Measuring Reputation

Transcription

1 Measuring Reputation Daniel Diermeier and Mathieu Trepanier Kellogg School of Management Abstract Reputation management has moved to the top of the agenda for many companies, yet corporate reputation remains an elusive concept which is difficult to measure and manage. In this paper we investigate whether linguistic measurements of reputational shocks contain useful information about short term future corporate performance. Using news articles for for a large sample of firms listed on NASDAQ and NYSE, we argue that useful information about corporate reputation can be derived from transient signals (reputational shocks). We create measures of reputational shocks based on the sentiment and emotions captured from the news coverage about a corporation. We find evidence supporting a link between some of our measures and next day stock return. Our results suggest that measures of sadness are the most reliable and substantial predictors of performance among our set of linguistic measurements. We also find that the relationship between reputational shocks and short term performance varies substantially across industries, with the more consistent results for manufacturing, retails trade, and transportation industries. This Version: February 2009 Very preliminary: DO NOT QUOTE WITHOUT PERMISSION. * Acknowledgements: Viorel Maxim provided able research assistance. All errors are our own. Author contact information: Daniel Diermeier (d-diermeier@kellogg.northwestern.edu) and Mathieu Trepanier (mtrepanier@kellogg.northwestern.edu), Kellogg Graduate School of Management, 2001 Sheridan Road, Evanston, IL

2 Introduction Reputation management has moved to the top of the agenda for many companies, yet corporate reputation remains an elusive concept which is difficult to measure and manage. A common approach is to interpret corporate reputation as public opinion for corporations but with multiple publics, i.e. constituencies, such as customers, employees, investors, regulators and the like. While plausible at first, the approach has limited practical use, both for companies and researchers. Public opinion is usually measured by surveys, a very expensive and inflexible tool, which only the largest companies can afford. Moreover, even when surveys exists (e.g. McDonald s FastTrack survey), they are not commonly available to researchers. An alternative method relies on an indirect approach. The idea is that constituents beliefs about a company or product will be significantly shaped by the information and opinion received through the media (both mass and user generated). Moms may stop taking their daughters to McDonald s not because the staff was unfriendly during their last visit, but because they saw a feature on The Today Show linking higher rates of breast cancer with French fries. Indeed potential customers may never become actual ones because of a company s reputation. Recent laboratory studies (e.g. (Uhlmann, et al. 2008), (Jordan, Diermeier and Galinsky 2008)) have provided empirical support for the impact of reputational issues on customer perception and behavior. Customers, for example, will rate a company or a company logo lower if they are exposed to a news story alleging, e.g. a sexual harassment incident. Moreover, they also will rate product quality lower and consume less. Importantly, companies response strategies do have an effect on customer perception and behavior. Responses that focus on showing empathy, transparency, and commitment all have 2

3 positive effects. Finally, evidence of past virtuous behavior, a moral bank account, also has a positive effect, in the absence of other factors. (Uhlmann, et al. 2008). 1 These findings suggest an indirect approach to measuring reputation. Rather than using surveys or focus groups to assess the state of mind of constituencies, one can measure the inputs, i.e. the sentiment expressed in news paper articles, internet postings, etc. The behavioral link between media influence and stakeholder attitudes would be provided, by the experimental micro data on how stakeholder perception is formed. This was done by Uhlmann, et al. (2008) for the case of customers. This leads to the next question on how to measure the inputs, i.e. media sentiment about companies and products. Recent developments in information retrieval, machine learning, and natural language processing technologies provide a promising path in this direction. A standard approach (followed by many commercial providers and researchers alike) is to rely on annotated opinion corpora to train and test opinion retrieval, classification, and aggregation models. This approach has been used with considerable success in the classification of customer opinions, e.g. online movie reviews. In these applications, the goal is to correctly classify reviews as positive or negative. These methods provide a natural approach to classifying corporate sentiment. First, create a training set of articles about company X. Next, have human annotators create a training set by classifying each article as positive, neutral, and negative. Finally, train classification algorithms on the training set and then create indices based on the classification results. 1 Uhlmann, et al. (2008) have subjects judge the quality of bottled water. Subjects in the sexual harassment condition rate the taste lower and drink less. If a company uses a strategy focusing on transparency and empathy related to the sexual harassment case compared to a stone walling or defensive approach, the product quality is rated higher and a larger quantity is consumed. 3

