Evidence from analysts’ reports

two lines long – this does not give you much confidence in terms of quality of earnings.’ Accounting specialist The interview findings reveal that earnings qual- ity is a multifaceted concept. Analysts broadly consider two types of information in defining earn- ings quality. The first, accounting-based informa- tion, which is sourced directly from the financial statements themselves, comprises income about the sources of earnings, the impact of accruals and the effects of accounting policies. The second, non-accounting-based information, which is de- rived from data outside the financial statements, is more broadly concerned with the markets in which the entity operates and the effectiveness of man- agement in designing and implementing strategies to compete successfully in those markets. To illus- trate, two entities may have the same level of earn- ings, but one may have a greater proportion of those earnings in core businesses, backed by cash and with consistently-applied underlying account- ing policies, and so will have higher earnings qual- ity, as judged using accounting-based information. Alternatively, earnings quality could be judged to differ on the basis of non-accounting information. For example, two entities may be equivalent in terms of the source of earnings, cash-backing and accounting policy, yet the entity with the manage- ment team that inspires greater confidence will be deemed to have the higher quality earnings. An interesting implication of these findings is that non-accounting information is used to contex- tualise and add meaning to accounting data, i.e. the quality of earnings is not purely an accounting concept. This finding adds insight to previous studies, such as Barker 1999 and Breton and Taffler 2001, which have sought to contrast ac- counting vs non-accounting information, rather than viewing each as offering alternative perspec- tives on accounting data. A consistent finding of previous survey research, such as Pike et al. 1993, Barker 1999 and Holland 1998 is that perceptions of management quality and discus- sions with management on ostensibly non-ac- counting subjects are a more important source of information than the financial statements see also Day, 1986. If, however, these non-accounting in- formation sources are in practice important in the context of interpreting earnings quality i.e. ac- counting data then, especially in the light of the evidence reported earlier that the PE is the domi- nant valuation model, the importance of account- ing information is perhaps understated by these studies. This interpretation is consistent with Hussain 2002, who finds a relationship between the size of firms of analysts and forecasting per- formance, which he suggests may be due to larger firms having superior access to company manage- ment; this finding suggests that non-accounting- based information is used to enhance the assess- ment of the quality of current-period earnings and, so, the forecasting of future earnings. 11

5. Evidence from analysts’ reports

The data from the form-oriented analysis is sum- marised in Tables 3 and 4. It is worth repeating that this is not a simple word count but rather is a sum- mary of keywords in context – i.e. words are counted only if, first, they were used by analysts in the interviews and, second, they were used in the same context as in the interviews. Hence the con- tent analysis is a direct test of the evidence from the interviews. Table 4 reports total accounting and non-ac- counting keywords by sector. In total, there are somewhat more non-accounting keywords used in the reports, which reinforces the interview evi- dence above that earnings quality is not an issue of financial statement data alone. In other words, while analysts will try to understand earnings qual- ity from the financial statements themselves, they acknowledge the inherent limitations of financial statement data as a basis for understanding and predicting future performance, relying somewhat more heavily on information sources that are out- side the financial statements. A chi-square test, as reported in Table 4, finds that this difference in usage between accounting and non-accounting in- formation is highly significant. While one might think that the reliance on non- accounting-based information would vary by sec- tor, because the financial accounting model is more or less able to capture economic fundamen- tals across sectors see, for example, Hand and Lev, 2003, a striking feature of Table 4 is that this is not the case. Viewed on a sector basis, there is a remarkable consistency in the ratio of accounting to non-accounting keywords, with the former in the narrow range 41–46 across all five sectors. This consistency suggests that a standardisation of report-writing style and analytical approach domi- nates any inherent variation in the usefulness of accounting information that might exist across sectors. Possibly this is because fund managers’ valuation models and hence information demands are similar irrespective of sector and that they therefore prefer sell-side analysts to present infor- mation consistently. Alternatively, it demonstrates inherent limitation in fundamental analysis, be- cause a ‘one-size-fits-all’ approach is employed in spite of underlying differences in economic and accounting fundamentals across sectors. In Table 5, in addition to the accounting and 320 ACCOUNTING AND BUSINESS RESEARCH 11 Hussain 2002 notes that the significance of this effect appears to hold for short-term forecasts only. He notes but does not explore the issue of whether broker status has an in- cremental effect. Downloaded by [Universitas Dian Nuswantoro], [Ririh Dian Pratiwi SE Msi] at 20:38 29 September 2013 V ol . 