Forecasting the level of earnings management of Russian and Chinese companies

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The purpose of the current paper is to elaborate a model to forecast a particular type of earnings management by companies: upward earnings management, downward earnings management or the absence of significant manipulation.
The sample analyzed in the current paper comprises 664 Russian and 2,380 Chinese public companies for the period 2009-2014. The forecast was made for 2014 based on annual accounting data for 2009-2013. Regression analysis, as well as Classification and Regression Tree modelling (CART), were used. The data forecast for 2014 was compared with actual data for that year, and the accuracy of the forecasting model was assessed.
The paper outlines the main conditions under which a particular type of earnings manipulation is expected to take place in a company in the accounting period following the current one. It is shown that the main factor influencing the company’s level of earnings manipulation of the next accounting period for both Russian and Chinese companies is the debt ratio calculated as the ratio of total liabilities to total assets. The other important factors are: the company size, return on equity, earnings persistence, the level of earnings manipulation in the current period and stock emission.

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    • Figure 1a. Industrial affiliation of companies
    • Figure 1b. Industrial affiliation of companies
    • Figure 2a. Quantiles of distribution of discretionary accruals of the companies of the sample based on actual data for 2014 (Russia)
    • Figure 2b. Quantiles of distribution of discretionary accruals of the companies of the sample based on actual data for 2014 (China)
    • Figure 3a. Decision tree to determine clusters of earnings management (Russia)
    • Figure 3b. Decision tree to determine clusters of earnings management (China)
    • Table 1. Variables used to forecast companies’ level of earnings manipulation
    • Table 2. Descriptive statistics of Russian and Chinese samples of data
    • Table 3. The Jones model parameters estimation for 2014 (Russian and Chinese companies)
    • Table 4. Parameters of company X compared to the decision tree conditions for Cluster 1 “Earnings decreasing companies”
    • Table 5. Parameters of company Y compared to the decision tree conditions for Cluster 2 “Insignificant earnings manipulation”
    • Table 6. Parameters of company Z compared to the decision tree conditions for Cluster 3 “Earnings increasing companies”
    • Table 7. Parameters of company X compared to the decision tree conditions for Cluster 1 “Earnings decreasing companies”
    • Table 8. Parameters of company YY compared to the decision tree conditions for Cluster 2 “Insignificant earnings manipulation”
    • Table 9. Parameters of company ZZ compared to the decision tree conditions for Cluster 3 “Earnings increasing companies”