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Detecting false financial statements using published data: some evidence from Greece

Charalambos T. Spathis Aristotle University of Thessaloniki, Department of Economics, Division of Business Administration, Thessaloniki, Greece

Keywords Financia l statements , Fraud, Regression analysis, Greece

Abstract This paper examines published data to develop a model for detecting factors associate d with false financia l statements (FFS). Most false financial statements in Greece can be identified on the basis of the quantity and content of the qualification s in the reports filed by the auditors on the accounts. A sample of a total of 76 firms include s 38 with FFS and 38 non-FFS. Ten financial variables are selected for examination as potential predictors of FFS. Univariate and multivariat e statistica l technique s such as logistic regression are used to develop a model to identify factors associate d with FFS. The model is accurate in classifyin g the total sample correctly with accuracy rates exceedin g 84 per cent. The results therefore demonstrat e that the models function effectivel y in detecting FFS and could be of assistance to auditors, both internal and external, to taxation and other state authorities and to the banking system.

Managerial Auditing Journal 17/4 [2002 ] 179±191 # MCB UP Limited [ISSN 0268-6902] [DOI 10.1108/0268690021042432 1]

Introduction References to false financial statement (FFS) are increasingly frequent over the last few years. Falsifying financial statements primarily consists of manipulating elements by overstating assets, sales and profit, or understating liabilities, expenses, or losses. When a financial statement contains falsifications so that its elements no longer represent the true picture, we speak of fraud. Management fraud can be defined as ``deliberate fraud committed by management that injures investors and creditors through misleading financial statements’’ (Eliott and Willingham, 1980). For Wallace (1995), fraud is `à scheme designed to deceive; it can be accomplished with fictitious documents and representations that support fraudulent financial statements’’. The International Federation of Accountants issued in 1982 the International Statement of Auditing (ISA) No. 11: Fraud and Error and explains that the characteristic which differentiates error from fraud is intent. Errors result from unintentional actions (Colbert, 2000). The American Institute of Certified Public Accountants (AICPA) (1983) in Statement on Auditing Standards (SAS) No. 47 notes that `èrror refers to unintentional misstatements or omissions of amounts or disclosures in the financial statements’’. SAS No. 82 (AICPA, 1997) reiterates the idea that fraud is an intentional act, and fraud frequently includes the perpetrator(s) feeling pressure or having an incentive to commit fraud and also perceiving an opportunity to do so. Fraud and white-collar crime has reached epidemic proportions in the USA. Some estimates suggest that fraud costs US business more than $400 billion annually (Wells, 1997). The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/0268-6902.htm

In Greece, the issue of false financial statements has lately been brought more into the limelight in connection primarily with: the increase in the number of companies listed on the Athens Stock Exchange and the raising of capital through public offering; and attempts to reduce the level of taxation on profits. The year 2000 has been very difficult for the Greek stock market, which has suffered from stagnation both in terms of share prices and liquidity. This fact, along with the recent pervasive record of false financial statements, increased the interest of the authorities, stock market, Ministry of the Economy and the banking sector in earlywarning systems. In this context, the absence of a Greek study on the subject is striking. This paper intends to address this need in the existing literature. For this purpose, univariate and multivariate statistical tools were employed to investigate the usefulness of publicly available variables for detecting FFS. A total of ten variables were found to be possible indicators of FFS. These include the ratios: debt to equity, sales to total assets, net profit to sales, accounts receivable to sales, net profit to total assets, working capital to total assets, gross profit to total assets, inventory to sales, total debt to total assets, and financial distress (Z-score). Using stepwise logistic regression, two models were developed with a high probability of detecting FFS in a sample. The models include the variables: the inventories to sales ratio, the ratio of total debt to total assets, the working capital to total assets ratio, the net profit to total assets ratio, and financial distress (Z-score). The paper is organised as follows: the second section reviews research on false financial statements carried out up to now. The third section underlines the methodologies employed, the variables, the method and the sample data used in the present study. The fourth section describes

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Charalambos T. Spathis Detecting false financial statements using published data: some evidence from Greece

the empirical results and discussion obtained using univariate tests and multivariate logistic regression analysis. Finally, in the fifth section come the concluding remarks.

Managerial Auditing Journal 17/4 [2002] 179±191

Previews research Characteristics of FFS No one knows how many business failures are actually caused by fraud, but undeniably lots of businesses, especially small firms, go bankrupt each year due to fraud losses. In the USA incidences of fraud cut across all industries with greatest losses apparent (fraud losses by industry) in real estate financing, manufacturing, banking, oil and gas, construction, and in health care (Wells, 1997). Losses can occur in almost any area, certainly not just in cash areas. Losses in cash actually represent the lowest level of fraud. Accounts receivable, expenditures for services, and inventory losses are each three times higher than those in cash. Fraud is not just a problem in large firms. Small businesses with 1-100 employees are also susceptible. This is a serious problem because fraud in a small firm has a greater impact, as the firm does not have the resources to absorb the loss (Wells, 1997). In a global economy and multinational trade, the trend of international fraud affects all countries (Vanasco, 1998). A report by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) compiled by Beasley et al. (1999) examined fraudulent financial reporting from 1987-1997 by US public companies. Some of the most critical insights of the study are: The companies committing fraud generally were small, and most (78 per cent of the sample) were not listed in the New York or American Stock Exchanges. Incidences of fraud went to the very top of the organisations concerned. In 72 per cent of the cases, the CEO appeared to be associated with the fraud, and in 43 per cent the CEO was associated with the financial statement fraud. The audit committees and boards of the respective companies appeared to be weak. Most audit committees rarely met, and the companies’ boards of directors were dominated by insiders and others outsiders ``grey’’ directors, with significant equity ownership and apparently little experience of serving as directors of other companies. A total of 25 per cent of the companies did not have an audit committee. The founders and board members owned a significant portion of the companies. In

