Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information around Earnings Releases

Transcription

1 Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information around Earnings Releases By Jim Kyung-Soo Liew 1, Shenghan Guo 2, and Tongli Zhang 3 Version 1.7 Abstract In this work we examine the confluence of two important financial social media databases -- Estimize and isentium. Both data capture crowdsourced information that has begun to appear increasingly more important for financial market research. In particular we investigate the event of the earnings announcement. First we confirm that crowdsourced/estimize s consensus earnings have slightly more accuracy than Wall Street s consensus earnings and this has been robust over the past two years Second, we document that the objectivity of the crowd has been one reason why it is more accurate. Wall Street s consensus is biased due to the lowballing phenomenon pervasive in the industry. Wall Street s consensus are 65-68% lower than the actual reported earnings versus 52-54% lower from the crowd s consensus Third, we find economically and statistically significant evidence that tweet sentiment contains distinct information that is not contained in the traditional preannouncement variables such as Forecasts Error, Earnings Surprise, Bias, Coverage, Track Record, and Earnings Volatility. Fourth, we show that Tweet sentiment prior to the earnings announcement date can actually predict post-announcement risk-adjusted excess returns over the short-term (few days). This predictive relationship holds even in the presence of the Earnings Surprise variable. Fascinatingly enough the market quickly incorporates this information and after only a few days the statistical significance of this relationship wanes. We estimate that gross of costs, the alpha from tweet sentiments post-earnings announcement may be as high as ~10-20% per year. 1 Jim Kyung-Soo Liew is an Assistant Professor in Finance at the Johns Hopkins Carey Business School, 100 International Drive, Baltimore MD Shenghan Guo is a Masters in Financial Mathematics student in the Department of Applied Mathematics and Statistics in the Whitting School of Engineering at the Johns Hopkins University, [email protected]. 3 Tongli Zhang is a Masters in Finance student in the Department of Applied Mathematics and Statistics in the Whitting School of Engineering at the Johns Hopkins University, [email protected]. 1

2 One frontier of empirical finance that has continued to rapidly expand due to a proliferation of new and exciting data is the realm of research based on listening to the crowd. Recently, we have witnessed an explosion of research activity examining such crowd-sourced data generated by social network sites such as Twitter/StockTwits (Bollen, Mao, and Zeng (2009), Zhang, Fuehres and Gloor (2011), Ruiz (2012), Forbergskog and Blom (2013), Sul, Dennis, and Yuan (2014)), Seeking Alpha (Chen, De, Yu, and Hwang(2014)), Estimize (Drogen and Jha (2013), Bliss and Nikolic (2015), Jame, Johnston, Markov, and Wolfe (2015)), and isentium (Liew and Wang (2015)), to name a few. Researchers have started to determine with alarming success that the crowd matters with regard to financial markets. However, should it really come as a surprise given that Wikipedia, Yelp, TripAdvisor and Amazon, which rely on peer-reviews by the masses, i.e. the crowd, are sites that have woven into our daily lives? It appears that we search for what the crowd has to say when it comes to our own personal lives. In fact, it appears that we have already started to trust the crowd s opinion more and more. The theory of the wisdom of crowds argues that a diverse group of responses often times, surprisingly, outperforms responses given by knowledgeable experts, Surowiecki (2004). What does the crowd say about the recently opened boutique ice cream shop down the street, just Yelp it. What about which hotel to stay when you take your family trip to Harry Potter World in Florida, just TripAdvisor it. What about some subject you want to learn because you have to teach it? Just Wiki it! What about the quarterly earnings of a company, say Apple? Just Estimize it!?! Even the Tech guru Philip Elmer-DeWitt (@philiped) has started to listen in on what the crowd has to say. Recently he wrote about the estimated earnings per share of Apple (AAPL). All 450 armchair analysts opined in on AAPL s earnings for FQ on the Estimize platform. This time the Estimize consensus of $1.86 per share was much closer to the actual EPS of $1.85 per share, well a penny off, versus that of the Wall Street consensus of $1.79 per share. 4 Antidotal evidence aside, clearly, some have taken notice and started to examine the data from an academic rigorous vantage. In light of the Efficient Market Hypothesis (EMH) of Fama (1970, 1991), what does this new source of information have to do with security price behavior? Can such information help determine the cross-section of expected returns beyond what CAPM (Sharpe (1964), Lintner(1965)) predicts? CAPM has since been adjusted to include factors related to size (Banz (1981)) and value (Rosenberg et al (19xx)). The Fama-French 4-factor model (Fama and French (1993, 1996)) has been extended successfully by Carhart (1997) to include the momentum factor attributed to Asness(1994) and Jegadeesh and Titman(1993). Will there be some factors related to social networks? It appears that we as a community are headed in that direction. Antidotal evidence appears to be building and soon we may have to adjust the Carhart 5-factor model for a social-media factor. Only time will tell. In this paper we are concerned with better understanding two data sets and their information content on earnings announcement and consequential security price behavior. We will examine the earnings announcement period for our sample of companies spanning the period from November 2011 to December Each year companies are mandated to disclose their earnings four times. It s a heighted event for Wall Street and for the companies. Equity analysts read their valuation models and try to predict the level of earnings. Successful Wall Street analysts are reward handsomely with lucrative bonuses and name recognition. Evidence appear to be built that Wall Street analysts have been beaten by the crowd, see Drogen and Jha (2013) and Bliss and Nikolic (2014). Also, the divergence of opinion on earnings appears to have influence on the velocity of dissemination of security prices. We continue to investigate this vein of research by examining the Estimize dataset. However, in this work we link tweet sentiment dataset provided by isentium. isentium have their proprietary sentiment engine that takes text tweets and coverts them to a score between -30 and 30 with 30 being the most bullish and -30 the most bearish. Prior work has shown that these sentiment can predict the cross-section of IPOs, Liew and Zhang(2015). In this paper we merge the two databases and bring in the security prices. We are interested in testing the following questions: (1) Does tweet sentiment help reduce the earning s announcement forecast error? (2) Does this gain in accuracy hold with inclusion of standard variables known-to influence earning s announcement forecast error? (3) Does tweet sentiment have any relationship to on post-earnings announcement risk-adjusted drift? Literature Review A considerable amount of studies have demonstrated the "wisdom of crowds" in various disciplines. Page (2007) argues that the diversity in a group leads to collective wisdom. He provides empirical evidence in a cognitive sense that gives strong support to such a statement. Collective wisdom is also analyzed by Landemore and Elster (2012) from both theoretical and empirical aspects. Predicting the market is introduced in their work as one of the most important applications of such collective wisdom. In finance, the "wisdom of crowds" in forecasting equity markets is supported by prior studies. Schijven and Hitt (2012) analyze the cause of the "wisdom of crowds" and conclude that the buy-side crowds attempt to assess managerial perceptions based on publically available information and thus lead to a vicarious form of wisdom. In economic benchmarking, Lee, Ma and Wang (2014) display evidence that the "wisdom of crowds" results in peer identification schemes that systematically outperform standard classification schemes

