ACCOUNTING WORKSHOP

Do Weather-Induced Moods Affect the Processing of Earnings News?

By

Ed deHaan* Stanford University

Joshua Madsen

University of Minnesota

Joseph D. Piotroski Stanford University

Thursday, Nov. 19th, 2015 1:20 – 2:50 p.m.

Room C06 *Speaker Paper Available in Room 447

Do Weather-Induced Moods Affect the Processing of Earnings News?

Ed deHaan* Stanford University

Joshua Madsen

University of Minnesota

Joseph D. Piotroski Stanford University

November 15, 2015

Preliminary Draft

We exploit a dataset of equity analysts’ locations and work output to investigate how unpleasant weather affects capital market participants’ responses to information events. We draw from psychology to develop a new prediction that weather-induced moods reduce market participants’ activity levels. We also investigate an existing prediction in financial economics that weather-induced negative moods induce pessimism. Within-firm-quarter analyses find support for both predictions that do not appear to be driven by physically disruptive severe weather. Additional price association tests indicate that our new prediction - that unpleasant weather reduces activity - potentially delays equilibrium price adjustments following earnings announcements. Our study contributes to financial economics by providing new evidence both extending and reconfirming predictions of a relation between unpleasant weather and market activities. We contribute to psychology and economics more broadly by providing large-scale evidence of an impact of weather-induced mood on labor productivity.

* Corresponding author: edehaan@stanford.edu. We thank David Hirshleifer, Ryan McDonough, Siew Hong Teoh, Joanna Wu and workshop participants at LBS, Michigan, MIT, Stanford, Washington University, and Wharton for valuable comments and suggestions. We are sincerely grateful to Sue Barnstable, Mae Bethel, Su Elliot, John Johnson, and Stanford GSB CIRCLE for research support. Financial support was provided by the Stanford University Graduate School of Business and the University of Minnesota Carlson School of Management.

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“My work has been murky today because the weather was murky.” (attributed to Voltaire, 1694-1778) 1 “I have been amazed at the faulty deductions and misconceptions which were made in damp, foggy weather, or on days in which the air was charged with electricity and thunder storms were impending… The psychology of the weather should be a most pregnant new land for study.” (Crothers [1894]) 1. Introduction

Research in psychology and casual observation suggests that mood has significant effects on an

individual’s behavior and judgments. In particular, mild depression (a.k.a., negative mood) is

characterized by symptoms including difficulty concentrating, sadness, irritability, and decreased activity

levels.2 The conjecture that weather-induced mood (e.g., “rainy day blues”) impacts economic activities

has been investigated as early as Lemon [1894], but empirical evidence of such a link is mixed and often

relies on small sample sizes.3 In this paper we exploit a dataset containing the locations and work output

of 5,456 sell-side equity analysts over eight years to develop and test predictions about how unpleasant

weather (i.e., cloudy, rainy, windy weather), through its effect on mood, impacts professional financial

market participants’ responses to important information events.

In particular, we examine how weather-induced moods affect geographically dispersed analysts’

simultaneous responses to firms’ quarterly earnings announcements. Earnings announcements, which

cause analysts to update their beliefs about firm value, have several features that make for a powerful

setting to study the consequences of weather-induced mood on work quality. First, earnings

announcements are well-defined events, during which analysts have salient and relatively homogenous

incentives to act in a timely manner (Abdel-Khalik and Espejo [1978]; Stickel [1989]). Thus, earnings

announcements are a setting where analysts across the U.S. simultaneously perform a similar function

1 The date of the quote attributed to Voltaire’s letters is unknown but was referenced as early as 1887 by Thomas North in “Storm-effects on mentality” (The North American Review, volume 144, p427). 2 This study focuses on mild depression, which is often characterized as the type of short-term negative mood that most people experience from time to time. The link between mild depression and the aforementioned symptoms is widely accepted; e.g., see the U.S. National Institute of Health website on depression: http://www.nlm.nih.gov/medlineplus/ency/article/003213.htm. Major depression or simply “depression” is a mood disorder in which these same symptoms interfere with daily life for a minimum of two weeks (APA [1994]). 3 See Cunningham [1979], Sanders and Brizzolara [1982], Schwarz and Clore [1983], Howarth and Hoffman [1984], Clark and Watson [1988], and Keller, Fredrickson, Ybarra, Côté, Johnson, Mikels, Conway, and Wager [2005]).

