Security Analysts’ Earnings Forecasts: Distributions Normality and a Comparative Analysis of Fitted Distribution Types in the Development of a Surrogate Consensus

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(1)Security Analysts’ Earnings Forecasts: Distributions Normality and a Comparative Analysis of Fitted Distribution Types in the Development of a Surrogate Consensus. by. Henry Leung. A thesis presented in fulfilment of the requirement for the degree of Doctor of Philosophy. Faculty of Economics and Business The University of Sydney Sydney NSW 2006 Australia ©Henry Leung 2011.

(2) Abstract The employment of IBES (Institutional Brokers’ Estimate System) analysts’ earnings forecast consensus in capital markets research literature presupposes normality in per period, per firm distributions of analysts’ earnings estimates on the basis that: (i) the central limit theorem holds true because analysts’ earnings estimates are independently and identically distributed (iid); and (ii) the unweighted consensus mean and/or median are the best estimator(s) of a normal distribution. However, monthly distributions of IBES analysts’ earnings forecasts for all Australian stocks from 11 months through to actual reported earnings between 1988 and 2002 were found to be significantly non-normal, with this result common across four different deflator types; (i) firm’s share price at 11 months prior to actual reported earnings; (ii) firm’s share price at each period; (iii) actual reported earnings; and (iv) average of forecast plus actual. Furthermore, respective distribution skewness and kurtosis were significantly positive in corroboration with evidence of distribution non-normality. These findings are consistent with the principal hypothesis of this thesis, which propounds the contrarian notion that distributions of IBES analysts’ earnings estimates are non-normal because IBES analysts’ earnings forecasts are neither independent nor identically distributed in practice for various reasons: post earnings announcement drift effects; serial correlations of IBES individual analysts forecast revisions; and analyst herding behaviour.. Further investigation of the same distributions found that they adhere variously to the Extreme Value distribution, the Uniform distribution and other unspecified non-normal distribution types. This information was utilised to construct a best estimate of distributions, the surrogate consensus, which was found to be more accurate than the IBES consensus for the actual forecast error deflator type. It was benchmarked against four other surrogate consensus generation techniques: weighted consensus based on prior year end’s lead analyst, analysts’ prior accuracy, earnings forecast age and analyst forecast frequency, with none of these alternatives offering improvements over the IBES consensus.. ii.

(3) Declaration of Original Authorship I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners.. I understand that my thesis may be made electronically available to the public.. iii.

(4) Dedication. To my parents, and my dear wife Jennifer, who made all this possible, for their endless support and patience.. iv.

(5) Acknowledgements I would like to thank the members of my thesis committee at the University of Sydney: Philip Lee, Michael McKenzie and Marcus O’Connor for their valuable guidance, helpful discussions and suggestions. Part of this research has been conducted using the facilities of the Faculty of Business and Economics PhD Research Centre.. Finally, I gratefully. acknowledge the contribution of Thomson Financial for providing earnings-per-share forecast data, available through the Institutional Brokers’ Estimate System. These data have been provided as part of a broad academic program to encourage earnings expectation research.. I extend my gratitude to Maxwell Stevenson at the University of Sydney, who provided assistance with the BestFit distribution fitting software. I would also like to thank Tigger Wise, the head editor of The University of Sydney Oceania Publications, for her comments in the final revision of the thesis. Finally, I give my sincerest thanks to my family for their love, support, and encouragement. Thank you.. All errors remain my responsibility.. v.

(6) Table of Contents Reference and Header. Page. Abstract .................................................................................................................................................. ii Declaration of Original Authorship ....................................................................................................... iii Dedication ............................................................................................................................................. iv Acknowledgements ................................................................................................................................ v Table of Contents .................................................................................................................................. vi List of Figures ....................................................................................................................................... xi Abbreviations ...................................................................................................................................... xvi Chapter 1 Introduction ........................................................................................................................... 1 1.1 Introduction.................................................................................................................................. 1 1.2 Background to the Research ........................................................................................................ 1 1.3 Research Problem and Hypotheses .............................................................................................. 2 1.4 Contribution to Analysts’ Earnings Forecast Literature .............................................................. 4 1.5 Methodology ................................................................................................................................ 5 1.6 Outline of the Thesis .................................................................................................................... 6 1.7 Proposed Research Framework and Delimitations ...................................................................... 6 1.8 Conclusions.................................................................................................................................. 8 Chapter 2 Core Literature Review – Properties of Analysts’ Earnings Forecasts ................................ 10 2.1 Introduction................................................................................................................................ 10 2.2 Analysts’ Earnings Forecast Framework: Analysts’ Earnings Forecasts as a Demand Driven Entity ............................................................................................................................................... 10 2.3 Justification for the Delimitation of Properties of Analysts’ Earnings Forecasts ...................... 11 2.4 Accuracy .................................................................................................................................... 13 2.4.1 Accuracy of Analysts’ Earnings Forecasts in Relation to Thesis Aims ...................................... 13 2.4.2 Literature Review of Accuracy of Analysts’ Earnings Forecasts ................................................ 14 2.4.3 Definition of Accuracy: Consensus and Individual Analysts’ Earnings Forecasts ..................... 18 2.5 Bias ............................................................................................................................................ 20 2.5.1 Bias of Analysts’ Earnings Forecasts in Relation to Thesis Aims .............................................. 20 2.5.2 Literature Review of Bias of Analysts’ Earnings Forecasts ........................................................ 21 2.5.3 Definition of Bias: Consensus and Individual Analysts’ Earnings Forecasts.............................. 27 2.6 Revision ..................................................................................................................................... 28 2.6.1 Revision of Analysts’ Earnings Forecasts in Relation to Thesis Aims ....................................... 29 2.6.2 Literature Review of Revision of Analysts’ Earnings Forecasts ................................................. 29 2.7 Analyst Following/ Neglect (Analyst Coverage) ....................................................................... 32 2.7.1 Analyst Coverage in Relation to Thesis Aims............................................................................. 33 2.7.2 Literature Review of Analyst Coverage ...................................................................................... 33 2.8 Dispersion .................................................................................................................................. 36 2.8.1 Dispersion of Analysts’ Earnings Forecasts in Relation to Thesis Aims .................................... 36 2.8.2 Literature Review of Dispersion of Analysts’ Earnings Forecasts .............................................. 37. vi.

