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Good versus Bad Earnings Management: Is Income Smoothing a Deliverance?

Good versus Bad Earnings Management: Is Income Smoothing a Deliverance?. Dipl.-Pol. Matthias Johannsen, MSc. Doktorandenseminar des Competence Centers Corporate Finance der Universität Hohenheim, 27. Januar 2006. theoretical considerations conceptual definitions theoretical motivation

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Good versus Bad Earnings Management: Is Income Smoothing a Deliverance?

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  1. Good versus Bad Earnings Management: Is Income Smoothing a Deliverance? Dipl.-Pol. Matthias Johannsen, MSc. Doktorandenseminar des Competence Centers Corporate Finance der Universität Hohenheim, 27. Januar 2006

  2. theoretical considerations conceptual definitions theoretical motivation research question existing literature set up of empirical investigation research design variable computation hypotheses to be tested results descriptive statistics hypothesis tests fixed effects panel regressions references agenda

  3. conceptual definitions “purposeful intervention in the external financial reporting process, with the intent of obtaining some private gain (as opposed to say, merely facilitating the neutral operation of the process)” (Schipper 1989: 92) earnings management “actions [by the management of the firm] to dampen fluctuations of the firms’ publicly reported net income” (Trueman/Titman 1988: 127) income smoothing note: income smoothing can be achieved by earnings management activities

  4. theoretical motivation I opportunistic e.m. and information uncertainty • upward management of reported earnings in order to avoid reporting of loss by reducing depreciation example • increase in current reported earnings • increase in net assets reduces future reported residual earnings • increase in net assets requires adjusted future depreciation and hence reduction of future earnings effects • opportunistic earnings management has only a transitory effect in general • current earning are of little use for predicting future earnings • opportunistic earnings management increases information uncertainty

  5. theoretical motivation II income smoothing and information uncertainty • upward management of reported earnings in case of temporary reduction of earnings • downward management of reported earnings in case of temporary increase of earnings example • reported earnings follow more closely the general trend • reported earnings have lower fluctuations effects • current earning are very useful for predicting future earnings • earnings management to smooth income increases information uncertainty

  6. research question effect of earnings management on the information uncertainty of earnings opportunistic earnings management earnings management to smooth income no significant effect increase in information uncertainty decrease in information uncertainty does the market react differently to earnings management depending on the degree of income smoothing present? scheme by Guay/Kothari/Watts (1996)

  7. existing literature I earnings management • verification of opportunistic earnings management • Teoh/Welch/Wong (1998), before IPO • Burgstahler/Eames (1998), to meet analysts’ forecasts • Detzler/Machuga (2002), in cases of non-routine change of CEO • decreasing effect on information uncertainty • Francis et al. (2003), earnings management leads to larger cumulative abnormal returns • Marquardt/Wiedmann (2004), earnings management reduces the explanatory power of in a price-earnings regression • Francis et al. (2004), earnings management increases the cost of equity • increasing effect on information uncertainty • Subramanyam (1996), discretionary accruals which are often taken as a measure of earnings management have significant information content in price-earnings regressions

  8. existing literature II income smoothing • verification of income smoothing • Schmidt (1979), in an older sample for the German Market • Kasanen/Kinnunen/Niskanen (1996), in order to sustain a smooth dividend stream • Lim/Lustgarten (2002), for the US • decreasing effect on information uncertainty • Bitner/Dolan (1996) show that equity markets pay a premium for shares of income smoothing firms • Zarowin (2002), income smoothing increases the value relevance of earnings • Tucker/Zarowin (2005), income smoothing increases the information content of reported earnings

  9. theoretical considerations conceptual definitions theoretical motivation research question existing literature set up of empirical investigation research design variable computation hypotheses to be tested results descriptive statistics hypothesis tests fixed effects panel regressions references agenda

  10. research design I event study methodology and its timing the event estimation period post event period time t0= x trading days after end of fiscal year t3= z trading days after end of fiscal year t1 = begin of estimation period t2 = end of last fiscal year

  11. research design II structure of the analysis compare average monthly absolute abnormal returns income smoothing high earnings management high income smoothing low separation of sample firm years income smoothing high earnings management low income smoothing low time to, the event