38 N o. 4. 2008 321 Table 3 Keywords dictionary based on keywords in context from interview data Accounting-based Frequency Non-accounting-based Frequency Keywords related theme In reports percentage of total Keywords related theme In reports percentage of total keywords in the category keywords in the category Growth 3,294 37.00 Market 3,396 29.53 Cash flow 1,108 12.45 Business model 2,361 20.53 Operating 662 7.44 Customer 1,592 13.84 CertainPredictable 403 4.53 Acquisition 923 8.03 CoreSource 401 4.50 Management 886 7.70 Sustainable 281 3.16 Strategy 564 4.90 AdjustedNormalised 278 3.13 Contract 478 4.16 Trading 278 3.13 DisclosureGuidance 458 3.98 Organic 243 2.73 Disposal 274 2.38 PredictableStable 220 2.47 Restructuring 133 1.16 ConservativeDefensive 203 2.30 CredibleReliable 91 0.79 Aggressive 201 2.26 Pessimistic pessimism 89 0.77 Underlying 187 2.10 Optimistic optimism 88 0.77 ConsistentConsistency 177 1.99 ManifestDistort 85 0.74 One-offExceptional 172 1.93 Transparency 65 0.56 Continuing 165 1.85 Discretionary 17 0.15 Ongoing 116 1.30 Cyclicality 115 1.18 Visible 99 1.12 Persistence 88 0.99 VariabilityVolatile 88 0.99 Transitory 56 0.63 Unusual 37 0.41 RecurringRepeat 30 0.33 Total 8,902 100 Total 11,500 100 Note: We only selected those words that analysts used to define earnings quality during the interviews after doing appropriate adjustments by KWIC. Although these cover only a small percentage of total words, they capture the keywords that the interviewees used during the interviews in defining earnings quality. 322 A CCO U N T IN G A N D BU S IN E S S RE S E A RCH Table 4 Total keywords across sectors Keywords Financial Industrial Media Retail Technology Total Total accounting 1,488 45 1,060 46 1,588 43 2,835 44 1,931 41 8,902 44 Mean per report 88 71 84 109 92 91 Total non-accounting 1,815 55 1,264 54 2,081 57 3,547 56 2,793 59 11,500 56 Mean per report 107 84 110 136 136 118 Total keywords 3,303 100 2,324 100 3,669 100 6,382 100 4,724 100 20,402 100 Note: This table is based on form-oriented content analysis. χ 2 for Accounting v. Non-accounting words across sectors is 22.1, p = 0.0002. Table 5 Theme score across sectors Financial Industrial Media Retail Technology Total Classification Positive Negative Positive Negative Positive Negative Positive Negative Positive Negative Positive Negative Total Accounting 105 48 81 37 83 40 182 48 151 86 602 259 861 0.022 0.01 0.026 0.012 0.014 0.007 0.028 0.007 0.026 0.015 Non-accounting 202 186 114 86 167 166 209 241 279 206 971 885 1,856 0.042 0.039 0.037 0.028 0.028 0.028 0.032 0.04 0.045 0.035 Total 307 234 195 123 250 206 391 289 430 292 1,573 1,144 2,717 Chi-sq test χ 2 =12.27 χ 2 =4.24 χ 2 =10.89 χ 2 =66.54 χ 2 =2.53 χ 2 =74.75 p-value 0.0005 0.0394 0.001 0.000 0.1117 0.000 Note: The figures in parentheses show the theme variable which is calculated as total theme score divided by total sentences. non-accounting classification used earlier, and in the light of evidence on analysts’ bias towards companies, sentences are also classified according to whether keywords are used in a positive or neg- ative context. This theme-based analysis adds power to Table 4 in that the emphasis is now more active – rather than simply counting keywords in context, the analysis is directional and indicates whether or not the analyst has a favourable view of the company being analysed. So, for example, in the industrial sector, there are 195 positive refer- ences to earnings quality, of which the majority 114 are from non-accounting sources, while there are 123 negative references. 12 Chi-square tests, reported in Table 5, show a significant dif- ference within sectors in the frequency of positive and negative related themes relating to accounting vs non-accounting information; the results are sig- nificant at the 1 level for financial, media and re- tail, and at the 5 level for industrial although not significant for technology. Consistent with the evidence in Table 4, non-ac- counting keywords are used more often than ac- counting keywords. However, the directional data in Table 5 offer four additional insights. First, there are significantly more positive references to earn- ings quality than there are negative references. This is reassuringly consistent with the empirical evidence summarised earlier that analysts have a favourable bias towards companies, at least in the context of their public communications. Second, non-accounting words are used relatively more – approximately twice as often – than for the non-di- rectional data reported in Table 4. Alternatively stated, when an analyst is expressing a positive or negative opinion about a company’s earnings qual- ity