(7) 2.9 Implications of Literature Review in Relation to Research Aims.............................................. 38 Chapter 3 Subsidiary Literature Review – Non-Normal Models of Earnings and Financial Ratios, Statistical Analyses and Forecast Metrics ........................................................................................ 40 3.1 Introduction................................................................................................................................ 40 3.2 Justification for Literature Review of Non-Normal Models of Earnings and Financial Ratios, Statistical Analyses and Forecast Metrics ........................................................................................ 40 3.3 Non-Normal Models of Earnings and Financial Ratios ............................................................. 42 3.4 Research Framework of Statistical Analyses ............................................................................. 46 3.4.1 The Normal Distribution ............................................................................................................. 46 3.4.2 The Central Limit Theorem......................................................................................................... 48 3.4.3 Moments of a Distribution .......................................................................................................... 49 3.4.4 Parameters of a Distribution ........................................................................................................ 50 3.4.5 Types of Distributions ................................................................................................................. 51 3.4.6 Goodness of Fit Tests .................................................................................................................. 52 3.4.7 Estimation of Parameters ............................................................................................................ 54 3.4.7.1 Least Squares Estimation ............................................................................................ 55 3.4.7.2 Maximum Likelihood Estimation ............................................................................... 56 3.4.8 Combination of Forecasts............................................................................................................ 58 3.4.8.1 Simple Unweighted Average and Optimised Unequally Weighted Average .............. 59 3.4.8.2 Optimised Weighting Methods ................................................................................... 61 3.5 Research Framework of Forecast Metrics.................................................................................. 64 3.5.1 Bias and Accuracy of Analysts’ Earnings Forecasts ................................................................... 65 3.5.2 Appropriate Error Deflators ........................................................................................................ 67 3.5.3 Forecast Error Metrics ................................................................................................................. 68 3.6 Implications of Literature Review In Relation to Research Aims ............................................. 72 Chapter 4 Hypotheses .......................................................................................................................... 75 4.1 Introduction................................................................................................................................ 75 4.2 Hypotheses Development .......................................................................................................... 75 4.2.1 Phase 1: Distributions Non-Normality of IBES Analysts’ Earnings Forecast ............................ 77 4.2.1.1 Hypothesis 1 Proposal ................................................................................................. 77 4.2.1.2 Application of the Bias and Accuracy Metrics in Relation to Research Aims ............ 79 4.2.2 Phase 2: Fitting Distributions of IBES Analysts’ Earnings Forecasts to Different Distribution Type(s) ........................................................................................................................................ 82 4.2.2.1 Hypothesis 2 Proposal ................................................................................................. 83 4.2.3 Phase 3: Development of Surrogate Consensus (Versus IBES Consensus Accuracy) ................ 86 4.2.3.1 Hypothesis 3: Equally Weighted Surrogate Consensus Based on Non-normal Distribution Type(s) ................................................................................................................ 89 4.2.3.2 Hypotheses 4 to 7: Unequally Weighted Surrogate Consensus Based on Different Optimisation Schemes............................................................................................................. 92. vii.

(8) 4.2.3.3 Hypothesis 4: Unequally Weighted Surrogate Consensus Based on Prior Year End’s Lead Analyst ........................................................................................................................... 94 4.2.3.4 Hypothesis 5: Unequally Weighted Surrogate Consensus Based on Analysts’ Prior Accuracy ................................................................................................................................. 95 4.2.3.5 Hypothesis 6: Unequally Weighted Surrogate Consensus Based on Forecast Age..... 97 4.2.3.6 Hypothesis 7: Unequally Weighted Surrogate Consensus Based on Forecast Frequency ................................................................................................................................................ 98 4.3 Conclusions................................................................................................................................ 99 Chapter 5 Source Data ....................................................................................................................... 101 5.1 Introduction.............................................................................................................................. 101 5.2 Usage of IBES Analysts’ Earnings Forecasts – The Rationale ................................................ 101 5.3 IBES Data Description............................................................................................................. 102 5.4 Forecast Horizon and Observation Interval ............................................................................. 104 5.5 Sample Data Selection Process ................................................................................................ 105 5.6 Sample Data Cleansing Process ............................................................................................... 107 5.7 Comparison of Individual Estimates with the IBES Consensus .............................................. 107 5.8 IBES Internal Rules ................................................................................................................. 109 5.8.1 IBES Individual Analysts’ Earnings Estimates Exclusion Rule................................................ 110 5.8.2 IBES Individual Analysts’ Earnings Estimates Stoppage Rule ................................................. 110 5.9 Comparing Mean of IBES Consensus and Mean of IBES Individual Analysts’ Earnings Estimates - Before and After Application of IBES Internal Rules................................................. 111 5.10 Descriptive Statistics of Cross-sectional Distributions of Analysts’ Earnings Forecasts Post Treatment of IBES Internal Rules.................................................................................................. 115 5.11 Further Data Partitioning Due to High Actual Forecast Error of Initial Results .................... 122 5.12 Conclusions............................................................................................................................ 125 Chapter 6 Phase 1: Non-Normality of Distributions of IBES Analysts’ Earnings Forecasts Methodology & Analyses of Results .............................................................................................. 127 6.1 Introduction.............................................................................................................................. 127 6.2 Methodology and Analyses of Results..................................................................................... 127 6.3 Distribution Normality of IBES Analysts’ Earnings Forecasts................................................ 127 6.4 Phase 1 Rerun: Partitioning Data by Time Period, Market Capitalisation and Industry As a Result of High Actual Forecast Error ............................................................................................ 133 6.5 Conclusions.............................................................................................................................. 144 Chapter 7 Phase 2: Conformance of Distributions of IBES Analysts’ Earnings Forecasts to Different Distribution Type(s) - Methodology & Analyses of Results .......................................................... 146 7.1 Introduction.............................................................................................................................. 146 7.2 Methodology ............................................................................................................................ 146 7.2.1 Data Partitioning and Testing .................................................................................................... 150 7.3 Analyses of Results .................................................................................................................. 152. viii.

