful indicators for FFS (Spathis 2002). Further, this study also aims to develop a model that includes, if possible, all the variables at the same time. In order to determine whether there is any correlation between the variables, a number of multivariate tests with stepwise logistic regression were applied to find which of the examined ratios best fits and is most illustrative of the financial statements. Table 4 presents the results for the gradual accounting regression of the model.

The final proposed model correctly classifies the total sample with an accuracy of 78.4%. In particular, 91.7% of companies without FFS and 53.8% of companies with FFS were classified correctly. The relationship between the dependent variable, concerning the nonexistence or existence of FFS, and the independent variables is statistically significant (χ2=13.65, p<0.001), while RL2=0.43, which points to a satisfactory relationship.

Table 4: Stepwise logistic regression results for FFS and non-FFS companies. [***, ** and * denote the rejection of the null hypothesis at significance levels of 0.01, 0.05 and 0.1, respectively.]
Independent Unstandardized Standard  
variables coefficient (β) error Significance
VAR15 0-0.006 0.002 0.019**
VAR23 0-0.006 0.003 0.045**
Constant 0-2.697 0.832 0.001***
χ2 -13.65   0.001***
RL2 0-0.425    
N 23    
Correctly predicted      
FFS firms 53.8%    
Non-FFS firms 91.7%    
Overall 78.4%    

Specifically, the first variable in Table 4 (VAR15) shows an increased probability of correctly classifying a company as having FFS (b=0.006, p=0.019). This fact shows that companies with a high collection period ratio are very likely to falsify their financial statements. On the other hand, companies without FFS achieve higher values in VAR23. Spathis (2002) found similar results for Greek companies before the financial crisis. The variable VAR23 has a negative impact (b=-0.006, p=0.045) and this reveals that companies with a high interest coverage have an increased probability of being classified as being trustworthy.

5 Conclusions

It is a fact that in the modern economic environment, which is characterized by its instability, companies make great efforts to survive. The shocks brought about by the Greek financial crises create insurmountable obstacles for companies and make them vulnerable. The purpose of this research was to investigate the presence of FFS for companies listed on the Athens Stock Exchange during the period 2008–18. The financial data of 23 companies whose financial statements include falsifications, according to the auditors’ reports, were analyzed, as well as the corresponding data of 23 other companies in the same sector that did not present falsifications of their financial statements. The research was carried out through the use and analysis of 30 ratios used as variables. The specific ratios were those relating to the size of the examined companies and belonged to four main categories: profitability ratios, liquidity ratios, capital structure ratios and cashflow ratios.

From the results obtained after the statistical analysis of the ratios, it emerged that FFS significantly affects 9 out of 25 of the ratios. These 9 ratios could be used alongside other measures to audit for FFS. In particular, the results of the investigation showed that the average values of the ratios for companies for which FFS was found do not exceed the corresponding values of the companies that do not present falsification. Also, in the present work, multiple varying combinations of all the financial ratios were attempted via stepwise logistic regression in order to develop a comprehensive model that can detect factors related to FFS. The proposed model contains two ratios, or variables, with significant coefficients. These ratios are “collection period” and “gearing”, and could be red flags in an audit process.

The proposed model is able to correctly classify the total sample with an accuracy rate of over 78%. Therefore, based on the results of the research, it appears that it is possible to detect FFS through the analysis of published financial statements, as in a time of financial crisis the model works effectively in detecting FFS. The resulting model could be used for accounting research and for auditing to detect FFS, in combination with alternative methods (multicriteria analysis, neural networks), for both listed and unlisted companies as well as for different time periods. The model could be used as a tool by internal and external auditors, the banking system and tax and other government authorities in order to reliably inform those directly concerned, especially in times of economic crisis, about the real financial situation of examined companies. Further research could be carried out to identify the specific characteristics, relating to the sector in which they operate, size, corporate governance and the effectiveness of internal and external audits, of companies that are more likely to falsify financial statements.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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