reading between the lines: an empirical examination of qualitative attributes of financial...

J. Account. Public Policy 31 (2012) 1–21

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J. Account. Public Policy

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Reading between the lines: An empirical examinationof qualitative attributes of financial analysts’ reports

Brady Twedt 1, Lynn Rees ⇑Mays Business School, Texas A&M University, College Station, TX 77843-4353, United States

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⇑ Corresponding author. Tel.: +1 979 845 6078.E-mail addresses: [email protected] (B. T

1 Tel.: +1 979 845 6070.

a b s t r a c t

This paper examines whether two qualitative attributes of financialanalysts’ reports, detail and tone, are significant in explaining howthe market responds to analysts’ reports, after controlling for theinformation contained in the reports’ quantitative summary mea-sures. Report detail is hypothesized to reflect the level of effortexpended by the analyst in preparing the report, and thereforethe usefulness of their intrinsic firm value estimates. Report toneis predicted to signal the analyst’s underlying sentiment regardingthe firm and may be used to assess the extent to which analysts’conflicts of interest interfere with the mapping of firm value esti-mates into stock recommendations. Consistent with these hypoth-eses, we find that the tone of financial analyst reports containsignificant information content incremental to the reports’ earn-ings forecasts and recommendations, and report complexity (onecomponent of report detail) helps explain cross-sectional variationin the market’s response to the reports’ recommendations.

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1. Introduction

Sell-side financial analysts have been the subject of extensive empirical and experimental researchin accounting. The high demand for this research stems from several sources; including, the need forearnings expectations proxies in market research, the wide use of their outputs across the investmentcommunity, and the fact that analysts can be used as investor proxies to address interesting questionsabout the usefulness of accounting data. The significant role of financial analysts in evaluatingaccounting data and disseminating their analysis to the public ensures that they will continue to

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wedt), [email protected] (L. Rees).

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2 B. Twedt, L. Rees / J. Account. Public Policy 31 (2012) 1–21

attract substantial interest from investors and academic researchers. Our knowledge of the influenceof accounting information in capital markets is enhanced as we gain a deeper understanding of howfinancial analysts process accounting data, how they transmit the results of their analysis to investors,and how the market reacts to their reports (Bradshaw, 2009; Huang and Zhang, 2011). In this study,we contribute to research on the outputs generated by analysts to communicate their private informa-tion, and the investor response to these outputs.

When examining outputs from analysts, researchers have historically focused on two quantitativesummary measures included in the majority of analysts’ reports: earnings forecasts and stock recom-mendations.2 However, as the following quote expresses, analyst reports consist of more informationthat presumably supports their ultimate recommendation:

‘‘Stock ratings and price targets are just the skin and bones of analysts’ research. The meat of suchreports is in the analysis, detail, and tone’’ (Tsao, 2002; emphasis added).

If all information contained in analysts’ reports is captured by the stock recommendation and/orearnings forecast, then the other content (which make up the majority of the report) becomes unim-portant. However, given the significant effort expended by analysts in producing and disseminatingtheir reports, we conjecture that incremental information can be obtained by examining the contentsof the reports, including an analysis of non-quantitative information (i.e., detail and tone). For exam-ple, analysts are generally constrained to five broad categories when issuing stock recommendations;strong buy, buy, hold, sell, and strong sell, and the most negative categories are rarely used (Barberet al., 2006). Thus, the detail and tone of analyst reports may provide investors with a valuable sourceof additional information. Indeed, Ramnath et al. (2008) argue that ‘‘examining analyst reports basedsolely on quantitative information may not capture the complex nature of the analyst’s task’’ (p. 9).Just as prior research has examined the incremental value-relevance of separate earnings componentsto assess the quality of the summary measure EPS (e.g., Lipe, 1986; Dechow, 1994), in this study, weexamine qualitative attributes of analyst reports to assess whether they contain incremental informa-tion about the quality of the reports’ summary measures – EPS forecasts and stock recommendations.3

Our sample consists of 2057 analyst reports published in the year 2006 that initiate coverage of aspecific firm. We exploit innovations in the content analysis and computational linguistics fields to de-velop objective and reliable measures for the reports’ detail and tone – the second and third compo-nents of the ‘‘meat’’ of the reports listed in the above quote. Report detail is broadly defined asanything that may signal to investors the amount of effort the analyst expended in preparing the re-port. Due to its multiple dimensions, three aspects of the report are used to assess detail: complexity,length, and visual aids. Report complexity is measured using the Fog Index, a commonly used measureof writing sophistication. The second and third measures of detail are the length of the report and thenumber of visual aids, including charts, tables, and graphs, used by the analyst. Report tone is mea-sured using the content analysis software General Inquirer, and is intended to represent the analyst’sunderlying opinion of firm value.4

We hypothesize that report detail and tone provide capital market participants with insightsregarding the value of analysts’ earnings forecasts and stock recommendations. Beyond the significantcosts expended by analysts to produce their reports, additional support is provided for this hypothesisfrom evidence in prior research on analysts’ behavior and incentives. Several studies document resultsconsistent with analysts’ herding behavior (e.g., Trueman, 1994; Hong et al., 2000; Clement and Tse,2005), which is the tendency for analysts to issue forecasts or recommendations close to the

2 See Ramnath et al. (2008) for an in-depth review of the extant literature regarding these quantitative components of financialanalyst reports.

3 Consistent with the notion that the contents of analysts’ reports have value implications, Kothari et al. (2009) show that anincrease in the aggregate amount of negative content within all analyst reports for a firm leads to an increase in stock returnvolatility. Our research question fundamentally differs from Kothari et al. (2009) in that we examine the association betweenqualitative attributes of analysts’ reports and the stock price response to those reports within a narrow window surrounding theirdisclosure.

4 As reviewed in more detail later in the paper, the detail and tone metrics used in this study have recently gained prominence inthe finance and accounting literatures. See, for example, Li (2008), Lehavy et al. (2011), Biddle et al. (2009), Callen et al. (2011),Muino and Trombetta (2009), Tetlock (2007), Tetlock et al. (2008), and Kothari et al. (2009).

B. Twedt, L. Rees / J. Account. Public Policy 31 (2012) 1–21 3

consensus. An analyst who lacks sufficient private information to produce an accurate forecast orrecommendation, either through lack of effort or ability, is more likely to mimic forecasts issued bystrong analysts. We argue that the level of detail in a report represents an analyst’s attempt toconvey to clients that he has invested the necessary effort to produce a credible opinion. Thus, wepredict a more pronounced market response to the summary outputs of a report (i.e., forecasts andrecommendations) when they are supported by more detail in the report (an interaction effect).

With respect to tone, anecdotal and empirical evidence suggests that analysts’ stock recom-mendations are often compromised by conflicts of interest. Analysts are influenced by theirown compensation structures and will tend to issue more favorable recommendations to facilitatepotential underwriting relationships (Groysberg et al., 2011). In addition, analysts have been ac-cused of issuing overly positive recommendations in order to curry favor with management,which presumably offers the analyst greater access to management’s private information (Daset al., 1998; Libby et al., 2008; Mayew, 2008). Michaely and Womack (1999) and Barber et al.(2007) demonstrate that these conflicts of interest adversely affect the quality of analysts’ stockrecommendations. Given these conflicts of interest, report tone may better reflect the analyst’sunderlying sentiment about the firm, and therefore can be used as an additional piece of infor-mation to assess the analysts’ underlying opinion of the firm. We examine the effect of tone oninvestor response to analyst reports as a main effect and interacted with the reports’ quantitativesummary measures. A significant positive coefficient on the tone interaction effect indicates thattone provides investors with incremental information that is supportive of the quantitative sum-mary measures; whereas a significant positive main effect suggests that tone by itself providesvalue-relevant information.