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nearly 40 per cent of the companies, authorizations for votes by proxy provided evidence of family relationships among the directors and/or officers. The founder and current CEO were the same person or the original CEO/President was still in place in nearly half of the companies. Severe consequences resulted when companies commit fraud, including bankruptcy, significant changes in ownership, and suspension from trading in national exchanges. Most techniques for manipulating profits can be grouped into three broad categories ± changing accounting methods, fiddling with managerial estimates of costs, and shifting the period when expenses and revenues are included in results (Worthy, 1984). Other false statements include manipulating documents, altering test documents, and producing false work reports (Comer, 1998). For example, recording revenue on shipments after year-end by backdating shipment documents. Asset misappropriation schemes include the theft of company assets e.g. cash and inventory. The study of Vanasco (1998) examines the role of professional associations, governmental agencies, and international accounting and auditing bodies in promulgating standards to prevent fraud in financial statements and other white-collar crimes. It also examines several fraud cases. The cases examined show that the cash, inventory, and related party transactions are prone to fraud. Auditors assign a high-risk index to the potential misappropriation of inventory, cash defalcation, and conflict of interest. Typical financial statement fraud techniques involved the overstatement of revenues and assets (Beasley et al., 1999). Over half the frauds involved overstating revenues by recording revenues prematurely or fictitiously. Many of those revenue frauds only affected transactions recorded right at the end of significant financial reporting periods (i.e. quarter-end or year-end). About half the frauds also involved overstating assets by understating allowances for receivables, overstating the value of inventory, property, plant and equipment and other tangible assets, and recording assets that did not exist. Fraudulent statements are the most costly schemes per case. In spite of being the most common and the smallest loss per case, asset misappropriation presents in total the largest losses of the categories. Fraudulent statements, on the other hand, have the lowest total losses. Corporate falsification

Charalambos T. Spathis Detecting false financial statements using published data: some evidence from Greece Managerial Auditing Journal 17/4 [2002] 179±191

can also be classified as the committed by insiders for the company (violation of government regulations, i.e. tax, securities, safety and environmental laws). Senior managers might perpetrate financial statement falsifications to deceive investors and lenders or to inflate profits and thereby gain higher salaries and bonuses. Higson (1999) analysed the results of 13 interviews with senior auditors/forensic accountants on whether their clients report suspected fraud to an external authority. Although some companies do report it, quite a number seem reticent about reporting. There appear to be three contributing factors: 1 the imprecision of the word ``fraud’’; 2 the vagueness of directors’ responsibilities; and 3 the confusion over the reason for the reporting of suspected fraud. In 1997, the Auditing Standards Board issued Statement on Auditing Standards (SAS) No. 82: Consideration of Fraud in a Financial Statement Audit (AICPA, 1997). This Standard requires auditors to assess the risk of fraud on each audit and encourages auditors to consider both the internal control system and management’s attitude toward controls, when making this assessment (Caplan, 1999). This SAS No. 82, which supersedes SAS No. 53, clarifies but does not increase auditors’ responsibilities to detect fraud (Mancino, 1997). Risk factors ``red flags’’ that relate to fraudulent financial reporting may be grouped in the following three categories (SAS No. 82): 1 Management’s characteristics and influence over the control environment. These pertain to management’s abilities, pressures, style, and attitude relating to internal control and the financial reporting process. For example, strained relationships between management and the current or previous auditor. 2 Industry conditions. These involve the economic environment in which the entity operates. For example, a declining industry with increasing business failures. 3 Operating characteristics and financial stability. These pertain to the nature and complexity of the entity and its transactions, the entity’s financial condition, and its profitability. For example, significant related-party transactions not in the ordinary course of business or with related entities not audited or audited by another firm. Moreover, similarly to the well-examined area of bankruptcy prediction, research on FFS has been minimal. In an important