  12. variable computation I earnings management variable alternative 1 according to the model of Dechow/Sloan/Sweeney (1995): • total accruals are given by • these are regressed on • the first earnings management measure = absolute value of the one period ahead forecast error

  13. variable computation II earnings management variable alternative 2 according to the model of Dechow/Dichev (2002): • regress changes in working capital and changes on cash • the residuals are changes in working capital unrelated to past, current and future cash realizations • the second earnings management measure is the standard deviation of all current and past firm specific forecast errors:

  14. variable computation III income smoothing variable according to the model of Lang/Ready/Yetman (2003): • measurement of dampening of fluctuations in performance • In order to keep a large sample modifications are made (as robustness check, cash from operations and operating income were used without changing the results)

  15. variable computation IV control variables • unexpected earnings (Ball/Brown (1968) and subsequent) • Momentum effect (Jegadeesh/Titman (1993)) • extreme financial performance (own computation) unexpected earnings X-E[X] are the residuals from an AR(1) specification of earnings

  16. variable computation V absolute cumulative abnormal returns according to the Fama/French (1992, 1996) Three Factor Model • the risk premium is estimated by • abnormal returns, denoted , are the one period ahead forecast errors of the above equation absolute cumulative abnormal returns are given by • absolute cumulative abnormal returns serve as the proxy variable for information uncertainty

  17. hypotheses to be tested • firm years with high (low) degrees of earnings management show high (low) absolute cumulative abnormal returns hypothesis 1 • firms years with high (low) degrees of income smoothing show low (high) absolute cumulative abnormal returns hypothesis 2 • assuming that hypotheses 1 and 2 cannot be rejected • assuming that income smoothing is a special form of earnings management • It is expected that the differences in absolute cumulative abnormal returns between high and low earnings management firm years disappear for income smoothing firm years hypothesis 3

  18. theoretical considerations conceptual definitions theoretical motivation research question existing literature set up of empirical investigation research design variable computation hypotheses to be tested results descriptive statistics hypothesis tests fixed effects panel regressions references agenda

  19. descriptive statistics moments of key variables Sample Statistic EMGMT_DAC EMGMT_SD INCSM SUE MOMENTUM EXTRZ EMGMT_DAC mean 0.1463 0.8596 17.0393 0.1143 0.0344 0.3025 total STDEV 0.2259 1.939 33.8848 1.0994 0.4312 0.5261 mean 0.0331 0.3822 18.9547 0.1999 0.0414 0.2091 below the median STDEV 0.0207 0.8745 35.305 1.0407 0.3443 0.3014 mean 0.2595 1.3239 15.1231 0.0279 0.0273 0.3985 above the median STDEV 0.2758 2.4979 32.2972 1.1494 0.5047 0.6709 EMGMT_SD mean 0.1469 0.9326 14.1706 0.1704 0.0465 0.2789 total STDEV 0.2148 2.0167 2 6.9995 1.2717 0.4419 0.4501 mean 0.078 0.0957 17.9297 0.3961 0.039 0.1907 below the median STDEV 0.1033 0.0511 32.4916 1.2561 0.3185 0.2285 mean 0.2225 1.7695 10.4116 -0.064 0.054 0.3766 above the median STDEV 0.2722 2.5949 19.3583 1.246 0.5388 0.592 INCSM mean 0.1459 0.9326 20.3974 0.0735 0.0468 0.384 total STDEV 0.2251 2.0167 49.1006 1.1073 0.4453 0.7134 mean 0.1736 1.4238 2.9593 -0.0588 0.0433 0.447 below the median STDEV 0.2474 2.6243 2.0863 1.1697 0.4982 0.796 mean 0.1201 0.4468 37.8403 0.1951 0.0504 0.3233 above the median STDEV 0.1988 0.8985 64.8854 1.0319 0.3858 0.6176

  20. below the median above the median below the median above the median below the median above the median hypothesis test I hypotheses 1 and 2: separation of sample average monthly ABS(CAR) all observations for EMGMT_DAC T for equality to average T for equality to average average monthly ABS(CAR) all observations for EMGMT_SD T for equality to average T for equality to average average monthly ABS(CAR) all observations for INCSM T for equality to average T for equality to average