(9) 7.3.1 Goodness of Fit Tests: Conformance of Analysts’ Earnings Relative Forecasts Distributions to Certain Distribution Type(s): All Firms .................................................................................... 152 7.3.2 Goodness of Fit Tests: Conformance of Analysts’ Earnings Relative Forecasts Distributions to Certain Distribution Type(s): By Sector, Industry and Group (S/I/G) ...................................... 156 7.3.3 Goodness of Fit Tests: Conformance of Analysts’ Earnings Relative Forecasts Distributions to Certain Distribution Type(s): By Firms..................................................................................... 176 7.4 Phase 2 Rerun: Partitioning Data by Time Period, Market Capitalisation and Industry As a Result of High Actual Forecast Error ............................................................................................ 179 7.5 Conclusions.............................................................................................................................. 187 Chapter 8 Phase 3: Surrogate Consensus Development (Versus IBES Consensus Accuracy) Methodology & Analyses of Results .............................................................................................. 189 8.1 Introduction.............................................................................................................................. 189 8.2 Methodology ............................................................................................................................ 189 8.2.1 Equally Weighted Surrogate Consensus Based on Non-normal Distribution Type(s) (Hypothesis 3) ............................................................................................................................................... 190 8.2.2 Unequally Weighted Surrogate Consensus Based on Prior Year End’s Lead Analyst (Hypothesis 4) ............................................................................................................................................... 195 8.2.3 Unequally Weighted Surrogate Consensus Based on Analysts’ Prior Accuracy (Hypothesis 5) ................................................................................................................................................... 200 8.2.4 Unequally Weighted Surrogate Consensus Based on Forecast Age (Hypothesis 6) ................. 204 8.2.5 Unequally Weighted Surrogate Consensus Based on Forecast Frequency (Hypothesis 7) ....... 208 8.3 Analyses of Results .................................................................................................................. 212 8.3.1 Hypothesis 3 Results: Equally Weighted Surrogate Consensus Based on Non-normal Distribution Type(s) .................................................................................................................. 212 8.3.2 Hypothesis 4 Results: Unequally Weighted Surrogate Consensus Based on Prior Year End’s Lead Analyst.............................................................................................................................. 216 8.3.3 Hypothesis 5 Results: Unequally Weighted Surrogate Consensus Based on Analysts’ Prior Accuracy.................................................................................................................................... 220 8.3.4 Hypothesis 6 Results: Unequally Weighted Surrogate Consensus Based on Forecast Age ...... 223 8.3.5 Hypothesis 7 Results: Unequally Weighted Surrogate Consensus Based on Forecast Frequency ................................................................................................................................................... 226 8.3.6 Comparison of Equally and Unequally Weighted Surrogate Consensus Performance ............. 227 8.4 Phase 3 Rerun: Partitioning Data by Time Period, Market Capitalisation and Industry As a Result of High Actual Forecast Error ............................................................................................ 236 8.5 Conclusions.............................................................................................................................. 239 Chapter 9 Conclusions and Implications ............................................................................................ 245 9.1 Introduction.............................................................................................................................. 245 9.2 Conclusions Recapitulation ..................................................................................................... 245 9.2.1 Phase 1 Conclusions .................................................................................................................. 245 9.2.2 Phase 2 Conclusions .................................................................................................................. 246. ix.

(10) 9.2.3 Phase 3 Conclusions .................................................................................................................. 248 9.3 Resolution of Core Research Problems: Contribution to the Literature of Analysts Earnings Forecasts ........................................................................................................................................ 248 9.4 Further Research ...................................................................................................................... 250 9.5 Conclusions.............................................................................................................................. 251 Bibliography....................................................................................................................................... 252 Bibliography Annotations .................................................................................................................. 264 Appendices ......................................................................................................................................... 266 Appendix A - Miscellaneous Tables .............................................................................................. 266 A1. Analysts' Forecast Accuracy ....................................................................................................... 266 A2. IBES Consensus Actual Forecast Error by Economic Cycles ..................................................... 269 A3. IBES Consensus Actual Forecast Error by Market Capitalisation .............................................. 270 A4. Industry Decomposition of Positive and Negative Actual Reported Earnings Firms by Time Period ........................................................................................................................................ 271 A5. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) - All Firms: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Chi-Squared, KolmogorovSmirnov and Anderson-Darling Statistics. ................................................................................ 280 A6. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) - Firms by Sector: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Chi-Squared, Kolmogorov-Smirnov and Anderson-Darling Statistics............................................................ 284 A7. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) - Firms by Industry: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Chi-Squared, Kolmogorov-Smirnov and Anderson-Darling Statistics............................................................ 295 A8. Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using KolmogorovSmirnov and Anderson-Darling Statistics: By Sector ............................................................... 304 A9. Phase 3 Hypothesis 3 Rerun: Statistics from Tests for Difference in the Mean of IBES Consensus and Surrogate Consensus Samples ............................................................................................ 321 A10. Phase 3 Hypothesis 4 Rerun: Statistics from Tests for Difference in the Mean of IBES Consensus and Surrogate Consensus Samples .......................................................................... 324 A11. Phase 3 Hypothesis 5 Rerun: Statistics from Tests for Difference in the Mean of IBES Consensus and Surrogate Consensus Samples .......................................................................... 327 A12. Phase 3 Hypothesis 6 Rerun: Statistics from Tests for Difference in the Mean of IBES Consensus and Surrogate Consensus Samples .......................................................................... 330 A13. Phase 3 Hypothesis 7 Rerun: Statistics from Tests for Difference in the Mean of IBES Consensus and Surrogate Consensus Samples .......................................................................... 333. x.

(11) List of Figures Reference and Header. Page. Figure 1-1. Analysts’ Earnings Forecast Framework with Research Scope Highlighted in Grey.......... 9 Figure 2-1. Thesis Literature Delimitation – Properties of IBES Analysts’ Earnings Forecasts .......... 12 Figure 3-1. Binomial probability distributions of sample size n = 10 and parameter w = 0.2 (top) and w = 0.7 (bottom). ................................................................................................................................ 52 Figure 3-2. The likelihood function given observed data y = 7 and sample size n = 10 for the one parameter Binomial distribution model. ......................................................................................... 57 Figure 3-3. Computation of the Distribution of Mean Consensus Earnings Estimates. ....................... 60 Figure 4-1. Accuracy versus Bias as the Means of Constructing Distributions of Analysts' Earnings Forecasts ......................................................................................................................................... 81 Figure 4-2. Estimates of population mean between the MLE (μ) versus equally weighted sample mean ( x ). (Reproduced from Hayes and Levine, 2000). .............................................................. 91 Figure 5-1. A Graph of IBES Individual Analysts’ Earnings Forecasts Illustrating the Relations Between IBES Data Attributes. .................................................................................................... 104 Figure 5-2. Time line prior to actual reported earnings release illustrating analysts' earnings estimates carry forward rule. ........................................................................................................................ 109 Figure 5-3. Time line prior to actual reported earnings release illustrating analysts' earnings estimates exclusion rule................................................................................................................................ 110 Figure 5-4. Time line prior to actual reported earnings release illustrating analysts' earnings estimates stoppage rule................................................................................................................................. 111 Figure 8-1. Standardised Differences Between Analysts' Earnings Actual Forecast Accuracy of All Surrogate Consensuses and the IEBS Consensus Using Deflator Share Price at 11 Months Prior to Actual Earnings Reporting. .......................................................................................................... 234 Figure 8-2. Standardised Differences Between Analysts' Earnings Actual Forecast Accuracy of All Surrogate Consensuses and the IEBS Consensus Using Deflator Share Price at Each Period. .... 234 Figure 8-3. Standardised Differences Between Analysts' Earnings Actual Forecast Accuracy of All Surrogate Consensuses and the IEBS Consensus Using Deflator Actual Reported Earnings. ..... 235 Figure 8-4. Standardised Differences Between Analysts' Earnings Actual Forecast Accuracy of All Surrogate Consensuses and the IEBS Consensus Using Deflator Average of Forecast and Actual. ...................................................................................................................................................... 235. xi.