4 The Classification Approach Methodological Problems While initially plausible, there are at least three potential problems with this approach. The first problem is known as the domain dependency problem. Opinion classifiers have achieved accuracy levels as high as 88% for product reviews (Dave et al., 2003) and 82% for movie reviews (Pang, Lee and Vaithyanathan 2002). However, Finn and Kushmerick (2006) found that an opinion classifier trained on movie reviews was not effective in predicting the polarity of restaurant reviews, and vice versa. For example, in their analysis classifiers that are able to predict movie reviews with high accuracy (77%) fail to predict restaurant reviews (40%). The reason for the domain dependence lies in the importance of expressive adjectives for classification success. While there are some universal adjectives (like good or bad) that express opinions, most adjectives that are typical for movie reviews (like gripping or boring), however, are unlikely to occur in restaurant reviews (like tasty or delicious). This issue is particularly important in the case of corporate reputations which cross various issue domains. The second problem has to do with the way opinions are expressed. While customer opinions are frequently expressed directly ( the food was delicious ) opinions about corporations are frequently expressed indirectly, i.e. through some form of argument. This is especially true of news articles. For examples, while news editorials may contain some direct opinion expression, reporting on negative events, such as lawsuits, strikes, or decreasing stock price, may actually have the same effect on the audience, even though we would not usually consider them expressions of opinion. Here we are interested on the effect on the audience, i.e. a company s customers and other stakeholders. From this perspective it does not matter whether, e.g. a customer s opinion of company X drops because of a critical editorial or a report about a pending government investigation. The third problem is practical and consists in the absence of existing text corpora related to corporate reputation that could be used to reliably train classifiers. To investigate these issues Yu, Diermeier and 4

5 Kaufmann (2009) built a new corporate opinion corpus, the Wal Mart Corpus. The practical goal of the corpus was to facilitate future algorithm development. However, methodologically it also allowed an evaluation of the reliability and validity of human annotation of corporate opinions. Unless typical subjects can clearly distinguish positive from neutral or negative news about a company, here Wal Mart, the classification based approach to reputation metrics becomes problematic. Yu, Diermeier and Kaufmann (2009) collected more than 130,000 news articles which mentioned Wal Mart in 2006, and sampled from them 1,080 articles based on the distributions of their publication dates, the document lengths, and the reach of the publishers. Three coders were then asked to annotate the polarity (a choice among the three options positive, negative, or neutral ) of the 1080 articles at both the paragraph and the document level. To test if paragraph is an appropriate opinion text unit (without much ambiguity), the fourth category mixed was added to the paragraph level annotation. Cohen s κ, a standard measure in the content analysis literature, was used to measure inter coder. A minimal κ > 0.60 customarily indicates an acceptable level of reliability. However, none of the polarity annotation tasks passed this threshold. For example, the average κ was at the document level is 0.30 and 0.39 at the paragraph level. 2 What is the reason for this low level of agreement? First of all, news articles report both opinions and facts. Many facts about corporations easily evoke various opinions among readers, for example, after reading an article on robbery at a Wal Mart parking lot, some readers would worry about the safety when shopping at Wal Mart while others might not feel the same way. 2 These two numbers are not directly comparable with each other because of the additional mixed category at the paragraph level. Interestingly, average κ was higher at the title level (0.42). 5

6 Secondly, Yu, Diermeier and Kaufmann (2009) observe a large grey area at the boundary between neutral and polarized ( positive or negative ) categories at all three levels. Further marginal distribution analysis results demonstrated that individual coders have unique personal biases toward the polarity category distribution. Even when they annotated different data subsets, the coders exhibited similar marginal category distributions. In other words, some coders are just more positively or negatively inclined than others. This phenomenon poses another challenge to classification methods in that the ground truth or gold standard is hard to obtain for algorithm training and evaluation purposes. A possible counter argument could state that, perhaps, the annotators (university undergraduates) were not trained well enough to make the proper distinctions. But this argument misses the main purpose of the whole exercise of reputation metrics which is about finding measures of company s public image. The relevant public may consist of experts, e.g analysts, but most members of the public will lack any specific knowledge or expertise. Yet, as customers and other stakeholders their opinion still matters. To summarize, the promising approach of machine based classification faces various challenges in the context of corporate reputations. First, corporate reputations cross multiple domains, yet classifiers are typically highly domain specific. Second, opinions about corporations are shaped directly (e.g. an editorial) or indirectly (e.g. a negative news story). Third, the attempt to design specific corpora for corporate reputation classification faced the problem of low inter coder agreement which made the establishment of a ground truth, essential for any classification task, impossible. At a deeper level, existing classification approaches focus on the wrong end of the communicative relation: the sender, while the real concern of corporate reputation metrics lies in the receiver. This leads to a different approach. 6