The above arguments suggest that investors will assess higher levels of credibility to outputsassociated with more detailed reports, and that positive report tone will result in a favorablemarket response. However, an alternative argument could also be made for the opposite result.Specifically, less-informed analysts could use detail in their reports as a means to obfuscate theirlack of firm-specific information, and report tone could reflect analysts’ cognitive biases and lin-guistic preferences. While our ex-ante expectation is that these alternative arguments are not aspersuasive as the above predictions, the validity of our hypotheses is ultimately an empiricalquestion.

Our results are consistent with our hypotheses that the detail and tone of financial analyst reportsprovide the market with incremental information content beyond the reports’ quantitative summarymeasures. Specifically, one component of report detail; namely, report complexity, causes a strongermarket reaction to the news contained in the analyst’s stock recommendation, as measured by three-day size-adjusted returns centered on the reports’ publication dates. This interaction effect suggeststhat report complexity provides support to the quantitative measures issued by analysts. We also findthat more optimistic reports are associated with larger returns independent of any quantitative newsprovided by the reports. This main effect indicates that investors view report tone as a valuable sourceof incremental information by itself, regardless of the recommendation or forecast contained in thereport.

In supplementary analysis, we examine whether abnormal returns can be earned through long-term trading strategies based on report detail and tone, controlling for the analysts’ stock recommen-dations and firm characteristics. These investment portfolio tests provide evidence on whether themarket fully impounds in prices the valuation implications of report detail and tone at the time theyare disclosed. If the market fails to properly value this information, then trading strategies that exploitthis knowledge may yield abnormal returns. Our evidence suggests that abnormal returns cannot beearned through long-term trading strategies based on these qualitative attributes, which supports thenotion that investors recognize and correctly respond to the value of these attributes when the reportsare published.

Our results are robust to a battery of sensitivity tests. We employ two alternative measures of re-port complexity, as well as an alternative measure of tone designed specifically for use in a financialcontext, and find qualitatively similar results. We also continue to find similar results when we exam-ine ‘‘abnormal’’ levels of report complexity and tone after controlling for the average levels of thesequalitative report attributes across industries and recommendation levels.


Our study is related to Asquith et al. (2005), who manually examine 1126 analyst reports and findthat the written analysis made by analysts to support their opinions provides incremental informationcontent to the market. Our study complements and extends Asquith et al. along several dimensions.First, whereas Asquith et al. examine specific analysis contained in analyst reports, we study the othertwo distinct qualitative attributes that comprise the ‘‘meat’’ of financial analyst reports; detail andtone (see above quote by Tsao). Second, our measures of detail and tone are objective and amenableto replication, and avoid any bias that might be inherent in human classification.5 Third, our measuresare based on an analysis of the entire report instead of focusing on only a few elements containedtherein.

This study has implications for investors and researchers interested in the process through whichfinancial analysts transmit their private information to their clients, as well as how the market reactsto their reports. By providing evidence that investors are concerned about not only the quantitativenews contained in the reports of information intermediaries, but also the qualitative content of theirreports, this study should be of broad public interest. Additionally, extant research has documentedsituations where the stock return around an information event and the quantitative news providedby the event are of opposite signs, particularly with regards to firms’ earnings announcements (Kinneyet al., 2002; Johnson and Zhao, 2011). By examining the ‘‘soft’’ information contained in one frequentlystudied information event, namely the publication of financial analyst reports, we provide evidenceregarding one potential determinant of this interesting phenomenon. The study should also be ofinterest to analysts because it shows that levels of report complexity and tone affect how investorsinterpret and respond to their reports.

The remainder of the paper proceeds as follows. Section 2 develops hypotheses regarding the infor-mation content of these qualitative report attributes. The empirical measures of detail and tone, aswell as the research design, are introduced in Section 3. Section 4 presents the empirical results re-lated to our main tests, and Section 5 describes additional analyses. The final section presents ourconclusions.

2. Hypotheses development

2.1. Report detail as an indicator of analyst knowledge

Clement (1999) finds that financial analyst forecast accuracy is associated with variables thatproxy for ability (i.e., experience), extent of resources available to the analyst (i.e., broker size),and the complexity of the task (i.e., number of firms and industries followed by the analyst). Var-iation in these factors can lead to information asymmetries among analysts, which in turn causessome analysts’ reports to be more valuable to investors than others. Theoretical studies posit thatwhen analysts lack sufficient private information to produce accurate forecasts or recommenda-tions, either through lack of effort or ability, they will tend to mimic outputs from strong analysts(Trueman, 1994; Arya et al., 2005). This herding behavior among analysts is an attempt to alleviatethe observable effects of their lack of information and has been documented in empirical studies(e.g., Hong et al., 2000; Clement and Tse, 2005; Mensah and Yang, 2008). Bloomfield and Hales(2009) provide evidence that in some situations, analysts may use the consensus forecast as a sub-stitute for individual effort.

2.1.1. Positive market response to report detailOne manifestation of this type of herding behavior among less informed analysts would likely be

shorter, simpler research reports, as it is more difficult to provide unique, thorough justification forforecasts and recommendations that are mere reproductions of the existing consensus. In contrast,more informed analysts may issue longer, more complex reports as they attempt to convince their

5 It could be argued that human classification is less rote and amenable to the consideration of context. However, in almost allstudies that use humans for classification, the research process is still rote in the sense that typically, simple checkmarks are madefor the presence or absence of a particular word, phrasing, or type of analysis.


clients that they have invested the necessary effort and resources to produce a credible opinion. In es-sence, because analysts have little incentive to retain private information when publishing their re-ports, the extent of their disclosure may proxy for their level of informativeness. Thus, betterinformed analysts are more likely to produce accurate forecasts and valuable recommendations. Thisleads to a predicted positive relation between the level of detail in an analyst report and the market’sresponse to the quantitative news contained in the report (an interaction effect). While the level ofdetail in the report may serve as a signal about the overall quality of the report, the direction of themarket’s response to this signal depends on the positive or negative nature of the news found inthe report.

2.1.2. Negative market response to report detailAs an alternative hypothesis, greater detail in analyst reports could be an attempt by less-informed

financial analysts to conceal or obscure their lack of firm-specific information. Li (2008) argues thatbecause longer and more complex annual reports have higher information processing costs, managerswill produce annual reports that are more difficult to read during times of poor economic performanceto distract the reader from value-relevant information. Consistent with this hypothesis, he finds thatthe annual reports of firms reporting lower earnings tend to be longer and more complex than those ofother firms. Similarly, Baginski et al. (2011) compare the Management Discussion and Analysis sec-tions of fraud firms’ 10-Ks to those of non-fraud firms and find that firms engaging in financial state-ment fraud issue longer, more complex reports than other firms. Just as managers may use detail toobfuscate poor performance or fraudulent behavior, analysts may use it to hide their lack ofinformation.

The above arguments are not mutually exclusive. The market reaction to analyst report detail likelyreflects the net effect of both influences. Additionally, detail is likely to be at least partially a result ofidiosyncrasies of the analysts themselves, and therefore not entirely dependent on their informationenvironment. Given the empirical tension with regards to the capital market implications of the extentand complexity of disclosure in financial analyst reports, the report detail hypothesis is stated in nullform:

H1. The detail of financial analyst reports does not have an effect on the market’s reaction to theinformation contained in earnings forecasts and stock recommendations (interaction effect).

It is important to note that the above hypothesis predicts an interaction effect betweenreport detail and the summary outputs of analysts’ reports (i.e., earnings forecasts andrecommendations). Specifically, we predict that the report detail affects how investors view thesesummary outputs, but that detail by itself cannot be interpreted unambiguously as good or bad news.

