NEW BUSINESS COMBINATIONS ACCOUNTING RULES AND THE MERGERS AND ACQUISITIONS ACTIVITY HUMBERTO NUNO RITO RIBEIRO Doctor of Philosophy DE MONTFORT UNIVERSITY LEICESTER BUSINESS SCHOOL November 2009 This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without proper acknowledgement Leicester Business School, De Montfort University New Business Combinations Accounting Rules and the Mergers and Acquisitions Activity Humberto Nuno Rito Ribeiro Doctor of Philosophy 2009 Thesis Summary The perennial controversy in business combinations accounting and its dialectic with stakeholders’ interests under the complexity of the Mergers and Acquisitions (M&A) activity is the centrepiece of analysis in this thesis It is argued here that the accounting regulation should be as neutral as possible for the economic activity, although it is recognised that accounting changes may result in economic effects In the case of the changes for business combinations accounting in the USA, lobbying was so fierce that in order to achieve the abolition of accounting choice in M&A accounting, it forced the standard-setter to compromise and to change substantially some of its earlier proposals Such fierce lobbying cast doubts about whether it was effectively possible to mitigate such economic effects, resulting in a possible impact of the accounting changes on the M&A activity The occurrence of M&A in waves is yet to be fully theorised Nevertheless, existing literature established relationships between M&A activity and some key economic and financial factors, and has provided several interesting theories and other meaningful contributions for this thesis It was therefore possible to examine whether the changes in the accounting rules produced any significant impact on the M&A activity The findings obtained from the testing of the research hypotheses suggest that the new M&A accounting rules did not result in significant impacts on overall M&A activity Nevertheless, from the study of managers’ perceptions, and from the examination of annual reports of S&P 500 companies, a considerable impact on the financial reporting was found Key words: Mergers and Acquisitions (M&A), M&A activity and waves, Accounting regulation, Economic consequences, Business combinations, Accounting choice, Pooling of interests method, Purchase method, Goodwill amortisation, Goodwill impairment Dedication This thesis is dedicated to my family in Portugal and in Brazil, in thanks for their continuing support and encouragement during its preparation Iria, Franklin, Stella, Nita, and godmother Lúcia, I love you all In memory of José Maria Pereira who could not live long enough to witness the accomplishement of this thesis Many thanks for everything, may you enjoy eternal life Post scriptum: I had the confirmation of my PhD on my Godfather’s first anniversary Acknowledgements I would like to acknowledge in particular my director of studies, Professor David Crowther, Leicester Business School, De Montfort University, for his supervision and constant support I also would like to acknowledge Peter Scott, De Montfort University, and Bode Akinwande, London Metropolitan University, for their advice during the development of the research; Maria Simatova, London Business School, for her assistance with data collecting and treatment; and Ken Westmoreland, London, and Rachel Alves, Chichester, for their continuous reviews; and, finally, Stuart Cooper, Aston University, and Ashok Patel, De Montfort University, for their substantive reviews Finally, yet not least important I address my recognition to other friends and colleagues, anonymous referees, discussants and conference participants, which helped in some way to develop this major research undertaking I also would like to acknowledge