7 A Different Approach Emotional Lexica Our new approach is based on the desire to relate to constituency attitudes more directly. To do so, we utilize an automated text analysis program called Linguistic Inquiry and Word Count (LIWC) (Pennebaker, Booth and Francis 2006). LIWC identifies the linguistic structure of a text by counting the number of words associated with a series of pre defined dictionaries. These include rudimentary linguistic features such as pronoun or verb use, but also words associated with mental states such as emotions, beliefs and attitudes. For any given text, LIWC will calculate the number of words that matches its pre defined dictionaries. For example, if a word such hate, which exists in the negative emotions dictionary, appears in a text, it would be scored as a one. If it appears again, it would receive an additional score of one. If the word ugly, also in the negative emotion dictionary, appears in the text the total score would be three. In other words, LIWC counts word tokens, not types. At the end of the text analysis, LIWC will calculate the total times these dictionary word appear in the dialogue divided by the total number of words in the text, creating a percentage. This represents the linguistic footprint or summary of a particular text. What makes LIWC promising in our context is that LIWC has demonstrated external validity across a variety of studies, demonstrating how language can represent personality types. Chung and Pennebaker (2007), Pennebaker and Lay (2002), and Pennebaker, Mehl and Niederhoffer (2003) distinguish deceptive or ironic speech. Hancock, et al. (2005) represents how speakers tend to converge upon each other s speech styles. Niederhoffer and Pennebaker (2002) and Kahn, et al. (2007) distinguish verbal expressions of emotion. Stirman and Pennebaker (2002) show evidence of differences in self and collective linguistic references in writings of suicidal and non suicidal poets. Further, each dictionary has 7

8 been compared with a text analysis by human coders to insure reliability, and examined for internal validity by using a variety of text corpora 3 Notice that LIWC constitutes a universal dictionary that has been refined over many studies rather than the outcome of specific classification experiments. The hope is that these categories correspond with the mental state evoked in a typical reader of a text. To provide some prima facie credibility to the measure we discuss a brief example. In 2006, a multinational healthcare company was faced with some activist pressure concerning one of its products. The following figures show an analysis of annual news coverage for the company processed by LIWC 4. Each of the spikes in the Anger and Sadness category reflects media response to a clearly identifiable action including aggressive actions by the company such as lawsuits and product registration decisions. In early March, news articles echoed criticism of the company by a well known health related activist organization over the slow registration of a life saving drug in developing countries. Activist criticism intensified around mid April and the company registered the product in several developing countries leading to higher levels of positive feelings and optimism. In August, a major international conference attracted substantial coverage of the company s actions, most of it critical. The highest level of optimism occurred when a government took drastic regulatory action against the company in response to pressure from activists and the public more generally. 3 See for example Pennebaker, Booth and Francis (2006) and Pennebaker and Francis (1996). 4 Text Data for the period January 1, 2006 to January 31, 2007 were provided by Lexis/Nexis. 8

9 Figure 1: LIWC analysis of sample reputational environment While this case study certainly looks like a promising proof of concept, we are still measuring text characteristics, not the impact on constituencies. Of course, the existing applications of LIWC suggest that, on average, a similar emotional state will be triggered in the audience, but this is a hypothesis. To investigate the effects of LIWC directly, we look towards a standard measure of corporate performance, their stock price. Notice that, in our context, stock price effects are to be interpreted as summary measures of the attitudes of multiple constituencies. For example, investors may consult analyst reports, whichh assess the impact of a certain event or news story on customers, suppliers, investors, regulators and so forth. It would perhaps be preferable to have a more granular outcome measure for each stakeholder group, e.g. customers, but this requires the use of sales data which are not commonly available. 9