(12) List of Tables Reference and Header. Page. Table 3-1. Consensus and Actual Error Metrics - Accuracy and Bias ................................................. 67 Table 5-1. Elimination of Firms and Estimates Due to Data Cleansing and Employment of IBES Internal Rules................................................................................................................................ 112 Table 5-2. Comparison Between Mean of IBES Individual Analysts’ Earnings Estimates and Mean of IBES Consensus - Prior and Posterior Application of IBES Internal Rules. ................................ 114 Table 5-3. Effects on sample size after observations with zero deflators (PRICEt=-11, PRICEt, CONS, ACTUAL and mPE) are removed. ............................................................................................... 116 Table 5-4. Descriptive Statistics on Distributions of Analysts' Relative Forecast Bias (AERF_BIAS) Using Deflators PRICEt=-11, PRICEt, CONS and mPE - Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 ............................................................................................. 119 Table 5-5. Sample Size Changes Due to Subsequent Application of Selection Criteria .................... 123 Table 6-1. Distribution Normality of Analysts’ Earnings Relative Forecast Bias ............................ 130 Table 6-2. Effects on Analysts’ Earnings Actual Forecast Error deflated by Actual Due to Data Partitioning ................................................................................................................................... 135 Table 6-3. Descriptive Statistics of Partitioned Data: Monthly Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) Using Deflators PRICEt=-11, PRICEt, CONS and mPE ... 137 Table 6-4. Monthly Distributions of Analysts’ Earnings Relative Forecast Bias Due to Data Partitioning ................................................................................................................................... 141 Table 7-1. Description of Data Partitioning Scenarios. ...................................................................... 151 Table 7-2. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) Using Deflators PRICEt=-11, PRICEt, mPE and CONS at 1, 6 and 11 Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All Firms: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Chi-Squared, Kolmogorov-Smirnov and Anderson-Darling Statistics. ...................................................................................................................................................... 154 Table 7-3. Australian Listed Firms 1st July, 1988 Through 30th June, 2002: IBES Sector, Industry/ Group Classification Percentage Breakdown ............................................................................... 157 Table 7-4. Distributions of IBES Analysts' Earnings Relative Forecast Bias (AERF_BIAS) Using Deflators PRICEt=-11, PRICEt, mPE and CONS at 1, 6 and 11 Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 for Firms by Sector: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Chi-Squared, Kolmogorov-Smirnov and AndersonDarling Statistics. ......................................................................................................................... 160 Table 7-5. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) Using Deflators PRICEt=-11, PRICEt, mPE and CONS at 1, 6 and 11 Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 for Firms by Industry: Ranking of Goodness of Fit Against PreSpecified Distribution Types Using Chi-Squared, Kolmogorov-Smirnov and Anderson-Darling Statistics........................................................................................................................................ 170. xii.

(13) Table 7-6. Distributions of Analysts' Earnings Relative Forecast Bias (AERF_BIAS) Using Firm’s Consensus as Deflator for 11 Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 for Individual Firms: Ranking of Goodness of Fit Against Pre-Specified Distribution Types Using Kolmogorov-Smirnov and Anderson-Darling Statistics.......................................... 178 Table 7-7. Distributions of Analysts' Earnings Relative Forecast Bias Using Firm's Consensus as Deflator for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 for All Firms: Percentage of Distribution Fitted Using Ranking of Goodness of Fit Against PreSpecified Distribution Types Using Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) Statistics........................................................................................................................................ 181 Table 7-8. Distributions of Analysts' Earnings Relative Forecast Bias Using Firm's Consensus as Deflator for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 for All Sectors: Percentage of Distribution Fitted Using Ranking of Goodness of Fit Against PreSpecified Distribution Types Using Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) Statistics........................................................................................................................................ 184 Table 7-9. Distributions of Analysts' Earnings Relative Forecast Bias Using Firm's Consensus as Deflator for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 for All Industries: Percentage of Distribution Fitted Using Ranking of Goodness of Fit Against PreSpecified Distribution Types Using Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) Statistics........................................................................................................................................ 185 Table 7-10. Distributions of Analysts' Earnings Relative Forecast Bias Using Firm's Consensus as Deflator for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 for Individual Firms: Percentage of Distribution Fitted Using Ranking of Goodness of Fit Against PreSpecified Distribution Types Using Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) Statistics........................................................................................................................................ 186 Table 8-1. Comparison of Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of Surrogate Consensus (Equally Weighted Based on Phase 2 Non-normal Distribution Type(s)) versus the IBES Consensus Using Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ...................................................................................................................................................... 214 Table 8-2. Comparison of Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of Surrogate Consensus (Unequally Weighted Based on Prior Year End’s Lead Analyst) versus the IBES Consensus Using Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ............. 218 Table 8-3. Comparison of Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of Surrogate Consensus (Unequally Weighted Based on Analysts’ Prior Accuracy) versus the IBES Consensus Using Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ...................... 221 Table 8-4. Comparison of Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of Surrogate Consensus (Unequally Weighted Based on Forecast Age) versus the IBES Consensus Using. xiii.

(14) Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ............................................. 224 Table 8-5. Comparison of Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of Surrogate Consensus (Unequally Weighted Based on Forecast Frequency) versus the IBES Consensus Using Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ............................................. 228 Table 8-6. Standardised Differences Between Analysts' Earnings Actual Forecast Accuracy (AEAF_ACC) of All Surrogate Consensus and the IBES Consensus Using Deflators PRICEt=-11, PRICEt, ACTUAL and mPE at each of the 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 30/6/2002 for All ASX Firms. ........................................................................ 231 Table 8-7. Hypothesis 3 Surrogate Consensus Based on Distribution Type versus the IBES Consensus: Analysts' Actual Forecast Error For 4 Deflator Types for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 ..................................................................................... 237 Table 8-8. Hypothesis 4 Surrogate Consensus Based on Prior Year’s Lead Analyst versus IBES Consensus: Analysts' Actual Forecast Error For 4 Deflator Types for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 ....................................................................... 241 Table 8-9. Hypothesis 5 Surrogate Consensus Based on Analysts’ Prior Accuracy versus IBES Consensus: Analysts' Actual Forecast Error For 4 Deflator Types for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 ....................................................................... 242 Table 8-10. Hypothesis 6 Surrogate Consensus Based on Forecast Age versus IBES Consensus: Analysts' Actual Forecast Error For 4 Deflator Types for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 ..................................................................................... 243 Table 8-11. Hypothesis 7 Surrogate Consensus Based on Forecast Frequency versus IBES Consensus: Analysts' Actual Forecast Error For 4 Deflator Types for 11 Months Prior to Reporting for Year End Period 1/7/1988 Through 28/2/1999 ..................................................................................... 244. xiv.