the funding for this research and for presentations in seminars and conferences obtained from the following programs and institutions: PRODEP, Ministério da Ciência e Ensino Superior, Portugal and EU ESTiG, Instituto Politộcnico de Braganỗa, Portugal Leicester Business School, De Montfort University, UK Fundaỗóo para a Ciờncia e a Tecnologia, MCTES, Portugal Fundaỗóo Calouste Gulbenkian, Portugal London Metropolitan University, UK Universidad del País Vasco, Spain The Institute of Chartered Accountants in England and Wales, UK Finally, I acknowledge the institutions that provided training and logistical support during the early stage of the research: Universidad de Santiago de Compostela, Spain Universidad Carlos III, Madrid, Spain Contents Page Chapter Introduction 16 1.1 Summary 16 1.2 The development of accounting theory and regulation 17 1.3 Ontology, theoretical identification and theory validation 31 1.4 Accounting, finance, and globalisation 35 1.5 Conclusions 38 Chapter Political Nature of Accounting Standard Setting and Developments on Business Combinations Accounting 43 2.1 Introduction 43 2.2 Towards a regulated conceptual framework for accounting 44 2.3 Lobbying and political influences on standard setting 55 2.4 Business combinations in the USA: an under pressure accounting issue 67 2.5 Accounting choice and business combinations accounting 2.6 Lobbying and pressures on FASB’s new M&A accounting proposals 2.7 Chapter 80 86 Conclusions 113 M&A Activity and M&A Accounting 115 3.1 Introduction 115 3.2 Terminology and definitions 116 3.3 M&A activity pattern and M&A waves 118 3.4 New age for business combinations and goodwill accounting 129 3.5 Neutrality versus economic effects in M&A accounting 137 3.6 Possible impacts on M&A activity and M&A accounting 143 3.7 Conclusions 146 Chapter Hypotheses 148 4.1 Introduction 148 4.2 M&A activity during the 2000-2002 period 150 4.3 M&A activity during the 1994-2008 period 157 4.4 Research methodology 159 4.5 Factors and theories explaining M&A activity 163 4.6 Reasons explaining M&A abandonment 167 4.7 Conclusions 171 Chapter Survey and Financial Reporting Analysis 173 5.1 Introduction 173 5.2 Survey on companies’ reactions to the new M&A accounting rules 174 5.3 5.4 Chapter Analysis of annual reports 189 5.3.1 Methodology of analysis 190 5.3.2 Data sources 190 5.3.3 Data collection 192 5.3.4 SFAS 142 impacts sample 208 5.3.5 Basic descriptive statistics and analysis 212 5.3.6 Cross-sectional analysis 215 Conclusions 220 Data Collection 223 6.1 Introduction 223 6.2 Data sources 224 6.3 M&A data selection 227 6.4 M&A sample 234 6.5 Data aggregation 236 6.6 Conclusions 243 Chapter Models Development and Testing Results 244 7.1 Summary 244 7.2 Introduction 245 7.3 Construction of metrics 252 7.4 Statistical models for hypotheses testing 259 7.5 Variable definitions and predictions 264 7.6 Univariate descriptive statistics 276 7.7 Empirical tests and discussion of results 280 7.7.1 Bivariate analysis 280 7.7.2 Multivariate analysis 284 7.7.3 Outliers and influential points 303 7.7.4 Sensitivity analysis 312 7.7.5 Forecasting model validation 319 Conclusions 321 7.8 Chapter Discussion and Interpretation 8.1 Introduction 8.2 Accounting regulation and accounting choice: an international 323 perspective for business combinations 8.3 325 Accounting choice in business combinations accounting and M&A activity 8.4 323 336 New M&A accounting rules and M&A activity: business as usual? 