10 Our approach is similar to that of Tetlock, Saar Tsechansky and Macskassy (2008) who find supportive evidence for a link between daily measures of negative sentiment in newswire articles and next day stock performance. However, our work differs from Tetlock, Saar Tsechansky and Macskassy (2008) in three key respects. First, aside from obtaining scores for positive and negative sentiment, we also use linguistic measurements for a variety of emotions (LIWC). As discussed above, an important advantage of using LIWC comes from its demonstrated external validity. Second, while Tetlock, Saar Tsechansky and Macskassy (2008) rely on pooled estimations for firms in the S&P500, we are concerned by industrylevel differences. Last, our interest in studying reputation rather than market efficiency leads us to aggregate our linguistic measurements over longer horizons. The remainder of this article is organized as follows. Section II details our statistical and linguistic methodologies. In section III, we describe our data. Section IV, presents our results. Finally, section VI concludes. Methodology We report results for the OLS estimation of, Ψ,, Equation 1 10

11 Where, is the stock return for firm 1, on day t=1, T 5 and is the risk free rate on day 6. is a 1X4 matrix of coefficients, is a 4XT matrix containing Fama and French s 3 factors (Fama and Kenneth 1993) and Cahart s fourth factor (Cahart 1997). The factors allow us to control for returns of the contemporaneous market (market), size (SMB), book to market (HML), and momentum factors (UMD). Ψ is 1X9 matrix of coefficients., is a 9X(J*T) matrix containing the reputation measures. Finally,, is an error term. We define the reputation measures in the following way 7. From each article j considered in our study, we obtain a total word count (# of word in article j) as well as a word count for each linguistic category (e.g. # of positive words in article j). For each linguistic category, we then compute an article level proportion. For example: # # Equation 2 The reputation measure is the constructed as follows: 5 Daily return is defined using closing stock prices. 6 We use the monthly t bill rate divided by the number of trading days in the month as the measure of the risk free rate. 7 We use the positive sentiment measure (pos) for the exposition. The adaptation to all other measures (,,,,,,,,,,,,,, and, ) is straightforward. 11

12 ,, Equation 3,,,, Equation 4 Were, and, are computed on a 7 day rolling basis. The concept of a corporate reputation is often understood to be defined over a horizon longer than 7 days. For our purposes, reputation measures can be conceptualized as reputational shocks impacting the stock of reputation. We first estimate the model by pooling over all firms in the sample. We then proceed with estimations at the industry level 8. Data We use newswire articles from Dow Jones News Service for 2288 (2661) NASDAQ listed firms for 2005 (2006) and 1613 (1991) NYSE listed firms for 2005 (2006) 9. News articles are obtained from the Dow Jones News Service for 2005 and To eliminate articles containing only tables, numbers, or company names, we require that an article contain at least 50 words. We also require that they contain at least 5 positive words. Ticker symbols are obtained from the articles metadata. To avoid problems of attribution, we require that no more than 3 ticker symbols be listed in the metadata of an article. Finally, 8 When estimating at the industry level, we require that there be at least 10 firms in an industry. 9 The sample of firms was selected to match all firms listed on NYSE and NASDAQ at the beginning of