2.2. Circumventing analysts’ conflicts of interest via report tone

While some research shows that following analysts’ stock recommendations can be profitable (e.g.,Womack, 1996; Barber et al., 2001), more recent studies question their investment value. Indeed, evi-dence suggests that analyst recommendation levels may actually hinder the market’s price discoveryprocess, and these studies show a negative relation between analysts’ recommendations and futurestock performance (Bradshaw, 2004; Barniv et al., 2009; Drake et al., 2011). One rationale for this neg-ative relationship is analysts’ conflicts of interest. Analysts are influenced by their own compensationstructures and might tend to issue more favorable recommendations to facilitate potential underwrit-ing relationships, even when the recommendation cannot be justified by the firm’s fundamentals (Linand McNichols, 1998; Agrawal and Chen, 2008).6 Groysberg et al. (2011) show that analyst compensa-tion is driven more by the investment banking business generated from the firms they cover than by theperformance of their stock recommendations or accuracy of their earnings forecasts. In addition, analysts

6 Indeed, a majority of the reports used in this study contain the following (or a similar) disclaimer: ‘‘. . . does and seeks to dobusiness with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict ofinterest that could affect the objectivity of this report.’’


have been accused of issuing overly positive recommendations in order to curry favor with managementso as to gain or keep access to private information (Das et al., 1998; Libby et al., 2008; Mayew, 2008).7

Michaely and Womack (1999) and Barber et al. (2007) demonstrate that these conflicts of interest ad-versely affect the quality of analysts’ stock recommendations.8

2.2.1. Positive market response to report toneFinancial analyst reports provide a unique setting to study the capital market implications of tone.

While the information content of analysts’ summary outputs might be compromised by their conflictsof interest, the tone of analysts’ reports is more likely to be free from bias in conveying their overallsentiment of the firm. Thus, we examine whether report tone provides information to the market thatis incremental to the analysts’ earnings forecasts and recommendations.

Prior research has shown that tone can significantly affect the capital market response to the re-lease of other types of information. Tetlock (2007) establishes that the tone used by influential marketparticipants can have a significant effect on stock prices. Specifically, he documents a significantlynegative market reaction to pessimistic articles in a popular Wall Street Journal column. Expandingon this concept, there has been a surge of recent research that examines the significance of tone in firmreports. In general, the evidence suggests that optimism in a firm report is indicative of superior per-formance. For example, Davis et al. (forthcoming), find that the use of optimistic language in earningspress releases is positively associated with future ROA. Further, Feldman et al. (2010) show that opti-mism in a 10-K’s Management Discussion and Analysis section is a predictor of short-term excess re-turns, and Loughran and McDonald (2011) find a negative market reaction to the use of pessimisticlanguage in 10-Ks. These results suggest that the tone of a financial document contains information,and that investors are capable of recognizing this information and assessing higher values to firmswith more optimistic reports. Although the same influences that introduce bias in analysts’ recom-mendations might also cause analysts’ tone to be similarly biased, we expect that managers are morelikely to focus their attention on the quantitative summary measures contained within an analyst’sreport than the tone used by the analyst in the report, and accordingly, analysts encounter fewerincentives to intentionally bias the tone of their reports than they face when issuing theirrecommendations.

2.2.2. No market response to report toneThe above reasoning suggests that analyst report tone may function as a useful source of informa-

tion for investors. However, just as we did for report detail, we also present countering arguments thatprovide empirical tension to our research question. For example, cognitive biases such as overopti-mism and overconfidence, rather than conflicts of interest, may explain at least some of the positivebias in analyst stock recommendations (Mokoalele-Mokoteli et al., 2009).9 If these cognitive biases arealso reflected in the tone of an analyst’s report, the ability of tone to provide investors with valuableinformation content will be reduced.

Additionally, Winchel (2010) utilizes an experimental setting to demonstrate that in some situa-tions, analyst reports with no negative argumentation are less favored by investors than reports withboth positive and negative language. This implies that investors may discount the information pro-vided by analysts if their reports appear overly optimistic. Also, Kothari et al. (2009) argue that both

7 Tom Larsen, a former Credit Suisse Group analyst, stated, ‘‘An analyst cannot issue a sell rating because he doesn’t want to loseaccess.’’ Bloomberg.com, December 3, 2007.

8 Regulators have expressed concern in recent years regarding these conflicts of interest. For example, see the House ofRepresentatives’ Subcommittee on Capital Markets’ hearing on this subject, June 14, 2001, entitled ‘‘Analyzing the Analysts.’’Available at http://commdocs.house.gov/committees/bank/hba73368.000/hba73368_0f.htm. Attempts have been made to addressthese issues through regulation (NASD Rule 2711, NYSE Rule 472, and Regulation Fair Disclosure), and even litigation (GlobalResearch Analyst Settlement). However, recent evidence suggests that they have yet to be fully resolved, and analystrecommendations are still subject to conflicts of interest (Kolasinki and Kothari, 2008; Agrawal and Chen, 2008; Chen and Chen,2009).

9 Another explanation for the observed positive bias in recommendations is that the analyst/optimistic recommendationrelationship is endogenous. That is, analysts tend to cover firms that they feel positive about.

http://www.commdocs.house.gov/committees/bank/hba73368.000/hba73368_0f.html


positive and negative news disclosures from analysts are discounted by market participants due tosource credibility and timeliness problems.

Lastly, there is a concern that financial analysts, like all people, have natural variations in their lin-guistic styles, and the more optimistic tone of some reports relative to others may simply be a reflec-tion of idiosyncrasies in their linguistic styles rather than their private estimates of firm value(Pennebaker and King, 1999). Because the existing evidence is unclear as to the capital market impli-cations of financial analyst report tone, the relationship between tone and the market reaction is anempirical question.

If analysts transmit incremental information through report tone, as suggested in Section 2.2.1,then it can be a useful source of information to investors by itself (a main effect), or it couldguide investors on how they should respond to the summary outputs (an interaction effect). Inour empirical analysis, we test for both a main effect for tone, as well as how it interacts withthe market’s response to the earnings forecasts and stock recommendations found in the reports.Even if the same influences that cause bias to creep into analysts’ recommendations also resultin biased tone, we might still observe a significant interaction effect based on the strength ofthe support that tone gives to the report’s forecasts and recommendations. This isespecially true for recommendations, since analysts are generally constrained to issue recom-mendations in one of five broad categories; and the sell categories are rarely used (Barberet al., 2006).

We state our report tone hypotheses in null form as follows:

H2a. There is no market reaction to the tone of financial analyst reports that is incremental to theinformation contained in earnings forecasts and stock recommendations (main effect).

H2b. The tone of financial analyst reports does not affect the market’s reaction to the informationcontained in earnings forecasts and stock recommendations (interaction effect).

3. Empirical measures and research design

3.1. Detail measures

We broadly define detail as anything that may signal to investors the amount of effort the analystexpended in gathering information and producing the report. We use three aspects of the analyst re-port in capturing its detail: complexity, length, and visual aids. Complexity is measured using the FogIndex, a readability formula developed by Robert Gunning in 1952. Designed to measure writingsophistication as a function of syllables per word and words per sentence, it provides an approxima-tion of the years of education a reader would need to understand the text. Used extensively in thecomputational linguistics literature, the Fog Index has recently become more prominent amongaccounting researchers (Li, 2008; Lehavy et al., 2011; Biddle et al., 2009; Callen et al., 2011).10 It is cal-culated as follows:

10 Witchairmstock ocomplia

FOG ¼ 0:4 � ðaverage number of words per sentenceþ percent of complex wordsÞ; ð1Þ

where complex words are defined as words with three or more syllables. A Fog Index in the range of12–14 is considered average, below 12 is easy, 14–18 is relatively difficult, and above 18 indicates thedocument is unreadable (Gunning, 1968). A key advantage of the Fog Index is that it provides an objec-tive and reliable quantitative measure of writing complexity. We expect that a more complex analystreport provides a more credible signal about the analyst’s effort and thus, the value of the report.

h regards to the use of writing complexity measures such as the Fog Index in a financial reporting context, former SECan Christopher Cox recently stated, ‘‘Just as the Black-Scholes model is commonplace when it comes to compliance with theption compensation rules, we may soon be looking to the Gunning-Fog and Flesch-Kincaid models to judge the level ofnce with the plain English rules’’ (Cox, 2007).