3 In this paper, we consider the "wisdom of crowds" phenomenon in a financial context. Recently, some online financial social media platforms, such as Estimize and Seeking Alpha, have been established so that the general public can share opinions on stock market moves and forecast future market statistics. Increasing attentions are drawn to these crowd-sourced financial service platforms. Johnston, Kang and Wolfe (2013) show that the buy-side analysts give estimates for future earnings and revenues that are at least as accurate as the sell-side analysts, which confirms the findings by Drogen and Jha (2013) as well as Bliss and Nikolic (2015). By conducting cross-sectional regression analysis, they find that the buy-side estimates are optimistic compared to the sell-side forecasts but the accuracy does hold across firm size and coverage segments. Drogen and Jha (2013) compare the crowd-sourced forecasts with the Wall Street professional forecasts and conclude that the crowd-sourced forecasts are more accurate. Bliss and Nikolic (2015) state that the Internet provides a crucial channel for sharing opinions and information, thus improving price efficiency. The accuracy of the crowd-sourced forecasts is shown to be positively related to the number of analysts making the forecasts. Moreover, investor reactions to the earnings surprise on the information announcement day of the actual information are more prominent and the post-earnings drift is less significant for companies followed by buy-side analysts than the ones not followed. Jame, Johnston, Markov and Wolfe (2015) find crowdsourcing important in the sense that the buy-side and sell-side forecasts combined bring in more accuracy than either of them alone. Further, they show that the earnings announcement returns are more sensitive to EPS news when using the crowd-sourced forecast consensus as the expected earnings per share. The "wisdom of crowds" is believed to have certain influences on stock markets. One venue that many researchers consider highly possible through which the influences can be implemented is social media textual sentiments. Baruch, Saar and Zhang (2014) confirm the viewpoint that news can introduce a shared error into stock prices by rigorously modeling it with a probabilistic trading framework. Their model, when applied to empirical data, shows that the price impact related to the news is initially strengthened by the influence yet the convergence to the true value could be slowed down later on. Chen, De, Hu and Hwang (2014) incorporate the textual sentiments from a popular crowd-sourced financial website, Seeking Alpha, in their research and find that a strong link exists between investors' opinions and security prices. Their regression results show that stock reviews on Seeking Alpha have remarkable predictive power for future equity returns and earnings surprises. In the next section we describe our data. We summarize the relevant statistics and discuss potential drawback to our data and research results. Next, we discuss our framework to examine earning forecast errors and describe the relevant hypothesis tests. Following this section, we present our main results and our discussion of the implication of our results. Finally, we end with our conclusions and thought of future work. Data Description In this paper, we employ two sets of data, one is from Estimize and the other is from isentium. Here we give a short description of both datasets. 1. Estimize Estimize is an online financial estimates platform open to the general public. It was founded in 2011, aiming at boosting the crowd-sourced market forecasting. Analysts making forecasts on Estimize include students, independent researchers, private investors, sell-side professionals and buy-side analysts. Till the time of composition of this paper, there are more than 34,000 registered users making contributions to the community. The resulted forecast consensus is uploaded to major financial research platforms such as Bloomberg and referenced by prestigious financial media like Forbes, Barron s, CNN Money and etc. The diversified backgrounds of analysts distinguish Estimize from the traditional financial groups whose analysts purely consist of sell-side experts. Market estimates made solely by financial professionals are demonstrated to be biased. Typical sell-side biases, such as the herding effect (Trueman, 1994; Hong et al, 2000) and institutional bias, distort the forecast consensus from the professional analysts. As a result of the herding behavior, a sell-side analyst may be reluctant to make estimates too deviate from the forecasts given by the sell-side majority even if such deviate estimates are well supported. Apparently this is a compromise of individual wisdom. Also, the forecast consensus from the sell-side could be over-optimistic as there are institutional pressures (Boni and Womack (2002), Michaely and Womack (1999)). As a contrast, Estimize consensus forecast is crowd-sourced, thus minimizing the sell-side biases. Different from some online platforms that provide monetary incentives to the contributors, Estimize doesn t compensate its analysts. Contributors may make forecasts purely for reputation-building purpose as the accuracy ranking for analysts are visualized on the website. Opinions from a variety of sources are collected in addition to those from sell-side professionals. In this sense, a portion of market consensus previously ignored by the sell-side forecasts is reflected in Estimize forecasts. However, a disadvantage of Estimize is that contributors may not add valid information about market fundamentals. Instead, they may make unreliable estimates as a result of insufficient financial knowledge or an intention to manipulate market consensus for personal benefits. In order to deal with the problem, Estimize algorithmically checks the reliability of each estimate submitted and reviews the first five forecasts made by new analysts. Besides, the affiliations of analysts from a financial background are confirmed by the registered accounts. The raw data from Estimize contains the earnings and revenue estimates made by each contributor concerning a piece of future information release, as well as the date and time that the estimates were submitted. Each pair of earnings and revenue estimates is assigned a unique ID. The analyst made that pair of estimates has a unique user ID, which is also included in the dataset. Estimates and the actual information released are connected by the release ID 3

4 assigned to that information announcement. With the release ID, we can reference the estimation to the actual earnings and revenue and calculate the bias, earnings surprises and other important accuracy measurements. Important data features include reliability, integrity and seasonality. Estimize dataset has a binary field varying between true and false, indicating whether a pair of earnings and revenue forecasts is reliable. Being marked false means that the forecasts are reasonable estimations, otherwise incredible. In terms of the data integrity, the Estimize data is of high quality and contains no missing values. Strong seasonality also exists. The data dates from the beginning of 2010 to the end of However, the majority of the estimates were submitted after January 2011, when the Estimize community was formally established. When considering the number of stocks covered by Estimize as a function of date, we find obvious seasonality (Figure 1). Also, define coverage as the number of analysts contributing to Estimize, we show that coverage monotonously increases as the number of days prior to announcements decreases (Figure 2), implying that most users make forecasts few days in advance of the actual information release date. For additional fundamental statistical features of the dataset, interested readers can refer to Appendix (Table 1, 2). 2. isentium Established in 2010 by a group of linguistic and computer scientists, isentium is expertised in providing market sentiments indicators. Instruments covered include stocks, indices and ETFs, whose price moves have been proven highly related to sentiments. Deriving real time market related texts from Twitter, isentium uses its patented Natural Language Processing (NLP) technology to process the textual contents and assign them sentiment scores ranging from -30 to 30. Empirically, positive scores imply optimistic investor expectations while negative scores indicate pessimistic expectations for the market price. Real time market sentiment scores are generated and sent to isentium users to analyze the market movements and produce their own trading signals. In this article, we use isentium API data during January 1 st, 2013 and April 18 th, 2015 to analyze the effects of market sentiments on earnings surprises. The dataset has 8,388 comma separated values files with each file contains the API data during the aforementioned time span for one stock. An individual file has 15 variable fields that include 5 place holders and multiple observations which we call tweet. We attach the field descriptions in Appendix (Figure 3). Excluding the place holders, we mainly use the fields time, keyword and sentiment score. time is the time that a single tweet was generated. keyword is actually the ticker representing the stock, by which we link that tweet to the Estimize dataset. sentiment score is the score assigned by isentium NPL system to that tweet to indicate the direction and strength of sentiment. In order to incorporate isentium data into our analysis, we need to build a connection between sentiment scores and earnings surprises. Specifically, since we want to find out the influence of market sentiments on stock prices, we consider the market sentiments 1 day, 2 days, 3 days and 1 week prior to the earnings surprises. For a detected earnings surprise, we extract the time it happens and the ticker representing the stock and then find the sentiment scores of tweets regarding that stock in previous 1 day, 2 days, 3 days and 1 week. Taking average of all the sentiment scores 1 day, 2 days, 3 days and 1 week prior to the earnings surprise, respectively, we get the mean sentiment scores 1 day, 2 days 3 days and 1 week before the occurrence of that earnings surprise. We then regress earnings surprises on the mean sentiment scores 1 day, 2 days, 3 days and 1 week ahead the earnings announcement date, respectively, and explore the effects of tweet sentiments on earnings surprises. 3. Standard Academic Data Sources Besides Estimize and isentium, we employ well-known academic data sources in our study. Complementary data sources include Center for Research in Security Prices (CRSP), Compustat and Kenneth French Data Library. We give a thorough demonstration of the data collection procedure. i. CRSP and Compustat CRSP is a database for security prices and returns established by Chicago Booth in It is famous for its data quality and comprehensiveness. Available data is classified into two main groups, i.e. research products and investment products. Typical research products include U.S. Stock Databases, U.S. Index Historical Files, U.S. Treasury Databases and etc. Investment products are further classified into three types, i.e. Market Cap indexes, Value indexes and Growth indexes, with each type contains several sub-classes such as U.S. Mega Cap index, U.S. Large Cap Value index and U.S. Small Cap Growth index. CRSP provides an accurate and completed data source for researchers and investors and is demonstrated to be the first choice for academic study. Similar to CRSP, Compustat became a well-known financial database ever since its foundation in It is an exhaustive data source for statistical and market information on global companies, either active or inactive. A slightly different character of Compustat from CRSP is that it focuses more on the fundamentals of companies and markets. For example, a considerable portion of the Compustat data consists of historical financial statement information such as quarterly total assets and normalized earnings of relevant companies. A lot of times Compustat data is combined with CRSP data in financial studies as the two databases can be complementary to each other. 4