13 we consider only articles published between 12am and 3pm Eastern time. Our sample contains 1,855,266 valid newswire articles for 2005 and 1,989,360 for Stock return information for is obtained from CRSP. The selected sample consists of the set of firms listed on either NASDAQ or NYSE at the beginning of 2005 and for which we have at least 120 trading days. The Fama French factors are obtained from Kenneth French s personal webpage 10. Table 3 provides summary statistics. We obtain linguistic measurements from two sources. First, we use the well known Harvard IV 4 psychosocial dictionary word classifications (General Inquirer (GI)) 11 for sentiment or tonality scoring.,,, are thus derived using the General Inquirer. Second, we use a series of lexicons from the Linguistic Inquiry and Word Count (LIWC) to measure psychological processes 12. For each newswire article meeting our selection criterion, we obtain a general word count (word), as well as the relevant linguistic measures from GI and LIWC. The core measures obtained from LIWC are affect, negemo, posemo, anger, anx, sad, optim, and posfeel. Table 1 provides an overview of the linguistic measures. Table 3 contains descriptive statistics for key financial variables, article features, emotions, sentiment, and other linguistic measurements. We see that in our sample, a firm listed on NYSE is mentioned in a newswire article about twice as often as a NASDAQ listed firm, but that a typical article about a NASDAQ listed firm is longer than one about a NYSE listed firm. For both NYSE (NASDAQ) firms, the Dow Jones newswire articles are about 17% (20%) shorter in 2006 than in The average numbers of words from the positive, affect, positive emotions, optimism, and sadness lexicons per article are higher See the General Inquirer s Web site lists each word in the positive and negative categories: and 12 See (Pennebaker, Chung, et al. n.d.) for more details. THE LIWC website ( also contains useful information. 13

14 in both years for NASDAQ listed firms than for NYSE listed firms, but the reverse is true for words from the positive feeling, negative emotions, anxiety, and anger lexicons. Industry information for firms in the sample is obtained from Bloomberg. We used the two digit North American Industry Classification System (NAICS) codes. An extra category (NAICS=0) was created to contain 477 (438) NYSE listed firms and 108 (117) NASDAQ listed firms for 2005 (2006) for which a NAICS code was not available from Bloomberg. A non ambiguous industry classification was obtained for 3316 (4097) firms for 2005 (2006). Table 4 provides descriptive statistics for 2005 at the industry level for NYSE firms. Table 2 gives the NAICS labels. Firms in industry 49 (transportation and warehousing: postal services) have the highest average number of newswire articles per day with an average of 1 article every 2.63 days. They are followed by firms in industries 51 and 45 (information and retail trade: sporting goods, hobby, books, music, and general merchandise) with averages of 1 article every 5 days. Firms in industry 55, 61, and 81 (management of companies and enterprises, educational services, and other services) have the lowest coverage intensity with averages of 1 article every 33.3, 14.3, and 14.3 days respectively. Overall, intensity measures 13 for affect, negative, negative emotions, and sadness tend to be higher for firms industries 11, 31, 32, 44, and 45 and lower for firms in industries 55, 61, 23, and This result seems consistent with intuition that firms in agriculture, manufacturing, and retail would exhibit a newswire coverage that is more intense in sentiment and emotions than that of firms in management of companies, educational services, construction, and public administration services. Figure 2 depicts key intensity measures per industry for Intensity measures for the emotion and sentiment variable are computed by dividing the total number of words for the given emotion/sentiment in an article by the article s total word count. 14 See Table 4. 14

15 Results Table 5 shows estimates for the Ordinary Least Squares (OLS) estimation of Equation 1 where the matrix, contains one of the measures listed in the first column. The first two sections (emotions and sentiment) contain the measure of interests. Columns 2 and 3 present the results for firms listed on either NYSE or NASDAQ while columns 4 7 show the results for NYSE and NASDAQ listed firms separately. With the exception of positive feelings, all emotions and sentiment coefficients have the expected signs in the three regressions 15. Two emotional measures are consistently significant (at the 0.01 level) across the three sets of results. Negative emotion and sadness shocks to a company s reputation are systematically associated with lower next day stock returns. The magnitude of the coefficients for these two variables is also much greater than for the other measures. Overall, firms listed on NASDAQ seem more responsive to our reputational shocks. For example, the coefficients for negative emotions and sadness are roughly twice as large for NASDAQ listed firms as they are for NYSElisted firms. For a typical firm listed on NASDAQ, a one standard deviation negative emotion shock to its reputation is associated with a lower next day stock return by 4.5 basis points. Still for a NASDAQ listed firm, a one standard deviation sadness shock to its reputation leads to a lower next day return by 5.0 basis points. Table 5 shows OLS estimates for Equation 1 for the core reputational shock measures at industry level for both NYSE and NASDAQ listed firms 16. We report the coefficients for the parameters Ψ at the 2 digit NAICS code industry level 17. Consistent with what we observed in Table 5, we find that negative emotion and sadness shocks present the most consistency in terms of expected signs and significance. In both cases, of the 21 industries for which we have estimates, 17 have the expected negative sign. The 15 As affect includes emotions for which intuition would suggest a positive and a negative impact, there is no clear intuition for the coefficient sign. 16 Each regression uses a single reputation measure. 17 We include an industry if it contains at least 10 firms in our sample. 15