study, Matsumura and Tucker (1992) discuss an extensive theoretical foundation of auditor/manager strategic interaction. This study investigates the effects of the following variables: auditor’s penalty; auditing standard requirements; the quality of the internal control structure; and audit fee. The interaction between manager and auditor is examined with fraud as the focus. The model encompasses all irregularities, which it categorises into two types: fraud (misrepresentation of fact) and defalcations (misappropriation of assets). For a strategy with reference to auditing and fraud, Morton (1993) notes that auditors often use sampling methods to audit probabilistically and the probability of auditing is usually contingent on information about that item. His model for optional audit policy yields an exact solution that can generate some useful comparative statistics. As the audit cost decreases, the audit region (or extent of the sample) expands and the amount of misreporting declines. Bloomfield (1997) uses behavioural laboratory experiments, which indicated amongst other conclusions that auditors have more trouble assessing fraud risk when they face high legal liability for audit failure and the firm they are auditing has strong internal controls in place. Another study examines how reliance on a mechanical decision aid is effected by decision consequences (Boatsman et al., 1997). Experimental participants made planning choices based on available input from actual management fraud cases before and after receiving the decision aid’s predictions of fraud probability. The experiment documents two types of nonreliance: 1 intentionally shifting the final planning judgement away from the aid’s prediction even through this prediction supports the initial planning judgement; and 2 ignoring the aid when its prediction does not support the initial planning judgement. The study of Bonner et al. (1998) examines whether certain types of financial reporting fraud result in a higher likelihood of litigation against independent auditors and develops a new taxonomy to document types of fraud that includes 12 general categories. They find that auditors are more likely to be sued when the financial statement frauds are of types that most commonly occur or when the frauds arise from fictitious transactions. Auditors’ sensitivity with respect to clients’

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ethical status and their assessment of the likelihood of fraud is examined by Abdolmohammadi and Owhoso (2000). The results indicate that senior auditors were sensitive to ethical information, e.g. regarding clients’ service to the community, in making their assessment of the likelihood of fraud, deeming such clients less likely to have committed fraud.

Detecting FFS Statement of Auditing Standards (SAS) No. 82 (AICPA, 1997) requires auditing firms to detect management fraud. This increases the need to detect management fraud effectively. Detecting management fraud is a difficult task using normal audit procedures (Porter and Cameron, 1987; Coderre, 1999). First, there is a shortage of knowledge concerning the characteristics of management fraud. Second, given its infrequency, most auditors lack the experience necessary to detect it. Finally, managers are deliberately trying to deceive the auditors (Fanning and Cogger, 1998). For such managers, who understand the limitations of an audit, standard auditing procedures may be insufficient. These limitations suggest the need for additional analytical procedures for the effective detection of management fraud.

` ... Auditors using the expert system exhibited the ability to discriminate better among situations with varying levels of management fraud risk and made more consistent decisions regarding appropriate audit actions.... ’

Recent work has attempted to build models to predict the presence of management fraud. Results from logit regression analysis of 75 fraud and 75 no-fraud firms indicate that no-fraud firms have boards with significantly higher percentages of outside members than fraud firms (Beasley, 1996). Hansen et al. (1996) use a powerful generalized qualitativeresponse model to predict management fraud based on a set of data developed by an international public accounting firm. The model includes the probit and logit techniques. An experiment was conducted to examine the use of an expert system developed to enhance the performance of auditors (Eining et al., 1997). Auditors using the expert system exhibited the ability to discriminate better among situations with varying levels of management fraud risk and made more consistent decisions regarding appropriate audit actions. Green and Choi (1997) presented the development of a neural network fraud classification model employing endogenous financial data. A

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classification model created from the learned behaviour pattern is then applied to a test sample. During the preliminary stage of an audit, a financial statement classified as fraudulent signals the auditor to increase substantive testing during fieldwork. Fanning and Cogger (1998) use an artificial neural network (ANN) to develop a model for detecting management fraud. Using publicly available predictors of fraudulent financial statements, they find a model of eight variables with a high probability of detection. Summers and Sweeney (1998) investigate the relationship between insider trading and fraud. They find, with the use of a cascaded logit model, that in the presence of fraud, insiders reduce their holdings of company stock through high levels of selling activity as measured by either the number of transactions, the number of shares sold, or the dollar amount of shares sold. Beneish (1999) investigates the incentives and the penalties related to earnings overstatements primarily in firms that are subject to accounting enforcement actions by the Securities and Exchange Commission (SEC). He finds that the managers are likely to sell their holdings and exercise stock appreciation rights in the period when earnings are overstated, and that the sales occur at inflated prices. The evidence suggests that the monitoring of managers’ trading behaviour can be informative about the likelihood of earnings overstatement. Eilifsen et al. (1999) and Hellman (1999) analyse the link between the calculation of taxable income and accounting income influences on the incentive to manipulate earnings, as well as the demand for regulation and verification of both financial statements and tax accounts. Abbot et al. (2000) examine and measure the audit committee independence and activity in mitigating the likelihood of fraud. Using the logistic regression analysis they find that firms with audit committees which are composed of independent directors and which meet at least twice per year are less likely to be sanctioned for fraudulent or misleading reporting. Prior work in this field has examined several variables related to data from audit work papers and from financial statements, with various techniques, for their usefulness in detecting management fraud (Fanning et al., 1995). In this study, we examine indepth publicly available data from firms’ financial statements for detecting FFS. Greece has entered the new millennium with a very positive economic picture despite some underlying structural inflation, and a