5 There are a variety of ways to accesse CRSP and Compustat data. One can use CRSPSift 5 or CRSP Cloud 6 to obtain CRSP research data. SAS users can extract CRSP data directly with common SAS applications. One can also use web based accesses such as WRDS (Wharton Research Data Services) and CHASS (Computing in the Humanities and Social Sciences) to obtain CRSP data. We use WRDS to extract CRSP and Compustat data. WRDS is a research platform managed by Wharton School of Business. It is a prevailing tool for both academic study and industrial applications. Researchers registered for WRDS have access to various global financial data, such as Thomson Reuters, SEC Order Execution, CRSP and Compustat. We download relevant CRSP and Compustat data types from WRDS and import the data into programming tools as the input of our calculation. Within all the available data, we particularly need daily historical stock quotes, daily returns and shares outstanding from CRSP; past diluted normalized EPS and total equity from Compustat. Daily historical stock quotes and daily returns are used in the calculation of forecast errors and cumulative abnormal returns. Past diluted normalized EPS, total equity and shares outstanding, combined with the data from Kenneth French website, also contribute to the computation of cumulative abnormal returns. As complementary information, we need to match the data from WRDS with related forecasts and announcements. CRSP historical stock data are broken down into multiple comma separated values (csv) files by ticker. For each announcement, we first find the ticker for the relevant stock, and then search for the daily historical quotes, returns and past quarterly financial statements by the ticker. Since the dates of announcements and forecasts are crucial to our research, we need to match the stock quote dates and financial report dates with them. For historical quotes, we simply grab the adjusted prices and 1-day holding period returns on the day that the actual information was announced and the day that the forecasts were made. If the quotes data is not available on that day, we treat it as a missing value. Note that occasionally we consider a few days prior to or after the announcements, in which case we match the historical quotes by the quote dates and the time span under consideration. For financial reports, we match the year and month only. Specifically, we use the latest financial statement information prior to the date of interest. ii. Kenneth French Data Library Kenneth French Data Library is part of the Kenneth R. French website. It obtains raw security data from academic data sources such as CRSP and Compustat, and then pools stocks into portfolios that meet different criteria. For instance, the stock universe can be sorted solely by market capitalization, book equity or operating profitability. Or it can be sorted by multiple variables such as market capitalization and book-to-market ratio, operating profitability and investment in assets and etc. Daily, weekly, monthly and yearly returns of the portfolios are made available for the general public as the benchmarks of markets. All the details of field constructions and classification criteria are listed in datasets. Factors used in the prestigious Fama-French model constructions and breakpoints of the individual portfolios are also accessible on the website. Datasets are updated at least once a year and the full historical of returns is reconstructed at the time of updating. We use the 25 Portfolios Formed on Size and Book-to-Market (5 x 5) [Daily] dataset. In this dataset, the stock universe is sorted first by market capitalization and then by book to market ratio, eventually broken down into 25 portfolios. It contains three data blocks, i.e. average value weighted returns, average equal weighted returns and number of firms in portfolios. We use the average value weighted returns as our benchmark. With the breakpoints for market capitalization and book to market ratio obtained from Kenneth French Data Library also, we match the stocks in Estimize with the corresponding market returns and compute the abnormal returns around the earnings announcement dates. The resulted values are treated as a dependent variable in our regression analysis to explore the connection between isentium and stock markets performance. Examination of Estimize versus Wall Street Consensus Before we begin our formal investigation we first look at our Estimize data to build some intuition with regards to this data set. Fortunately, we are given the Wall Street consensus estimate for each stock at each quarter. As such we compare the accuracy of Estimize s consensus earnings estimate versus the accuracy of Wall Street s consensus earnings estimates in predicting actual reported earnings. We employ a very simplistic definition of error as the difference between the consensus estimate and actual EPS for stock j at time period t employing the following notation: Consensus Error(j,t) = Consensus EPS(j,t) Actual EPS(j,t) As we have two consensus sources, namely Estimize s consensus EPS and Wall Street s consensus EPS, we examine: Estimize s Consensus Error(j,t) = Estimize s EPS(j,t) Actual EPS(j,t); Wall Street s Consensus Error(j,t) = Wall Street s EPS(j,t) Actual EPS(j,t) 5 CRSPSift is a Windows based software/data package, designed to run on the same Windows system where the CRSP database products are installed. Current edition works well on Windows 7 32-bit and 64-bit, as well as Windows 8 64-bit workstation. 6 CRSP data can be delivered via Cloud. CRSP subscribers can download data on demand or with scheduled processes. 5

6 In order to determine which source Estimize or Wall Street is more accurate, we compute the absolute value of the consensus error. The source that has a smaller absolute value of consensus error would be declared more accurate. Note that we do not distinguish between beating or missing the earnings at this point just that the absolute deviation is lower for the more accurate consensus. If Estimize s Consensus Error(j,t) < Wall Street s Consensus Error(j,t), then we say Estimize is more accurate than Wall Street Else, we say that Wall Street is more accurate than Estimize. For a given earnings announcement we count the number of times Estimize is more accurate than Wall Street. We plot the % of times Estimize has been more accurate that Wall Street over time using a rolling 1-year fiscal-year window. Since we have quarterly earnings data each quarterly point will contain that quarter and the prior three quarters of data. In Graph 1, we see that over time Estimize has been more accurate than Wall Street and this results has been very consistent over time. Over the whole period of our data we find that Estimize s consensus is 56.4% more accurate than Wall Street s consensus similar to prior studies. The graph shows that this results, however, has been surprisingly consistent over time. We present the raw data in Appendix 1. It appears that Estimize has a slight advantage, and this advantage is robust, in terms of earnings accuracy vis-à-vis Wall Street. There are clearly cases in the data set when Wall Street s consensus is much more accurate than Estimize s consensus, however Estimize has had the slight edge, 55%-58% over rolling 1-year fiscal years in most recent years. We suspect that it will continue to have this advantage in accuracy in the future. Graph 1: Estimize versus Wall Street s Consensus EPS Accuracy Over Time Many have documented that Wall Street analysts are notorious at lowballing earnings estimate, see xxx. In other words, Wall Street analysts provide lower estimates of earnings such that companies that they follow can swiftly beat their estimated earnings. If a company beats it s numbers then that company s management would be considered doing a superb job and remain intact, well, at least for another quarter. However, if that company consistently does not match or beat their numbers, then the board will eventually take notice and if this becomes a consistent pattern, i.e. not hitting their earnings targets, the board would have to take action. Such action could be replacing upper level management and in some instances asking the CEO to step down. With such dynamics in place, no doubt there are many subtle conflicts of interests that involve equity research analysts and upper-level management of companies that the analysts cover. Thus, we ask another simple question to the data, does Estimize analysts consistently low-ball their estimates giving companies a chance to beat their consensus forecasts? Presumably Estimize analysts have no motivation to lowball their estimates and we would expect that over time roughly 50% predict higher earnings estimates and 50% predict lower earnings estimates than actually reported. When we examine the data we see support for this hypothesis, well partially. In the earnings consensus data sample, we find that 53.0% of Estimize consensus lowballs reported earnings, that is the consensus is below the actual earnings. 42.4% of Estimize consensus is higher than the actual earnings and 4.6% of Estimize consensus actually perfectly match or equal the reported earnings. Compared to Wall Street consensus, 66.8% of Wall Street 6

7 lowballs reported earnings, 29.3% overshoots, and 3.9% perfectly matches reported earnings. We plot the robustness of these percentages over time in Graph 2 employing our 1-fiscal year rolling methodology described above. Graph 2: Estimize s (green)versus Wall Street s(red) Consensus EPS % (lower, higher, and equal) Over Time Graph 2 shows one reason why Estimize consensus has had a slight dominance over Wall Street consensus in regards to predicting earnings. Wall Street consensus has been consistently biased-lower than the actual reported earnings. In Graph 2, the top red line with the squares marks the percentage of earnings in the prior 1-fiscal year (4-quarters) rolling window, whereby Wall Street s consensus estimated EPS that were actually less than reported EPS. That is, firms beat their Wall Street numbers over 65%-68% of the time. Over this period Wall Street analysts have been lowballing their earnings estimates and in aggregate Wall Street s consensus estimates of earnings have been lower than the actual earnings. Indeed Estimize s analysts appears to lowball their consensus estimated earnings but not as egregiously as Wall Street analysts. Estimize consensus has been lower 52%-54% of the time, significantly less than Wall Street s 65%-68%. Interestingly enough, Estimize consensus earnings have a slight advantage in actually nailing the actual EPS 4.4%-4.7% of the time as compared to Wall Street % in the most periods. In this section we provided some evidence why the crowd, as captured by the Estimize consensus, can actually outperform Wall Street in predicting company s earnings. The notion of being more level-headed in giving independent and unfettered estimates by the crowd, and not being by overly influenced by the needs to gain access and favor from company executives appears to be a reasonable explanation supported by the data. Additionally, building relationships over time with conference calls, equity research events, and social gatherings may have led many Wall Street analysts to lose their objectivity when forecasting company s earnings. Variable Construction Regression analysis plays a key role in our study. Below we explain how we construct the dependent and independent variables used in regression. 1. Preliminary Variables In order to calculate these variables, we first define a few preliminary variables. Preliminary variables are not directly used to conduct regression analysis, instead, they are used to calculate dependent and independent variables. Preliminary variables include: (1) Earnings Forecast Consensus Before each earnings announcement, there are forecasts from Estimize made by Estimize analysts for that announcement. Earnings forecast consensus is calculated as the mean of all forecasts in prior 14 days of the announcement date, following Bliss and Nikolic (2015). (2) Relative Error 7