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using similar inputs. Second, analysts’ work output in response to earnings announcements is quantifiable

along multiple dimensions, including the accuracy and directional biases in analysts’ quantitative

forecasts, as well as the speed and efficiency with which analysts perform their tasks.

We draw from research in psychology, economics, and finance to develop two predictions about

how unpleasant weather affects analysts’ reactions to earnings announcements. Our predictions build on

evidence that the onset of negative moods is a normal part of human life. Although the causes of negative

moods are varied, one commonly cited determinant is unpleasant weather conditions (e.g., Howarth and

Hoffman [1984]). Our first prediction builds on evidence in psychology that negative moods are

frequently characterized by a lack of concentration, lethargy, apathy, and reduced cognitive capacity.4

Collectively, we refer to these symptoms as being reflective of “decreased activity levels.” Based on the

psychology literature, we predict that weather-induced negative moods impair individuals’ activity levels,

causing slower and/or muted reactions to earnings announcements. Empirically, we predict that analysts

experiencing unpleasant weather are less likely and/or slower to issue EPS forecasts, investment

recommendations, and stock price targets following an earnings announcement, relative to simultaneous

responses by analysts experiencing more pleasant weather. A plausible opposite prediction is that

unpleasant weather causes an intertemporal substitution of work for leisure, such that analysts are more

productive when the weather is bad (Connolly [2008]).

Our second prediction builds on evidence that negative moods are also associated with sadness

and irritability that can induce pessimistic thinking (Wright and Bower [1992]). Existing studies in

financial economics find evidence that unpleasant weather is associated with investor pessimism, as

reflected daily stock index returns (Saunders [1993]; Hirshleifer and Shumway [2003]), firm-level returns

(Chang, Chen, Chou, and Lin [2008]) and sophisticated market participants’ trading behavior (Goetzmann,

4 For example, The American Psychiatric Association’s criteria of a depressive episode, as listed in DSM-IV, include loss of energy (criteria 6) and “diminished ability to think or concentrate, or indecisiveness” (criteria 8). DSM-IV describes these symptoms in context: “Many individuals report impaired ability to think, concentrate, or make decisions. They may appear easily distracted or complain of memory difficulties. Those in intellectually demanding academic or occupational pursuits are often unable to function adequately even when they have mild concentration problems” (page 322). Most people experience these symptoms on occasion as part of transitory depressive episodes.

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Kim, Kumar, and Wang [2015]; Dolvin, Pyles, and Wu [2009]).5 We re-examine the prediction that

unpleasant weather induces pessimism in a new context that requires the timely processing of new

information by market professionals, using a research design which exploits the simultaneous activities of

analysts in geographically disperse locations. We predict that analysts experiencing unpleasant weather

issue more negatively biased EPS and target price forecasts than analysts experiencing pleasant weather.

We test these predictions using a sample of 635,826 firm-quarter-analyst observations spanning

1997 – 2004 and containing 5,456 unique analysts in 139 cities.6 We first document substantial

geographic variation in the location of a firm’s analysts: the average firm-quarter includes 7.6 analysts in

3.8 different cities, providing necessary exogenous variation in weather conditions during these earnings

announcements. We estimate the effect of weather on analyst behavior using OLS regressions including

firm-quarter, analyst, and city fixed effects, which implicitly control for all characteristics of the firm and

market at the time of the earnings announcement (e.g., earnings surprise and competing information

events), fixed analyst characteristics (e.g., education), and fixed location-specific characteristics (e.g.,

labor market competition). We match analyst locations to local weather station data to construct our

Unpleasant_Weather treatment variable, defined as the principal component of cloud cover, wind, and

rain. We also include controls for physically disruptive severe weather events to increase confidence that

our results are due to a psychological mood effect.7 By focusing on a common information event (as

opposed to general associations between weather and market activity), we create research designs that

exploit both cross-sectional and inter-temporal variation in our weather data, allowing us to potentially

better identify causal effects of weather-induced mood on market participants’ decision-making (Roberts

and Whited [2013]; Bertrand, Duflo, and Mullainathan [2004]).