stable social and political environment. In June 2000, the leaders of the member states of the European Union agreed to accept Greece as a member of the Economic and Currency Union. Greek enterprises continue to make progress in profitability and efficiency. The improvement in capital ratios, the increase in investment, the significant increase in profits and the return on capital are just some of the indicators which make those involved in Greek industries feel much more optimistic than they did a few years ago. The year 2000 has been very difficult for the Greek Stock Market, which has suffered from stagnation both in terms of share prices and liquidity. This fact, along with the recent pervasive record of false financial statements, increased the interest of the authorities, stock market, Ministry of the Economy and the banking sector in early warning systems. In Greece, the issue of falsification of FS has become of particular significance lately, and this is due to the following: Attempts to reduce the level of tax on profits. The increase in numbers of firms listed on the Athens Stock Exchange, the large increases in capital obtained by listed companies, the extensive cooperation achieved through takeovers and mergers and the attempt by management to meet the expectations of shareholders. There is currently a backlog of companies who are entering the Stock Exchange or parallel market for the first time. The new stock market (NEHA) for small and ``new economy’’ enterprises will begin its operation in 2001, and there is a strong interest in listing. To avoid instances of false financial statements being submitted by candidates and other companies, examination by an auditor is obligatory. Control by auditors is also obligatory regarding the use of capital raised through public floatation. The goal of this research is to identify financial factors to be used by auditors in assessing the likelihood of FFS. The following section discusses the methodology proposed to detect FFS.

Methodology Sample Most FFS in Greece can be identified on the basis of the quantity and content of the qualifications in the reports filed by the auditors on accounts for: depreciation, forecast payment defaults, forecast staff severance pay, participation in other companies, and fiddling of accounts for tax

purposes and other qualifications. The classification of a financial statement as false was based on: the inclusion in the auditors’ reports of opinions of serious doubt as to the correctness of accounts; the observations by the tax authorities regarding serious taxation intransigencies which seriously alter the company’s annual balance sheet and income statement; the application of Greek legislation regarding negative net worth; inclusion of the company in the Athens Stock Exchange categories of `ùnder observation’’ and ``negotiation suspended’’ for reasons associated with falsification of the company’s financial data; and the existence of court proceedings pending with respect to FFS or serious taxation contraventions. The sample of a total of 76 manufacturing firms includes 38 with FFS and 38 with nonFFS (the sample did not include financial companies). For the non-FFS firms of the sample, no published indication of FFS behaviour was uncovered in a search of databases and the relevant auditors’ reports. It has to be noted that while the process of ensuring that none of the considered non-FFS firms had issued FFS was extensive, it cannot guarantee that the financial statements in this group of firms were not falsified. It only guarantees that there was no publicly available FFS information in the auditors’ reports and other sources, such as the Stock Exchange and the Ministry of Finance. Since this only covers information known to this date, there is no guarantee that future information will not prove that members of the control group issue FFS. Auditors check all the companies included in the sample. All public limited companies (socieÂteÂ s anonymes) and limited liability companies are obliged to submit to an auditor’s control when they fulfil two of the three following criteria: 1 total revenues are over 1 billion Greek Drachmas (GRD) ($2,717 million, £1,852 million); 2 total assets are over 500 million GRD ($1.359 million, £926,000); and 3 the average number of employees is over 50 (Ballas, 1994; Caramanis, 1997). Some of the characteristics of the full sample companies are presented in Table I. There is a statistically significant difference between average profits of FFS firms, with losses averaging at 368 million GRD ($1 million, £680,000), and non-FFS companies averaging a profit of 895 million

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Table I Characteristics (means) of firms’ means and t-tests Characteristics

Non-false

False

t-test

Total assets Inventories Working capital Equity Sales Net profit

10,551 1,859 2,281 6,206 9,195 895

7,315 1,036 369 2,474 3,966 ±368

1.063 1.420 2.457** 1.686* 2.059** 2.547***

Notes: The amounts are reporting in million GRD t-test: df = 74, (two-tailed) * Significance at 10% level ** Significance at 5% level, *** Significance at 1% level GRD ($2.432 million, £1.657 million) (t ˆ 2:547, p < 0:000). Similarly a significant difference can be observed in average working capital between FFS firms with 369 million GRD ($1.003 million, £683,000), and non-FFS firms with 2,281 million GRD ($6.198 million, £4.224 million) (t ˆ 2:457, p < 0:05). Mean equity also gives a statistically significant difference between FFS firms and non-FFS firms with 2,474 million GRD ($6.723 million, £4.581 million), and 6,206 million GRD respectively ($16.864 million, £11.492 million) (t ˆ 1:686, p < 0:010). With regard to inventories, although mean value for FFS firms is 1,036 million GRD ($2.815 million, £1.918 million), and 1,859 million GRD ($5.051 million, £3.442 million) for non-FFS firms, the difference is not statistically significant (t ˆ 1:420, p < 0:160).