342 8.5 Chapter Conclusions 344 Conclusions and Further Research 346 9.1 Introduction 346 9.2 Limitations 347 9.3 Summary of main research and generalisation 350 9.4 Suggestions for further research 353 9.5 Contributions 354 References 359 Appendix A S&P 500 Companies List as of 31 December 2004 417 Appendix B Questionnaire Addressed to S&P 500 companies 430 Appendix C Crosstabulations for Questionnaire Data 431 Appendix D Goodwill and Other Intangible Assets (OIA), and Impact on Diluted EPS, by Industry 437 Appendix E Descriptive Statistics 438 Appendix F Pearson/Spearman Correlation Matrixes 442 Appendix G Regression Analysis for Hypotheses One and Two 446 Appendix H Residuals’ Autocorrelation Tables and Correlograms 453 Appendix I Durbin-Watson Distribution and Critical Values 467 Appendix J Plots for Normal Distribution of Residuals 469 Appendix K Outliers and Influential Points 475 Appendix L Sensitivity Analyses 487 Tables Page Table 3.1 Pro forma impact on EPS in 2001 of selected S&P 500 companies 145 Table 5.1 Estimated SFAS 142 impacts on diluted EPS by industry 216 Table 6.1 Summary of some major M&A data sources for the USA 225 Table 6.2 Sample description for the 2000-2002’s period 235 Table 6.3 Sample description for the 1994-2008’s period 236 Table 7.1 First lag autocorrelations and Durbin-Watson statistic values 294 Table 7.2 Key tests for residuals’ normality 299 Table 7.3 Tests for heteroscedasticity 303 Table 7.4 Outliers diagnostics 308 Table 7.5 Resume of samples for sensitivity analysis 315 Table 7.6 Regressions’ sensitivity to abnormal and non-trading day’s removals 316 Table 7.7 Sensitivity analysis using alternative event windows 317 Table 7.8 Out-of-sample accuracy measurement 321 Table 8.1 Accounting for business combinations worldwide in the 1990’s 327 Table 8.2 Accounting for M&A in Europe and in the USA: 1999-2000 332 10 Table K.1 Unusual residuals for hypothesis one (continued) Panel C: Model using daily data Row Studentised Studentised Modified Studentised Values Without Values With MAD Residual Residual Deletion Deletion Z-Score 21 28.2386 4.34 4.24816 4.30099 4.55937 37 26.2322 4.01 3.94632 3.98892 4.23865 65 20.6055 3.13 3.09985 3.12115 3.33921 66 -23.0304 -3.54 -3.46465 -3.49389 -3.63608 130 29.4753 4.51 4.43421 4.49413 4.75706 586 27.5797 4.21 4.14904 4.19834 4.45405 Excessive studentised residuals and modified MAD Z-scores in italic Panel D: Model using quarterly data Studentised Studentised Modified Studentised Values Without Values With MAD Row Residual Residual Deletion Deletion Z-Score 31 -0.09861 -3.26 -3.04919 -3.36114 -3.19579 Excessive studentised residual in italic 476 Table K.2 Unusual residuals for hypothesis two Panel A: Model using monthly data Row Studentised Studentised Modified Studentised Values Without Values With MAD Residual Residual Deletion Deletion Z-Score 26 7.52585 2.48 2.40341 2.68336 2.37721 36 5.13396 2.29 1.63955 1.73486 1.70375 There are no excessive studentised residuals or modified MAD Z-scores Panel B: Model using quarterly data Studentised Studentised Modified Studentized Values Without Values With MAD Row Residual Residual Deletion Deletion Z-Score 23 -38.9292 -3.35 -2.96044 -3.24522 -3.02038 47 -28.8337 -2.24 -2.19271 -2.31078 -2.2569 57 26.3563 2.09 2.00431 2.09642 1.91689 Excessive studentised residual in italic 477 Fig K.1 Box-and-Whisker plot of residuals for hypothesis one Panel A: Model using monthly data -80 -60 -40 -20 20 RESIDUALS 40 60 80 Panel B: Model using weekly data -45 -30 -15 15 30 45 RESIDUALS Panel C: Model using daily data -30 -20 -10 RESIDUALS 478 10 20 30 Box-and-Whisker plot of residuals for hypothesis one (Continued) Panel D: Model using quarterly data -0.1 -0.07 -0.04 -0.01 RESIDUALS 0.02 0.05 0.08 Fig K.2 Box-and-Whisker plot of residuals for hypothesis two Panel A: Model using monthly data -9 -6 -3 RESIDUALS Panel B: Model using quarterly data -39 -19 RESIDUALS 479 21 41 Fig K.3 Plot of outliers for hypothesis one Panel A: Model using monthly data Outlier Plot with Sigma Limits Sample mean = 