  21. hypothesis test II hypotheses 1 and 2: results EMGMT_DAC Panel A: Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6 total avg. monthly ABS(CAR) 0.0888 0.0633 0.0551 0.0458 below the avg. monthly ABS(CAR) 0.0787 0.0519 0.0438 0.0377 median T of H0 = total avg. ABS(CAR) -4.5167*** -7.721*** -8.9091*** - 7.6 582*** above the avg. monthly ABS(CAR) 0.1001 0.0761 0.0678 0.0548 median T of H0 = total avg. ABS(CAR) 3.7467*** 5.6939*** 6.192*** 5.6561*** Panel B: EMGMT_SD Statistic τ_0 τ_0_2 τ_0_4 τ_0_6 Sample total avg. monthly ABS(CAR) 0.1025 0.079 0.0738 0.0597 below the avg. monthly ABS(CAR) 0.0847 0.0552 0.0479 0.0405 median T of H0 = total avg. ABS(CAR) -4.9234*** -10.5592*** -14.5964*** - 12.3685*** above the avg. monthly ABS(CAR) 0.1253 0.1104 0.107 0.0842 median T of H0 = total avg. ABS(CAR) 4.0965*** 6.7129*** 7.5834*** 7.1775*** INCSM Panel C: τ_0 τ_0_2 τ_0_4 τ_0_6 Sample Statistic total avg. monthly ABS(CAR) 0.0885 0.0627 0.0535 0.0447 below the avg. monthly ABS(CAR) 0.1007 0.0762 0.066 0.0539 median T of H0 = total avg. ABS(CAR) 4.8748*** 6.9412*** 7.5434*** 6.9882*** above the avg. monthly ABS(CAR) 0.077 0.0506 0.0421 0.0363 median T of H0 = total avg. ABS(CAR) -5.803*** -10.326*** -11.9926*** - 10.1589***

  22. INCSM = below the median INCSM = above the median INCSM = below the median INCSM = above the median hypothesis test III hypothesis 3: separation of sample Z for: above the median minus below the median observations for EMGMT_DAC if INCSM is available Z for EMGMT_DAC : above the median minus below the median Z for EMGMT_DAC : above the median minus below the median observations for EMGMT_SD if INCSM is available Z for: above the median minus below the median Z for EMGMT_SD : above the median minus below the median Z for EMGMT_SD : above the median minus below the median

  23. hypothesis test IV hypothesis 3: results for EMGMT_DAC Panel A: EMGMT_DAC Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6 avg. monthly difference ABS(CAR):above – below EMGMT_DAC 0.0215 0.0242 0.0239 0.0171 total if is INCSM Z-ratio of H0: difference = 0 5.7174*** 9.0167*** 9.9537*** 8.9518*** available STDEV above] / [ABS(CAR) 1.2731 1.4323 1.5199 1.4301 STDEV [ below] ABS(CAR) avg. monthly difference ABS(CAR):above – below EMGMT_DAC total and 0.0226 0.0289 0.0308 0.022 = INCSM Z-ratio of H0: difference = 0 below the 3.7993*** 6.2943*** 7.4374*** 7372*** median STDEV above] / [ABS(CAR) 1.2729 1.2956 1.4203 1.3951 STDEV [ below] ABS(CAR) Z statistic of average Z-ratio over all event windows 16.6552*** avg. monthly difference ABS(CAR):above – below EMGMT_DAC total and 0.0155 0.0136 0.0115 0.0081 = INCSM Z-ratio of H0: difference = 0 above the 3.3272*** 4.898*** 4.9488*** 4.2926*** median STDEV above] / [ABS(CAR) 1.1866 1.4347 1.3455 1.1731 STDEV [ below] ABS(CAR) Z statistic of average Z-ratio over all event windows 12.2478***