Our second measure of detail is the length of the analyst report. This variable directly measures theamount of disclosure in the report, which is a central component of report detail. More disclosure islikely to provide a stronger signal about the analyst’s effort. The length of reports is simply defined as:

11 MoKothari

12 Quo

LENGTH ¼ word count: ð2Þ

The Fathom package of Perl, a dynamic computer programming language used in prior academicresearch, is employed to calculate both FOG and LENGTH (Collins-Thompson and Callen, 2005; Li,2008; Miller, 2010).

Our final measure for detail is the number of visual aids contained in the report. Research hasshown that graphs and other visual features of financial reports can convey considerable informationto their users (Beattie and Jones, 1992; Muino and Trombetta, 2009). This variable is formally definedas:

VIS ¼ chart countþ graph countþ table count: ð3Þ

In summary, we use three different measures to capture the level of detail in the analyst report. TheFog Index measures text complexity; length measures the overall level of text disclosure; and thenumber of visual aids measures the extent to which analysts convey ideas or results through meansother than text. While all three of these measures capture the level of detail in the analyst report, theyare likely independent from each other and associated with different constructs. Therefore, we exam-ine the information content of each component of report detail independently in our analysis, ratherthan attempting to create an aggregate measure of overall report detail.

3.2. Tone measure

We use the content-analysis software General Inquirer (GI) to measure the tone of analyst reports.Developed by Harvard professor Phillip Stone and colleagues, GI maps text files based on dictionary-supplied categories containing both word counts and senses.11 GI’s software algorithm uses word sensedisambiguation to accurately assign words to its various categories. For example, it is able to ‘‘distinguishbetween ‘race’ as a contest, ‘race’ as moving rapidly, ‘race’ as a group of people of common descent, and‘race’ in the idiom ‘rat race’.’’12 Another advantage of GI is its reliance on predetermined dictionaries anddisambiguation rules, which prevents researcher subjectivity or bias from entering the study.

Our primary measure of tone comes from two GI dictionary categories, the ‘‘Positiv’’ and ‘‘Negativ’’valence categories, which have been used extensively in prior finance and accounting tone analysisresearch (e.g., Tetlock, 2007; Tetlock et al., 2008; Kothari et al., 2009). These categories are comprisedof thousands of words and word meanings associated with a positive or negative outlook. Importantly,the disambiguation rules discussed above also enable GI to identify negative qualifiers, so that aphrase such as ‘‘earnings will not meet expectations’’ will be characterized as negative, rather thanpositive. We subtract the negative word count from the number of positive words and scale by thetotal word count of the report. To allow for a more intuitive interpretation of the results, we multiplythe outcome by 100. Formally:

TONE ¼ 100 � ½ðpositive word count� negative word countÞ=word count�: ð4Þ

TONE can thus be interpreted as the percentage of positive words used by the analyst minus thepercentage of negative words. Therefore, a higher value of TONE is indicative of a more optimistic re-port. A quantitative measure of something as inherently subjective and qualitative as the tone of awritten document is likely to result in substantial measurement error. Accordingly, we examine therobustness of our results in Section 5 to the use of an alternative measurement of tone recently devel-oped in Loughran and McDonald (2011). While their measure is less well-known and has not beenused extensively in the literature, it carries the advantage of being designed specifically for use in afinancial context.

re information regarding General Inquirer is available at its homepage, http://www.wjh.harvard.edu/~inquirer/. Also, seeet al. (2009) for a detailed explanation of General Inquirer’s content analysis capabilities, as well as examples of its use.tation retrieved from http://www.wjh.harvard.edu/~inquirer/3JMoreInfo.html on 10 Mar 2010.

http://www.wjh.harvard.edu/inquirer/

http://www.wjh.harvard.edu/~inquirer/3JMoreInfo.html


3.3. Research design

We analyze the information content of the qualitative report attributes ‘‘detail’’ and ‘‘tone’’ in orderto determine their usefulness to market participants. Information content is defined as the short-win-dow market reaction to the detail and tone of a report, controlling for the news provided by the re-port’s summary measures (earnings forecasts and stock recommendations). By restricting oursample to reports that are initiations in coverage, we eliminate any confounding effects from prior re-ports issued by the same analyst for the same company. Therefore, the level of news from the sum-mary measures can unambiguously be defined as the report’s deviation from the contemporaryconsensus earnings forecast and stock recommendation. In order to determine the extent to whichinvestors respond to the detail and tone of analyst reports upon their publication, the followingregression is estimated for each of the three measures of report detail:

13 The14 Infe

robust tzero ot

15 Weobservacalenda

CARij ¼ a0 þ b1FCASTDEVij þ b2RECDEVij þ b3DETAILij þ b4TONEij þ b5ðFCASTDEVij

� DETAILijÞ þ b6ðRECDEVij � DETAILijÞ þ b7ðFCASTDEVij � TONEijÞ þ b8ðRECDEVij

� TONEijÞ þ ck

X12

k¼1

Ii þ eij; ð5Þ

where CARij is the firm i’s three day (�1,0,1) size-adjusted return centered on the publication date ofanalyst j’s report13; FCASTDEVij is the deviation of analyst j’s one-year earnings per share forecast forfirm i from the current mean consensus forecast, deflated by stock price as of the publication date ofthe report; RECDEVij is the deviation of analyst j’s stock recommendation for firm i from the currentmean consensus recommendation; DETAILij is the quartile ranking of one of the three measures of detailof analyst j’s report for firm i: FOG, LENGTH, or VIS, scaled to range between 0 and 1; and TONEij is thequartile ranking of the tone of analyst j’s report for firm i, scaled to range between 0 and 1; and Ii is the aseries of industry indicator variables based on the Fama-French 12 industry classification scheme.

With regards to report detail, a positive and significant coefficient on FCASTDEVij � DETAILij (b5) orRECDEVij � DETAILij (b6) in the above regressions indicates that there is a more pronounced market re-sponse to the summary outputs of a report (earnings forecast or stock recommendation) when theyare supported by more detail in the report (test of H1). With regards to report tone, a positive and sig-nificant coefficient on TONEij (b4) indicates that the tone of financial analyst reports is a useful sourceof information to investors by itself (test of H2a), while a positive and significant coefficient on FCAST-DEVij � TONEij (b7) or RECDEVij � TONEij (b8) indicates that report tone has an effect on the market’sreaction to the information contained in forecasts and recommendations (test of H2b).

Given the difficulty in measuring the subjective constructs of detail and tone, we likely have mea-surement error in our variables. For this reason, we use quartile rankings in place of the interval mea-surement of the four detail and tone variables because quartiles do not require a monotonicassociation between these variables and stock returns (Armstrong et al., 2010).14 The use of quartilerankings also alleviates the effects of skewness in the distributions and eases the interpretation of theinteraction terms in the model. T-statistics are calculated using White’s (1980) heteroscedasticity robuststandard errors, clustered by analyst and firm to control for dependency in the error terms (Peterson,2009).15 To alleviate the effects of outliers on the analysis, all continuous variables are winsorized atthe 1st and 99th percentiles.

use of a two day (0,1) or five day (�2 to 2) return window does not qualitatively impact the results.rences are robust to the use of tercile, quintile, or decile rankings of the four detail and tone variables. Inferences are alsoo using interval measurement of the variables, and indicator variables equal to one if the measure is above the median, and

herwise.cluster standard errors by analyst and firm because these are the two most likely sources of dependence across

tions. Our results are robust to a variety of alternative one and two-way clustering techniques based on analyst, firm, andr day.

Table 1Sample.

