Detecting earnings management dechow sloan sweeney 1995
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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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Detecting earnings management dechow sloan sweeney 1995

Detecting Earnings ManagementDechow, Sloan, Sweeney (1995)

SeptianBayu K. (0806479080)


Outline

Outline

  • Introduction

  • Statistical Background

  • Measuring DA

  • Experimental Design

  • Data Analysis

  • Empirical Results

  • Conclusions

  • Implications


Introduction 1

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


Introduction 2

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)


Statistical background

Statistical Background

  • McNichols & Wilson (1988)

  • Problems:

    • Incorrectly attributing earnings management to PART

    • Unintentionally extracting earnings management caused by PART

    • Low power test


Measuring da 1

Measuring DA (1)

  • The Healy model (Healy, 1985)

  • The DeAngelo model (DeAngelo, 1986)

    • NDAτ = TAτ-1

  • The Jones model (Jones, 1991)


Measuring da 2

Measuring DA (2)

  • The Modified Jones model

  • The Industry model (Dechow & Sloan, 1991)

    • NDA τ = γ1 + γ2 median1 (TA τ)


Experimental design

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


Data analysis

Data Analysis

  • Total accruals (TA)

  • CFO = Earnings – TA

  • Using Z-statistic


Empirical results

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


Conclusions

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


Implications

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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