(15) Glossary of Terms Term. Description. Analyst code. This is a unique identifier for each analyst.. Broker code. This is a unique identifier for each brokerage firm.. Estimation date. This is the date an estimate was reported by an analyst to IBES.. Excluded date. This denotes the period during which individual analysts’ earnings forecasts have been excluded from IBES consensus calculation. These forecasts have deviated from accepted standard as defined by the majority of analysts covering a particular issue. IBES contacts the analysts making these forecasts for confirmation or to query the methodology behind the estimates.. Fiscal Period. This is the frequency by which a company performance measure is reported.. Forecast Horizon. This is the period of time from the date the earnings forecast is made to the next earnings announcement date.. IBES Ticker. This permanently and uniquely identifies an estimate made at a certain point in time.. Reporting date. This is the date actual earnings figures are announced. Companies may not report earnings to the marketplace ranging up to 6 months after the fiscal period end date.. Statistical Period. This is the date range (approximately a month in duration) between two subsequent IBES summary consensus statistics publication. This occurs after the IBES monthly production run which occurs on the evening of the Thursday before the third Friday of every month. It executes snapshots of individual analysts’ earnings estimates and calculates the consensus level data.. Stop date. This indicates when a brokerage firm’s analysts removed his/ her earnings forecast from the IBES database due to conflict of interest between the brokerage firm and the company for which earnings are estimated. Examples of causes of earnings publication stoppage include a brokerage firm underwriting a firm’s equity issues or when the investment banking arm of a brokerage firm is involved with the mergers or acquisition activity of the client firm.. xv.

(16) Abbreviations Acronym. Definition. AMEX. American Stock Exchange. CRSP. Centre for Research in Security Prices. EPS. Earnings Per Share. IBES. Institutional Brokers’ Estimate System. NYSE. New York Stock Exchange. OTC. Over The Counter. S&P. Standard and Poor’s. xvi.

(17) Chapter 1. Chapter 1 Introduction 1.1 Introduction This chapter provides an overview of the thesis. It begins with a discussion of the research problems and hypotheses in the context of security analysts’ earnings forecast literature. This is followed by a description of the rationale behind this study and the contribution of the thesis to the literature surrounding analysts’ earnings forecasts. Then an overview of the research design is presented and finally the delimitation of this study is outlined. Chapter 2 reviews the literature around analysts’ earnings forecasts and its relevance to this study. The next section provides the background to this thesis.. 1.2 Background to the Research Literature on security analysts’ earnings forecasts utilises forecasts through consensus measures (mean or median) published by the Institutional Brokers Estimate System (IBES). These consensus measures are point estimates of distributions of individual analysts’ earnings forecasts. Often, these consensus measures serve as informational proxies (Brown and Kim, 1991) in stock valuation models and in predicting stock returns, as well as in domains such as capital markets efficiency and information asymmetry research. Thus with the existing sources of demand for the earnings consensus, a high earnings accuracy (low earnings error) is necessary to avoid spurious conclusions.. Two forecast properties measures, analysts’ earnings bias (Butler and Lang, 1991, Duru and Reeb, 2002) and analysts’ earnings accuracy (Brown and Rozeff, 1978, Brown, 1999), are commonly applied to the IBES consensus mean and median to generate unsigned and signed consensus forecast error metrics to be used in capital markets research. This usage implies that distributions of IBES individual analysts’ earnings estimates are presumed to be normal1 because the unweighted mean or median are the best estimators of a normal distribution.. 1. The normal distribution, also called Gaussian distribution, is a family of distributions of the same. general form (see 3.4.1 for the mathematical form (Gu and Wu, 2003, p. 1184)), differing in their location and scale parameters: mean and standard deviation, respectively. A standard normal distribution has a location parameter of zero and a scale parameter of one.. Page 1.

(18) Chapter 1 Additionally, according to the central limit theorem criterion2, normality is assumed to be satisfied through the use of large sample sizes. These two assumptions are pivotal to the methodologies employed in analysts’ earnings forecast studies because conclusions drawn are not limited to the modelling of stock returns nor market efficiency research but also cover the different properties of analysts’ earnings forecasts.. These properties include. earnings forecast revisions (Bird, 1998; Barth and Hutton, 2004), analysts’ following (Branson, Guffey and Pagach, 1998; Ackert and Athanassakos, 2000; Ackert and Athanassakos, 2003) and earnings forecast volatility (Comiskey, Walkling and Weeks, 1987; Ajinkya, Atiase and Gift, 1991; Athanassakos and Kalimipalli, 2003).. 1.3 Research Problem and Hypotheses The core research problem of this thesis consists of three central questions. They correspond to the three phases of investigation presented in 4.2.1, 4.2.2 and 4.2.3 respectively. Furthermore, the set of related hypotheses is developed within each phase. An overview of the hypotheses and the three phases will first be outlined.. The first phase investigates whether distributions of IBES individual analysts’ earnings forecasts are non-normal.. Hypothesis 1 (see 4.2.1) tests the statistical significance of. distribution normality using significance tests of good fit.. Detailed justifications for. disputing the normality of sample distribution are outlined in the development of the hypothesis. The diagnostics of these distributions, such as skewness3 and kurtosis4, are also analysed to help determine whether their values correspond to values expected from a normal or non-normal distribution. These distributions are found to corroborate the results of the tests of good fit. Results in Chapter 6 conclusively show that distributions of IBES analysts’ earnings forecasts are significantly non-normal5.. 2. See 3.4.2 for a restatement of the central limit theorem in mathematical form (Kreyszig, 1993, p.. 1231) but in summary, it states that for a large sample size, the distribution of the sample mean tends to be normal, even when the distribution from which the mean is computed is decidedly non-normal. 3. The skewness defines whether there are more data on one side of the mean compared to the other.. 4. Kurtosis describes the peakiness of the distribution and is formally defined in 3.4.3.. 5. A non-normal distribution is any distribution that is significantly (in a statistical sense) not normal,. that is, given the null hypothesis that a certain distribution under investigation is normal, this hypothesis is rejected for a chosen significance level.. Page 2.

(19) Chapter 1 The second phase (see 4.2.2) is designed to find distributions that can be best fitted to specific non-normal distribution types. By doing so, a more accurate surrogate consensus can be developed based on these distribution families rather than utilising a normal distribution as assumed by the usage of the IBES consensus.. The third phase is designed to utilise the findings of Phase 2 to answer whether a more accurate surrogate consensus measure than the IBES consensus can be found. If so, research literature on individual6 and collective7 properties of analysts’ earnings forecasts may have to be re-examined because prior research conclusions drawn using IBES consensus data will be questionable. Additionally, future applications of the mean or the median as the a priori consensus earnings forecast would need to be carefully considered.. Phase 3 consists of Hypotheses 3 to 7 (see 4.2.3.1, 4.2.3.3, 4.2.3.4, 4.2.3.5 and 4.2.3.6). They test whether surrogate consensus based on non-normal distribution types (as well as prior ones based on year end’s lead analyst, analysts’ prior accuracy, analysts’ earnings forecast age and analysts’ forecast frequency) improves upon the forecast accuracy of the IBES earnings consensus.. It is found that surrogate consensus based on non-normal. distribution types demonstrate general accuracy improvement over the IBES consensus for the forecast error scaled by a firm’s share price. The surrogate consensus also improved over the four other consensus generation techniques for error deflators based on actual reported earnings, average of actual and forecast, and the period consensus.. As a result, the three central research questions of this thesis have been resolved. See 9.3 for a further discussion of the conclusions. The next section discusses the contributions of this thesis to the analysts’ earnings forecast literature.. 6. Individual properties refer to the examination of the accuracy, bias and revisions of individual. analysts’ earnings forecasts relative to a firm’s reported actual earnings vis-à-vis the consensus benchmark. 7. Collective properties refer to the distributional properties of a batch of individual analysts’ earnings. forecasts such as their forecast dispersion around the consensus forecast or their level of following/ neglect of a firm.. Page 3.