5 Building on Hirshleifer and Shumway [2003], Bushee and Friedman [2015] find that the sensitivity of returns to weather-induced mood varies with the quality of a country’s disclosure standards and Shon and Zhou [2009] find weak evidence that returns to earnings announcements are positively biased on sunny days in NYC. 6 As discussed in Section 2, the sample period is limited by availability of analyst names and location data. 7 Related literature also finds that physical impediments caused by severe weather (e.g., blizzards) disrupts local dissemination of information (Engelberg and Parsons [2011]) and causes lower trading volume in local stocks (Loughran and Schultz [2004]). Our discussion focuses on the emotional rather than physical affects of weather.

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Regarding our activity prediction, the unconditional likelihood of an analyst updating his annual

forecast following a quarterly earnings announcement is 47%, which indicates that responding to an

earnings announcement is an important but noncompulsory aspect of an analyst’s job. This is a useful

feature in our setting because we would unlikely find a behavioral effect of weather-induced mood for a

nondiscretionary task. Consistent with our predictions, we find that analysts experiencing unpleasant

weather are slower to revise their EPS forecasts and are less likely to update their reports within three

days following an earnings announcement, as compared to analysts responding to the same earnings

announcement but experiencing pleasant weather. For example, moving from the 5th to 95th percentiles of

our Unpleasant_Weather variable results in 9.4% to 17.9% relative decreases in the likelihood of an

analyst releasing an EPS forecast, buy/hold/sell recommendation, or target price recommendation.

Robustness tests indicate that these muted reactions are roughly symmetric for positive and negative

earnings surprises, indicating that inactivity caused by weather-induced moods is a separate effect from

weather-induced pessimism.

Regarding our pessimism prediction, we find that long-term forecasts and target prices issued in

response to earnings announcements by analysts experiencing unpleasant weather are more negative than

similar forecasts issued by analysts experiencing pleasant weather. For example, moving from the 5th to

95th percentile of Unpleasant_Weather is associated with a negative bias in two-year-ahead EPS forecasts

of roughly $0.013 per share and a negative bias in analysts’ twelve-month price targets of roughly 1.2%

of current price.

Prior research suggests that equity analysts’ investment-related decision-making is representative

of sophisticated market participants more broadly (Bradshaw [2011]; Richardson, Tuna, and Wysocki

[2010]) and that analysts’ forecasts directly affect stock prices (Givoly and Lakonishok [1979]; Lys and

Sohn [1990]; Francis and Soffer [1997]; Healy and Palepu [2001]).8 Thus, evidence that unpleasant

8 As noted by Bradshaw [2011], “analysts are a good proxy for beliefs held by investors in general, so examining properties of analyst data provides insight into how investors in general utilize and process accounting information” (page 10). Further, in their review of anomalies research, Richardson, et al. [2010] remark that equity analysts are “a reasonable proxy for the overall behavior of capital market participants,” and that it is useful to support apparent

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weather affects analysts’ behavior suggests that weather also impacts other sophisticated market

participants’ moods and activities. A logical question is thus whether the effects we observe among

analysts aggregate to affect the equilibrium stock market response to earnings announcements. As

mentioned, prior research finds evidence consistent with weather-induced pessimism affecting

equilibrium market pricing. We complement this stream of research by providing an initial examination

of the effects of weather-induced inactivity on the market’s price response to new information.

We predict that inactivity due to weather-induced mood causes investors to abstain from trading,

delay their trading activity, or to trade on an incomplete information set during the earnings

announcement window (e.g., due to a mood-affected investor missing the earnings signal and/or to a

reduced flow of timely supplemental information upon which to base their trades). These market pricing

predictions are closely related to evidence that reduced attention due to heightened information flows

(Hirshleifer, Lim, and Teoh [2009]) or temporal preferences for leisure (DellaVigna and Pollet [2009])

can delay equilibrium price responses to earnings announcements, resulting in observably smaller short-

window price responses (i.e., earnings response coefficients, or “ERCs”) and larger pricing drift (i.e.,

post-earnings announcement drift, or “PEAD”).9 We investigate the market pricing effects of mood-

related inactivity using a sample of 193,200 earnings announcements from 1990 through 2013. We follow

prior research in examining the effects of contemporaneous weather in NYC, which is the location of the

highest concentration of sophisticated capital market participants in the U.S (Saunders [1993]; Hirshleifer

and Shumway [2003]). Consistent with our predictions, we document smaller ERCs and larger PEAD

when the weather in NYC is unpleasant. We also find that trading volumes around earnings

announcements are lower during unpleasant weather conditions, that unpleasant weather-induced PEAD

is more concentrated around subsequent earnings announcements, and that reduced ERCs are present

market anomalies by investigating whether similar phenomenon are observable in analysts’ earnings and price forecasts. (p. 423) 9 See also Hong and Stein [1999]; Hirshleifer and Teoh [2003]; Cohen and Frazzini [2008]; Hirshleifer, Lim, and Teoh [2011]; deHaan, Shevlin, and Thornock [2015]; Madsen [2015], Niessner [2015]. As discussed by Hirshleifer and Teoh [2003], even if attentive investors can identify mispricing, mispricing can persist in equilibrium if those attentive investors are limited in the amount of risk they are willing to bear.