16 estimates are economically significant in many cases. For examples, a one standard deviation increase to our sadness measure is associated with a more than 14 basis point drop in next day stock price for firms in wholesale trade (NAICS 42), while the same shock is associated with an almost 12 basis points drop for firms in transportation and warehousing (NAICS 48). Smaller, yet significant impacts are found for finance and insurance (NAICS 52), one of the manufacturing classification (NAICS 33), and utilities (NAICS 22) with estimates of 2.1, 3, and 5.2 basis points respectively. Considering shocks to our negative emotion measure, the largest impact are for healthcare and social assistance (NAICS 62), wholesale trade (NAICS 42), real estate and rental and leasing (NAICS 53), and one of the retail trade classification (NAICS 44) with estimates of 11.3, 10.8, 10.3, and 10.1 basis points respectively. Our industry level estimates do not show a similar pattern to what was observed in Table 5 when comparing NYSE and NASDAQ estimates 18. Conclusion and Extensions Do linguistic measurements of reputational shocks impact corporate performance? We find some supportive evidence. We find that negative emotion/sentiment and sadness shocks are significantly associated with short term future stock performance with the expected signs. We further find that the reputational shock impacts are economically meaningful. For instance, a one standard deviation positive sadness shock is correlated with a 5.0 basis points lower next day return for NASDAQ listed firms or with an about 14.1 basis points drop for firms in wholesale trade. Consistent with intuition, our results suggest that the impact of linguistic shocks to corporate reputations vary substantially across industries. 18 Results not shown. Available upon request. 16

17 The central aim of this paper is to stimulate further research on the systematic use of publicly available information to study of corporate reputation. In terms of pushing the agenda further, it would be interesting to see how the perception of various stakeholder groups impact corporate performance or to investigate the role of context (e.g. articles about product defects versus earnings release) in which the linguistic reputation measures are obtained. 17

18 Works Cited Cahart, Mark M. "On the Persistence of Mutual Fund Performance." Journal of Finance, 1997: Chung, C. K., and J. W. Pennebaker. "The psychological function of function words." In Social Communication, by K. Fiedler (Ed.), New York: Psychology Press., Dave, K, Lawrence, S, & Pennock, D. M. (2003). Mining the peanut gallery: opinion extraction and semantic classification of product reviews. Proceedings of the 12 th international conference on World Wide Web, Retrieved May 28, 2007, from ACM Digital Library. Fama, Eugene F., and R. French Kenneth. "Common Risk Factors in the Returns of Stocks and Bonds." Journal of Financial Economics, 1993: Finn, A., and N. Kushmerick. "Learning to Classify Documents According to Genre." of American Society for Information Science and Technology, 2006: Hancock, J. T., L. Curry, S. Goorha, and M. Woodworth. "Automated linguistic analysis of deceptive and truthful synchronous computer mediated communication." Paper presented at the Hawaii International Conference on System Sciences, Hawaii, Jordan, J., D. Diermeier, and A. D. Galinsky. "When it s not the thought that counts: The double edged sword of care in corporate crisis responses." Working paper, Kahn, J. H., R. M. Tobin, A. E. Massey, and J. A. Anderson. "Measuring emotional expression with the Linguistic inquiry and Word Count." The American journal of psychology, 2007: Niederhoffer, K. G., and J. W. Pennebaker. "Linguistic style matching in social interaction." Journal of Language and Social Psychology, 2002: Pang, B., L. Lee, and S. Vaithyanathan. "Thumps up?: Sentiment classification using machine learning techniques." Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP2002), 2002: Pennebaker, J. W., and M. E. Francis. "Cognitive, emotional, and language processes in disclosure." Cognition & emotion, 1996: Pennebaker, J. W., and T. C. Lay. "Language use and personality during crises: Analysis of Mayor Rudolph Giuliani's press conferences." Journal of Research in Personality, 2002: Pennebaker, J. W., M. R. Mehl, and K. G. Niederhoffer. "Psychological aspects of natural language use: Our words, our selves." Annual Review of Psychology, 2003: Pennebaker, J. W., R. J. Booth, and M. E. Francis. Linguistic inquiry and word count: LIWC. Austin, Texas: Erlbaum Publishers,

