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Early Signals of Early Signals of disclosed accounting frauds- Chinese firms’ evidence (Discussion Draft) Yi Wei, Jianguo Chen and Jing ChiYi Wei, Jianguo.

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Presentation on theme: "Early Signals of Early Signals of disclosed accounting frauds- Chinese firms’ evidence (Discussion Draft) Yi Wei, Jianguo Chen and Jing ChiYi Wei, Jianguo."— Presentation transcript:

1 Early Signals of Early Signals of disclosed accounting frauds- Chinese firms’ evidence (Discussion Draft) Yi Wei, Jianguo Chen and Jing ChiYi Wei, Jianguo Chen and Jing Chi

2 Introduction China has experienced a rapid change from a planned economy to a market mechanism. 20% of listed firms had committed serious fraud since the Chinese stock market was established in the early 1990s (CSRC). China is rated at 71 out of 145 countries (Fan, Rui& Zhao, 2008) in terms of Corruption Perception Index of Transparency International.

3 Management and Controlling shareholders Management may manipulate financial report through tunnelling, insider trading, creative accounting and false statements (Bai, Yen & Yang, 2008) Controlling shareholders in Chinese listed companies is to maximize the proceeds from investors, and they have less care of improving corporate governance and financing subsequent investment and growth (Shi, 2004).

4 One form of management frauds Called fraud financial statements (FFS) It mainly refers to intentional misstatements of omission in financial statements (Rezaee, 2010)

5 Common tricks in the book of Financial Shenanigans: Recording revenue before it is earned; Creating fictitious revenue; Boosting profits with non-recurring transactions; Shifting current expenses to a later period; Failing to record or disclose liabilities; Shifting current income to a later period and Shifting future expenses to an earlier period

6 This Research The study do not ask the specific types. It is a general investigation of the chance of corruption behaviour related to the reported accounting balances. We do find some evidence that there exist a strong relation between the disposed corruption firms and the reported account balance ratios.

7 Relative literature Conflicts of interest between majority and minority shareholders (Berle &Means, 1932) “Tunneling” behaviour which refers to controlling shareholders expropriating minority shareholder in many ways when the corporate governance mechanisms are poor (Johnson, La Porta, Lopez- de-Silanes, and Shleifer, 2000). Fraudulent financial reporting (FFR) (Erickson, Hanlon, Maydew, 2004)

8 Relative literature Fei (2005) concluded four types of fraudulent in China: non-monetary transactions; related party transactions; assets restructuring and change of accounting estimates.

9 Recent studies Logit regression analysis of 75 fraud and 75 no-fraud firms have indicated that uncorrupt firms have boards with significantly higher percentages of outside members than fraud firms (Beasley, 1996) The Classification and Regression Tree (CART) was employed by Bai, Yen and Yang (2008) to indentify and predict the impacts of FFS. They included 24 FFS and 124 normal firms Fan, Rui and Zhao (2008) report that fraud and rent seeking have an influence on firms’ behaviour of leverage, especially obvious relation on long-term debt ….

10 Difference In our research, we consider every single account in balance sheet and income statement to detect the relation with the corruption.

11 Sample Sources (Web pages): China Security Regulation Committee (CSRC), Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE) Law Yearbook of China ( ) Others (Disciplinary Cases of the Communist Party of China, China securities news, Yahoo Finance, Xinhua News, Google China and Baidu)

12 Our Sample We have identified 76 firms involved in scandals in total. Sample period: 1990 through 2002.

13 Comparable firms We subcategorize all the corruption firms into 8 industry area: – Communication and Cultural Industry; – Farming, Forestry, Animal Husbandary and Fishery; – Information Technology; – Manufacturing; – Multiple (some firms not just major in one area); – Real Estate; Transportation and Warehousing; – Utilities; – Wholesale and Retail Trade.

14 Comparable firms Then we collect 76 size matched controlled firms in the same industry through GTA database with total assets closest to the fraud firm. We also include the data 5 years before and 5 years after the frauds (restricted by the listed information and availability of the data).

15 Model We hypothesize that corrupted firms can potentially manipulate any account. So we have tested each account in our model. Four types of account values are used: ratio (to the base of total assets); change rate; ratio deviation and absolute value of ratio deviation.

16 Logistic formula Y = F( a+b*X)=F(Z) = 1/(1+exp(-2Z)) Where Y=1 for corrupted firms and 0 for not-corrupted firms and X are account variables and b are coefficients

17 Every account is tried We have tried the regression separately with Asset accounts, Liability accounts, Equity accounts and Income Statement items. Only Assets and Liability accounts shows strong consistent relation. Only Asset and Liability regressions are reported. And the significant accounts (From Assets and Liabilities) are combined in one single regression as the final model.

18 Table 1: Balance Sheet Accounts AssetsLiabilitiesEquities AccountDescriptionsAccountDescriptionsAccountDescriptions A1Cash & equivalentsL1Accounts payableE1Share capital A2Short term investmentsL2Notes payableE2Retained earnings A3ReceivablesL3Short term debtE3Capital reserves A4InventoriesL4Other payablesE4 Other stockholder equity A5Net prepaymentsL5Other current liabilitiesE5 Total stockholder equity A6Other current assetsL6Total current liabilities A7Total Current AssetsL7Long term debt A8Long term AssetsL8Other long term liabilities A9GoodwillL9Deferred tax liabilities charges A10OthersL10Total non-current liabilities A11Long term investmentsL11Total liabilities A12Total Long term assets A13Total Assets