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following both positive and negative earnings announcements. Although these pricing tests lack the

identification strengths of our analyst-based tests, our results suggest that inactivity arising from weather-

induced negative mood has potential market implications, and that these effects are distinct from the

pricing behavior associated with weather-induced pessimism.

Together, our results suggest that unpleasant weather perturbs the processing of new information

and distorts the flow of supplemental information (e.g., analyst reports) after earnings events. This paper

offers several contributions. First, we contribute to the psychology and economics literatures by providing

large-scale empirical evidence on the effects of weather-induced mood on individuals’ professional output

and economic decision-making. Prior evidence on the economic effects of weather-induced mood on

individuals is mixed and often based on small samples, limited sample periods, and/or self-reported

emotional conditions.10 To our knowledge, our study is unique in using large-sample, archival data to

investigate the effects of weather-induced lethargy, apathy, and reduced cognitive capacity (i.e.,

“inactivity”) on individuals’ professional work product. Further, we provide compelling evidence to

reinforce existing studies of weather-induced pessimism on individuals’ decision-making (Goetzmann, et

al. [2015]).

Our second contribution is to introduce an “inactivity” prediction to the financial economics

literature. Our finding that weather-induced negative moods impede market participants’ responses to

important information events is consistent with behavioral biases causing muted or delayed responses to

earnings announcements. These findings are relevant to research on analysts in particular (e.g., see a

review by Bradshaw [2011]) as well as studies of sophisticated market participants more broadly. Further,

our stock pricing tests complement prior research finding that investor inattention affects market

outcomes, with local weather conditions serving as an additional exogenous channel by which “attention”

10 In psychology, Smith and Bradley [1994], Coleman and Schaefer [1990], Markham and Markham [2005], Lamare [2013], Busse, Pope, Pope, and Silva-Risso [2014]), and Tietjen and Kripke [1994] provide evidence on various economic consequences of weather-induced moods, while Watson [2000] and Keller, et al. [2005] find no consistent main effects of weather on mood. A few notable findings in the economics literature are that projection biases cause individuals to over-weight current weather in retail purchasing decisions (Busse, et al. [2014]; Conlin, O'Donoghue, and Vogelsang [2007]), and that weather-induced pessimism/optimism impact institutional investors’ stock purchases (Goetzmann, et al. [2015]) and art auction prices (De Silva, Pownall, and Wolk [2012]).

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varies (DellaVigna and Pollet [2009]; Hirshleifer, et al. [2009]). We view the potential relation between

weather, mood, inactivity, and market pricing as an interesting avenue for future research.

Finally, we inform the existing debate in financial economics about the influence of weather-

induced pessimism on stock market behavior. Despite ample psychology evidence justifying a link

between weather, pessimism, and stock prices, the existing finance studies remain controversial because

of their incongruence with rational pricing theory and concerns of spurious correlations or data mining

(Novy-Marx [2014]). Our evidence that weather-induced pessimism impacts analyst forecasts following

earnings announcement provides credible new evidence in a new setting that weather affects professional

market participants, supporting the notion that these effects plausibly aggregate to the market level.

2. Analyst data

The following subsections discuss our data, sample construction, and measures of analyst output

and local weather conditions. For all variables, the subscript i identifies the analyst, subscript j identifies

the firm, and subscript q identifies the fiscal quarter for which firm j recently announced earnings. All

variables are winsorized at the 1st and 99th percentile, and are further defined in the Appendix.

2.1 Sample construction

Table 1, Panel A details our sample construction for the analyst tests. First, we intersect

Compustat, IBES, and CRSP and, to maximize sample accuracy, retain only U.S. firm-quarter

observations with the same earnings announcement dates in both Compustat and IBES (deHaan, et al.