19 Pennebaker, James W., Cindy K. Chung, Molly Ireland, Amy Gonzales, and Roger J. Booth. "The Development and Psychometric Properties of LIWC2007." LIWC.net. Pennebaker, James W., M.E. Francis, and RJ. Booth. Linguistic Inquiry and Word Count (LIWC). Mahwah, New Jersey: Lawrence Erlbaum Associates, Stirman, S., W., and J. W. Pennebaker. "Word Use in the Poetry of Suicidal and Nonsuicidal Poets." Psychosomatic Medicine, 2002: Tetlock, Paul, Maytal Saar Tsechansky, and Sofus Macskassy. "More Than Words: Quantifying Language to Measure Firms' Fundamentals." Journal of Finance, 2008: Uhlmann, E.L., G. Newman, V.L. Brescoll, A. Galinsky, and D. Diermeier. "Corporate crisis communication and its effect on consumers." Manuscript under review, Yu, B., D. Diermeier, and S. Kaufmann. "The Wal Mart Corpus: A multi granularity corporate opinion corpus for opinion retrieval, classification and aggregation." Working paper,

20 Appendix Table 1: LIWC and GI Categories GI LIWC Category Abbreviation Examples Words in category Positive pos Ability, clean, hopeful 1915 Negative neg Abominable, empty, 2291 haphazard Linguistic processes Past tense past Went, ran, had 145 Present tense present Is, does, hear 169 Future tense future Will, gonna 48 Negations negate No, not, never 57 Psychological processes Affective processes affect Happy, cried, abandon 915 Positive emotion posemo Love, nice, sweet 406 Negative emotion negemo Hurt, ugly, nasty 499 Anxiety anx Worried, fearful, nervous 91 Anger anger Hate, kill, annoyed 184 Sadness sad Crying, grief, sad 101 Optimism optim Certainty, pride, win 69 Cognitive processes Insight insight Think, know, consider 195 Causation cause Because, effect, hence 108 Discrepancy discrep Should, would, could 76 Tentative tentat Maybe, perhaps, guess 155 Certainty certain Always, never 83 Inhibition inhib Block, constrain, stop 111 Personal concerns Money money Audit, cash, owe 173 The LIWC portion of the table is extracted from (Pennebaker, Chung, et al. n.d.) 20

21 Table 2: 2 Digit NAICS Labels 2 Digit Description NAICS code 11 Agriculture, Forestry, Fishing and Hunting 21 Mining, Quarrying, and Oil and Gas Extraction 22 Utilities 23 Construction 31 Manufacturing: Food, beverage, tobacco, and textile 32 Manufacturing: Wood, paper, printing, petroleum & coal, and chemical 33 Manufacturing: Metal, machinery, computers & electronics, electrical equipment, transport, and furniture 42 Wholesale Trade 44 Retail Trade: Motor vehicle, furniture, electronics & appliances, building materials, food & beverage, health, gasoline station, and clothing 45 Retail trade: Sporting goods, hobby, books, music, and general merchandise 48 Transportation and Warehousing: Air, rail, water, trucking, transit and ground passenger transport, pipeline, and scenic & sightseeing transportation 49 Transportation and Warehousing: Postal services 51 Information 52 Finance and Insurance 53 Real Estate and Rental and Leasing 54 Professional, Scientific, and Technical Services 55 Management of Companies and Enterprises 56 Administrative and Support and Waste Management and Remediation 61 Educational Services 62 Health Care and Social Assistance 71 Arts, Entertainment, and Recreation 72 Accommodation and Food Services 81 Other Services (except Public Administration) 92 Public Administration 0 NAICS not available 21

