Forensic Analytics of Financial Report in Philippines Property Sector. The Benford's Law Application

Forensic Analytics of Financial Report in Philippines Property Sector: Benford’s Law Application

Abstract

This paper examines the property sector financial misreporting using the Benford’s Law a logarithmic distribution technique. The study examined the financial and income statement of the 35 property companies listed in the Philippine Stock Exchange (PSE) for 2015 and 2016. Using the Chi-square test and Mean Absolute Deviation (MAD) t

he results show that the income statement before tax stockholder’s equity and stockholder’s equity-parent data set did not conform to the Benford’s Distribution. The study suggests that there is a probable misreporting of income before tax and stockholder’s equity in the property sector.

Inhaltsverzeichnis

1. Introduction

2. Framework

3. Literature

4. Method

5. Results and Discussions

6. Conclusions

7. References

1. Introduction

Misreported financial statements, whether intentional or unintentional is difficult to detect and eventually results in wrong financial decision (Peterson, 2012). Erroneous financial statement deceives investors and regulators leading into wrong capital allocation and misleading policy (Magrath & Weld, 2002). Consequently, the effects are often irreversible, affecting the firm's investors and employees. As early as 2003 the SEC launched a surveillance system called, "Advanced Warning and Control System," to detect fraud, manipulation, and other illegal practices. The system was placed on stock market transactions. In the last several years, the Security Exchange Commission (SEC) is vigilant in strictly detecting insider trading, but might be missing on detecting accounting fraud (Pentildea, 2003).

Due to a strong Philippine economy, the real estate industry is expected to project a sustained growth for 2017. The government’s ability to maintain strong macroeconomic fundamentals is the reason for the sustained economic growth in the country. Real Estate Investors are advised to take advantage of the opportunity to effectively market and sell projects (Gonzales, 2017). However, accompanying the sustained growth in the Philippines real estate industry are the different scams perpetrated by con artists (Tajar, 2015). Among the biggest scam perpetrated involved the P7 billion Pag-Ibig funds lent to “buyers” of Globe Asiatique, a housing development company. It was discovered that Pag-Ibig funds were lent to almost 60 percent non-existent borrowers. The developer siphoned the proceeds and sold the houses to other buyers most are overseas foreign workers (OFW). Buyers are warned to exercise due diligence in purchasing properties (Salazar, 2014).

In determining the company’s financial status investor analyze the financial statements (Baker & Haslem, 2015). A deliberate analysis on this document reveals whether the company is carefully managed or heading towards a crisis (Ravisankar, Ravi, Rao, & Bose, 2011). Companies listed in the Philippine Stock Exchange are required to publish regularly their financial statement (Unite, Sullivan, Brookman, Majadillas, & Taningco, 2008). The data gives the stockholder’s information about the company’s future and help them decide whether their stocks are worth investing. Lending institutions need these financial statements in deciding to grant loans. In the end, the financial statement reflects the company’s financial status (Kabigting, 2011).

Most fraudulent financial reporting involves projecting the company’s financial health better than its real condition (Lou & Wang, 2011). There are several techniques to overstate stockholder’s equity such as overstating assets and revenues; understating liabilities; inaccurate disclosure; and a combination of these methods (Norwani, Zam, & Chek, 2011). Understating the company’s liability results in overstated net income which, results in higher equity. However, these are not mutually exclusive. Similarly, overstating assets result in overstated revenues and consequently overstating equity. In addition, an overstated account receivable overstates revenue and net income, subsequently overstating equity. Understating fixed-assets depreciation expense each year, the smaller expense translates into larger income which overstates equity (Putra, 2017).

The stockholder’s equity has the least activity and fraudsters typically avoid it. However, some fraudsters are tempted to use the stockholders equity as a temporary hiding place (Kranacher, Riley & Wells, 2010). Typically, fraud in stockholder’s equity is listed in the Focus on Fraud Feature (Johnstone, Gramling, & Rittenberg, 2013). A Major cause of fraudulence is traceable on the management desire to meet analyst’ earnings estimate. In cases, where the stock market weakens, managers become motivated to maintain the stock price in the financial statement which is another cause to commit fraudulence (Lou & Wang, 2011).

This study attempted to empirically examine the property sector financial statement published in the Philippines Stock Exchange (PSE) using the Benford’s Law of distribution. It is possible to use the reported financial statements in the property industry and arrive at a meaningful statistical analysis to detect manipulations or errors in the reported stockholders’ equity (Henselmann, Scherr, & Ditter, 2013).

2. Framework

This study is based on the Benford Law which states that leading digits are distributed in a definite non-uniform manner (Diaconis, 1977). This theory is based on the logarithmic probability of digit occurrence (Hill, 1999). According to this law in a data set the number 1 as the first digit, appear about 30 percent of the time, while, 9 occur less than 5 percent of the time. The law suggests a formula to determine the probability that number 1 is the first non-zero digit or number 2 is the non-zero second digit.

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The d1 is the first digit in a number set. If the accounting transaction data set significantly did not follow the Benford’s distribution, there is a reason to believe that human intervention occurred in manipulating the data sets. It is suggested that an investigation must commence for possible fraud.

3. Literature

Several researchers have already used the Benford’s Law in measuring particular account items in several companies to identify management behavior. One successful study identified the occurrence of second-digit in income reported among the 220 listed New Zealand companies. The study suggested an abnormally high frequency of the digit zero and very low number nine in the reported income data set. The result confirms the companies’ behavior in rounding up income to impress higher earnings among their investors (Carslaw, 1988). Similar behavior was also detected in the US, where companies reported earnings have abnormally high zero digits and losses have very high number nines (Thomas, 1989). Likewise, in Finland, companies proceed to rounding of their net income (Niskanen & Keloharju, 2000), while British companies use round-off behavior in reporting pre-tax income (Van Caneghem*, 2004). An extensive research covering 22,000 firms in 18 countries confirmed the previous studies in companies rounding-off behavior (Kinnunen & Koskela, 2003).