19 Table 2. Simple statistics comparison of corruption and non-corruption groups (A) Non CorruptedCorrupted Diff Ratio MeanMedianMeanMedianMeanMedian Cash &equivalents Short term investments Receivables Inventories Net prepayments Other current assets Goodwill Others Long term investments Obs. # Non CorruptedCorrupted Diff Ratio MeanMedianMeanMedianMeanMedian Accounts payable Notes payable Short term debt Other payables Other current liabilities Other long term liabilities #DIV/0! Obs. #547548

20 Table 3: Regression Results: A: Coefficients for Asset variables Variable typesRatiosChange rateDeviation Absolute deviation Intercept *** *** *** (-5.16)(1.08)(-4.02)(-4.28) Cash &Equivalents0.6549* * (1.74)(0.62)(1.74)(2.55) Short Term Investments (0.76)(-0.72)(0.76)(-0.05) Receivables2.1958*** ***1.6323*** (6.93)(-0.57)(6.93)(4.00) Inventories (0.04)(-1.15)(0.04)(-1.26) Net Prepayments4.1880*** ***4.3950*** (4.57)(1.57)(4.57)(4.05) Other Current Assets (-0.39)(0.16)(-0.39)(-0.14) Goodwill2.9358*** ***2.1429*** (4.69)(0.57)(4.69)(3.40) Others1.0739* *1.0739* (1.90)(-1.67)(1.90)(1.43) Long Term Investment (1.22)(0.04)(1.22)(-0.25) No. of observations Likelihood Ratio®

21 Panel B: Coefficients for Liability variables Variable typesRatioschange ratedeviation absolute deviation Intercept ***0.4419*** ** (-3.64)(3.95)(-2.18)(0.59) Accounts Payable *** *** *** (-3.10)(-0.06)(-3.1)(-6.21) Notes Payable2.3695*** ***1.3553** (3.86)(0.66)(3.86)(2.35) Short Term Debt1.7533*** *** (5.75)(-1.08)(5.75)(1.52) Other Payables0.8853*** *** (2.73)(-1.12)(2.73)(0.18) Other Current Liabilities (-0.39)(-0.72)(-0.39)(-1.27) Other Long Term Liabilities *** ***3.3689*** (5.46)(-0.40)(5.46)(4.63) No. of observations Likelihood Ratio®

22 Table 4: Final regression results ratioschange ratesdeviation absolute deviation Intercept ***0.3677*** *** *** Cash &Equivalents0.8522** **1.3678*** Receivables2.0776*** ***1.6803*** Net Prepayments4.3843*** ***4.5288*** Goodwill2.2847*** ***1.8949*** Others1.3474** **1.1409* Accounts Payable *** *** *** Notes Payable2.2401*** ***1.6143*** Short Term Debt1.4213*** *** Other Payables0.7244** ** Other Long Term Liabilities3.5774*** ***2.8224*** No. of observations Likelihood Ratio®

23 Table 5. Prediction Results using Ratios model (Accuracy=68%) Actual 10 Predicted Actual 10 Predicted Actual 10 Predicted Actual 10 Predicted

24 Table 6: Typical examples: A (firm code: ) Years A1A3A5A9A10L1L2L3L4L8 Prob. Coeff

25 Table 6: Typical examples: A (firm code: ) Years A1A3A5A9A10L1L2L3L4L8 Prob. Coeff

26 Table 6: Typical examples: A (firm code: ) Years A1A3A5A9A10L1L2L3L4L8 Prob. Coeff

27 Table 1: Balance Sheet Accounts AssetsLiabilitiesEquities AccountDescriptionsAccountDescriptionsAccountDescriptions A1Cash & equivalentsL1Accounts payableE1Share capital A2Short term investmentsL2Notes payableE2Retained earnings A3ReceivablesL3Short term debtE3Capital reserves A4InventoriesL4Other payablesE4 Other stockholder equity A5Net prepaymentsL5Other current liabilitiesE5 Total stockholder equity A6Other current assetsL6Total current liabilities A7Total Current AssetsL7Long term debt A8Long term AssetsL8Other long term liabilities A9GoodwillL9Deferred tax liabilities charges A10OthersL10Total non-current liabilities A11Long term investmentsL11Total liabilities A12Total Long term assets A13Total Assets

28 Conclusions The results show that there exist a strong relation between FFR and the account ratios: – Receivables – Net Prepayments – Goodwill – Accounts payable (negative) – Notes payable – Short term debt; – Other long terms liabilities – We also get a high prediction accuracy of 68% with the estimated model result.

29 Conclusions The corruption firms tend to use more intangible assets (Goodwill) or paper records (A/R, and Net Prepayments), and less physical records (Inventory, fixed assets) It looks strange to find that the corruption firms have more Cash & Equivalents. (Maybe that is the reason for corruption, such as avoid dividends, and do “tunneling”) The negative relation with the accounts payable is consistent with Higher working capital level. (Higher than normal is consistent with Higher NI)

30 Comments Consistent with the literature (?) Fei (2005) concluded four types of fraudulent in China: non-monetary transactions; related party transactions; assets restructuring and change of accounting estimates.

31 Comments We hypothesize that corrupted firms can potentially manipulate any account. Our regression results have eight significant accounts, which is consistent with the hypothesis that different company may use different accounts as the form of corruption.

32 Future Research The relation between the types of corruption and corresponding accounts used. the “tunnelling” type, the cash raised by the equity market source is transferred to the other “related” company. Some other types are about illegal account profit adjustment so that the firm’s share becomes more attractive to the general investors. The type of corruption should be related to the profitability level or changes of the business. Another interesting direction is about the timing of the accounting frauds. (such as the ages after IPC)

33 Your Comments and Suggestions Thanks !


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