Panel A: Sample selection procedure Observations

Total 2006 initiation in coverage financial analyst investment research reports available from ThomsonFinancial’s Investext database

11,065

LessDuplicate reports (2545)Reports issued for foreign firms (2277)Reports authored by analysts not found in I/B/E/S (2841)Reports for firms lacking sufficient CRSP/Compustat data (1279)Reports issued within three days of an earnings announcement (66)Final sample 2057Unique analysts 786Unique firms 1404

Panel B: Sample composition by recommendation and industry

Industry Stock recommendation

Strong buy Buy Hold Sell Total Sample % Compustat %

Consumer nondurables 16 22 29 6 73 4 4Consumer durables 8 7 11 3 29 1 2Manufacturing 19 43 38 6 106 5 8Energy 49 37 52 6 144 7 6Chemicals 2 12 14 0 28 1 2Business Equipment 130 205 192 24 551 27 17Telecom 10 23 32 1 66 3 3Utilities 11 24 51 4 90 4 3Wholesale and retail 44 54 91 11 200 10 7Healthcare 69 121 78 14 282 14 11Finance 63 80 149 19 311 15 19Other 33 69 71 4 177 9 17Total 454 697 808 98 2057Sample % 22 34 39 5Barber et al. (2006) % 42 41 17

Industry groupings are based on the Fama-French 12-industry classification scheme. Compustat % is equal to the percentage ofall Compustat firms in each industry as of December, 2006. Barber et al. (2006) % represents the distribution of US stockrecommendations as found in Fig. 1 of Barber et al. (2006), with buys and strong buys grouped together.


4. Empirical results

4.1. Sample

We obtain financial analyst reports from Thomson Financial’s Investext database. The sample selec-tion process is detailed in Panel A of Table 1. All reports issued in 2006 that are initiations in coverageare included in the preliminary sample. We focus only on initiations of coverage in one year to managethe level of hand collected data required by the study. In addition, coverage initiations possess thedesirable attribute of mitigating potential confounding effects from prior reports by the same analyst.For example, while a current report could indicate significant effort on the part of the analyst, if it islargely a regurgitation of content in a previous report, this would induce significant measurement er-ror in our detail variables. Similarly, the favorability of a particular recommendation (forecast) be-comes ambiguous when a prior report exists (i.e., should the benchmark be the recommendation(forecast) in the prior report or the existing consensus?). The year 2006 was chosen as a relatively re-cent year with sufficient post-return data to conduct long-run returns test, as we describe later. More-over, our study requires that we merge analysts’ names with their code on I/B/E/S, and 2006 was themost recent year for which we have the information necessary to perform this merge.16

16 Thomson Reuters no longer provides access to files that allow for identification of I/B/E/S analyst codes in more recent years.

Table 2Descriptive statistics and correlations.

Panel A: Descriptive Statistics

Variable Mean Median Std. dev. 1st 25th 75th 99th

FOG 16.96 17.03 1.84 11.98 15.86 18.16 21.01LENGTH 7458 6604 4531 1309 4628 9060 23,385VIS 22 18 16 3 12 28 83TONE 4.16 4.06 1.81 0.00 2.97 5.30 8.86REC 2.73 3 0.86 1 2 3 4RECDEV �0.049 �0.050 0.901 �2.120 �0.670 0.550 2.000FCAST 1.44 1.10 2.16 �2.07 0.40 2.10 8.68FCASTDEV 0.000 0.000 0.032 �0.044 �0.002 0.001 0.054CAR 0.006 0.003 0.043 �0.095 �0.016 0.024 0.128

Panel B: Correlations (Pearson above diagonal/Spearman below diagonal)

FOG LENGTH VIS TONE REC RECDEV FCAST FCASTDEV CAR

FOG 0.126 0.042 0.101 �0.031 �0.067 �0.106 �0.012 0.000LENGTH 0.133 0.708 �0.041 �0.072 �0.093 �0.022 0.004 0.007VIS 0.069 0.744 �0.045 �0.147 �0.105 0.107 �0.008 �0.041TONE 0.098 �0.016 �0.008 0.210 0.158 �0.027 0.007 0.077REC �0.020 �0.077 �0.131 0.200 0.812 �0.081 0.040 0.238RECDEV �0.056 �0.100 �0.107 0.147 0.814 0.004 0.029 0.189FCAST �0.112 �0.033 0.097 �0.010 �0.116 �0.013 0.181 �0.055FCASTDEV 0.005 0.053 0.043 0.065 0.110 0.110 0.102 0.071CAR �0.004 0.021 �0.012 0.104 0.268 0.217 �0.060 0.087

Correlations are presented in bold when they are statistically significant at the a = .01 level using a two-tailed test.Variable definitions:FOG = 0.4 � (average number of words per sentence + percent of complex words).LENGTH = word count.VIS = chart count + graph count + table count.TONE = 100 � [(positive word count � negative word count)/word count].REC = stock recommendation coded as: 1 = Strong Sell/Sell, 2 = Hold, 3 = Buy, 4 = Strong Buy.RECDEV = deviation of the stock recommendation from the current consensus.FCAST = one-year ahead earnings per share forecast.FCASTDEV = deviation of the one-year-ahead earnings per share forecast from the current consensus, deflated by stock price asof the publication date of the report.CAR = three day size-adjusted return centered on the report publication date.


Reports for foreign firms are deleted, as these firms and the analysts covering them may face sig-nificantly different regulatory and reporting environments than their American counterparts. We thenmatch the remaining reports with data from the I/B/E/S, Compustat, and CRSP databases in order toobtain the analyst, firm, and market information necessary to calculate the other variables in the mod-els. Reports that are published within three days of a firm’s earnings announcement are also removeddue to the confounding effect of this simultaneous release of information to the market. This processresults in a final sample of 2057 reports from 786 unique analysts17 for 1404 different firms.

Panel B of Table 1 displays the composition of the sample by industry and stock recommendation.The distribution of firms across industries is relatively analogous to that of all Compustat firms with acouple of exceptions. There is a higher concentration in our sample of firms in the Business Equipmentindustry, and fewer firms in the Other category.

Similar to prior research, our sample is heavily weighted towards strong buy and buy recommen-dations, with relatively few sell recommendations. For comparison purposes, the distribution of stockrecommendations as reported in Barber et al. (2006) is displayed in the final row. The stock recom-mendations associated with the initiation of coverage reports have a comparable distribution to that

17 Of these 786 analysts, only 43 are ranked as All-Star analysts by Institutional Investor magazine in 2006. Thus, All-Starsrepresent roughly 5.5% of our sample, which is comparable to the 7% of all I/B/E/S analysts that achieved All-Star status during the1990s, as reported in Leone and Wu (2007).

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Sell Hold Buy Strong Buy

Ave

rage

Ton

e

Recommendation Level

Fig. 1. Tone distribution across stock recommendation levels.


found in Barber et al. (2006), although our sample has more buy recommendations and fewer sell rec-ommendations than what is found in their sample.18

4.2. Descriptive statistics and correlations

Panel A of Table 2 presents descriptive statistics for the empirical measures of detail and tone, aswell as the other variables used in the models discussed in the following section. The mean and med-ian values of the Fog Index (FOG) are 16.96 and 17.03, respectively. These values are considered ‘‘dif-ficult’’ according to the traditional interpretation of the index, although they are substantially belowthe ‘‘unreadable’’ 19.39 and 19.24 that Li (2008) finds with regards to firms’ annual reports. The meanlength of the reports (LENGTH) is 7458 words, with a median value of 6604. At a rough approximationof 400 words per page, this translates to an average report length of over 18 pages. While the averagereport contains 22 visual aids (VIS), the variation across reports is substantial, with a standard devi-ation of 16. Our measure of report tone (TONE) has a mean of 4.16, and is positive even at the firstpercentile, indicating that analyst reports contain significantly more positive content than negativecontent.

With regards to the quantitative news contained in the reports, the median stock recommendation(REC) is a buy, consistent with the distribution statistics observed in Panel B of Table 1. These recom-mendations deviate only slightly from the mean consensus recommendations, as the mean and med-ian values of RECDEV are �0.049 (�0.050). Keeping in mind that REC is an integer but the meanconsensus recommendation is continuous, RECDEV < 0.5 in absolute value suggests that analyst j’srecommendation is consistent with most other analysts following the same firm. Earnings forecasts(FCAST) are generally positive, with an average value of $1.44. Most analysts issue earnings forecaststhat do not deviate significantly from the current consensus, as the mean and median values forFCASTDEV are both equal to zero. Moreover, the inter-quartile range is only 0.003.