as opposed to a neutral opinion, he or she is more likely to do so by reference to non-account- ing information. This is consistent with analysts having the dual objective of generating commis- sion income by introducing news stories to the market in this case in the form of opinions about earnings quality while also retaining credibility as reliable providers of information Barker, 2000. Since accounting-based information is more read- ily verifiable than non-accounting-based informa- tion, analysts have more latitude in using non-accounting-based information without being shown to be wrong. Hence, they are more likely to use non-accounting-based information as the basis of news stories. This is consistent with Breton and Taffler’s 2001 conclusions on the greater usage of non-financial information, as well as with Fogarty and Rogers 2005, who argue that while accounting information provides essential support for an analyst’s arguments, it is typically not used as the main substance of a case. Finally, there is a parallel here with Clatworthy and Jones 2003, who identify inherent bias in the narrative of the chairman’s statement, relative to the more objec- tive and audited financial statements; the present study identifies a comparable bias in the non-ac- counting-based elements of analysts’ reports. The third insight from Table 5 is that, in aggre- gate, there are similar numbers of positive and negative non-accounting references to earnings quality, whereas accounting references are more than twice as often positive as negative. This rein- forces and adds to the first two insights above. Given that, first, analysts are inherently biased to- wards companies and, second, they are incen- tivised to generate non-refutable news stories, it is not surprising that negative opinions based upon readily-verifiable accounting information are used sparingly: of the total of 2,717 theme scores re- ported in Table 5, a little fewer than 10 are neg- ative and from accounting sources. If an analyst has a biased predisposition towards a company, yet he or she cannot credibly report only positive opin- ions about the company, then making a relatively objective, accounting-based case against the com- pany is less appealing than making a negative case using more subjective non-accounting-based in- formation. The final insight from Table 5, which is consis- tent with Table 4, is that the findings are broadly consistent across sectors. This applies in particular to the relative usage of accounting and non-ac- counting words, and also in most sectors to the ob- servation that there is a similar number of positive as negative non-accounting references to earnings quality, whereas accounting references are more than twice as often positive as negative. Hence, for example, analysts’ disincentive to report negative, accounting-based news is universal across sectors. Table 6 explores further analysts’ bias towards companies by classifying each report according to whether the analyst is on balance positive on both accounting-based and non-accounting-based information, negative on both, or positive on one and negative on the other. For example, if a report is placed in Category 2, then the analyst perceives earnings quality positively on the basis of account- ing information but negatively on the basis of non- accounting information. This might arise, for example, if earnings are generated within core ac- tivities and backed by operating cash flow, but the management is perceived not to have in place an effective strategy for sustainable performance. Overall, the reports are positive, which is again consistent with the evidence on analysts’ bias 53 of reports are in Category 1 and uniformly positive, against 19 in Category 4 that are uni- formly negative; the corresponding chi-square test, Vol. 38 No. 4. 2008 323 12 Note that the total number of keywords differs from Table 4 because neutral cases i.e. where there is neither a positive nor a negative opinion clearly expressed are excluded. Downloaded by [Universitas Dian Nuswantoro], [Ririh Dian Pratiwi SE Msi] at 20:38 29 September 2013 as reported in Table 6, is highly significant. For the most part, there is a strong congruence be- tween the analysts’ views of earnings quality measured according to both accounting-based and non-accounting-based criteria – taken together, Categories 1 and 4 account for 72 of the 98 reports, or 73. This is not surprising because it is likely that there are similar factors behind the analysts’ perceptions of earnings quality from both account- ing and non-accounting sources. Moreover, given that the analyst’s aim is to persuade clients to trade based upon an authoritative analysis and recom- mendation, a report that contains conflicting sig- nals is likely to be less effective. Of the remaining two categories where the sig- nals are conflicting, it is far more likely that ana- lysts are positive based upon accounting sources while being simultaneously negative based upon non-accounting sources Category 2, 22 of re- ports rather than vice versa Category 3, 4 of reports. Of the 57 cases where the analyst is pos- itive with respect to non-accounting-based percep- tions of earnings quality, it is