8 For an estimate made by analyst i on an announcement for stock s at time t, the relative error for this specific estimate is defined as: Forecast Earnings( i, s, t) Actural Earnings( s, t) Error( i, s, t). Actural Earnings( st, ) Averaging the relative errors for all estimates with respect to an earnings announcement of stock s at time t, we get the mean relative error regarding one Num( s, t) 1 announcement: Error( s, t) Error( i, s, t). Num( s, t) Relative error is not directly used in our analysis. Instead, we use this preliminary variable to calculate the Track Record for Estimize analysts and thedifficulty of forecasting for each stock. i 1 2. Dependent Variables Major dependent variables used are forecast error (FERR), earnings surprise (Surprise) and abnormal returns (AbnormalR). FERR is defined as: FERR= actual earnings - earnings forecast consensus share price at the announcement date. The absolute value of the forecast error is scaled by the daily stock quote on the earnings announcement date. This definition is similar to the one from Johnston, Kang and Wolfe (2013) but slightly varies in the sense that we take daily share price instead of quarterly share price. It measures the accuracy of the consensus forecast by the magnitude of discrepancy between the earnings forecast and the actual earnings. For Surprise, we calculate the variable using the definition form Drogen and Jha (2013): Surprise = actual earnings earnings forecast consensus earnings forecast consensus Surprise has been intensively studied in prior work. Generally, it is defined as the difference between the reported earnings and expected earnings 7. It is believed that stock markets react in the same direction as the Surprise, i.e. markets react positively to positive EPS Surprise and negatively to negative EPS Surprise 8. We will examine this viewpoint later and take a further step to detect the subtle influence of isentium tweets on EPS. AbnormalR is computed by subtracting the benchmark returns given by 25 Portfolios Formed on Size and Book-to-Market (5 x 5) [Daily] obtained from Kenneth French Data Library from the 1-day holding-period returns for the date windows before and after the earnings announcements: AbnormalR=1-day holding-period return - benchmark where the 1-day holding-period return is defined as the next day daily adjusted stock price minus the prior day s stock price divided by the prior day s stock price. In our analysis, we find isentium tweets have significant influence on AbnormalR, which will be illustrated in details later. 3. Independent Variables A group of independent variables are constructed to describe the properties of earnings forecast and related tweet sentiments, including traditional preannouncement variables such as Coverage, Track Record and Days to Release and variables measuring tweet sentiments. (1) Coverage Coverage is calculated as the number of Estimize analysts who made EPS forecasts within previous (90) (we also considered 7, 14, 90 days prior to an announcement) days of an announcement. This variable is often used as follows Bliss and Nikolic(2014): Coverage Ln[1 Num( s, t)], where Num( s, t ) is the number of analysts making forecasts for EPS of stock s at time t (2) Track Record Track Record measures analysts capability of making accurate forecasts by tracking their historical accuracy of forecasting.. For analyst i who made forecast for EPS of stock same sector as stock s p : p s at time p t, his/her track record before this forecast is measured by the mean relative error of all his forecasts on stocks in the p p p p Track Record ( i, s, t )=mean{ Error( i, s, t)} ( s sector, t t ), 7 Zhou, P. "Option Strategies for Earnings Announcements: Opportunities and Risks". FT Press, Pearson Education, Retrieved 13 January Pinto, J. E., Elaine H., Thomas R. R. and John D. S. (2010), Equity Asset Valuation (2nd ed.), John Wiley & Sons, ISBN X, Retrieved 18 January

9 where sector p stands for the sector of stock p s. For example, an analyst is going to make a forecast on the EPS of a utility company at time t, in the past he has made N 1 number of forecast for companies in utility sector, he also made N 2 number of forecast for companies in finance sector. His track record at time t will be calculated as the mean of N 1 number of forecast he made in the utility sector. On the other hand, if he is going to make a forecast for a finance company, we will only calculate his track record in the finance sector, etc. In our dataset, there are ten different sectors in total. For an announcement covered by several analysts, the group Track Record is calculated as the mean of Track Record of all analysts who cover this announcement: (3) Difficulty Track Record ( s, t) mean {Track Record ( i, s, t)} Difficulty of forecasting measures the level of difficulty faced by analysts to make precise earnings forecasts for individual stocks. Employing the standard deviation of historical forecast errors on stock s,, we define its Difficulty of Forecasting at time t as: where Std.Dev{ } refers to standard deviation. p Difficulty( s, t ) Std.Dev{ Error( s, t)} t<t p (4) Day to Release Days to Release is the number of days between the time a forecast was made and the time of the earnings release on which the forecast was based upon. For each announcement event, the Date to Release of the forecast consensus is calculated as the mean of all the forecast in that consensus: (5) Bias Num( s, t) 1 Date to Release( s, t) Date to Release( i, s, t) Num( s, t) Bias is the earnings forecast consensus. This variable measures non-linearity bias when we use our dependent variables to measure forecast accuracy. By introducing earnings forecast consensus as an independent variable, our model is performing a gage linearity and bias study. (Thomas, 2007) This study can test whether or not the forecast accuracy is a function of the magnitude of forecast EPS they are measuring. (6) Sentiment Variables We calculated a group of sentiment variables to describe properties of tweet sentiment from the isentium data. These variables include: a) Sent_1, Sent_2, Sent_3, Sent_w: These four variables describe the average sentiment scores of tweets about the company that is going to make an earnings announcement. For each earnings announcement event, we first find out all the tweets about the company that is going to make the announcement. Then we calculate Sent_1 as the mean of sentiment scores of those tweets posted in one day before the announcement. Sent_2, Sent_3, Sent_w are calculated respectively for tweets posted in two days, three days, one week prior announcement day. Here we define one day prior as 24 hours before the announcement time. For example, if the company made the announcement at 16:00 Jan 3 rd 2015, we will use all tweets about the company which was posted from 16:00 Jan 2 nd 2015 to 15:59 Jan 3 rd Sent_2, Sent_3, Sent_w follow the same rules of definition. b) Sent_shock Sent_shock describe the sentiment shock prior to the announcement. We define Sent_shock as one day pre-announcement sentiment subtract one week pre-announcement sentiment. i.e: Sent_shock Sent_1-Sent_w i 1 (7) Earnings Volatility Earnings Volatility is calculated as the standard deviation of the quarterly earnings of the prior year. Instead of using prior 4 years earnings as Bliss and Nikolic(2015), we only use the prior 1 year earnings data from CRSP. Regression Analysis 1. Statistical Description of Variables In our study, we use each announcement as an observation. In total, we have 16,840 observed earnings announcements date from November 1 st 2011 to December 31 st 2014, we restrict our period accordingly, given the limitations of the CRSP data which ends on December 31 st, We use data from three different sources, i.e. Estimize, isentium and CRSP, to construct dependent and independent variables. Table I panel A reports the descriptive statistics of variables; panel B reports the correlations among independent variables. We can see that sentiment variables are strongly correlated with other independent variables, as we would expect. Also, significant correlations exist among traditional pre-announcement variables (track record, difficulty, 9

10 Coverage, etc). Generally, correlations between sentiment variables and traditional variables are very small, although statistically significant. This result indicates that sentiment variables contain information that is neglected by traditional variables. 2. OLS Regressions With the dependent and independent variables discussed above, we conduct cross-sectional Ordinary Least Square (OLS) regressions. The results are presented in Table II, which confirm the relationships between Earnings Surprise and traditional pre-announcement variables such as Track Record, Difficulty, Coverage, Date to Release and Bias, as observed by Drogen and Jha (2014). In order to measure the magnitude of these variables, we take their absolute values. We find the forecast errors, either measured by FERR or Earnings Surprises, have strong positive correlations with Track Record, Difficulty, Date to Release and Earnings Volatility, respectively. In addition, forecast errors have strong negative correlations with Coverage and Bias. This result indicates that analysts with good track records generally make more accurate forecasts compared with analysts with less desirable track records. Given a 0.08 standard deviation and a regression coefficient, if lowering the Track Record by the magnitude of one standard deviation, the forecast error will drop from 0.28 to 0.23, which is 17% lower than the original value. Difficulty of Forecasting can also explain a large portion of forecast errors. With a regression coefficient, lowering the Difficulty of Forecasting by the magnitude of one standard deviation, 0.24, will decrease the forecast error from 0.28 to 0.15, which is a 45% reduction of the original value. The positive correlation between Earnings Surprise and Days to Release confirms the intuition that more recent forecasts tend to be more accurate than forecasts made a long time before the earnings release, as more effective information about the earnings is available when it comes closer to the announcement day. Roughly speaking, reducing Days to Release by one day can lower the forecast error by or 0.75%. In addition, the positive correlation between Earnings Surprise and Earnings Volatility supports the managerial beliefs that increasing Earnings Volatility reduces earnings predictability (Graham, Harvey and Rajgopal (2005)). Empirically, raising Earnings Volatility by the magnitude of one standard deviation, 0.054, will increase the forecast error from 0.28 to 0.33, approximately 19% of the unconditional mean of FERR. The negative correlation between Coverage and forecast errors is consistent with intuition and the notion Wisdom of Crowds. Given a 0.73 standard deviation and a regression coefficient, raising Coverage by the magnitude of one standard deviation will reduce the forecast error by 25.5%, decreasing it from 0.28 to This is consistent with the result of Bliss and Nikolic (2015). Their study shows that augmenting the number of contributors in Estimize by one standard deviation reduces the absolute forecast error by 20.04%. 9 The negative correlation between dependent variables and Bias indicates that our measurements of forecast accuracy suffer from a linearity gage bias. This means that when using FERR or Earnings Surprise to measure the forecast accuracy, we do not have an unbiased measurement that is only affected by exogenous variables. Instead, the forecast accuracy increases with the magnitude of, i.e. the earnings forecast consensus. The gage bias should be taken into account when evaluating the relationship between forecast accuracy and other independent variables. Other than confirming the relationship between FERR, Earnings Surprise and traditional pre-announcement variables, our analysis also reveals the relationship between pre-announcement tweet sentiments and dependent variables. Our result implies that FERR has a negative correlation with two-day, three-day and one-week pre-announcement tweet sentiment scores. Generally, increasing sentiment scores by one standard deviation decreases FERR by The decrease here is 4.2% of the unconditional mean of the FERR. On the other hand, Earnings Surprise has a different relationship with sentiment variables compared with the one between FERR and sentiment variables. Earnings Surprise does not show any significant correlations with preannouncement sentiment variables, instead it has a positive correlation with the sentiment shock defiend as one-day pre-announcement sentiments subtracted by one-week pre-announcement sentiments. Such differences between FERR and Earnings Surprise may be a result of different denominators used to normalize forecast errors, i.e. stock price for FERR and forecast consensus for Earnings Surprise. While the forecast consensus is mostly predetermined, stock price can change with tweet sentiments, which eventually leads to a different relationship between FERR and sentiment scores. 3. Correlations between Variables and Multiple Regression Univariate regression results reveal the potential predicting power of tweet sentiment scores on earnings forecast errors and confirm the impacts of traditional pre-announcement variables such as track record, coverage and etc. Hypothesis H1 implies that the predicting power of tweet sentiment scores does not exist in traditional pre-announcement variables. In order to test hypothesis H1, we conduct multivariate regression for dependent variables and sentiment variables, with all six traditional pre-announcement variables being the control variables. First we conduct multivariate regression for FERR and Earnings Surprise on all control. The result is presented in Table III panel A. The multivariate regression result is consistent with that of univariate regression for most of the traditional variables. All coefficients become smaller due to the correlations among independent variables. The coefficients of Track Record and Days to Release nearly become insignificant, which indicates that the explanatory power of Track Record and Days to Release may actually reflect their correlation with other independent variables. For example, a smaller Days to Release means that people don t make forecasts until it gets close to the announcement day, or it may also implies that more people contribute to this forecast 9 The small difference between our results and the results of Bliss and Nikolic is due to the different approach of data, since we are use the logarithm of the number of contributors plus one instead of using the number directly. In addition, they define absolute value by the median of forecast consensus instead of the mean of the consensus. When we use the same method as Bliss and Nikolic did in their paper, the result is 19.4%, which is close to their result. 10