The mean (median) three day size-adjusted return centered around the report publication date is0.60% (0.30%). The inter-quartile range of 4.0% suggests significant variation in the sign and magnitudeof news contained in the reports.

Correlations are shown in Panel B of Table 2. The Fog Index is significantly positively correlatedwith both the length and the tone of the reports, but not their visual aid counts. As would be expected,we observe significant positive correlations between the three day size-adjusted return and the devi-ation of the analyst’s earnings forecast and stock recommendation from the current consensus,although this association with returns is substantially stronger for the recommendation deviationthan the forecast deviation (Pearson correlations of 18.9% and 7.1%, respectively). Three day returns

18 This result is not surprising given that our sample is restricted to initiations in coverage. Brokerage houses are less likely tobegin coverage of companies they strongly dislike.

Table 3Market reaction to publication of analysts’ reports.

Variable Dependent variable is CARTable entries are estimated coefficients with p-values in parentheses

INTERCEPT �0.000 �0.001 0.001 �0.000 �0.001 0.002(.936) (.751) (.717) (.941) (.754) (.667)

FCASTDEV 0.322** 0.320** 0.323** 0.361 0.206 0.360*

(.013) (.014) (.012) (.146) (.307) (.087)RECDEV 0.008*** 0.008*** 0.008*** 0.007*** 0.010*** 0.011***

(.001) (.001) (.001) (.001) (.001) (.001)FOG 0.000 0.000

(.991) (.944)LENGTH 0.002 0.002

(.521) (.499)VIS �0.003 �0.003

(.264) (.224)TONE 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.007***

(.004) (.004) (.003) (.005) (.004) (.004)FCASTDEV � FOG �0.242

(.573)RECDEV � FOG 0.008***

(.009)FCASTDEV � LENGTH 0.090

(.764)RECDEV � LENGTH 0.001

(.827)FCASTDEV � VIS �0.244

(.438)RECDEV � VIS �0.001

(.742)FCASTDEV � TONE 0.190 0.158 0.182

(.583) (.634) (.584)RECDEV � TONE �0.004 �0.004 �0.004

(.142) (.158) (.125)Adjusted R2 6.2% 6.2% 6.3% 6.7% 6.3% 6.4%Number of observations 2057 2057 2057 2057 2057 2057

All continuous variables are winsorized at the first and 99th percentiles to alleviate the effects of outliers on the analysis. T-statistics are calculated using White’s (1980) robust standard errors clustered by analyst and firm to control for dependency inthe error terms (Peterson, 2009).

* Statistical significance at the 0.10 level, using a two-tailed test.** Statistical significance at the 0.05 level, using a two-tailed test.

*** Statistical significance at the 0.01 level, using a two-tailed test.


are also significantly correlated with the tone of the reports (7.7%), but not with any of the three mea-sures of report detail.

Importantly, we also find a strong positive correlation between the tone of the reports and theirstock recommendations (21%, p-value = .001). To further examine this relationship, we display the dis-tribution of tone across recommendation categories in Fig. 1. We observe a monotonic increase in toneacross all four recommendation levels, although this increase is greater in the lower recommendationcategories, and begins to level out between the buy and strong buy categories. This result suggeststhat, to the extent that report tone reflects analysts’ private estimates of firm value, the decision toissue a positive recommendation is at least partially based on these estimates. While this providessome initial validation of our measure of tone, if the tone of a report functions merely as a substitutefor the analyst’s recommendation, we would not expect to find that tone provides explanatory powerfor returns that is incremental to the recommendation in our multivariate analysis.19

19 To more fully address this issue, in Section 5.4, we introduce a measure of ‘‘abnormal’’ tone that is based on the deviation fromthe average tone for each recommendation level.


4.3. Multivariate analysis

Table 3 presents the results from estimating regression Eq. (5), both with and without the interac-tion terms (industry indicator variables not reported). In all six models, the coefficient on RECDEV issignificantly positive, indicating that the extent to which analysts’ stock recommendations deviatefrom the existing mean consensus recommendation is a key factor in the market reaction to analystreports.20 The deviation of the analysts’ earnings forecasts from the consensus forecasts also has the ex-pected positive relationship with three day size-adjusted returns, and this effect is significant in all buttwo of the regressions.

As shown in the fourth column, we find a significantly positive coefficient of 0.008 on the interac-tion term of our measure of complexity, the Fog Index, and RECDEV. In economic terms, if an analyst’sreport with a Fog Index in the lowest quartile (least complex) were to contain a buy recommendationfor a firm whose current consensus was hold, this would result in an average three day size-adjustedreturn of 0.7% (the coefficient on RECDEV), holding everything else constant. By comparison, if thissame report instead had a Fog Index in the highest quartile (most complex), the same recommenda-tion upgrade would result in an average three day size-adjusted return of 1.5% – a relative increase of114%.

This finding suggests that analyst report complexity conveys information to investors regarding thevalue of the stock recommendation found in the report, providing support for our first hypothesis. Amore complex report results in a more pronounced reaction to the analyst recommendation, suggest-ing that investors view report complexity as a signal of superior analyst knowledge rather than obfus-cation. This result stands in contrast to the finding in Li (2008) that companies provide more complexannual reports during times of poor economic performance to distract the reader from value-relevantinformation. These seemingly contradictory results are likely due to the fundamental differences inincentives between the producers of annual reports (i.e., managers) and the producers of analyst re-ports. Managers produce annual reports that reflect on the performance of their own company andtherefore, their incentives are to produce a report that presents their performance in the best lightpossible. In contrast, analysts produce reports that reflect the performance of other entities, and theirincentives are to convey their expertise and credibility to their clients.21

We do not find statistical significance for the interaction between FOG and the earnings forecast,suggesting that report complexity has no effect on how investors interpret the forecast. This resultis consistent with the notion that forecasts are a less biased source of information than stock recom-mendations (Bradshaw, 2004). The main effect for FOG is not significantly different from zero.

In the fifth and sixth columns of Table 3, we find that the interactions between the other two com-ponents of report detail – report length and the number of visual aids – and the earnings forecasts andstock recommendations are not significant. These results indicate that unlike report complexity, thelength and the number of visual aids in the reports do not affect how investors interpret stock recom-mendations or earnings forecasts.

In the first column of Table 3, we document a significantly positive main effect for report tone, andthis effect persists throughout all six regressions presented in Table 3. The coefficient on TONE is0.007, which implies that a change in the tone of a report from the lowest (most pessimistic) quartileto the highest (most optimistic) quartile would result in an average increase in the short-window

20 Altınkılıç and Hansen (2009) argue that analysts often revise their recommendations immediately following major corporatenews events, and thus studies finding that these revisions provide information to the market suffer from endogeneity problems.However, our study is not prone to this concern because we focus on the publication of full research reports, not recommendationrevisions. It seems unlikely that analysts could respond to a major corporate event by preparing and issuing an 18-page (onaverage) report in less than two days, particularly a report that is an initiation in coverage. In fact, prominent brokerage housesconsider nine months to be a reasonable amount of time for an analyst to produce a report when initiating coverage of a firm(Reingold and Reingold, 2006, p. 30).

21 While extant research has generally associated complexity measures such as the Fog Index with obfuscation (e.g., Li 2008;Biddle et al. 2009; Baginski et al. 2011), it is possible (and indeed the results here suggest) that in some contexts, complexity maysignal superior knowledge. Just as a college textbook would likely provide more information to an educated reader than a high-school text on a given subject, so too could a more complex analyst report be viewed by sophisticated investors as moreinformative than a simpler report.

Table 4Market reaction to publication of analysts’ reports. Alternative measures of complexity and tone.