Variables The variables in this study come from many sources. To find variables, prior work on the topic of FFS was carefully considered. Such work as that of Green and Choi (1997), Hoffman (1997), Hollman and Patton (1997), Zimbelman (1997), Beasley (1996), Bologna et al. (1996), Arens and Loebbecke (1994), Bell et al. (1993), Schilit (1993), Davia et al. (1992), Green (1991), Stice (1991), Loebbecke et al. (1989), Palmrose (1987), and Albrecht and Romney (1986) contained suggested indicators of FFS. Initially, a set of 17 financial ratios was formed. However, to avoid ratios providing the same information due to high correlations, it was decided to exclude highly correlated ratios, while retaining ratios describing all aspects of financial performance, including profitability, solvency/liquidity and managerial performance (Courtis, 1978). Except for the correlation analysis, the statistical significance of the financial ratios was also considered through t-tests. This

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combination of correlation analysis and ttests led to the selection of a limited set of ten financial ratios, which provide meaningful and non-overlapping information (as much as possible). The selected ratios for FFS detection are discussed below. There is a relationship between a company’s choice of accounting valuation methods and type of depreciation with FFS. Managers involved in fraudulent activities may attempt to disguise their actions through accounting choices. By selecting different valuation methods, management can increase or decrease stated values for various variables (Dhaliwal et al., 1982). It is an open question whether a high debt structure is associated with FFS (Persons, 1995). A high debt structure may increase the likelihood of FFS since it shifts the risk from equity owners and managers to debt owners. Research suggests that the potential for wealth transfer from debt holders to managers increases as leverage increases (Chow and Rice, 1982). Management may manipulate financial statements, given the need to meet certain debt covenants. This suggests that higher levels of debt may increase the probability of FFS. This is measured through the difference in the ratio of debt to equity (DEBT/EQ) and total debt to total assets (TD/TA). There are certain financial statements that are more likely to be manipulated by management. These variables include sales, accounts receivable, allowance for doubtful accounts and inventory (Shilit, 1993; Green, 1991; Loebbecke et al., 1989; Wright and Ashton, 1989). The subjective nature of the judgements involved with these accounts makes them more difficult to audit. Persons (1995), Schilit (1993), Stice (1991), Green (1991) and Feroz et al. (1991) suggest that management may manipulate accounts receivable. The fraudulent activity of recording sales before they are earned may show as additional account receivable. We tested this by considering the ratio of account receivable to sales (REC/SAL) (Fanning and Cogger, 1998; Green, 1991; Daroca and Holder, 1985). Accounts receivable and inventory depend on the subjective judgement involved in estimating uncollected accounts and obsolete inventory. Because subjective judgement is involved in determining the value of these accounts, management may use these accounts as tools for financial statement manipulation (Summers and Sweeney, 1998). Loebbecke et al. (1989) found that the inventory account and accounts receivable were involved in 22 per cent and 14 per cent, respectively, of frauds in their sample.


Many researchers such as Vanasco (1998), Persons (1995), Schilit (1993) and Stice (1991) also suggest that management may manipulate inventories. The company may not match sales with the corresponding cost of goods sold, thus increasing gross margin, net income and strengthening the balance sheet. Another type of manipulation involves reporting inventory at lower than cost or market value. The company may choose not to record the right amount of obsolete inventory. Consequently, the ratio of inventory to sales is considered (INV/ SAL). Another issue examined in this research is whether higher or lower gross margins are related to the issuing of FFS. For this purpose, the ratio of gross profit to total assets is used (GP/TA). The profitability orientation is tempered by the manager’s own utility maximization, defined (partially) by job security. Following this definition, the achievement of stable or increasing earnings streams maximizes the manager’s utility. This approach is based on the expectation that management will be able to maintain or improve past levels of profitability, regardless of what those levels were (Summers and Sweeney, 1998). If this expectation is not met by actual performance, then it provides a motivation for financial statement falsification. Loebbecke et al. (1989) found that profit relative to industry was inadequate for 35 per cent of companies with fraud in their sample. In this research some other financial statement red flag variables are examined, such as the sales to total assets ratio (SAL/ TA), net profit to sales (NP/SAL), net profit to total assets (NP/TA), working capital to total assets (WC/TA), for their ability to predict FFS. The sales to total assets ratio was a significant predictor in prior research (Persons, 1995). Financial distress may be a motivation for FFS (Bell et al., 1993; Stice, 1991; Loebbecke et al., 1989; Kreutzfeldt and Wallace, 1986). When the company is doing poorly there is greater motivation to engage in FFS. The results in Hamer (1983) suggest that most models predict bankruptcy with similar ability. Poor financial condition (Bell et al., 1993; Loebbecke et al., 1989) may motivate unethical insiders to take actions intended to improve the appearance of the company’s financial position, perhaps to reduce the threat of loss of employment, or to garner as many resources as possible before termination. In addition to motivating the commission of fraud, poor financial condition may indicate a weak

control environment, a condition that allows the perpetration of a fraud (AICPA, 1997). Loebbecke et al. (1989) found that 19 per cent of the fraud companies in their sample were experiencing solvency problems. Therefore, we use the Altman (1968, 1983) Zscore as a control variable to investigate the association of FFS and financial distress. The use of the Z-score is accompanied by some limitations, as was its use 30 years ago to develop a corporate failure prediction model for the US manufacturing sector. It is nevertheless still used today in many studies (Summers and Sweeney, 1998). With regards to Greek companies, despite the fact that a number of researchers have looked at bankruptcy, a generally accepted model has not been established (Theodosiou, 1991; Vranas, 1992; Negakis, 1995; Dimitras et al., 1995). We measure this variable through the difference in the Z-score in model (2) outlined below as a control variable for differences in financial condition between FFS and nonFFS firms.