11 consensus since people continue to make their forecasts as the announcement day approaches. The negative correlation between Coverage and Days to Release confirms the implications. We also conduct regression test between dependent variables and sentiment variables while controlling all six traditional variables. The result is presented in Table III panel C. We can see most of the pre-announcement sentiment, i.e. Sent_1, Sent_2, Sent_3 and Sent_w, have a significant negative correlation with FERR and Earnings Surprise. This is consistent with the univariate regression result between FERR and sentiment variables. Compared with the univariate results, the coefficients become larger with more significant t values. Among all the sentiment variables, one-week pre-announcement mean sentiment has the most significant correlations with both FERR and Earnings Surprise. Given a large coefficient -0.01, increasing Sent_w by the magnitude of one standard deviation decreases FERR by 0.02, about 7.5% of the unexplained FERR (i.e intercept). In conclusion, both univariate and multivariate regression support hypothesis H1 that tweet sentiments contain information that can be used to predict earnings forecast accuracy. The predicting power of tweet sentiments exist even if we control the traditional pre-announcement variables such as Track Record, Difficulty of Forecasting, Coverage, Days to Release and Bias. This result indicates that sentiments from social media contain effective information about earnings that is not covered by traditional methods that predict forecast accuracy. Post-Announcement Risk-Adjusted Excess Returns Predicted by Sentiment In this section we present evidence that the post-earning announcement risk-adjusted excess returns can be explained by the prior day sentiment, in our previous notation known as Sent_1. This result survives the inclusion of the Earnings Surprised (not normalized by price). Risk-adjusted excess returns are returns to the stocks minus a portfolio that has been matched along the similar characteristics of market-capitalization and book-to-market as this stock. Fascinatingly enough, the market appears to be very efficient as this relationship only exist for a few days after the announcement. The market quickly incorporates this information and the statistical significant quickly wanes. In Table IV we report the regression outputs. Each panel consists of the post-announcement risk-adjusted excess returns for cumulative excess returns from +1 day, +2 days, +3 days, +4 days, and +5 days, corresponding to Panels A, B, C, D, and E, regressed on the Sent_1 and then on Sent_1 and Earnings Surprise. We notice that both variables have positive coefficients across all our regressions. Positive sentiment prior to the earnings release persists up to four days after the earnings announcement. If the Sent_1 increase one standard deviation of 4.25 then the excess daily returns would be predicted to be increase by 4.25*0.0197= 0.08% or 8 basis points (bps). 8 bps does not appear to be very much but if we annualized this excess returns it would yield 0.08%*252 = 20.16%. Note that this risk-adjusted excess returns could be interpreted as gross of cost alpha. As we include the Earnings Surprise variable we notice that the Sent_1 still remains statistically significant and holds the magnitude of its coefficient ranging from to It becomes clear that Sent_1 and Earnings Surprise contains different information as their correlation is Thus, we conclude that Twitter sentiment contains information that appears to be distinct from the information released on the earnings announcement date relative to the Estimize consensus earnings. Post-Earnings Announcement Risk-Adjusted Cumulative Excess Returns by Sentiment and Surprise States-of-Nature Finally, we examine the four states-of-nature that naturally occur given the Tweet Sentiment and the Earnings Surprise data. We are interested in asking the following question: Does conditioning on prior Sentiment states, either positive or negative, and prior Earnings Surprise states, either positive or negative, affect the post-earnings risk-adjusted cumulative excess returns? Presumably a stock that has experienced lots of negative sentiment tweets occurring before the earnings announcement and beats consensus numbers would behave differently from a similar stock that has had lots of positive sentiment tweets and misses earnings numbers. The four states of natures are as follows: (1) Positive Tweet Sentiment and Positive Earnings Surprise, (2) Positive Tweet Sentiment and Negative Earnings Surprise, (3) Negative Tweet Sentiment and Positive Earnings Surprise and (4) Negative Tweet Sentiment and Negative Earnings Surprise. In the Graph 3 below we see that in fact conditioning on the direction of Tweet Sentiment and the direction of Earnings Surprise does matter. The gross of fees cumulative returns are the greatest for Negative Tweet Sentiment and Positive Earnings Surprise, +87 bps per month (the next 20-trading days), which translates into +0.87%*12 = % excess returns per year. Noticed that stocks with Negative Tweet Sentiment and Negative Earnings Surprise receives punishing cumulative excess returns within the week after the announcement but then appears to recover within the month. Shorting such stocks after the announcement would yield +60 bps over the next 5-days which translates into +0.53%*(252/5) = % excess returns per year. Now we should put in our many caveats: First, these results are gross of all transaction costs and market impact costs. Second, the capacity of such a strategy would have to be thoroughly examined. Third, the ability to short-these stocks would have to be determined and some may be hard-to-borrow. Fourth, data mining biases could affect results. Fifth, paper-publishing biases also infect our results. Nonetheless, there appears to be some indication that a wellformulated strategy incorporating both data sets could be an interesting avenue of future research and may lead to annualized excess gross returns in the 10%-20% range. Graph 3: Post-Earnings Risk-Adjusted Cumulative Excess Returns by (+/-) Sentiment and (+/-) Earnings Surprise By Days 11

12 Conclusions As more entrepreneurs birth companies that capture crowd sourced information in regards to financial securities and markets, and in turn provide such information to academics to research, more and more exciting discoveries will be no doubt be discovered. In this work we look at the intersection of two prominent crowd sourced information data sets, namely, Estimize and isentium. We take both data sets and examine an event that has been thoroughly examined by many prior academics, namely companies earnings announcement, and contribute to the overall understanding with a few distinct contributions. It appears that tweet sentiment has power to predict post-earnings risk-adjusted excess returns. And this unique information source does not appear correlated to Earning Surprise on the earnings release date. Some may venture to investigate if our results could hold up net of trading costs as we have only scratched the surface here. Our research has been performed gross of transaction costs but appears to indicate the risk-adjusted excess returns may be as high as 10%-20% per year. We note that conditioning on Tweet sentiment and Earnings Surprise states-of-nature appears very productive. However, a few caveats are in order. First off, such a strategy would have to monitor each security s prices, tweet feeds, and respective earnings announcements dates, and would only trade for a very short time periods with holding periods duration varying from weeks to months. Capacity may become a significant issues here as well as market impact costs would be significant. Also, our research methodology assumes that these data sets are not affected by wellknown biases such as survivorship biases and we have not done too much data-snooping in our own research process. None-the-less, we document that tweet sentiment can help to improve the accuracy of earnings consensus forecasts even in the presence of popular variables that have been previously document to influence accuracy. We present empirical evidence that suggests one of the reasons why the crowd is more accurate on consensus earnings is due to its objectivity. Wall Street s consensus unfortunately has a strong bias to lowball estimated earnings. As such, Wall Street generates consensus earning that are easily beaten by companies. Hopefully our results will increase the interest in social media data and financial market events. It is our hope to inspire other academics and practitioners to further this investigation into non-traditional financial data sources such as data generated from the crowd! 12

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14 Trueman, B. (1994), Analyst Forecasts and Herding Behavior, The Review of Financial Studies, Vol. 7, No. 1, Spring 1994 Zhang, X., Fuehres, H., & Gloor, P. (2011). Predicting Stock Market Indicators Through Twitter I hope it is not as bad as I fear. Procedia - Social and Behavioral Sciences, 26, Zhou, P. "Option Strategies for Earnings Announcements: Opportunities and Risks". FT Press, Pearson Education, Retrieved 13 January