INTERCEPT 0.000 0.001 0.000 0.001(0.962) (0.815) (0.958) (0.851)

FCASTDEV 0.258*** 0.257*** 0.180 0.263(0.009) (0.010) (0.415) (0.227)

RECDEV 0.008*** 0.008*** 0.007*** 0.006***

(0.000) (0.000) (0.000) (0.001)FLESCH 0.001 0.001

(0.797) (0.784)KINCAID �0.001 �0.001

(0.719) (0.837)FIN-NEG 0.005** 0.005** 0.005** 0.005**

(0.037) (0.039) (0.044) (0.045)FCASTDEV � FLESCH 0.093

(0.760)RECDEV � FLESCH 0.006**

(0.017)FCASTDEV � KINCAID �0.059

(0.841)RECDEV � KINCAID 0.007***

(0.008)FCASTDEV � FIN-NEG 0.065 0.052

(0.804) (0.844)RECDEV � FIN-NEG �0.003 �0.002

(0.281) (0.344)Adjusted R2 6.3% 6.3% 6.6% 6.7%Number of observations 2057 2057 2057 2057

All continuous variables are winsorized at the first and 99th percentiles to alleviate the effects of outliers on the analysis. T-statistics are calculated using White’s (1980) robust standard errors clustered by analyst and firm to control for dependency inthe error terms (Peterson, 2009).⁄ Statistical significance at the 0.10 level, using a two-tailed test.

** Statistical significance at the 0.05 level, using a two-tailed test.*** Statistical significance at the 0.01 level, using a two-tailed test.


return of 0.7%, holding everything else equal. This result indicates that investors view the tone ofanalyst reports as an important source of information, providing evidence in support of rejectingthe null H2a. However, the effect of report tone on the market reaction does not appear to be con-tingent on the news contained in the reports, as the interaction terms are both insignificant, sug-gesting that we cannot reject the null H2b.22 Together, these results for tone indicate that investorsdo not view report tone as information about how they should react to the report’s quantitative sum-mary measures, as we observed with report complexity; rather, tone is incrementally informative byitself. Thus, report tone may be used by investors to gain an understanding of the analyst’s underly-ing opinion about the firm that is not captured in potentially biased summary outputs due toanalysts’ conflicts of interest.

5. Additional analyses

In this section, we first investigate whether abnormal returns can be earned through long-termtrading strategies based on the detail and tone of analyst reports. We then examine the sensitivityof our primary results to a battery of robustness tests.

22 The results in Table 3 are qualitatively the same when we partition the sample based on the sign of FCASTDEV and RECDEV andrun separate analyses on these partitions.


5.1. Long-term investment value

In our primary analysis, we examined whether investors respond to qualitative attributes of ana-lyst reports. While we find a significant price response to some of our proxies, it is possible that themarket fails to properly value this information. Specifically, investors might underreact to report detailas an indicator of analyst informativeness, or to report tone as an unbiased reflection of analysts’intrinsic firm value estimates. Alternatively, the results we document in Tables 3 and 4 could be anoverreaction that is based on simple heuristics.

In order to assess the extent to which investors correctly impound the information contained in ourdetail and tone proxies, we examine long-term abnormal returns from trading strategies that arebased on these qualitative attributes, while controlling for the analyst’s stock recommendation andfirm characteristics. To determine the profitability of trading strategies based on report detail andtone, we construct hedge portfolios by taking long positions in stocks associated with reports withinthe highest quartile of each detail and tone measure, and short positions in stocks associated with re-ports within the lowest quartiles, conditional on the analysts’ stock recommendations. Specifically, foreach detail and tone measure, we form portfolios using a two-way conditional sorting technique asfollows:

1. Stocks are first sorted into four categories based on stock recommendation, with sells and strongsells grouped together due to their relative infrequency of use.23

2. Within each of the recommendation categories, stocks are further sorted into four quartiles basedon the detail or tone measure.24

3. Finally, a hedge portfolio is formed in each of the recommendation categories by going long in thestocks associated with reports within the highest quartile of the detail or tone measure, and shortin the stocks associated with reports within the lowest quartile of the measure.

The result is four hedge portfolios for each measure of detail or tone, one within each of the fourrecommendation categories.25 Abnormal returns are then utilized to examine the long-run performanceof the portfolios. Abnormal returns are defined as the firm’s twelve month raw buy-and-hold returnbeginning the month following the issuance of the report adjusted for the portfolio return from 125benchmark portfolios formed based on size, book-to-market, and momentum (5 � 5 � 5), as describedin Daniel et al. (1997).26

In untabulated analysis, we find no evidence of significant long-term abnormal returns to hedgeportfolios formed by taking long positions in the stocks recommended by analysts with the most de-tailed or optimistic reports and shorting those stocks associated with the least detailed or optimisticreports. While it is difficult to draw definitive conclusions from insignificant tests, the lack of signif-icant results is at least consistent with the notion that investors recognize and fully respond to theinformation provided by these report attributes when the reports are published, leaving little exploit-able mispricing.

In order to further assess the long-term investment value of report detail and tone, we estimatelong-window return regressions in place of the portfolio approach described above. In this model,twelve month buy-and-hold abnormal returns are regressed on the detail and tone measures as wellas several control variables including the reports’ quantitative news and Fama-French risk factors. The

23 Barber et al. (2006) find that even after NASD Rule 2711 and other reforms aimed at reducing analysts’ conflicts of interest,more than 40% of all stock recommendations were buy or strong buys, compared to less than 20% for sell and strong sells,consistent with Table 1, Panel B.

24 Inferences are unchanged when the detail and tone quartiles are formed independently of the stock recommendations, as wellas when portfolios are formed based solely on detail and tone quartiles, without regard to the recommendations.

25 This approach requires perfect foresight regarding the formation of the detail and tone quartile rankings. An alternative is touse monthly calendar time portfolios based on the reports published each month. However, this results in a substantially reducedsample size, with only 171 (2057/12) stocks used in each month’s portfolios. While our investment techniques are notimplementable, they achieve our objective of assessing the extent to which investors properly impound the value implications ofanalyst reports at the time they are published.

26 The Daniel et al. (1997) benchmarks are available via http://www.rhsmith.umd.edu/faculty/rwermers.

http://www.rhsmith.umd.edu/faculty/rwermers


detail and tone variables are measured as defined previously, with the exception of LENGTH, wherethe natural logarithm of the word count is now used because of some extreme values and skewnessin the number of words across reports (Li, 2008). In untabulated analyses, we continue to find no asso-ciation between long-term returns and either report detail or tone, corroborating the evidence foundin the portfolio analysis.

5.2. Alternative complexity measures

To provide additional evidence on the capital market implications of analyst report complexity, wereplace the Fog Index with the Flesch Reading Ease and Kincaid Indexes, two other popular measuresof writing complexity (Dubay, 2007; Li, 2008). While the Kincaid Index, similar to the Fog Index, cal-culates a grade level necessary for comprehension of a text, the Flesch Reading Ease Index rates text ona 100 point scale, with higher values corresponding to less complex text. To make the interpretationscomparable across the different measures, we multiply the Flesch Reading Ease Index by �1, so that alarger value represents greater complexity.27

We report the results of the information content model using these two alternative measures ofcomplexity in Table 4. The significant and positive RECDEV � FLESCH coefficient of 0.006 and REC-DEV � KINCAID coefficient of 0.007, reported in the 3rd and 4th columns of Table 4, respectively,are nearly identical to the effect of our primary measure of complexity, the Fog Index, as documentedin Table 3. Additionally, using either alternative measure, we continue to find insignificant returns tolong-term investment strategies, other than an occasional exception that we would expect to find bychance.

5.3. Alternative tone measure

Loughran and McDonald (2011) show that the ‘‘Negativ’’ valence category utilized by GI often mis-classifies common words in financial documents. For example, some words identified by GI as nega-tive, such as capital, expense, and tax, are typically neutral in tone when used in a financial context, asthey simply describe company operations. Additionally, extant finance and accounting research hasfound that positive word lists typically add little incremental value beyond the analysis of negativewords (Tetlock, 2007; Kothari et al., 2009). Tetlock et al. (2008) note that this is consistent with psy-chology literature which argues that negative information tends to be both more impactful and morethoroughly processed than positive information (Baumeister et al., 2001; Rozin and Royzman, 2001).