Method The statistical method selected was logistic regression analysis (DeMaris, 1992; Mendenhall and Sincish, 1993; Menard, 1995). The following logit model was estimated using financial ratios from the firms to see which of the ratios were related to FFS. By including the data set of FFS and non-FFS we may find out what factors significantly influence the firms with FFS. We therefore formulated the following equation: E…y† ˆ

exp…bo ‡ b1 x1 ‡ b2 x2 ‡ . . . ‡ bk xk † 1 ‡ exp…bo ‡ b1 x1 ‡ b2 x2 ‡ . . . ‡ bk xk †

Where y ˆ 1 if FFS firm occurs y ˆ 0 if non-FFS firm occurs Q E…y† ˆ p (FFS firms occurs) ˆ Q ˆ denotes the probability that yˆ1 bo ˆ the intercept term b1 ; b2 ; . . . ; bk ˆ the regression coefficients of independent variables x1 ; x2 ; . . . ; xk ˆ the independent variables The models are presented as: FFS ˆbo ‡ b1 …DET=EQ† ‡ b2 …SAL=TA† ‡ b3 …NP=SAL† ‡ b4 …REC=SAL† ‡ b5 …NP=TA† ‡ b6 …WC=TA† ‡ b7 …GP=TA† ‡ b8 …INV=SAL† ‡ b9…TD=TA† ‡ e

…1†

Where FFS ˆ 1 if FFS discovered group, 0 otherwise.

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Charalambos T. Spathis Detecting false financial statements using published data: some evidence from Greece

FFS ˆbo ‡ b1 …DEBT=EQ† ‡ b2 …SAL=TA† ‡ b3 …NP=SAL† ‡ b4 …REC=SAL† ‡ b5 …NP=TA† ‡ b6 …WC=TA† ‡ b7 …GP=TA† ‡ b8 …INV=SAL†

Managerial Auditing Journal 17/4 [2002] 179±191

…2†

‡ b9 …TD=TA† ‡ b10 …Z† ‡ e

For model (2) the variable Z was added into the above model (1). The Z-score (Altman, 1968, 1983) was used to investigate the association of FFS and financial distress: Z ˆ1:2 …working capital=total assets†

‡ 1:4 …retained earnings=total assets† ‡ 3:3 …earnings before interest and taxes= total assets†

‡ 0:06 …market value of equity=

book value of total debt†

‡ 1:0 …sales=total assets†: The models will classify firms into FFS and non-FFS categories based upon financial statement ratios that have been documented as diagnostic in prior studies.

Results and discussion Table II reports the mean values, standard deviation and t-tests of ratios for non-FFS and FFS firms. The univariate tests suggest several variables may by helpful in detecting FFS. The large differences in average values

Table II Tests for the differences in the means of each group Variables DEBT/EQ SAL/TA NP/SAL REC/SAL NP/TA WC/TA GP/TA INV/SAL TD/TA Z

Mean Non-false False 1.075 1.055 0.067 0.456 0.074 0.252 0.274 0.179 0.437 1.990

2.705 0.699 ±0.459 1.755 ±0.021 0.054 0.144 0.359 0.629 0.778

Notes: DEBT/EQ: debt/equity SAL/TA: sales/total assets NP/SAL: net profit/sales REC/SAL: receivable/sales NP/TA: net profit/total assets WC/TA: working capital/total assets GP/TA gross profit/total assets INV/TA: inventories/total assets TD/TA: total debt/total assets Z: Z-score [ 186 ]

Std dev. Non-false False 0.937 0.576 0.159 0.349 0.064 0.205 0.140 0.158 0.196 0.730

3.351 0.416 2.434 5.897 0.095 0.238 0.121 0.656 0.242 0.936

t-test

Sig. (two-tailed)