(20) Chapter 1. 1.4 Contribution to Analysts’ Earnings Forecast Literature The justification for this thesis stems from its contributions to the analysts’ earnings forecast literature. This section discusses the existing research gaps. There exists analysts’ earnings forecast literature (detailed in Chapter 2) which utilises the IBES consensus. The IBES consensus is computed from the unweighted average of individual analysts’ earnings forecasts, and the use of the unweighted mean as the best estimate. This implies a normal distribution and as such, conclusions drawn in these studies rely on the assumption of distribution normality. As part of Phase 1 analyses, it is proposed for the reasons detailed in 4.2.1 that distributions of IBES analysts’ earnings forecasts are non-normal. If so, a more accurate and rational surrogate consensus can be generated given that better fitted distributions of IBES analysts’ earnings forecasts may be found (Phases 2 and 3). As part of future research, conclusions of existing analysts’ earnings forecast literature will need to be re-visited to examine whether the use of a consensus based on better fitted distributions of IBES individual analysts’ earnings forecasts would change conclusions previously drawn.. In particular, the results of this thesis may be relevant to the study of properties of analysts’ earnings forecasts such as accuracy, bias, revision, coverage and dispersion. The use of a more accurate surrogate consensus of distributions of IBES analysts’ earnings forecasts in place of the IBES consensus in literature may produce conclusions different to those previously found. This may further impact the relations found between the properties of analysts’ earnings forecasts and other components of the analysts’ earnings forecast research framework such as share price returns and volatility (Johnson, 2004) or forecast analyst behaviour (Clement and Tse, 2003, 2005). Some possible effects of each property are detailed in 2.4.1, 2.5.1, 2.6.1, 2.7.1 and 2.8.1 respectively.. Furthermore, the consideration of analysts’ earnings forecast distributional characteristics prior to their application have faced relative neglect in previous research. Distributional skewness has been examined by Gu and Wu (2003) but only to the extent of its relation with analyst forecast accuracy. For these reasons, the objective to find a more accurate surrogate consensus using known non-normal distribution types has evolved from the above research gap. The key research gap and the possible contributions of this thesis have been discussed. They form the motivation for carrying out the investigation of the three phases in this study. Next an overview of the methodologies used in the three phases will be provided.. Page 4.

(21) Chapter 1. 1.5 Methodology The research designs of the hypotheses considered under the three phases of study are based on robust significance tests that have been utilised in many prior research studies. An overview of the methodology for each phase is presented in turn.. In Phase 1, the IBES consensus and the individual analysts’ earnings forecasts are first compared and analysed for the rules IBES applies to the individual forecasts to obtain their consensus. These rules ascertain whether the IBES computes its consensus based on the unweighted average of analysts’ earnings forecasts to determine its best point estimate. Then the Kolmogorov-Smirnov, Anderson-Darling and the Cramér-von Mises (see 3.4.6 for an explanation of these tests) goodness-of-fit tests are applied to cross-sectional distributions of individual analysts’ earnings forecasts across the sample data to examine whether distributions are in general normal. Additionally, tests of location such as the Student’s t-test, Sign test and Wilcoxon Signed Rank test are applied to determine whether the skewness and kurtosis (see 3.4.3) of the same distributions significantly deviate from the location of zero.. In Phase 2, the maximum likelihood estimation technique (see 3.4.7.2) is carried out on the distributions of IBES analysts’ earnings forecasts used in Phase 1. The method enables the determination of which distribution, out of the many possible within the same distribution family, maximises the probability of obtaining the observed data. The distribution types are ranked according to their goodness-of-fit statistics from the chi-square test, the KolmogorovSmirnov test and the Anderson-Darling test (see 3.4.6 for an in-depth review of these tests). Initially, tests are applied to distributions aggregated across all firms, followed by application at the sector and industry level and finally at the individual firm level.. Phase 3 then compares the accuracy of the IBES consensus against the accuracy of surrogate consensus using Phase 2 distribution type information.. Additionally, the surrogate. consensus is generated using prior year end’s lead analyst, analysts’ prior accuracy, earnings forecast age and analyst forecast frequency. Next the surrogate consensus is tested for forecast accuracy improvement over the IBES consensus. Tests of location such as the Student’s t-test, Sign test and Wilcoxon Signed Rank test are applied to investigate whether there are significant directional variability between the forecast accuracy of each surrogate consensus methodology and the IBES consensus.. The following section outlines the. structure of the thesis.. Page 5.

(22) Chapter 1. 1.6 Outline of the Thesis This section presents an overview of the thesis structure and what each chapter entails. Chapter 1 provides the introduction to the thesis.. This includes a summary of the. background to the research problem and the hypotheses, the justification and contributions this study hopes to make to the analysts’ earnings forecast framework, the methodology applied, the outline of the thesis and the proposed research framework and delimitations.. Chapter 2 reviews the literature surrounding the properties of analysts’ earnings forecasts and Chapter 3 reviews the subsidiary statistical analyses and forecasting literature. Literature related to the properties of analysts’ earnings forecasts consists of works investigating analysts’ earnings forecast accuracy, bias, revision, coverage and dispersion. The rationale underlying the review of each property and how each relates to the aims of the thesis are discussed in turn.. Chapter 4 addresses the concerns of the research questions by developing hypotheses for each of the 3 phases of investigation. Chapter 5 then describes the IBES data set, the forecast horizon and observation interval and finally the sample data selection process.. The analyses of results for phases 1, 2 and 3 are subsumed under Chapters 6, 7 and 8 respectively. Each chapter begins with the methodology followed by the analyses of results. Chapter 9 first summarises the conclusions of chapters 6, 7 and 8 followed by a discussion of how the core research problems have been resolved and in particular the contributions to the analysts’ earnings forecast literature. Finally, the implications on the literature surrounding the properties of analysts’ earnings forecasts and the possible future research directions are highlighted. The following section discusses the proposed research framework and related delimitations.. 1.7 Proposed Research Framework and Delimitations The importance of analysts’ earnings forecasts is derived mainly from its wide use by investors as a proxy of earnings expectations so that future share prices may be approximated. However, the utility of earnings forecasts as an input to valuation models is. Page 6.