most unlikely that he or she would be negative on accounting criteria this happens in only four cases, or 7. In contrast, however, if an analyst is, on balance, negative with respect to non-accounting-based perceptions of earnings quality, then he or she is more or less equally likely to be positive or negative on ac- counting criteria 54 and 46, respectively. Hence, if, in spite of inherent bias in favour of companies, an analyst is willing to be, on balance, negative about accounting-based and so relatively verifiable earnings quality, then this is typically inconsistent with being positive about non-ac- counting and so relatively subjective perceptions of earnings quality. In other words, it is difficult for an analyst to be glowing about a company hav- ing great prospects and strong management if the financials do not back up the claims. On the other hand, a positive view of accounting-based earn- ings quality does not guarantee that the non-ac- counting view will be positive also. Here the analyst has greater choice to express a subjective opinion, for example, that the company has done well but does not have the right strategy to sustain performance. Overall, an analyst can be sceptical if financial performance is strong but cannot be optimistic if financial performance is weak. On balance, therefore, accounting information matters more than at first apparent. This conclusion on the relative importance of accounting information links in with the findings from Tables 4 and 5. Taking Table 4 alone, it would appear that non-accounting-based informa- tion is somewhat more important in analysts’ per- ceptions of earnings quality, while Table 5 adds to this conclusion by showing that analysts’ direc- tional views are even more likely to be based upon non-accounting sources. Yet Table 5 also shows that accounting-based information is more dis- criminatory, in that negative views are relatively infrequent. The evidence in Table 6 suggests this infrequency of negative, accounting-based views understates its importance, because taking a nega- tive accounting-based view effectively rules out taking a positive non-accounting-based view, whereas the reverse is not true. This finding sup- ports and provides insight into market-based evi- dence that, given analysts’ inherent bias in favour of companies, the market reacts more strongly to analysts’ adverse opinions than to their favourable opinions e.g. Hirst et al., 1995. These findings complement prior research into qualitative information in financial reporting see, for example, Smith and Taffler, 1992a, 1992b and 2000. To the extent that there is obfuscation and speculative content in analysts’ reports, the evi- 324 ACCOUNTING AND BUSINESS RESEARCH Table 6 Classification of individual reports Accounting information Positive dominance Negative dominance Total Non-accounting information Positive dominance Category 1 53 Category 3 4 57 Negative dominance Category 2 22 Category 4 19 41 Total 75 23 98 Note: The figures in parentheses show the number of reports in each category. ‘Positive dominance’ for ac- counting information means that within an individual report there were more positive than negative sentences concerning accounting-based information relating to earnings quality, i.e. 75 reports were on balance positive with respect to accounting-based information on earnings quality. Chi-square is 20.53 p = 0.000 which sug- gests there are significant differences in accounting and non-accounting positive v. negative dominance. Downloaded by [Universitas Dian Nuswantoro], [Ririh Dian Pratiwi SE Msi] at 20:38 29 September 2013 dence here is that this is more likely to arise where non-accounting information is being used. In con- trast, accounting-based opinions are more reliably associated with analysts’ overall opinions, and where there are conflicting messages in the ana- lyst’s report, which may confuse rather than assist the reader as reported in a similar context by Smith 1993, greater weight should be assigned to the accounting-based signal. This importance for accounting-based informa- tion is reinforced further in Table 7, which tests the validity of the findings reported so far by compar- ing the outcome of Table 6 with analysts’ recom- mendations. There is an implicit assumption being made here, which must be acknowledged. In an ef- ficient market, a company’s share price, against which an analyst’s recommendation is made, em- bodies a given information set, for example, pub- licly-available information in the case of semi-strong form efficiency. If an analyst is mak- ing a recommendation, then he or she is implicitly declaring a state of inefficiency, at least with re- spect to the information on which the analyst is recommending the trade. The assumption being made in Table 7 is that the analyst’s positive or negative statements with respect to earnings qual- ity correspond to his or her views on information that is not impounded in the share price, and which is therefore the basis of the recommendation. This is a fairly strong assumption but not an unreason- able one. After all, if the analyst is making the case to buy or sell, then he or she will stress the reasons for this in the report on the company