15 T a b l e I S t a t i s t i c a l C h a r a c t e r i s t i c s o f S a m p l e s T a b l e I r e p o r t s t h e s t a t i s t i c a l c h a r a c t e r i s t i c s o f t h e v a r i a b l e s w e u s e i n o u r r e g r e s s i o n t e s t. O u r s a m p l e c o n t a i n s e a r n i n g s a n n o u n c e m e n t s d a t a f r o m t o a v a i l a b l e o n E s t i m i z e, h a v e n e c e s s a r y d a t e a n d t i c k e r s i n C R S P a n d i S e n t i u m t o c a l c u l a t e r e l a t e d v a r i a b l e s. P a n e l A p r e s e n t s t h e d e s c r i p t i v e s t a t i s t i c s o f e a c h v a r i a b l e. F E R R a n d E a r n i n g s S u r p r i s e m e a s u r e t h e a c c u r a c y o f e a r n i n g s f o r e c a s t c o n s e n s u s b y c a l c u l a t i n g t h e d i f f e r e n c e b e t w e e n e a r n i n g s f o r e c a s t c o n s e n s u s a n d a c t u a l e a r n i n g s p e r s h a r e. E a r n i n g s f o r e c a s t c o n s e n s u s a r e c a l c u l a t e d a s t h e m e a n o f a l l n o n - f l a g g e d E s t i m i z e f o r e c a s t s i n 9 0 d a y s b e f o r e t h a t s p e c i f i c a n n o u n c e m e n t. F E R R i s s c a l e d e a r n i n g s f o r e c a s t c o n s e n s u s a c t u a l e a r n i n g s b y u s i n g t h e s t o c k p r i c e o n a n n o u n c e m e n t d a y w h i l e E a r n i n g s S u r p r i s e i s s c a l e d ( a c t u a l e a r n i n g s - e a r n i n g s f o r e c a s t c o n s e n s u s ) b y u s i n g n o n z e r o f o r e c a s t c o n s e n s u s. T r a c k r e c o r d i s t h e h i s t o r i c a l f o r e c a s t e r r o r o f E s t i m i z e a n a l y s t s w h o m a d e t h e s e e a r n i n g s f o r e c a s t s. D i f f i c u l t y i s t h e s t a n d a r d d e v i a t i o n o f h i s t o r i c a l e a r n i n g s f o r e c a s t e r r o r o f t h e s a m e c o m p a n y m a k i n g t h e e a r n i n g s a n n o u n c e m e n t. C o v e r a g e i s t h e n u m b e r o f i n d i v i d u a l s w h o m a k e e a r n i n g s f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t o n E s t i m i z e, t h i s n u m b e r i s t a k e n a s n a t u r a l l o g a r i t h m a f t e r p l u s o n e. D a t e t o r e l e a s e i s t h e n u m b e r o f d a y s f r o m t h e d a y o f m a k i n g e a r n i n g s f o r e c a s t t o t h e a c t u a l a n n o u n c e m e n t d a y. F o r m o s t a n n o u n c e m e n t s, t h e r e a r e m o r e t h a n o n e f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t, d a t e t o r e l e a s e i s c a l c u l a t e d a s t h e a v e r a g e o f a l l t h e s e f o r e c a s t s. B i a s i s t h e e a r n i n g s c o n s e n s u s i t s e l f. E a r n i n g s v o l a t i l i t y i s s t a n d a r d d e v i a t i o n o f q u a r t e r l y e a r n i n g s o f t h e s a m e c o m p a n y i n t h e p a s t o n e y e a r b e f o r e t h e a n n o u n c e m e n t. S e n t _ 1, S e n t _ 2, S e n t _ 3, S e n t _ w a r e a v e r a g e t w e e t s e n t i m e n t a b o u t t h e c o m p a n y i n 1 d a y, 2 d a y s, 3 d a y s, 1 w e e k p r i o r t h e a n n o u n c e m e n t d a y, r e s p e c t i v e l y. S e n t _ s h o c k i s t h e d i f f e r e n c e b e t w e e n S e n t _ 1 a n d S e n t _ w. A l l v a r i a b l e s a r e c u r t a i l e d a t 1 % a n d 9 9 % q u a n t i l e t o e x c l u d e o u t l i e r s, t h e n t a k e n a s a b s o l u t e v a l u e. F E R R h a s b e e n m u l t i p l i e d b y f o r e x p o s i t i o n a l p u r p o s e s. P a n e l B r e p o r t s t h e u n i v a r i a t e c o r r e l a t i o n b e t w e e n i n d e p e n d e n t v a r i a b l e s, t v a l u e s a r e r e p o r t e d i n p a r e n t h e s i s. * * *, * *, * r e p r e s e n t s i g n i f i c a n c e l e v e l o f 1 %, 5 %, a n d 1 0 % r e s p e c t i v e l y. Panel A O b s e r v a t i o n s M e a n M e d i a n S t d. D e v S k e w n e s s K u r t o s i s M i n i m u m M a x i m u m F E R R 10, E a r n i n g s 11, S u r p r i s e T r a c k 12, R e c o r d D i f f i c u l t y 8, C o v e r a g e 12, D a t e t o 10, R e l e a s e B i a s 12, E a r n i n g s 13, V o l a t i l i t y S e n t _ 1 10, S e n t _ 2 10, S e n t _ 3 11, S e n t _ w 12, S e n t _ s h o c k 10,

16 Panel B Correlation between Independent Variables Track Record Difficulty Coverage Date to release Bias Sent_1 Sent_2 Sent_3 Sent_w Sent_shock Earnings Volatility Track Record (6.41)*** (-6.62)*** 0.12 (12.46)*** (-8.47)*** 0.01 (0.78) 0.01 (0.59) 0.00 (-0.20) (-0.71) 0.01 (1.13) 0.05 (5.28)*** Difficulty (-0.10) 0.08 (6.80)*** (-18.30)*** (-3.58)*** (-5.39)*** (-6.26)*** (-6.36)*** (-1.45) 0.11 (9.90)*** Coverage (-10.95)*** 0.16 (17.73)*** (-24.26)*** (-23.46)*** (-22.82)*** (-21.57)*** (-16.44)*** 0.02 (1.67)* Date to release (-0.31) (-3.41)*** (-4.53)*** (-4.29)*** (-4.75)*** (-0.79) 0.02 (1.62) Bias (-5.13)*** (-5.39)*** (-5.46)*** (-4.56)*** (-2.12)*** 0.18 (19.71)*** Sent_ (124.12) 0.68 (93.74) 0.46 (52.67) 0.35 (37.07) (-3.97) Sent_ (185.25)*** 0.59 (75.25)*** 0.16 (15.74)*** (-3.97)*** Sent_ (95.89)*** 0.08 (8.17)*** (-4.21)*** Sent_w (-7.04)*** (-5.61)*** Sent_shock (-0.67) Earnings Volatility 1 16