To address these concerns, Loughran and McDonald (2011) develop a new measure of tone, coined‘‘Fin-Neg,’’ which is specific to the finance discipline and focuses exclusively on the use of negativewords in a document.28 As an alternative measure of tone, we use the textual-analysis software Linguis-tic Inquiry and Word Count to count the number of words in each analyst report classified by Fin-Neg asnegative.29 We then scale the result by the total word count of the report, and multiply by negative oneso that a larger value indicates a more optimistic report. In equation form:

27 Thetral.commeasur

28 Routone. Th

29 For

FIN-NEG ¼ �1 � ðnegative word count=word countÞ: ð6Þ

Table 4 presents results from re-estimating Eq. (5) using this alternative measure for tone. We con-tinue to find that tone has a significant main effect on the market reaction to the publication of finan-cial analyst reports. In untabulated analysis, the 12-month market-adjusted and abnormal returns toan investment strategy formed using this measure of tone remain statistically equivalent to zero.These results demonstrate that the findings from the main analysis regarding analyst report toneare not driven by bias or measurement error in the General Inquirer word lists or software.

Flesch Reading Ease and Kincaid Indexes are calculated using Java Programming code available at http://www.editcen-/gwt1/EditCentral.html. For more information regarding the calculation, development, and use of these alternative

es of complexity, refer to http://www.readabilityformulas.com.ghly half of the 2337 words in the Fin-Neg word list overlap with the Negativ GI word list used in our primary measure ofe Fin-Neg word list is available on Bill McDonald’s website: http://www.nd.edu/~mcdonald/Word_Lists.html.information about Linguistic Inquiry and Word Count, visit http://www.liwc.net.

http://www.editcentral.com/gwt1/EditCentral.html

http://www.editcentral.com/gwt1/EditCentral.html

http://www.readabilityformulas.com

http://www.nd.edu/mcdonald/Word_Lists.html

http://www.liwc.net


5.4. Information content of abnormal complexity and tone

In our main analysis, after calculating the complexity and tone of each analyst report, we rank theseraw measures into quartiles across all sample reports, and then use these quartile rankings in theregression models. We believe this method is appropriate because it resolves issues associated withmeasurement error and eliminates the need to establish benchmarks for what the complexity or toneof an analyst report should be. However, the limitation of this approach is that it implicitly assumesthat investors evaluate the extent to which they should respond to these qualitative report attributesbased on how an analyst report differs from other reports, without regard to other characteristics thatthe reports may or may not have in common. The concern with this assumption is that investors mayexpect reports with certain shared characteristics to have similar levels of these attributes. For exam-ple, as seen in Fig. 1, reports with buy recommendations are substantially more optimistic than re-ports with sell recommendations. This finding is fairly intuitive, and it is likely that investorsevaluate the tone of a report relative to the tone of other reports within the same stock recommenda-tion category. If this is true, then the raw tone measure we use in our models could be redundantinformation to the news in the recommendation, which biases our tests against finding any results.Similarly, we would rationally expect analyst reports written for firms in certain industries to be gen-erally more complex than those written for firms in other industries.

For these reasons, we conduct an additional analysis wherein we use industry and recommenda-tion level averages to establish measures of baseline, or expected, report complexity and tone, respec-tively. Next we calculate an ‘‘abnormal’’ complexity and tone for each report, where abnormalcomplexity is defined as the difference between a report’s raw complexity measure and the averagecomplexity of all reports from the same industry. Similarly, abnormal tone is defined as the differencebetween the raw tone measure of a report and the average tone for all reports with the same recom-mendation level.

Table 5Market reaction to publication of analysts’ reports. Analysis of abnormal complexity and tone.


INTERCEPT 0.001 0.001(0.883) (0.903)

FCASTDEV 0.257*** 0.260(0.010) (0.148)

RECDEV 0.008*** 0.007***

(0.000) (0.000)ABN_FOG 0.001 0.001

(0.759) (0.690)ABN_TONE 0.004** 0.004*

(0.048) (0.053)FCASTDEV � ABN_FOG �0.017

(0.957)RECDEV � ABN_FOG 0.005*

(0.092)FCASTDEV � ABN_TONE 0.011

(0.969)RECDEV � ABN_TONE �0.000

(0.858)Adjusted R2 6.3% 6.4%Number of observations 2057 2057

All continuous variables are winsorized at the first and 99th percentiles to alleviate the effects of outliers on the analysis. T-statistics are calculated using White’s (1980) robust standard errors clustered by analyst and firm to control for dependency inthe error terms (Peterson, 2009).

* Statistical significance at the 0.10 level, using a two-tailed test.** Statistical significance at the 0.05 level, using a two-tailed test.

*** Statistical significance at the 0.01 level, using a two-tailed test.


Table 5 presents results from re-estimating Eq. (5) using these measures of abnormal report toneand complexity (industry indicator variables not reported). Again, we report results with and withoutthe interaction terms included in the model. In the second column, the marginally significant coeffi-cient of 0.005 on the interaction term of abnormal report complexity and RECDEV suggests that if ananalyst’s report in the lowest quartile of abnormal complexity were to contain a strong buy recom-mendation for a firm whose current consensus was buy, we would observe an average three daysize-adjusted return of 0.7%. However, if this same report was instead in the highest quartile of abnor-mal complexity, this strong buy recommendation would result in an average return of 1.2% (a 71%increase).

In the first column of Table 5, we find a significantly positive main effect for abnormal report tone.The coefficient of 0.004 indicates that a change in the abnormal tone of a report from the lowest (mostpessimistic) quartile to the highest (most optimistic) quartile would result in an average 0.4% increasein the three day return. As in Table 3, the effect of abnormal report tone on the market reaction doesnot appear to depend on the quantitative news found in the reports, as both interaction terms remaininsignificant. Thus, even after explicitly controlling for industry and recommendation average levels ofcomplexity and tone, respectively, we continue to find that these qualitative report attributes provideinvestors with significant information content. This suggests that our results are not being driven byinherent differences in report complexity or tone across industries or recommendation levels. How-ever, the results are slightly weaker compared to what we observe in Table 3, suggesting that investorsreceive more information from the raw attributes than the measures adjusted for some baseline.

6. Conclusion

Prior research on financial analysts has generally focused on two quantitative summary measurescontained in the majority of financial analysts’ reports: the earnings forecast and the stock recommen-dation. In this paper, we empirically examine the capital market implications of two qualitative compo-nents of analyst reports, detail and tone. Detail is predicted to reflect the level of knowledge possessed byanalysts in gathering information and preparing their reports, and therefore the usefulness of theirintrinsic firm value estimates. Tone is hypothesized to signal analysts’ underlying sentiment aboutthe firms, and can thus be used by investors to determine the extent to which conflicts of interest inter-fere with the mapping of analysts’ firm value estimates into their stock recommendations.

Consistent with these predictions, we find that analyst report complexity (one dimension of reportdetail) and tone provide incremental information content to the market beyond quantitative summarymeasures. We continue to observe these effects using alternative measures of complexity and tone, aswell as an industry adjusted measure of report complexity and a recommendation level adjusted mea-sure of report tone. Further, we do not find evidence that abnormal returns can be earned throughlong-term trading strategies based on these qualitative report attributes. Taken together, these resultsindicate that investors recognize and properly respond to the value of these attributes when the re-ports are published. They are relevant to investors and researchers interested in both the methodsused by analysts to transmit their information to the market, as well as how the market reacts to ana-lyst reports. A caveat when interpreting our results is that our analysis is limited to analyst coverageinitiations in 2006. While we have no reason to believe our results might differ when extended tocontinuing analyst coverage reports and during other time periods, nevertheless, caution must be ta-ken when generalizing our results.

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