and, consis- tent with the theory and evidence outlined in Section 2, earnings quality is likely to be a major focus. Like other studies Bradshaw, 2002; Demirakos et al., 2004, Table 7 reports a dominance of posi- tive recommendations, which is again consistent with an underlying bias in favour of companies there are slightly more than three times as many buy recommendations as sells. There is a strong relationship between buy recommendations and analysts being positive on both accounting-based and non-accounting-based information relating to earnings quality and, similarly, between sell rec- ommendations and negativenegative. For exam- ple, out of 53 Category 1 reports, an overwhelming majority of reports 45 reports had positive rec- ommendations and only two had negative recom- mendations; similarly, there were no buy recommendations in Category 4. This evidence is reassuring because it is consistent with earnings quality as defined in this paper being decision-rel- evant i.e. the concept of earnings quality as de- scribed by analysts in the interviews, and as then measured in the content analysis of reports by those analysts is indeed consistent with the invest- ment recommendations made by the analysts. Category 2 is noteworthy because it provides some exception to the rule. The evidence is that if accounting-based information on earnings quality is positive, then when it comes to a recommenda- tion the analyst is unlikely to propose a sell, even if reservations based upon non-accounting infor- mation are expressed. This suggests that when an- alysts are positive on the basis of accounting data, they tend to provide at least neutral recommenda- tions and hardly any negative recommendations. The reverse is not true. When negative accounting- based information dominates i.e. 23 reports in Category 3 and 4, only one report had a positive recommendation and 13 reports had negative rec- ommendations. As reported in Panel A of Table 8, we fitted a lo- gistic regression model to examine the association Vol. 38 No. 4. 2008 325 Table 7 Association with recommendations Financial Industrial Media Retail Technology Total Category 1 Buy 8 7 9 12 9 45 Hold 1 2 1 2 6 Sell 2 2 Category 2 Buy 5 1 2 2 10 Hold 1 3 3 3 10 Sell 1 1 2 Category 3 Buy 1 1 Hold 1 1 2 Sell 1 1 Category 4 Buy Hold 1 1 3 1 1 7 Sell 1 1 6 4 12 Total 17 15 19 26 21 98 Downloaded by [Universitas Dian Nuswantoro], [Ririh Dian Pratiwi SE Msi] at 20:38 29 September 2013 between positive accounting dominance i.e. Category 1 and Category 2 and buy recommenda- tions. Logistic regression is a generalised linear model for binary dependent variables that uses the logit as its link function, and has binomially dis- tributed errors. The model takes the form logitp i = log e p i 1 – p i = β + β 1 x i 1 where p i is the predicted probability of Buy for re- port i given values for the explanatory variable x i . Model 1 can readily be rearranged to give the following expression for p i p i = exp β + β 1 x i {1 + exp β + β 1 x i } The model parameters β = β , β 1 are estimated by iterative reweighted least squares and are inter- preted as effects on the odds ratio. In the case of a dichotomous explanatory factor, with levels A and B, the antilog of the estimated parameter for that factor, exp β 1 , is an estimate of the odds-ratio of level A of the factor versus level B. For our di- chotomous categories, the model takes the form 2 The predicted probability p that analysts will recommend Buy for report i is given by substitut- ing the parameter estimates from Panel A of Table 8 into equation 2. We find, at the 1 significance level, that positive accounting views relating to earnings quality are positively associated with buy recommendations. We infer that when analysts have positive accounting-based views relating to earnings quality, it is likely that they will recom- mend a buy, irrespective of whether the positive accounting view is expressed alongside either pos- itive or negative non-accounting-based views. We tested another version of the model to see which category individually best explains ana- lysts’ buy recommendations. Consistent with ex- pectations, Panel B reports positive coefficients on Categories 1, 2 and 3. These are statistically sig- nificant on Category 1 and 2 at the 1 level, al- though not significant on Category 3. 13 The odds ratio for Category 1 suggests that the odds of ana- lysts recommending a stock ‘buy’, having both positive accounting and non-accounting views re- lating to earnings quality, is 28.6 times higher ver- sus Category 4, as opposed to 17.1 5.1 times in case of holding positive negative views of ac- counting while simultaneously holding negative positive views on non-accounting – all other fac- tors being equal. We infer that more positive ac- counting and non-accounting views relating to earnings quality should result in more buy recom- mendations, in particular when the accounting per- spective is positive. These findings reinforce further the importance of accounting-based infor- mation on earnings quality, which plays a domi- nant role in analysts’ recommendations despite a greater prevalence of non-accounting words and themes in analysts’ reports.

6. Conclusion