17 T a b l e II C o e f f i c i e n t s C a l c u l a t e d w i t h U n i - v a r i a b l e O L S R e g r e s s i o n T a b l e I I r e p o r t s t h e r e s u l t o f u n i v a r i a t e O L S r e g r e s s i o n b e t w e e n e a c h d e p e n d e n t v a r i a b l e a n d e a c h i n d e p e n d e n t v a r i a b l e. O u r s a m p l e c o n t a i n s e a r n i n g s a n n o u n c e m e n t s d a t a f r o m t o a v a i l a b l e o n E s t i m i z e, h a v e n e c e s s a r y d a t e a n d t i c k e r s i n C R S P a n d i S e n t i u m t o c a l c u l a t e r e l a t e d v a r i a b l e s. D e p e n d e n t v a r i a b l e s i n c l u d e f o r e c a s t e r r o r ( F E R R ) a n d E a r n i n g s S u r p r i s e. F E R R i s d e f i n e d a s s c a l e d e a r n i n g s f o r e c a s t c o n s e n s u s a c t u a l e a r n i n g s b y u s i n g t h e s t o c k p r i c e o n t h e a n n o u n c e m e n t d a y. E a r n i n g s S u r p r i s e i s t h e ( a c t u a l e a r n i n g s - e a r n i n g s f o r e c a s t c o n s e n s u s ) s c a l e d b y u s i n g n o n z e r o f o r e c a s t c o n s e n s u s. T h e i n d e p e n d e n t v a r i a b l e s a r e l i s t e d i n t h e l e f t c o l u m n. T r a c k r e c o r d i s t h e h i s t o r i c a l f o r e c a s t e r r o r o f E s t i m i z e a n a l y s t s w h o m a d e t h e s e e a r n i n g s f o r e c a s t s f o r t h e s p e c i f i c a n n o u n c e m e n t e v e n t. D i f f i c u l t y i s t h e s t a n d a r d d e v i a t i o n o f h i s t o r i c a l e a r n i n g s f o r e c a s t e r r o r o f t h e s a m e c o m p a n y m a k i n g t h e e a r n i n g s a n n o u n c e m e n t. C o v e r a g e i s t h e n u m b e r o f i n d i v i d u a l s w h o m a k e e a r n i n g s f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t o n E s t i m i z e, t h i s n u m b e r i s t a k e n a s n a t u r a l l o g a r i t h m a f t e r p l u s o n e. D a t e t o r e l e a s e i s t h e n u m b e r o f d a y s f r o m t h e d a y o f m a k i n g e a r n i n g s f o r e c a s t t o t h e a c t u a l a n n o u n c e m e n t d a y. F o r m o s t a n n o u n c e m e n t s, t h e r e a r e m o r e t h a n o n e f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t, d a t e t o r e l e a s e i s c a l c u l a t e d a s t h e a v e r a g e o f a l l t h e s e f o r e c a s t s. B i a s i s t h e e a r n i n g s c o n s e n s u s i t s e l f. E a r n i n g s v o l a t i l i t y i s s t a n d a r d d e v i a t i o n o f q u a r t e r l y e a r n i n g s o f t h e s a m e c o m p a n y i n t h e p a s t o n e y e a r b e f o r e t h e a n n o u n c e m e n t. S e n t _ 1, S e n t _ 2, S e n t _ 3, S e n t _ w a r e a v e r a g e t w e e t s e n t i m e n t a b o u t t h e c o m p a n y i n 1 d a y, 2 d a y s, 3 d a y s, 1 w e e k p r i o r t h e a n n o u n c e m e n t d a y, r e s p e c t i v e l y. S e n t _ s h o c k i s t h e d i f f e r e n c e b e t w e e n S e n t _ 1 a n d S e n t _ w. H o r i z o n i s t h e c o e f f i c i e n t o f e a c h i n d e p e n d e n t v a r i a b l e s. H e t e r o s k e d a s t i c i t y a d j u s t e d t v a l u e s a r e r e p o r t e d i n p a r e n t h e s i s. A l l d e p e n d e n t v a r i a b l e s a n d i n d e p e n d e n t v a r i a b l e s a r e c u r t a i l e d a t 1 % a n d 9 9 % q u a n t i l e t o e x c l u d e o u t l i e r s a n d t h e n c a l c u l a t e d a s a b s o l u t e v a l u e. F E R R h a s b e e n m u l t i p l i e d b y * * *, **, * r e p r e s e n t s i g n i f i c a n c e l e v e l o f 1 %, 5 %, a n d 1 0 % r e s p e c t i v e l y. F E R R T r a c k R e c o r d ( ) * * * D i f f i c u l t y ( ) * * * C o v e r a g e ( ) * * * D a t e t o R e l e a s e ( ) * * * B i a s ( ) * * * E a r n i n g s V o l a t i l i t y ( ) * * * S e n t _ ( ) S e n t _ ( ) * * S e n t _ ( ) * * S e n t _ w ( ) * * S e n t _ s h o c k ( ) E a r n i n g s S u r p r i s e ( ) * * * ( ) * * * ( ) * * * ( ) * * * ( ) * * * ( ) * * * ( ) ( ) ( ) ( ) ( ) * * 17

18 T a b l e I I I M u l t i - v a r i a b l e R e g r e s s i o n T a b l e I I I r e p o r t s t h e r e s u l t o f m u l t i - v a r i a b l e r e g r e s s i o n b e t w e e n e a c h d e p e n d e n t v a r i a b l e a n d e a c h i n d e p e n d e n t v a r i a b l e. O u r s a m p l e c o n t a i n s e a r n i n g s a n n o u n c e m e n t s d a t a f r o m t o a v a i l a b l e o n E s t i m i z e, h a v e n e c e s s a r y d a t e a n d t i c k e r s i n C R S P a n d i S e n t i u m t o c a l c u l a t e r e l a t e d v a r i a b l e s. P a n e l A p r e s e n t t h e r e g r e s s i o n r e s u l t b e t w e e n d e p e n d e n t v a r i a b l e s a n d t r a d i t i o n a l p r e - a n n o u n c e m e n t v a r i a b l e s. Dependent variables a r e f o r e c a s t e r r o r ( F E R R ) a n d E a r n i n g s S u r p r i s e. F E R R i s d e f i n e d a s s c a l e d e a r n i n g s f o r e c a s t c o n s e n s u s a c t u a l e a r n i n g s b y u s i n g t h e s t o c k p r i c e o n t h e a n n o u n c e m e n t d a y. E a r n i n g s S u r p r i s e i s t h e ( a c t u a l e a r n i n g s - e a r n i n g s f o r e c a s t c o n s e n s u s ) s c a l e d b y u s i n g n o n z e r o f o r e c a s t c o n s e n s u s. T r a d i t i o n a l p r e - a n n o u n c e m e n t v a r i a b l e s i n c l u d e T r a c k R e c o r d, D i f f i c u l t y o f F o r e c a s t i n g, C o v e r a g e, D a t e t o R e l e a s e, B i a s, a n d E a r n i n g s V o l a t i l i t y. T r a c k r e c o r d i s t h e h i s t o r i c a l f o r e c a s t e r r o r o f E s t i m i z e a n a l y s t s w h o m a d e t h e s e e a r n i n g s f o r e c a s t s f o r t h e s p e c i f i c a n n o u n c e m e n t e v e n t. D i f f i c u l t y i s t h e s t a n d a r d d e v i a t i o n o f h i s t o r i c a l e a r n i n g s f o r e c a s t e r r o r o f t h e s a m e c o m p a n y m a k i n g t h e e a r n i n g s a n n o u n c e m e n t. C o v e r a g e i s t h e n u m b e r o f i n d i v i d u a l s w h o m a k e e a r n i n g s f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t o n E s t i m i z e, t h i s n u m b e r i s t a k e n a s n a t u r a l l o g a r i t h m a f t e r p l u s o n e. D a t e t o r e l e a s e i s t h e n u m b e r o f d a y s f r o m t h e d a y o f m a k i n g e a r n i n g s f o r e c a s t t o t h e a c t u a l a n n o u n c e m e n t d a y. F o r m o s t a n n o u n c e m e n t s, t h e r e a r e m o r e t h a n o n e f o r e c a s t a b o u t a s p e c i f i c e a r n i n g s a n n o u n c e m e n t, d a t e t o r e l e a s e i s c a l c u l a t e d a s t h e a v e r a g e o f a l l t h e s e f o r e c a s t s. B i a s i s t h e e a r n i n g s c o n s e n s u s i t s e l f. E a r n i n g s v o l a t i l i t y i s s t a n d a r d d e v i a t i o n o f q u a r t e r l y e a r n i n g s o f t h e s a m e c o m p a n y i n t h e p a s t o n e y e a r b e f o r e t h e a n n o u n c e m e n t. P a n e l B p r e s e n t s t h e r e s u l t o f l i n e a r r e g r e s s i o n t e s t b e t w e e n d e p e n d e n t v a r i a b l e s a n d s e n t i m e n t v a r i a b l e s w h i l e c o n t r o l l i n g a l l t r a d i t i o n a l v a r i a b l e s. S e n t _ 1, S e n t _ 2, S e n t _ 3, S e n t _ w a r e a v e r a g e t w e e t s e n t i m e n t a b o u t t h e c o m p a n y i n 1 d a y, 2 d a y s, 3 d a y s, 1 w e e k p r i o r t h e a n n o u n c e m e n t d a y, r e s p e c t i v e l y. S e n t _ s h o c k i s t h e d i f f e r e n c e b e t w e e n S e n t _ 1 a n d S e n t _ w. U n i v a r i a t e r e g r e s s i o n r e s u l t a r e l i s t e d f o r c o m p a r a t i v e p u r p o s e. I n b o t h P a n e l A a n d P a n e l B, h o r i z o n i s t h e c o e f f i c i e n t o f t r a d i t i o n a l v a r i a b l e s f o r e a c h d e p e n d e n t v a r i a b l e s. H e t e r o s k e d a s t i c i t y a d j u s t e d t v a l u e s a r e r e p o r t e d i n p a r e n t h e s i s. A l l d e p e n d e n t v a r i a b l e s a n d i n d e p e n d e n t v a r i a b l e s a r e c u r t a i l e d a t 1 % a n d 9 9 % q u a n t i l e t o e x c l u d e o u t l i e r s a n d t h e n c a l c u l a t e d a s a b s o l u t e v a l u e. F E R R h a s b e e n m u l t i p l i e d b y * * *, * *, * r e p r e s e n t s i g n i f i c a n c e l e v e l o f 1 %, 5 %, a n d 1 0 % r e s p e c t i v e l y. Panel A Regression of Traditional Variables I n t e r c e p t T r a c k D i f f i c u l t y C o v e r a g e D a t e t o B i a s E a r n i n g s R e c o r d R e l e a s e V o l a t i l i t y F E R R ( ) * * * ( ) ( ) * * * ( ) * * * ( ) ( ) * * * ( ) * * * E a r n i n g s ( ) * * * S u r p r i s e ( ) * ( ) * * * ( ) * * * ( ) ( ) * * * ( ) * * * Panel B Regression of Sentiment Variables F E R R E a r n i n g s S u r p r i s e Uni v a r i a t e C o n t r o l l e d Uni v a r i a t e C o n t r o l l e d S e n t _ ( ) ( ) * * ( ) ( ) * * S e n t _ ( ) * * ( ) * ( ) ( ) S e n t _ ( ) * * ( ) * * * ( ) ( ) * S e n t _ w ( ) * * ( ) * * * ( ) ( ) * * * S e n t _ s h o c k ( ) ( ) ( ) * * ( ) 18