22 Table 3: Descriptive Statistics NASDAQ NYSE NASDAQ NYSE Mean Std Mean Std Mean Std Mean Std Emotions Affect Positive Negative Optimism Anxiety Anger Sadness Positive feelings Sentiment Positive Negative Direction Up Down Cognition Causation Insight Discrepancy Tentative Inhibition Certainty Verb tense Past Present Future Other Negation Money Articles Daily articles/firm Total words/articles Financial variables # of firms 2288 NA 1613 NA 2661 NA 1991 NA Daily return 0.03% % % % 0.02 Market (X 1000) SMB (X 1000) HML (X 1000) UDM (X 1000) Risk free rate 0.01% % % %

23 Table 4: Descriptive Statistics by Industry for NYSE Listed Firms in 2005 Industry NAICS Codes # of Firms Average Daily # of Articles /Firm Average # of Words /Article Average Affect Intensity Average Negative Words Intensity Average Negative Emotion Intensity Average Sadness Intensity Average Daily Return % % % % % % % % % % % % % % % % % % % % % % % % % 23

24 Figure 2: Intensity Measures for NYSE Listed Firms (2005) Affect Intensity Negative Intensity Negative Emotion intensity Sadness intensity 24

25 Table 5: OLS Estimates for Various Measures ( ) Measures NYSE NASDAQ NYSE NASDAQ Coefficient (X100) t stat Coefficient (X100) t stat Coefficient (X100) t stat Emotions Affect 0.013* * Positive Negative 0.032*** *** *** Optimism Anxiety Anger Sadness 0.038*** *** *** Pos. feel Sentiment Positive 0.000** ** * Negative 0.016** ** Direction Up * Down ** Cognition Causation Insight Discrepancy Tentative Inhibition Certainty Verb Tense Past ** Present Future ** Other Negation Money 0.016** *** OLS estimates of next day stock return on a linguistic measure, the four factors (market, SMB, HML, UDM), and a constant. Robust SEs are used. *** Significant at the 0.01 level ** Significant at the 0.05 level * Significant at the 0.10 level 25

26 Table 6: Industry Level OLS Estimates for Various Measures ( ) Industries Positive Negative Positive Negative Sadness Optimism Anxiety Anger Sentiment Sentiment Emotions Emotions ** ** ** *** * 0.034* * * 0.030** ** *** 0.141*** * 0.068* ** 0.093*** * * 0.119*** * * ** ** 0.082* * * ** 0.049** 0.044* 0.042** * ** ** * * ** OLS estimates at the industry level of next day stock return on a linguistic measure, the four factors (market, SMB, HML, UDM), and a constant. Robust SEs are used. *** Significant at the 0.01 level ** Significant at the 0.05 level * Significant at the 0.10 level The marginal effects reported are computed at the sample mean of the underlying variables. 26

27 Table 7: Measures per industries: Measures Industries Sign (# + / # /nonsignificant) Emotions Affect 42;53 0/2/19 Positive emotions 54 1/0/20 Positive feelings 22;71 1/1/19 Negative emotions 42;44;53;54;62 0/5/16 Optimism 54 1/0/20 Anxiety 0/0/21 Anger 21 1/0/20 Sadness 0;22;33;42;44;48 0/6/15 Sentiment Positive 22;81 0/2/19 Negative 42;62 0/2/19 OLS estimates at the industry level of next day stock return on a linguistic measure, the four factors (market, SMB, HML, UDM), and a constant. Robust SEs are used. Column 2 gives the 2 digit NAICS codes for the industries with coefficients significant at the 0.05 level for the relevant measures. Column 3 gives the % of 2 digit NAICS codes in our sample for which we have significant coefficients at the 0.05 level. Column 4 gives x/y/z such that x (y) is the # of 2 digit NAICS codes for which we have positive (negative) significant coefficient and z is the number of non significant coefficients. 27

28 Figure 3: Positive and Negative Reputation Shock Coefficients by 2 Digit NAICS Codes (NYSE NASDAQ ) 28

29 Figure 4: Positive and Negative Emotional Reputation Shock Coefficients by 2 Digit NAICS Codes (NYSE NASDAQ ) 29

30 Figure 5: Sadness and Optimism Shock Coefficients by 2 Digit NAICS Codes (NYSE NASDAQ ) 30

31 Figure 6: Anger and Anxiety Shock Coefficients by 2 Digit NAICS Codes (NYSE NASDAQ ) 31