(23) Chapter 1 only one aspect of the overall security analysts’ earnings forecast research framework. Other users such as managers, policymakers and analysts themselves draw on individual or consensus earnings forecast data to gain a better understanding of the environment they work in. The resulting benefits are twofold. First, these stakeholders are able to, a priori, judge the best form of available strategies to use and second, so they may be able to probe, observe and analyse the ex post degree of success of the execution of their strategies.. Henceforth, by drawing together the three aspects above and also their interrelationships, the final composition of the analysts’ earnings forecast framework may be illustrated as shown in Figure 1-1. With this framework, a pertinent question often raised is whether researchers are able to draw any conclusive evidence such as statistical significance supporting across period or cross-sectional (firm, industry or country) trends from different facets of the framework. However, the scope of this study concentrates on the properties of analysts’ earnings forecasts8, which is highlighted in grey in Figure 1-1.. As previously described in 1.3, the focus of this thesis is to investigate whether distributions of IBES individual analysts’ earnings forecasts are normal. If distributions are found to be significantly non-normal, then, given the non-normal distributional characteristics, a surrogate consensus more accurate than the IBES mean/ median may be determined. The first hypothesis involving the analysis of distributional normality requires the use of the relative bias9 property of analysts’ earnings forecasts whilst the analysts’ earnings forecast accuracy property serves as a benchmark to gauge the performance of the developed surrogate consensus over the IBES consensus.. 8. These earnings forecast properties are derived from analyst earnings expectations data and are sub-. categorised into specific and collective components. Properties in which earnings forecasts are compared against a firm’s actual earnings figures are termed specific because they deal exclusively with the accuracy, bias and revision trends of individual or consensus forecasts. Collective properties result from the comparison of clusters of individual forecasts of the same firm against the consensus forecast and include the tendency of following or neglect of a firm and the degree of dispersion of individual forecasts. 9. Bias was originally defined by Duru and Reeb (2002) as the signed forecast error of the forecast. minus actual forecast deflated by price. However, in the case of this thesis, the use of term relative augments the original definition to redefine bias as the forecast minus the consensus deflated by the consensus. See 3.5.1 for details of the definition’s development.. Page 7.

(24) Chapter 1 As for the properties of analysts’ earnings forecasts such as revisions, dispersion and following/ neglect, these areas of research must also be examined because by definition, they directly depend on and describe the different characteristics of both the individual and the IBES consensus analysts’ earnings forecasts. If a more accurate surrogate consensus is found to replace the IBES mean or median, then conclusions drawn using these properties may need to be re-examined.. Future research work may involve the investigation of the conclusions of studies utilising the IBES consensus. See 4.2 for a detailed discussion of the hypotheses development given the research problem and hypotheses.. 1.8 Conclusions This chapter laid the foundations for the thesis. It provided background to the analysts’ earnings forecast research matter in relation to research aims and introduced the research problem, the research questions and the hypotheses. The research was justified given its possible contributions to the existing analysts’ earnings forecast literature. The methodology was briefly described and justified, the thesis structure was outlined, the research framework was proposed and the limitations were set out. On these foundations, the thesis can proceed based on a detailed description of the research that was carried out.. The next chapter proposes and discusses the analysts’ earnings forecast framework followed by a review of the literature surrounding the properties of analysts’ earnings forecasts. More importantly, the rationale behind this review is clarified.. Page 8.

(25) Chapter 1 Figure 1-1. Analysts’ Earnings Forecast Framework with Research Scope Highlighted in Grey. Characteristics Factors Firm. Analyst. Characteristics. Behaviour. Company size, prominence, institutional shareholding, industry or country company operates or listed in, regulatory environment, relative or past performance. Bias, revisions trends, interpretation of information, herding mentality, size and resources of brokerage firm, relationship with companies followed. Action. Skill. Accounting practices, disclosure policy, corporate actions, earnings management, management forecasts/ changes, compensation or trading. Experience, forecast accuracy, workload, risk tolerance. Analyst(s) Expectations. Properties of Analysts’ Earnings Forecasts Specific Accuracy. Collective Bias. Revisions. Following/. Dispersion. Neglect. Interactions with Market. Surprise Whether company’s earnings announcement surpassed or missed analyst consensus. Revisions Continuous feedback of information into investment decision making process, custom revision strategies. Implications on Firm Characteristics. Risk Relationship between the dispersion of forecasts and effects on volatility of share price. Aggregation Cross sectional examination of forecasts on industry, sector or country level to allow country comparisons, calculating equity risk premiums, econometric predictions. Valuation Consensus forecasts as inputs to valuation models multi-stage dividend discount valuation models, P/E ratios. 2nd Order Earnings management, insider trading, investment plans, implications on regulations, strategists.. Page 9.

(26) Chapter 2. Chapter 2 Core Literature Review – Properties of Analysts’ Earnings Forecasts 2.1 Introduction The previous chapter provided an introduction to the thesis by outlining the background to the research and defined the research problem and hypotheses. The rationale behind the initiation of this thesis was presented followed by a description of the research design, an outline of the thesis structure and a summary of the proposed research framework and delimitations.. This chapter reviews the literature surrounding the properties of analysts’ earnings forecast. The motivation underlying the literature review is also discussed. Finally, the chapter closes with an examination of how the literature review may relate to the research aims.. 2.2 Analysts’ Earnings Forecast Framework: Analysts’ Earnings Forecasts as a Demand Driven Entity One major aspect of capital markets research is the investigation of the complex relationship between financial information and capital markets. The demand in this area of research is mainly driven by the need for a better understanding of sub-domain research areas such as fundamental analysis and valuation, tests of market efficiency and the added value of financial reporting. In turn, the study of their interrelationships brings about the need for the employment of individual and consensus earnings forecasts. The ways by which these earnings forecasts are used within each of the four research areas have been classified Kothari (2001) in his Capital Markets review paper, originally discussed in detail by Brown (1993) and Schipper (1991). A discussion of these four categories serves to delineate the wide range of research that depends on individual analysts’ earnings forecasts and the IBES consensus earnings forecast according to their sources of demand.. First, earnings forecasts are used as proxies in valuation models to forecast future share prices. Examples include (a) the discounted cash flows method which uses consensus forecasts to proxy for future cash flows; and (b) the residual income model which uses the discount of forecasted earnings net of normal earnings to calculate the residual (abnormal). Page 10.