19 T a b l e I V P o s t - A n n o u n c e m e n t E x c e s s R i s k - a d j u s t e d R e t u r n s R e g r e s s i o n s T a b l e I V r e p o r t s t h e s i m p l e a n d m u l t i - v a r i a b l e r e g r e s s i o n s b e t w e e n e a c h d e p e n d e n t v a r i a b l e e m p l o y i n g t h e p o s t - a n n o u n c e m e n t r i s k - a d j u s t e d e x c e s s r e t u r n s o n a n d e a c h i n d e p e n d e n t v a r i a b l e. O u r s a m p l e c o n t a i n s e a r n i n g s a n n o u n c e m e n t s d a t a f r o m t o a v a i l a b l e o n E s t i m i z e, h a v e n e c e s s a r y d a t e a n d t i c k e r s i n C R S P a n d i S e n t i u m t o c a l c u l a t e r e l a t e d v a r i a b l e s. P a n e l A p r e s e n t s t h e r e g r e s s i o n r e s u l t b e t w e e n d e p e n d e n t v a r i a b l e s f o r t h e + 1 d a y r i s k - a d j u s t e d e x c e s s r e t u r n s. P a n e l B p r e s e n t s t h e r e g r e s s i o n f o r t h e + 2 d a y r i s k - a d j u s t e d e x c e s s r e t u r n s a n d P a n e l C p r e s e n t s t h e r e g r e s s i o n r e s u l t s f o r t h e + 3 d a y s r i s k - a d j u s t e d e x c e s s r e t u r n s. W e d e f i n e E a r n i n g s S u r p r i s e i s t h e a c t u a l e a r n i n g s - e a r n i n g s f o r e c a s t c o n s e n s u s. S e n t _ 1 i s t h e a v e r a g e t w e e t s e n t i m e n t a b o u t t h e c o m p a n y i n 1 d a y p r i o r t h e a n n o u n c e m e n t d a y. A l l d e p e n d e n t v a r i a b l e s a n d i n d e p e n d e n t v a r i a b l e s a r e c u r t a i l e d a t 1 % a n d 9 9 % q u a n t i l e t o e x c l u d e o u t l i e r s a n d t h e n c a l c u l a t e d a s a b s o l u t e v a l u e. t v a l u e s a r e r e p o r t e d i n p a r e n t h e s i s. E x c e s s R i s k - a d j u s t e d r e t u r n s h a s b e e n m u l t i p l i e d b y * * *, * *, * r e p r e s e n t s i g n i f i c a n c e l e v e l o f 1 %, 5 %, a n d 1 0 % r e s p e c t i v e l y. Panel A: +1 Day Risk-Adjusted Excess Returns Intercept Sent_1 Earnings Surprise Nos. Obs. R-squared , % (-1.88) (2.26**) , % (-1.89) (2.24**) (1.26) Panel B: +2 Days Cumulative Risk-Adjusted Excess Returns Intercept Sent_1 Earnings Surprise Nos. Obs. R-squared , % (-1.17) (1.91*) , % (-1.19) (1.88*) (1.84*) Panel C: +3 Days Cumulative Risk-Adjusted Excess Returns Intercept Sent_1 Earnings Surprise Nos. Obs. R-squared , % (-1.63) (1.71*) , % (-1.65*) (1.69*) (1.67*) Panel D: +4 Days Cumulative Risk-Adjusted Excess Returns Intercept Sent_1 Earnings Surprise Nos. Obs. R-squared , % (-1.06) (1.76*) , % (-1.08) (1.73*) (1.71*) Panel E: +5 Days Cumulative Risk-Adjusted Excess Returns Intercept Sent_1 Earnings Surprise Nos. Obs. R-squared , % (-1.11) , % (-1.13) (1.12) (1.74*) 19

20 A p p e n d i x I A t t h e e n d o f e a c h f i s c a l y e a r l o o k b a c k o v e r t h e p r i o r f o u r q u a r t e r s ( i n c l u d i n g t h e c u r r e n t q u a r t e r ) a n d e m p l o y t h e d a t a f o r a l l f o u r q u a r t e r s o f t h a t f i s c a l y e a r t h e n r o l l t h i s p r o c e d u r e e a c h q u a r t e r. B e l o w t a b l e c o n t a i n s t h e f i s c a l q u a r t e r - e n d, n u m b e r o f e a r n i n g s e m p l o y e d a t e a c h r o l l i n g p e r i o d, t h e E s t i m i z e % a c c u r a c y, a n d t h e W a l l S t r e e t % a c c u r a c y. A c c u r a c y i s c o m p u t e d a s t h e a b s o l u t e v a l u e o f t h e d i f f e r e n c e b e t w e e n t h e c o n s e n s u s f o r e c a s t a n d t h e a c t u a l e a r n i n g s a s r e p o r t e d o n t h e e a r n i n g s d a y. I f E s t i m i z e h a s l o w e r a b s o l u t e e r r o r w e c l a s s i f y t h a t e a r n i n g s e v e n t w i t h E s t i m i z e a s m o r e a c c u r a t e t h a n W a l l S t r e e t. % a r e b a s e d o n c o u n t i n g t h e n u m b e r o f t i m e s E s t i m i z e w a s m o r e a c c u r a t e t h a n W a l l S t r e e t d i v i d e d b y t h e t o t a l n u m b e r o f e a r n i n g s i n t h a t r o l l i n g 1 - f i s c a l y e a r. W a l l S t r e e t % a c c u r a c y i s e q u a l t o ( 1 E s t i m i z e % a c c u r a c y ). Fiscal Yr (1-yr rolling) Number of Earnings Estimize % more accurate Wall Street % more accurate 2012Q % 49.3% 2012Q3 1, % 49.8% 2012Q4 2, % 46.5% 2013Q1 3, % 46.2% 2013Q2 4, % 44.6% 2013Q3 4, % 43.2% 2013Q4 4, % 42.7% 2014Q1 4, % 42.0% 2014Q2 4, % 41.9% 2014Q3 5, % 42.3% 2014Q4 4, % 42.6% 2015Q1 3, % 42.5% 2015Q2 1, % 42.9% 20

21 A p p e n d i x I I A t t h e e n d o f e a c h f i s c a l y e a r l o o k b a c k o v e r t h e p r i o r f o u r q u a r t e r s ( i n c l u d i n g t h e c u r r e n t q u a r t e r ) a n d e m p l o y t h e d a t a f o r a l l f o u r q u a r t e r s o f t h a t f i s c a l y e a r t h e n r o l l t h i s p r o c e d u r e e a c h q u a r t e r. B e l o w t a b l e c o n t a i n s t h e f i s c a l q u a r t e r - e n d, n u m b e r o f e a r n i n g s e m p l o y e d a t e a c h r o l l i n g p e r i o d, t h e l o w e r, h i g h e r, a n d e q u a l p e r c e n t a g e s f o r E s t i m i z e s c o n s e n s u s a n d t h e n t h e l o w e r, h i g h e r, a n d e q u a l p e r c e n t a g e s f o r W a l l S t r e e t s c o n s e n s u s, w i t h r e s p e c t t o a c t u a l E P S. L o w e r / h i g h e r / e q u a l r e p r e s e n t t h a t t h e c o n s e n s u s E P S w a s l o w e r t h a n / h i g h e r t h a n / e q u a l t o t h e a c t u a l E P S. Estimize's Consensus Estimates EPS Wall Street's Consensus Estimates EPS Fiscal Yr (1-yr rolling) Number of Earnings lower higher equal lower higher equal 2012Q % 41.1% 5.1% 68.2% 22.0% 9.8% 2012Q3 1, % 41.1% 4.7% 66.8% 22.0% 11.1% 2012Q4 2, % 43.5% 4.4% 65.1% 26.1% 8.7% 2013Q1 3, % 44.7% 4.1% 64.6% 28.5% 6.9% 2013Q2 4, % 43.6% 4.5% 65.3% 29.1% 5.6% 2013Q3 4, % 43.2% 4.7% 66.4% 29.6% 4.0% 2013Q4 4, % 42.2% 4.6% 67.8% 29.4% 2.8% 2014Q1 4, % 41.6% 4.6% 68.1% 29.6% 2.3% 2014Q2 4, % 42.0% 4.4% 67.3% 30.7% 2.0% 2014Q3 5, % 42.1% 4.4% 67.1% 31.0% 1.9% 2014Q4 4, % 41.8% 4.5% 66.6% 31.3% 2.1% 2015Q1 3, % 41.3% 4.7% 66.8% 31.0% 2.2% 2015Q2 1, % 41.5% 4.6% 67.7% 29.8% 2.5% 21