  24. hypothesis test V hypothesis 3: results for EMGMT_SD Panel B: EMGMT_SD Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6 avg. monthly difference ABS(CAR):above – below EMGMT_SD 0.0406 0.0552 0.0591 0.0436 total if is INCSM Z-ratio of H0: difference = 0 6.1202*** 10.7159*** 12.5258*** 11.6522*** available STDEV above] / ABS(CAR) 1.358 1.7773 2.1748 1.9471 STDEV [ below] ABS(CAR) avg. monthly difference ABS(CAR):above – below EMGMT_SD total and 0.0455 0.0647 0.0506 0.0694 = INCSM Z-ratio of H0: difference = 0 7.7967*** 9.422*** below the 4.604*** 8.75 67*** median STDEV above] / ABS(CAR) 1.5433 1.5335 1.8971 1.7487 STDEV [ below] ABS(CAR) Z statistic of average Z-ratio over all event windows 20.7142*** avg. monthly difference ABS(CAR):above – below EMGMT_SD total and 0.0339 0.0281 0.0309 0.0 257 = INCSM Z-ratio of H0: difference = 0 above the 5.5974*** 3.3628*** 6.7725*** 5.9831*** median STDEV above] / ABS(CAR) 1.0603 1.6231 1.933 1.7487 STDEV [ below] ABS(CAR) Z statistic of average Z-ratio over all event windows 14.4842***

  25. fixed effects panel regressions I hypotheses 1- 3: model specification first estimation: hypotheses 1, 2 H1: coefficient on > 0 coefficient on < 0 second estimation: hypothesis 3 (only last three rows of next table) H1: coefficient on < 0

  26. fixed effects panel regressions II hypotheses 1- 3: estimation results EMGMT_DAC Panel A: EMGMT_DAC Dependent V ariable Independent ABS ABS ABS ABS Variable (CAR_0) (CAR_0_2) (CAR_0_4) (CAR_0_6) intercept 0.0706*** 0.0568*** 0.0531*** 0.0468*** EMGMT_DAC_50 -0.0008 0.0039 0.004 0.0023 -0.0085* INCSM_50 -0.0043 -0.0109** -0.0123*** ABS(SUE) 0.0129*** 0.0041*** 0.0008 -0.0012 ABS(MOMENTUM) 0.0375* 0.022*** 0.0186*** 0.0069 EXTRZ 0.0071 0.0049 0.0047** 0.0051** 0.2417 0.4218 0.5027 0.4712 adjusted R2 EMGMT_DAC_50 * INCSM_50 0.0042 -0.0016 -0.0076 -0.0046 multiplicative effect: high – low earnings management (low income smoothing) -0.0031 0.0048 0.0083 0.0049 multiplicative effect: high – low earnings management (high income smoothing) 0.0011 0.0032 0.0006 0.0003

  27. fixed effects panel regressions III hypotheses 1- 3: estimation results EMGMT_SD Panel B: EMGMT_SD Dependent V ariable Independent ABS ABS ABS ABS Variable (CAR_0) (CAR_0_2) (CAR_0_4) (CAR_0_6) 0.0725*** 0.0867*** 0.0829*** 0.0745*** intercept EMGMT_SD_50 0.007 -0.01 -0.0006 -0.0045 INCSM_50 0.009 -0.0174 -0.0186*** -0.0149*** ABS(SUE) 0.0098*** -0.0016 -0.0035 -0.0047 ABS(MOMENTUM) 0.0303 0.0251*** 0.011* -0.0074 EXTRZ 0.0028 -0.0054* -0.001 -0.0007 adjusted R2 0.1594 0.3646 0.4564 0.4009 EMGMT_SD_50 * INCSM_50 -0.0016 0.003 -0.0086 -0.0128 multiplicative effect: high – low earnings management (low income smoothing) 0.0077 -0.0114 0.0034 0.0016 multiplicative effect: high – low earnings management (high income smoothing) 0.0061 -0.0084 -0.0051 -0.0112

  28. theoretical considerations conceptual definitions theoretical motivation research question existing literature set up of empirical investigation research design variable computation hypotheses to be tested results descriptive statistics hypothesis tests fixed effects panel regressions references agenda