0.00000462857, std deviation = 24.5373 100 RESIDUALS 60 20 -20 -1 -2 -60 -3 -4 -100 10 20 Row number 30 40 Panel B: Model using weekly data Outlier Plot with Sigma Limits Sample mean = 0.00000287355, std deviation = 16.8155 80 RESIDUALS 50 20 -10 -1 -2 -40 -3 -4 -70 40 80 Row number 480 120 160 Fig K.3 Plot of outliers for hypothesis one (continued) Panel C: Model using daily data Outlier Plot with Sigma Limits Sample mean = 2.10986E-7, std deviation = 6.64725 33 RESIDUALS 23 13 -1 -7 -2 -17 -3 -4 -27 200 400 Row number 600 800 Panel D: Model using quarterly data Outlier Plot with Sigma Limits Sample mean = -1.35593E-9, std deviation = 0.0323609 0.17 0.12 RESIDUALS 0.07 0.02 -1 -0.03 -2 -3 -0.08 -0.13 -4 10 20 30 Row number 481 40 50 60 Fig K.4 Plot of outliers for hypothesis two Panel A: Model using monthly data Outlier Plot with Sigma Limits Sample mean = -2.E-8, std deviation = 3.13132 17 -1 -2 -3 -4 RESIDUALS 12 -3 -8 -13 10 20 Row number 30 40 Panel B: Model using quarterly data Outlier Plot with Sigma Limits Sample mean = 0.00000183051, std deviation = 13.1498 60 RESIDUALS 40 20 0 -1 -20 -2 -40 -3 -4 -60 10 20 30 Rownumber 482 40 50 60 Table K.3 Influential points for hypothesis one Panel A: Model using monthly data Mahalanobis Row Leverage Distance DFITS COOK’s D 0.478847 29.3505 1.14009 0.1421550 0.230409 8.90934 -1.91005 0.2834294 11 0.517912 34.4816 -1.10874 0.1358353 15 0.259749 10.6089 -1.27837 0.1591881 20 0.421596 23.0829 -1.1974 0.1535954 32 0.429162 23.8391 1.32439 0.1853844 Average leverage of single data point = 0.257143 Excessive Leverage, DFITS, and Cook’s D values in italic Panel B: Model using weekly data Mahalanobis Row Leverage Distance DFITS COOK’s D 0.109032 17.7298 0.86116 0.0716604 26 0.034742 4.51349 0.58512 0.0323388 79 0.081673 12.6139 0.72401 0.0507079 89 0.351172 81.8164 0.47447 0.0226034 103 0.057117 8.27487 0.65633 0.0413354 104 0.084174 13.0688 -0.53353 0.0280598 126 0.064021 9.4718 0.55860 0.0304556 135 0.077157 11.7986 0.62250 0.0378041 153 0.134014 22.6837 -0.91145 0.0806455 155 0.146825 25.3366 1.35482 0.1720823 Average leverage of single data point = 0.0645161 Excessive Leverage, DFITS, and Cook’s D values in italic 483 Table K.3 Influential points for hypothesis one (continued) Panel C: Model using daily data Mahalanobis Row Leverage Distance DFITS COOK’s D 0.030427 23.4481 0.28761 0.0068790 0.026583 20.2752 -0.33444 0.0092836 11 0.050007 40.0081 0.35620 0.0105543 21 0.031430 24.2803 0.78096 0.0496760 22 0.054978 44.3212 0.27497 0.0062986 35 0.018717 13.8602 -0.33411 0.0092446 36 0.055278 44.5831 -0.09700 0.0007850 37 0.025604 19.4713 0.64974 0.0345048 38 0.027045 20.6551 -0.30808 0.0078849 42 0.029542 22.7151 0.31873 0.0084404 43 0.036637 28.6275 0.30267 0.0076204 47 0.017167 12.6087 0.27599 0.0063199 62 0.012332 8.72782 -0.31476 0.0081823 65 0.024179 18.3038 0.49327 0.0200466 66 0.040116 31.5584 -0.72318 0.0429398 67 0.012496 8.85912 0.26313 0.0057367 80 0.049909 39.9232 0.06519 0.0003546 85 0.022290 16.7617 -0.25582 0.0054407 86 0.022381 16.8355 -0.29274 0.0071161 87 0.009944 6.82568 0.25909 0.0055530 106 0.044460 35.2476 -0.32462 0.0087675 130 0.021191 15.8667 0.66297 0.0357309 131 0.126369 111.682 -0.69386 0.0399993 132 0.052073 41.7947 -0.39653 0.0130718 146 0.010851 7.54744 0.26542 0.0058296 168 0.012287 8.69229 -0.27878 0.0064330 176 0.047155 37.5531 -0.37465 0.0116695 177 0.042470 33.5531 0.44089 0.0161283 217 0.021992 16.5186 -0.28031 0.0065269 234 0.046511 37.0009 -0.20338 0.0034476 235 0.076349 63.3938 -0.05276 0.0002323 256 0.042095 33.235 -0.36887 0.0113081 257 0.080274 66.9929 0.16676 0.0023194 272 0.011685 8.21165 0.28717 0.0068193 327 0.018359 13.5705 0.28821 0.0068913 Average leverage of single data point = 0.0153649 Excessive Leverage, DFITS, and Cook’s D values