±2.750 3.087 1.329 ±1.356 5.110 3.892 4.333 ±1.643 ±3.783 6.292

0.007 0.003 0.188 0.179 0.000 0.000 0.000 0.105 0.000 0.000

of ratios between FFS and non-FFS firms and the high statistical significance (p < 0:000) indicate that the above ratios may indeed be related to FFS. The ratios NP/TA, WC/TA, GP/TA, TD/TA and Z score are statistically significant. The very low values for NP/TA and NP/SAL for the FFS firms compared to the corresponding ones for non-FFS indicate that the companies facing difficulties of low returns in relation to assets and sales try to manipulate the financial statements either by increasing revenue or by reducing expenditure so as to improve the profit and loss account. The same holds for the GP/TA ratio where FFS companies show on average half the gross profit of that of non-FFS firms with respect to total assets. The ratio WC/TA shows that FFS firms have a very low WC and present liquidity problems such that they cannot meet their obligations. Low WC is associated with financial distress according to the bibliography (Bonner et al., 1998). FFS firms seem to have on average both a higher TD/TA and DEBT/EQ. The higher debt to equity, the lower sales to total assets and the Z-score values for the FFS firms may indicate that many firms issuing FFS were in financial distress (Fanning and Cogger, 1998; Summers and Sweeney, 1998). This could provide the motivation for management fraud. The ability to manipulate the values in accounts receivable (REC/SAL) was clearly reflected in the results (t ˆ ¡1:356, p < 0:179). This is a very difficult area due to the subjective nature of estimating accounts receivable. These results suggest that additional time is necessary for auditing accounts receivable. The INV/SAL indicated that those firms with FFS keep high inventories and cost of goods sold. The univariate tests provide valuable information regarding a large number of variables over a sample. While some of the suggested variables were not statistically significant in this study, the results should be viewed cautiously. This study examined these variables at the aggregate level on one sample and generalizations to specific cases should be made with care. Every instance of falsification of financial statements is an individual case and the variables not significant in the aggregate may still be useful indicators for a particular case. Consequently the aim was to develop a model with variables which auditors can find useful. While several of these were good predictors in this study, they lacked the ability to transfer to other samples. Ratios allow better generalization and are easily derived from published financial statements,


and they have been used in the model development. There were ten possible variables in this study with the results of the univariate tests. The next step in securing a model was the application of multivariate testing with stepwise logistic regression. The univariate results were informative but there was the question of whether the association was a direct association or whether there was a joint correlation with a third variable. A univariate test does not allow detection of interaction effects which multivariate tests may find. This study used model (1) with nine variables (without Z scores) and model (2) with ten variables, Z score included. Table III reports the results for the stepwise logistic regression for model (1), without Z scores. According to the results, the overall per cent of correct classification, by means of the proposed model, is 82.89 per cent. This implies that 30 (78.95 per cent) out of the 38 non-FFS firms and 33 (86.84 per cent) out of the 38 FFS firms were classified correctly. The relationship between the dependent ± non-FFS and FFS firms ± and the independent variables is statistically significant ( 2 ˆ 50:539, p < 0:000). The association strength between the dependent and independent variables is R2L ˆ 0:480, indicating a medium-efficient strong relationship. The results indicate that only three variables with significant coefficients entered the model. These ratios are: INV/ SAL, NP/TA, WC/TA. The ratio INV/SAL has an increased probability of being classified with FFS firms (b ˆ 2:252, p < 0:097) and this ratio has a positive effect. This implies that firms with high inventories to sales have an increased probability of being

classified with FFS firms. NP/TA has an increased probability of being classified with FFS firms (b ˆ ¡33:029, p < 0:000) and this ratio has a significant negative effect. That means that firms with high net profit to total assets have an increased probability of being classified with the non-FFS firms. The same strong effect of being classified with the FFS firms’ group appears to exist for the working capital to total assets ratio (b ˆ ¡6:878, p < 0:003). This ratio has a significant negative effect, meaning that firms with increased WC/TA ratio have an increased probability of being classified with the nonFFS firms. That means that non-FFS firms have higher WC/TA values. Table IV reports the results for the stepwise logistic regression for model (2) ± with the Z score. According to the results the overall per cent of correct classification, by means of the proposed model, is 84.21 per cent. This implies that 32 (84.21 per cent) out of the 38 non-FFS firms and 32 (84.21 per cent) of the 38 FFS firms were classified correctly. The relationship between the dependent ± non-FFS and FFS firms ± and the independent variables is statistically significant ( 2 ˆ 54:487, p < 0:000). The association strength between the dependent and independent variables is R2L ˆ 0:517, indicating a medium-efficient strong relationship slightly higher than for model (1). The results indicate that only three variables with significant coefficients entered the model. These ratios are: INV/ SAL, TD/TA, Z score. INV/SAL has an increased probability of being classified with FFS firms (b ˆ 2:659, p < 0:005) and this ratio has a positive effect. This implies that firms with high inventories to sales are more likely

Table III Stepwise logistic regression results of nonfalse and false financial statements ± model (without z)

Table IV Stepwise logistic regression results of nonfalse and false financial statements ± model 2 (with z)

Independent variables

Independent variables

Unstandardized coefficient

Model 1 (without Z) INV/SAL 2.252 NP/TA ±33.029 WC/TA ±6.878 Constant 1.250 2

R2L N

50.539 0.480 76

Correctly predicted: Non-false 78.95% False 86.84% Overall 82.89%

S.E. 1.359 9.638 2.350 0.560

Sig. 0.097 0.000 0.003 0.025 0.000

Model 2 (with Z) INV/SAL TD/TA z Constant 2

R2L N

Unstandardized coefficient

S.E.

Sig.