Consistent with earlier studies, they find out an upward rounding of profit and reverse pattern in a net loss. Rounding off behavior was observed in reporting the earning per share (Das & Zhang, 2003). Japanese firms were also observed to perform rounding off behavior in reporting their earning per share (Skousen, Guan, & Wetzel, 2004).

The Majority of accounting data are expected to follow the Benford’s Law of distribution and possibly convenient for digital analysis (Hill, 1995). Benford’s law as a tool for detecting an error reveals unusual management behavior (Guan, He, & Yang, 2006). However, not all “nonconforming” accounts are flagged as fraudulent. Auditor has to drill down on these transactions.

4. Method

This study used the expected frequencies based on the Benford’s Law to analyze the digit frequencies of the property sector financial and income statement reported at the Philippine Stock Exchange (PSE). The data were from the fiscal year 2015 and 2016. There were 35 companies listed in the PSE property sectors and are fully compliant with the SEC regulations.

The Mean Absolute Deviation (MAD) statistic is used to calculate the sum of the absolute difference between the actual data digit frequency from 1 to 9 and the expected Benford’s frequency distribution reflected in Table 1 below, divided by nine. The variance aspect of the MAD statistics (Nigrini, 2012) developed makes it useful to examine the data sets conformity over time, since the companies listed in the property sector submitted their financial and income statement vary in the last two periods. (Nigrini, 2012) indicated that MAD between 0 and 0.004 is in close conformity; 0.004 to 0.008 is an acceptable conformity; 0.008 to 0.012 is in marginally acceptable conformity; while greater than 0.012 is nonconformity.

Table 1: Expected First Digit Frequencies Based on Benford's Law

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The degree of deviation from the expected Benford’s Law reflected in Table 1 can be evaluated in several ways. The study used the Chi-square goodness-of-fit statistic. The Chi-square test determines clearly whether the observed distribution diverges from the expected distribution (Cleary & Thibodeau, 2005). This is similar to the technique (Nigrini, 2012) used in their Benford Checker Chi-square test.

5. Results and Discussions

The Benford Law is applied in this study to the following accounting data sets (current assets, current liabilities, gross expense, gross revenue, income before tax, net income after tax, net income attributed to parent, retained earnings, stockholder’s equity, stockholder’s equity-parent, total assets and total liabilities) which Property Companies reported in the PSE.

Figure 1 demonstrates the digits 6, 8 and 9 appear more often than the distribution Benford’s Law suggests. However, the digit 1 and 3 and 5 appear less often than the distribution Benford’s Law suggested. Figure 2, and 3 demonstrate the distribution of the digits 1 and 2 in the stockholder’s equity and stockholder’s equity-parent, respectively. It appears more often in the first and second digit than the distribution Benford’s Law suggests. This raises concern that data manipulation existed, probably a rounding off behavior among property companies.

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Figure 1. PSE Property Sector Income Before Tax First Digit Pattern

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Figure 2. PSE Property Sector Stockholder’s Equity First Digit Pattern

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Figure 3 PSE Property Sector Stockholder’s Equity-Parent First Digit Pattern

The three figures presented above demonstrate the power of the Benford’s methodology. Many of the companies misreporting can be discovered by chance. A recent survey reveals that 32.2 percent of intentional misreporting was discovered through tips, 25.4 percent by chance and only 20.2 percent through internal audits (Asllani & Naco, 2014). Utilizing the Benford’s Law methodology allow auditors to scan vast amount of data sets from companies and possibly eliminate the tedious internal audits. Instead, auditors can focus on companies with anomalous digit distribution in their data sets.

People fabricating data randomly choose numbers and their data set do not follow the logarithmic distribution (Hill, 1999). Although, people fabricating data have goals in mind the digital frequencies still do not follow well to Benford’s Law. The income before tax, property companies reported first digit distribution Chi-square calculated 22.85 exceeds the critical value of 15.5073 at 0.05 with 8 degrees of freedom which indicates non-conformity to Benford’s Law. The MAD is 0.0473, a larger than average difference between the actual and expected, a MAD greater than 0.012 signal non-conformity (Nigrini, 2012). Similarly, the stockholder’s equity reported a chi-square 21.105 higher than the critical value of 15.507 at 0.05 with 8 degrees of freedom and a MAD 0.0557 a clear indication of non-conformity with Benford’s Law. Lastly, the stockholder’s equity-parent resulted in calculating chi-square 28.652 higher than the critical value of 15.507 at 0.05 with 8 degrees of freedom and a MAD 0.0668 which indicates nonconformity with Benford’s Law.

Table 2: Reported Financial Item Actual Frequencies Based on Benford's Law

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6. Conclusions

The first significant digit 1 and 2 in Stockholder’s equity and stockholder’s equity-parent were much higher than expected, while the frequency of number digit 3, 4, 8 and 9 were much lower than expected (Carslaw, 1988; Thomas, 1989). Similarly, the income before tax first significant digit 8 and 9 was much higher than expected (Niskanen and Keloharju, 2000). This study suggests that Benford’s Law can be used as a tool to detect financial misreporting among property companies listed in the PSE and is further strengthened by the aggressive earning behavior in the property sector. This study finds that as the property sector income before tax and stockholder’s equity diverge from the Benford’s Law the management misreporting behavior is trying to meet the company’s earning objectives.

7. References

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