in italic 484 (continues on the next page) Table K.3 Influential points for hypothesis one (continued) Panel C: Model using daily data (continued) Mahalanobis Row Leverage Distance DFITS COOK’s D 335 0.05006 40.0543 0.25033 0.005221 393 0.04780 38.1076 -0.07673 0.000491 413 0.02409 18.2347 0.29243 0.007103 436 0.04637 36.8804 -0.06182 0.000318 442 0.07672 63.736 -0.03144 0.000082 443 0.07589 62.9748 -0.12603 0.001325 444 0.07727 64.2353 -0.13218 0.001457 445 0.07470 61.8941 0.30710 0.007857 456 0.02584 19.6693 0.36268 0.010905 494 0.04710 37.5109 0.02547 0.000054 495 0.07260 59.9862 0.06853 0.000391 516 0.07908 65.8974 -0.37068 0.011441 517 0.05076 40.6661 -0.10999 0.001009 518 0.07615 63.2157 0.20277 0.003428 521 0.02019 15.0597 -0.33030 0.009041 522 0.04716 37.5593 0.34871 0.010114 585 0.05139 41.2101 0.59025 0.028830 586 0.02283 17.2056 0.64398 0.033823 587 0.03972 31.2273 0.29031 0.007013 623 0.01013 6.97882 0.25717 0.005472 627 0.01617 11.8073 0.29641 0.007280 654 0.04780 38.1122 0.11357 0.001075 655 0.07164 59.1185 0.21141 0.003726 673 0.02308 17.4135 0.36830 0.011234 674 0.03633 28.3721 0.38758 0.012470 694 0.02489 18.8884 -0.31824 0.008407 703 0.07468 61.8811 0.15856 0.002097 758 0.03217 24.9009 -0.44833 0.016641 759 0.05442 43.8368 0.03935 0.000129 760 0.07908 65.896 0.02335 0.000045 761 0.03281 25.4355 -0.47291 0.018502 774 0.01660 12.1518 0.37057 0.011338 775 0.02317 17.4821 0.40128 0.013319 777 0.08290 69.4202 -0.02678 0.000059 778 0.05791 46.8929 -0.06689 0.000373 779 0.08346 69.9384 0.09498 0.000752 Average leverage = 0.0153649 Excessive Leverage, DFITS, and Cook’s D values in italic 485 Table K.3 Influential points for hypothesis one (continued) Panel D: Model using quarterly data Mahalanobis Row Leverage Distance DFITS COOK’s D 19 0.141933 8.44565 0.76054 0.11057273 20 0.137918 8.13625 -0.66221 0.08496698 31 0.045455 1.73158 -0.71167 0.08595706 60 0.407693 38.251 -1.43693 0.39820474 Average leverage of single data point = 0.0847458 Excessive Leverage, DFITS, and Cook’s D values in italic Table K.4 Influencial points for hypothesis two Panel A: Model using monthly data Mahalanobis Leverage Distance DFITS COOK’s D 10 0.49152 30.928 1.12271 0.12453303 26 0.16657 5.6252 1.10826 0.10186089 34 0.44419 25.403 -1.50888 0.21199466 36 0.55808 40.703 2.5698 0.56483247 Row Average leverage of single data point = 0.285714 Excessive Leverage, DFITS, and Cook’s D values in italic Panel B: Model using quarterly data Mahalanobis Row Leverage Distance DFITS COOK’s D 23 0.150704 9.13169 -1.41316 0.2788794 31 0.268098 19.8966 -0.75469 0.0939430 32 0.156596 9.60052 -0.70661 0.0806471 33 0.225137 15.5786 0.90226 0.1312186 57 0.10271 5.54188 0.70562 0.0780503 Average leverage of single data point = 0.101695 Excessive Leverage, DFITS, and Cook’s D values in italic 486 Appendix L Sensitivity Analyses Fig L.1 Box-and-Whisker plot of residuals with major outliers removed -25 -15 -5 RESIDUALS 15 25 Model using daily data with major outliers removed Fig L.2 Outlier plot with major outliers’ elimination Outlier Plot with Sigma Limits Sample mean = -0.143534, std deviation = 6.35444 34 -1 -2 -3 -4 RESIDUALS 24 14 -6 -16 -26 200 400 Row number 600 800 Model using daily data with major outliers removed (red X’s) 487 Table L.1 Additional regression model estimation and tests results j =1 i =1 MAt = α + ∑ β j Pert j + ∑ δ iWeekdayi ,t + λ SP500t + ζ FEDt + ϕ Holt + (21) +ϖ HS _ Extt + φ E _ BoM t + θ Eventt + ψ Event _ EDt + ξ MAt −1 + ε t Backward elimination regression, with all variables left in the model significant at the 0.05 level: Estimate P-Value T Statistic 22.1014 -0.04491 0.00004 -3.04442 -4.87279 -5.24635 -9.3084 12.6057 0.01009 0.10468 0.0002 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0104 0.0005 3.73514 -8.52012 7.21886 -3.68739 -5.97421 -6.52683 -11.5858 15.0404 2.56297 3.45684 86.57 51.032% 740 (741) 0.0000 Durbin-Watson D 2.03524 0.3159 Chi-Squared (50 d f.) Shapiro-Wilk W Skewness Z-score Kurtosis Z-score 58.5726 0.98280 3.26643 4.70102 0.189843 0.120424 0.001089 0.000002 Kolmogorov-Smirnov Modified K-S D Cramer-Von Mises W2 Watson U2 Anderson-Darling A2 Kuiper V Box-Pierce Test 0.03136 0.85703 0.16416 0.12940 1.19931 0.05535 24.2939 0.469434 ≥ 0.10 ≥ 0.10 ≥ 0.10 ≥ 0.10 ≥ 0.10 0.444899 Parameter Intercept Per Per_2 Tue Wed Thu Fri E_BoM SP500 MA_lag ANOVA F value R2 (adjusted for d f.) N used (read) 488 Standard Error 5.91715 0.005271 0.000006 0.825631 0.815637 0.803812 0.803433 0.83812 0.003938 0.030282 Table L.2 Estimated autocorrelations for additional regression’ residuals Standard Lower 95.0% Upper 95.0% Autocorrelation Error Prob Limit Prob Limit -0.019282 0.036760 -0.072049 0.072049 0.057553 0.036774 -0.072076 0.072076 0.066627 0.036895 -0.072314 0.072314 0.061606 0.037058 -0.072632 0.072632 0.043876 0.037196 -0.072903 0.072903 -0.009975 0.037266 -0.073040 0.073040 0.031927 0.037269 -0.073047 0.073047 -0.003524 0.037306 -0.07312 0.07312 -0.031673 0.037307 -0.073120 0.073120 10 0.013436 0.037343 -0.073192 0.073192 11 0.017129 0.03735 -0.073204 0.073204 12 -0.043115 0.037360 -0.073225 0.073225 13 -0.042528 0.037427 -0.073357 0.073357 14 -0.04463 0.037493 -0.073485 0.073485 15 0.031350 0.037564 -0.073625 0.073625 16 -0.045995 0.037600 -0.073695 0.073695 17 0.028128 0.037676 -0.073843 0.073843 18 -0.027576 0.037704 -0.073899 0.073899 19 -0.002347 0.037731 -0.073952 0.073952 20 -0.032499 0.037731 -0.073953 0.073953 21 0.022204 0.037769 -0.074027 0.074027 22 0.005533 0.037787 -0.074062 0.074062 23 -0.066840 0.037788 -0.074064 0.074064 24 -0.003839 0.037947 -0.074376 0.074376 Lag 489 Table L.3 Estimated partial autocorrelations for additional regression’ residuals Partial Standard Lower 95.0% Upper 95.0% Autocorrelation Error Prob Limit Prob Limit -0.019282 0.0367607 -0.0720498 0.0720498 0.057202 0.0367607 -0.0720498 0.0720498 0.069027 0.0367607 -0.0720498 0.0720498 0.061563 0.0367607 -0.0720498 0.0720498 0.039461 0.0367607 -0.0720498 0.0720498 -0.019576 0.0367607 -0.0720498 0.0720498 0.018427 0.0367607 -0.0720498 0.0720498 -0.010177 0.0367607 -0.0720498 0.0720498 -0.038463 0.0367607 -0.0720498 0.0720498 10 0.009095 0.0367607 -0.0720498 0.0720498 11 0.020773 0.0367607 -0.0720498 0.0720498 12 -0.040660 0.0367607 -0.0720498 0.0720498 13 -0.043352 0.0367607 -0.0720498 0.0720498 14 -0.045118 0.0367607 -0.0720498 0.0720498 15 0.035872 0.0367607 -0.0720498 0.0720498 16 -0.028456 0.0367607 -0.0720498 0.0720498 17 0.037468 0.0367607 -0.0720498 0.0720498 18 -0.020681 0.0367607 -0.0720498 0.0720498 19 -0.000313 0.0367607 -0.0720498 0.0720498 20 -0.031893 0.0367607 -0.0720498 0.0720498 21 0.022987 0.0367607 -0.0720498 0.0720498 22 0.005176 0.0367607 -0.0720498 0.0720498 23 -0.060646 0.0367607 -0.0720498 0.0720498 24 -0.006545 0.0367607 -0.0720498 0.0720498 Lag Fig L.3 Autocorrelations correlogram for additional regression model Estimated Autocorrelations for RESIDUALS Autocorrelations 0.6 0.2 -0.2 -0.6 -1 10 15 lag 490 20 25 ...Leicester Business School, De Montfort University New Business Combinations Accounting Rules and the Mergers and Acquisitions Activity Humberto Nuno Rito Ribeiro Doctor of Philosophy 2009 Thesis... standard setting 55 2.4 Business combinations in the USA: an under pressure accounting issue 67 2.5 Accounting choice and business combinations accounting 2.6 Lobbying and pressures on FASB’s new. .. Summary The perennial controversy in business combinations accounting and its dialectic with stakeholders’ interests under the complexity of the Mergers and Acquisitions (M&A) activity is the centrepiece