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Detecting Earnings Management Dechow , Sloan, Sweeney (1995)

Detecting Earnings Management Dechow , Sloan, Sweeney (1995). Septian Bayu K. (0806479080). Outline. Introduction Statistical Background Measuring DA Experimental Design Data Analysis Empirical Results Conclusions Implications. Introduction (1).

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Detecting Earnings Management Dechow , Sloan, Sweeney (1995)

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  1. Detecting Earnings ManagementDechow, Sloan, Sweeney (1995) SeptianBayu K. (0806479080)

  2. Outline • Introduction • Statistical Background • Measuring DA • Experimental Design • Data Analysis • Empirical Results • Conclusions • Implications

  3. Introduction (1) • Analysis of earnings management focuses on discretionally accruals (DA) • Separate total accruals to DA & NDA • The aim of research • Finding the sophisticated model(s) to measure to detect earnings management with DA/NDA • Research gap • Modified Jones Model

  4. Introduction (2) • Prior research • DA: Healy (1995), DeAngelo (1996), Jones (1991) • Accounting procedure changes: Healy (1985), Healy & Palepu (1990), Sweeney (1994) • Specific components of DA: McNichols & Wilson (1988), DeAngelo et al (1994) • Components of Discretionary Cash Flow (Dechow & Sloan (1991)

  5. Statistical Background • McNichols & Wilson (1988) • Problems: • Incorrectly attributing earnings management to PART • Unintentionally extracting earnings management caused by PART • Low power test

  6. Measuring DA (1) • The Healy model (Healy, 1985) • The DeAngelo model (DeAngelo, 1986) • NDAτ = TAτ-1 • The Jones model (Jones, 1991)

  7. Measuring DA (2) • The Modified Jones model • The Industry model (Dechow & Sloan, 1991) • NDA τ = γ1 + γ2 median1 (TA τ)

  8. Experimental Design • Randomly 1000 firm-years (1950-1991) • Firm-years experiencing extreme financial performance • Firm-years with accrual manipulation • Expense manipulation • Revenue manipulation • Margin manipulation • 32 firms that are subject to SEC enforcement actions

  9. Data Analysis • Total accruals (TA) • CFO = Earnings – TA • Using Z-statistic

  10. Empirical Results • Random sample of firm-years • Table 1, table 2 • Samples of firm-years experiencing extreme financial performance • Figure 1, table 3, figure 2, table 4 • Samples of firm-years with artificially induced earnings management • Figure 3, figure 4 • Sample of firm-years in which of the SEC alleges earnings are overstated • Figure 5, table 5, table 6, table 7

  11. Conclusions • All of models appear well specified when applied to random sample of the firm-years • The models all generate test of low power of earnings management • All models reject the null hypothesis of no earnings management • Modified Jones model generate the revenue –based earnings management

  12. Implications • Regardless of the model used to detect earnings management • Further research: develop new model with more powerful test to detect earnings management • Correlation between PART ad firm performance, considered the models • Consider about earnings management context

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