[2015]). Availability of the IBES Translation File, which is needed to identify analysts’ names, limits our

sample period to 1997 through 2004.11 This process yields 141,712 firm-quarter observations. Next, we

11 Several attempts to purchase additional Translation File(s) (or similar data) from IBES were unsuccessful. Further, the Nelson’s Directories necessary to obtain analyst location information ceased publication in 2008 (with location data current through 2007), which further restricts our ability to extend the sample. We note that, due to what appears to be an oversight by IBES, the IBES target price file on WRDS no longer masks analysts’ names. We do not use these names because: i) using those names would bias our sample towards the subset of analysts that issue

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identify the analysts following each firm as those that both: (i) issue at least one annual forecast for the

sample firm within the one-year period preceding the earnings announcement; and (ii) issue at least one

annual forecast for the firm within one-year following the earnings announcement. This approach yields a

total of 873,362 firm-quarter-analyst observations. We eliminate 45,875 observations for analysts that are

missing from the IBES Translation File. Due to the high cost of collecting data on analyst locations, we

reduce sample size by dropping analysts that cover fewer than two firms or four firm-quarters over the

sample period.12 We also eliminate firm-quarters with fewer than two analysts following the firm, which

is a requirement for our within-firm-quarter model discussed below.

We obtain location data for 82.4% of the remaining firm-quarter-analyst observations from the

annual Nelson’s Directories. Finally, we drop 18,609 observations for analysts outside of the U.S. and

9,090 observations for which weather data are unavailable, leaving a maximum sample of 635,826 firm-

quarter-analyst observations for analysis. These data relate to 94,469 unique firm-quarters, 5,456

individual analysts, and 139 unique cities. The average firm-quarter unit of analysis includes 7.6 analysts

in 3.8 unique cities after excluding firm-quarters with only a single city (which are also dropped in all

tests below). Sample sizes are further reduced in subsequent tests depending on availability of the

dependent variables.

2.2 Analyst measurements

We examine the effect of local weather conditions on equity analyst behaviors within the three

trading days starting with firms’ earnings announcements (i.e., trading days [0, 2]). We choose an analyst

measurement window of days [0, 2] to allow analysts at least two trading days to release a report

following the earnings announcement.13 Untabulated analysis shows that, of all analyst forecasts issued

target prices; ii) it is not clear whether using those names are within our IBES license; and iii) doing so would only allow us to extend the data through 2007, when the Nelson’s Directories ceased publication. 12 We have no ex ante expectation that the excluded analysts are any more or less susceptible to unpleasant weather than other analysts. However, it is possible that our results do not generalize to such analysts. 13 deHaan, et al. [2015] find that roughly 50% of earnings announcements occur after hours. Further, many during- or after-hours announcers hold their conference call on the following day, in which case an analyst likely will not

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within 10 trade days following an earnings announcement during our sample period, days 0 through 5

contain 14.3%, 52.3%, 10.7%, 3.9%, 5.8%, and 3.9%, respectively, while days 6+ contain fewer than

3.1% each. Thus, there appears to be a natural break in the distribution of earnings forecasts between days

+2 and +3 which supports our use of a [0, 2] window.14

Our four analyst activity measures reflect whether the analyst issues an earnings forecast, stock

recommendation, or target price forecast following the firm’s earnings announcement and, if a forecast is

issued, the number of days after the earnings announcement that the analyst releases the report:

1. Make_Fcast i,j,q: An indicator variable equal to one if analyst i issues at least one annual earnings

forecast within days [0, 2] of firm j’s earnings announcement for quarter q, zero otherwise.

2. Make_Reci,j,q: An indicator variable equal to one if analyst i updates a previously-issued buy/hold/sell

recommendation within days [0, 2] of the earnings announcement, zero otherwise. To avoid

characterizing previously dropped coverage as inactivity, we require that the analyst’s last

recommendation was issued or reviewed within one year before the earnings announcement.

3. Make_Tpricei,j,q: An indicator variable equal to one if the analyst issues a target price

recommendation within days [0, 2] of the firms’ earnings announcement, zero otherwise. We again

require that the analyst has a previous target price that was issued or reviewed within one year before

the earnings announcement. We use target prices with a 12-month horizon, which comprise 98% of

all target prices on IBES in our sample period.