  29. references I • Ball, Ray, and Philip Brown, 1968, An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research, vol. 16, p. 159 – 177. • Bitner, Larry N., and Robert C. Dolan, 1996, Assessing the Relationship between Income Smoothing and the Value of the Firm, Quarterly Journal of Business & Economics, vol. 35, no.1, p. 16 – 35. • Burgstahler, David C., and Michael J. Eames, 1998, Management of earnings and analysts forecasts, University of Washington working paper. • Dechow, Patricia M., and Ilia D. Dichev, 2002, The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors, The Accounting Review, vol. 77, supplement, p. 35 – 59. • Dechow, Patricia M., Richard G. Sloan, and Amy P. Sweeney, 1995, Detecting Earnings Management,, The Accounting Review, vol. 70, no. 2, p. 193 – 225.

  30. references II • Detzler, Miranda Lam, and Susan M. Machuga, 2002, Earnings Management Surrounding Top Executive Turnover in Japanese Firms, Review of Pacific Basin Financial Markets and Policies, vol. 5, no. 3, p. 343 – 371. • Fama, Eugene F., and Kenneth R. French, 1992, The Cross-Section of Expected Stock Returns, The Journal of Finance, vol. 47, no. 2, p. 427 – 465. • Fama, E.; French, K.; 1996; Multifactor Explanations of Asset Pricing Anomalies, Journal of Finance, vol. 51, no.1, p. 55. • Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2003, Accounting Anomalies and Information Uncertainty, Duke University Fuqua School of Business working paper. • Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2004, The Market Pricing of Accruals Quality, Stockholm Institute of Financial Research working paper.

  31. references III • Guay, Wayne R., S. P. Kothari, and Ross L. Watts, 1996, A Market-Based Evaluation of Discretionary Accrual Models, Journal of Accounting Research, vol. 34, supplement, p. 83 – 105. • Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, Journal of Finance, vol. 48, no. 1, p. 65 – 91. • Kasanen, Eero, Juha Kinnunen, and Jyrki Niskanen, 1996, Dividend-based earnings management: Empirical evidence from Finland, Journal of Accounting and Economics, vol. 22, p. 283 – 312. • Lang, Mark, Jana Smith Raedy, and Michelle Higgins Yetman, 2003, How Representative Are Firms That Are Cross-Listed in the United States? An Analysis of Accounting Quality, Journal of Accounting Research, vol. 41, no. 2, p. 363 – 386.

  32. references IV • Lim, Steve C., and Steven Lustgarten, 2002, Testing for Income Smoothing Using the Backing Out Method: A Review of Specification Issues, Review of Quantitative Finance and Accounting, vol. 19, p. 273 – 290. • Marquardt, Carol A., and Christine I. Wiedman, 2004, The Effect of Earnings Management on the Value Relevance of Accounting Information, Journal of Business Finance & Accounting, vol. 31 (3) & (4), p. 297 – 332. • Schipper, Katherine, 1989, Commentary on Earnings Management, Accounting Horizons, vol. 3, issue 4, p. 91 – 102. • Schmidt, Franz, Bilanzpolitik deutscher Aktiengesellschaften, Empirische Analyse des Gewinnglättungsverhaltens, (Gabler, Wiesbaden, 1979). • Subramanyam, K. R., 1996, The pricing of discretionary accruals, Journal of Accounting and Economics, vol. 22, p. 249 – 281.

  33. references V • Teoh, Siew Hong, Ivo Welch, and T. J. Wong, 1998, Earnings Management and the Long-Run Market Performance of Initial Public Offerings, The Journal of Finance, vol. 53, no. 6, p. 1935 – 1974. • Trueman, Brett, and Sheridan Titman, 1988, An Explanation for Accounting Income Smoothing, Journal of Accounting Research, vol. 26, supplement, p. 127 – 143. • Tucker X. Jenny, and Paul Zarowin, 2005, Dose Income Smoothing Improve Earnings Informativeness?, University of Florida, Warrington College of Business and New York University, Stern School of Business Working Paper. • Zarowin, Paul, 2002, Does Income Smoothing Make Stock Prices More Informative?, New York University, Stern School of Business working Paper.

  34. Thank you very much for your attention.

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