2.659 6.685 ±3.327 0.230

0.951 2.430 0.897 1.401

0.005 0.005 0.000 0.869

54.487 0.517 76

0.000

Correctly predicted: Non-false 84.21% False 84.21% Overall 84.21% [ 187 ]


to be classified as FFS. The ratio TD/TA has an increased probability of being classified with FFS firms (b ˆ 6:685, p < 0:005) and this ratio has a significant positive effect. That means that firms with high total debt to total assets values have an increased probability of being classified with the FFS group. The same strong effect of being classified with FFS firms appears to be attributed to the Z score (b ˆ ¡3:327, p < 0:000). This ratio has a significant negative effect, meaning that firms with increased Z score values have increased probability of being classified with the non-FFS firms. That means that non-FFS firms have higher Z scores. In both models, the ratio indicated as an important variable is INV/SAL, demonstrating that FFS firms keep higher stocks, indicating a lower stock turnover with respect to sales. The coefficients of WC/TA, NP/TA, and Z score have negative signs. This is consistent with the hypothesis that an improvement in the liquidity position of a firm, an improvement in the profitability of the firm, or an improvement in the Z score of the firm will have a negative effect on the probability of FFS.

` ... The analysis shows that higher TD/TA may indicate that many firms issuing FFS were in financial distress . . . This could provide the motivation for management fraud ... ’

The coefficient of the debt ratio TD/TA and INV/SAL ratio has a positive sign, which conforms to the hypothesis that more leverage and large inventories make the firm more vulnerable to FFS. The analysis shows that higher TD/TA may indicate that many firms issuing FFS were in financial distress (Persons, 1995; Fanning and Cogger, 1998; Summers and Sweeney, 1998). This could provide the motivation for management fraud. On the other hand, the identification of INV/SAL as a crucial factor agrees with the results of previous studies in this field. The inventory is likely to be manipulated by management (Loebbecke et al., 1989; Schilit, 1993; Summers and Sweeney, 1998). SAS No. 47, Audit Risk and Materiality in Conducting and Audit (AICPA, 1983) states that any account that requires subjective judgement in determining its value increases audit risk. Inventory is noted as such an account due to the subjective judgement involved in estimating obsolete inventory. A further examination indicates that firms with FFS were less profitable (lower NP/TA) since they get less profit for the same total assets. This result agrees with the existing

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research (Loebbecke et al., 1989; Summers and Sweeney, 1998; Beasley et al., 1999).

Concluding remarks The primary objective of this study has been the development of a reliable false financial statement detection model for Greek firms. In order to achieve this goal we used a sample of FFS and non-FFS firms. We used univariate and multivariate statistical techniques such as logistic regression to develop a model to identify factors associated with FFS. A total of ten financial ratios are selected for examination as potential predictors of FFS. These variables appeared to be important in prior research and constitute ratios derived from published financial statements. The variables selected by the above techniques as possible indicators of FFS are: the inventories to sales ratio, the ratio of total debt to total assets, the working capital to total assets ratio, the net profit to total assets ratio, and financial distress (Z-score). Both models are accurate in classifying the total sample correctly with accuracy rates exceeding 84 per cent. The results of these models suggest there is potential in detecting FFS through analysis of publicly available financial statements. In general the indicators selected are associated with FFS firms. Companies with high inventories with respect to sales, high debt to total assets, low net profit to total assets, low working capital to total assets and low Z scores are more likely to falsify financial statements according to the results of the stepwise logistic regression. Alternative methods for FFS detection can be used, such as discriminant analysis, adaptive logit networks, neural networks and multicriteria analysis. There were several publicly available variables that remain for future study. These variables include standing within industries and long-term trends. Industry standing probably would provide additional valuable information in the growth and financial distress variables. A further possibility would be to examine variables other than those in financial statements, such as the number of members of the board of directors, the rate of turnover of the financial manager, the type of auditor used and the frequency with which they are changed, auditors’ opinions, the size of the company, the existence of company branches, inventory evaluation methods, depreciation methods. This study did not use a holdout sample to validate the model that is presented. Further research with larger


samples will be necessary to validate these results. The results are encouraging in that we have developed a reliable model for assessing the likelihood of false financial statements of businesses in Greece. The use of the proposed methodological framework could be of assistance to auditors, both internal and external, to taxation and other state authorities, individual and institutional investors, stock exchanges, law firms, economic analysts, credit scoring agencies and to the banking system. For the auditing profession, moving to address its responsibility to detect FFS, the results of this study should be beneficial. The auditors can provide this model as effective expert witness testimony and computer litigation support regarding FFS at a low cost to auditor and client. The auditors with suitable software will be able to apply it to scan financial statements posted on Internet Web sites and analyse the differences between trends of the company’s reports. It also identifies ``red flags’’ that substantially differ from the norms of the industry. Therefore, the present study contributes to auditing and accounting research by examining the suggested variables to identify those that can best discriminate cases of FFS. This study suggests certain variables from publicly available information to which auditors should be allocating additional audit time. With more improved statistical techniques and a greater number of variables it is possible to develop a more powerful analytical tool for the detection of FFS.

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