4. Fcast_Delayi,j,q: The log of one plus the number of weekdays between the earnings announcement

and the date analyst i provides his/her first annual EPS forecast, within days [0, 2] of the earnings

announcement.15

release a report until after the call. Thus, in practice, for roughly half of our sample, the [0, 2] window likely provides analysts up to two days to respond to the earnings announcement. Accurate earnings announcement time stamp data are unavailable for much of our sample period, so we cannot adjust for after-hours announcements. 14 Our measurement window may systematically eliminate analysts who tend to be slower to respond to earnings news. Section 3.4.4 discusses qualitatively unchanged robustness tests using a one-week measurement window. 15 Untabulated robustness tests using unlogged Fcast_Delay are qualitatively unchanged, as are untabulated tests using a binary specification where forecasts are identified as “delayed” if they occur more than one day after the earnings announcement. Throughout this paper, we use the term “qualitatively unchanged” to mean that the

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Weather-induced pessimism likely impacts analysts’ deliverables in several ways. First, we

expect that analysts are more likely to issue buy/hold/sell recommendation downgrades when the weather

is unpleasant. Second, we expect that analysts experiencing unpleasant weather issue target prices that are

biased downward compared to future price realizations. Finally, we expect that analysts experiencing

unpleasant weather will issue negatively biased one- and two-year-ahead EPS forecasts, relative to either

future earnings realizations or to the prevailing analyst consensus. Accordingly, we measure analyst

pessimism using the following variables:

1. Rec_Chgi,j,q: a trinary variable equal to one if the analyst upgrades his/her previously-outstanding

buy/hold/sell recommendation, zero if there is no change, and negative one for downgrades.

2. Fcast_Biasi,j,q,y: the difference between analyst i's first EPS forecast for year-end y less the future

realized EPS, scaled by price as of the day prior to the earnings announcement, multiplied by 100. We

retain only diluted EPS forecasts to improve comparability with the IBES actual EPS value. We

examine both one- and two-year-ahead annual forecasts, designated as y1 and y2. Negative values

represent pessimistic forecasts.

3. Fcast_Bias_v2i,j,q,y: the difference between analyst i's first EPS forecast for year-end y less the

consensus forecast value as of the previous day, scaled by price as of the day prior to the earnings

announcement, multiplied by 100. Consensus forecasts are calculated as the median of all outstanding

EPS forecasts issued or reviewed within the trailing 180 days, retaining only the most recent forecast

per analyst. Again, we retain only diluted, annual EPS forecasts for both one- and two-years horizons.

4. Tprice_Biasi,j,q – 12-month target price less the actual stock price 12 months in the future, scaled by

price as of the target price announcement date. Only populated for analysts that issue a target price

after the earnings announcement.

To the extent that unpleasant weather adversely impacts analyst activity levels, we predict lower

average Make_Fcast, Make_Rec and Make_Tprice realizations and larger average Fcast_Delay for

coefficients of interest are the same sign and remain statistically significant at the 10% level. Or, in the case of insignificant results, “qualitatively unchanged” means the results remain insignificant at the 10% level.

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analysts experiencing unpleasant weather relative to analysts responding to the same earnings

announcement, but experiencing pleasant weather. If weather-induced pessimism negatively biases

analysts’ decision-making, then we expect lower average Rec_Chg, Tprice_Bias, Fcast_Bias, and

Fcast_Bias_v2.

We also construct several analyst-related control variables. Allstar is a binary variable for

whether the analyst is voted as an “all-star analyst” in the year of the earnings announcement, as

identified in the Nelson’s Directory. Analyst_Exp is a control for analyst experience, calculated as the

logged number of years between the analyst’s first forecast in IBES and the firm’s earnings

announcement. Broker_Size is the log number of unique analyst codes in IBES associated with the

brokerage firm and that issue at least one forecast in the year of the firm’s earnings announcement.16

Rec_Before is the analyst’s outstanding buy/hold/sell recommendation from before the earnings

announcement, and Tprice_Before is the pre-announcement target price scaled by the current market price.