(27) Chapter 2 income. Second, research into the relationship between financial statement information and security returns involves the examination of the effect of surprise earnings components (actual reported earnings figure net of analysts’ earnings forecasts) on future stock returns. Third, research investigating capital markets efficiency examines whether security returns follow a pattern expected in an efficient capital market. Finally, analysts’ earnings forecasts may be employed as a source of information for managers, policymakers and analysts to predict future stock price movements. This area of research examines the value of analysts’ earnings forecasts contained within financial reports and its impact on investment decisions.. 2.3 Justification for the Delimitation of Properties of Analysts’ Earnings Forecasts The rationale underlying the literature review of the properties of analysts’ earnings forecasts (accuracy, bias, revision, following/ neglect and dispersion) is examined in this section. Three main reasons substantiate this literature focus and are outlined below.. First, IBES publishes a consensus as an unweighted mean of individual analysts’ earnings estimates as defined in the IBES Glossary (I/B/E/S International Inc., 2000) and statistical theory states that the best estimator of a normal distribution is its unweighted mean (Kreyszig, 1993). Hence, the usage of the IBES consensus implies the assumption of normality in the distribution of individual analysts’ earnings forecasts. However, the first hypothetical proposition is that distributions of IBES analysts’ earnings forecasts are significantly non-normal (see 4.2.1). The relative bias10 property of IBES analysts’ earnings forecasts is used to develop distributions to undergo tests of distribution fitting. Thus the dependency of the first hypothesis on the bias property calls for an examination of the relevant research literature.. Second, the subsequent hypothesis investigates the distribution types that conform to the distributions of individual analysts’ earnings forecasts.. This knowledge is required to. develop a more accurate surrogate consensus vis-à-vis the IBES consensus, as proposed in. 10. Bias was originally defined by Duru and Reeb (2002) as the signed forecast error of the forecast. minus actual forecast deflated by price. However, in the case of this thesis, the use of term relative augments the original definition to redefine bias as the forecast minus the consensus deflated by the consensus. See 3.5.1 for details of the definition’s development.. Page 11.

(28) Chapter 2 Hypotheses 3 and 4.. In this case, the accuracy property is required to gauge any. improvement in the surrogate consensus’ earnings forecast accuracy over the IBES consensus. Consequently, a review of the literature surrounding the accuracy property is required in order to develop an appropriate accuracy metric.. Third, the successful development of a surrogate consensus would be relevant to earnings forecast studies based upon the IBES consensus. The research areas directly related are the different aspects of literature surrounding the properties of analysts’ earnings forecasts such as revisions, following/ neglect, dispersion, accuracy and bias. These properties depend on the different characteristics of earnings forecasts sourced from both individual analysts and the IBES consensus.. The above three reasons justify the delimitation of the properties of the IBES analysts’ earnings forecast framework depicted in Figure 2-1, which shows examples of research studies related to each property of analysts’ earnings forecasts. The literature surrounding each property will be reviewed in turn.. Figure 2-1. Thesis Literature Delimitation – Properties of IBES Analysts’ Earnings Forecasts. Accuracy (Theil 1966, Brown and Rozeff 1978, O’Brien 1988) Bias (Abarbanell 1991, Gu and Wu 2003) Properties of Analysts’ Earnings Forecasts. Revisions (Stickel 1991, Lys and Sohn 1990). Following/ Neglect (Carvell 1987, Ackert and Athanassakos 2000). Dispersion (Barron and Stuerke 1998, Baik and Park 2003). Page 12.

(29) Chapter 2. 2.4 Accuracy This section’s literature exposition is centred upon the role of the accuracy of analysts’ earnings forecasts in relation to the aims of this thesis.. Additionally, the historical. development of the literature surrounding the accuracy of analysts’ earnings forecasts is discussed.. 2.4.1 Accuracy of Analysts’ Earnings Forecasts in Relation to Thesis Aims Literature on the accuracy of analysts’ earnings forecasts establishes the superior predictive accuracy of analysts’ earnings forecasts over other types of earnings forecasts (Barefield and Comiskey, 1975; Brown and Rozeff, 1978; Collins and Hopwood, 1980). This led to the widespread usage of the IBES consensus forecasts. However, this thesis questions whether the usage of the IBES consensus may be excessive because a more accurate surrogate consensus may exist11 (see 4.2.3 and 4.2.3.2).. Additionally, the literature review of the accuracy of analysts’ earnings forecasts elucidates how different types of forecast accuracy metrics are used as performance indicators. Their definitions and development are examined in further detail at a later stage in this chapter in 2.4.3. Subsequently, in relation to the research gap characterised by the third and fourth hypotheses, the forecast accuracy of the IBES consensus is used as a benchmark against which new surrogate consensuses are measured.. 11. This proposition is based on the first hypothesis purporting the non-normality of the distributions of. individual analysts’ earnings forecasts. If empirical evidence supports the hypothesis, then the IBES consensus will not be the most accurate estimator because the unweighted mean estimate of a distribution is only the most accurate in the normal case. Thus, if a surrogate IBES consensus can be generated based on a reconfiguration of the weightings of analysts’ earnings forecasts (rather than equal weightings as in the case of the IBES consensus mean) that best fit the distribution shape, then conclusions drawn by literature utilising the IBES consensus may be questionable. The greatest impact would be on those research literature related to analyst forecast properties such as bias, revision, following/ neglect and dispersion because they are directly dependent on the IBES consensus by way of their metric definitions.. Page 13.

(30) Chapter 2 2.4.2 Literature Review of Accuracy of Analysts’ Earnings Forecasts Literature investigating the different properties of analysts’ earnings forecasts include Malkiel and Cragg (1968), who examined the accuracy of different sources of earnings estimate predictors, including analysts’ earnings forecasts, market price to earnings ratios and past growth rates. Malkiel and Cragg (1968) used individual analysts’ earnings forecasts to manually compute an unweighted consensus mean that is similar to the consensus produced by IBES. The use of an unweighted mean as the best estimator of the distributions of individual analysts’ earnings forecasts presupposes distribution normality.. This. assumption was specifically noted by the authors in their comparison of earnings predictors between different industry categories. It was found that analysts’ forecasts of the earnings growth of firms were no better than using past growth rates to make forecasts. However, both analysts’ earnings forecasts and past growth rates were found to be more accurate than using market price to earnings ratios as estimates of future earnings growth. Thus evidence suggested analysts’ earnings forecasts as a possible but not the only source of corporate earnings prediction. The accuracy of analysts’ earnings forecasts over all other sources of earnings prediction remained an area to be investigated.. The study of the accuracy of the consensus of analysts’ earnings forecasts progressed to accuracy comparisons against benchmarks such as time series models in the late 1970s. Proponents included Brown and Rozeff (1978) and Brown and Rozeff (1980) in which analysts’ forecast performance was measured against the Box and Jenkins time series models (Box, Jenkins and Reinsel, 1994). During this period Thomson Financial’s IBES forecasts publication service was not yet established and researchers resorted to earnings forecast sources such as the Value Line Investment Survey. This differs from the IBES consensus in that the Value Line publishes a single, independent analyst earnings forecast per firm per period versus the IBES which generates its consensus forecasts by aggregating individual analysts’ earnings forecasts on a per firm per period basis.. The prior study by Brown and Rozeff (1978) utilised Value Line Investment Survey forecasts from 1972-1975 and compared its performance against three other forecast methods, namely seasonal martingale (random walk), seasonal sub-martingale (random walk with or without a drift) and the Box-Jenkins. It was found the Value Line Investment Survey consistently made significantly better earnings forecasts than the Box and Jenkins and other types of time series models.. Page 14.

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