2.3 Weather measures

Hourly weather data for cloud coverage (ranging from 0 to 8 oktas), liquid precipitation (i.e.,

“rain,” in millimeters), and wind speed (in miles per hour) are obtained from the National Oceanic and

Atmospheric Administration (NOAA) ISD-Lite dataset.17 We do not examine temperature because it

likely has a nonlinear relation with mood (i.e., because mood is decreasing with both extreme heat and

cold), and therefore cannot be included in our principal component analysis discussed below. We do not

examine humidity and air pressure due to limited data availability. We retain only weather data during

daylight hours (6AM to 6PM), and require that non-missing data are available for a minimum of four

16 IBES analyst codes can relate to a single person or, in a small minority of cases, to a team of people. Thus, a single person can be associated with multiple codes if she issues individual forecasts as well as team forecasts. We treat each analyst code, whether for a person or team, as a separate “analyst.” This treatment is appropriate for the purposes of our analyst fixed effects, which are intended to control for innate characteristics of the analyst(s) producing the work output. However, this treatment means that Broker_Size does not measure the number of individuals working at a broker, but rather the number of individuals and teams associated with a broker (which could be substantially higher than the number of people due to permutations in team memberships). 17 ISD-Lite is a subset of data from NOAA’s Integrated Surface Data dataset and is available at http://www1.ncdc.noaa.gov/pub/data/noaa/isd-lite/. Accessed December 2014.

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hours each day. Cloud, wind, and rain data for each weather station are aggregated to the daily level by

taking averages of the hourly data.

We match each analyst observation to weather data from the closest available NOAA weather

station, not more than 50 miles away. We determine the location of each individual analyst based upon

his/her city listed in Nelson’s Directory for the year of his/her forecast.18 Distances are calculated based

on the longitude-latitude coordinates of the weather station and the central coordinates of the zip code

within the analysts’ city that has the highest population.

We measure weather conditions for each analyst observation for the time period starting with the

earnings announcement and ending with the date of the analysts’ first forecast or recommendation update

within days [0, 2] of the earnings announcement. For analysts that do not issue a recommendation, price

revision, or earnings forecast, we measure weather conditions over the full three-day window. We

calculate the average daily cloud cover, wind speed, and rainfall over the respective measurement window

for each analyst. The resultant variables Cloud, Wind, and Rain are logarithmically transformed in order

to de-emphasize severe weather, give greater weight to differences between heuristic classifications of

good versus unpleasant weather (such as zero rain versus non-zero rain), and mitigate a significant right

skew in the wind and rain data.19 Finally, Cloud, Rain, and Wind are each standardized, which has no

impact on significance tests but facilitates comparing coefficient magnitudes across the weather variables.

We use all three aspects of Cloud, Wind, and Rain as proxies for the construct of “unpleasant

weather” because individuals’ moods are likely to be affected not only by lack of sunlight, but also rainy

or blustery conditions (Denissen, Butalid, Penke, and van Aken [2008]). However, including all three

weather variables in our regression tests introduces issues of multicollinearity. Like Denissen, et al.

18 During our sample period, Nelson’s Directories were published at the beginning of each calendar year and identified analyst locations as of the previous year. Thus we use the “2004” directory to identify analyst locations during 2003. If an analyst does not appear in a previous or subsequent edition of the directory, then we assume that the analyst was in the same city for all forecasts made within six months before and after the end of the year (e.g., July 2002 through June 2004). 19 Hirshleifer and Shumway [2003] deseasonalize their weather variables to isolate the effects of weather from other seasonal trends in stock market returns (see description on page 1015). Our analyst activity tests eliminate seasonal trends in analyst forecasts by examining strictly within-firm-quarter variation. Section 3.4.6 discusses tests using abnormally unpleasant weather.

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[2008], we instead create a combined measure of weather, denoted Unpleasant_Weather, defined as the

first principal component of Cloud, Rain, and Wind.20 Our Unpleasant_Weather variable further allows us

to capture multiple unpleasant weather states (e.g., gray, rainy and windy weather), which are likely to

have a larger impact on mood than one unpleasant weather state alone (e.g., windy weather).

Our tests also control for severe weather events that have potential physical instead of

psychological effects on analyst behaviors.21 We use the NOAA’s Storm Events Database to create an

indicator variable, Severe_Weather, which takes the value of one if any land-based storm event occurs

within the analyst’s county during the weather measurement window.22

2.4 Descriptive statistics

Table 1, Panel B provides descriptive statistics. For completeness we provide information on both

transformed and untransformed variables (e.g., unlogged and unstandardized for the weather variables),

but transformed values are used in all tests below. In our sample, 46.9% of analysts issue an annual EPS

forecast within days [0, 2] of the earnings announcement, 5.3% of analysts issue a buy/hold/sell

recommendation change, and 26% issue a target price recommendation.23 The average unlogged forecast

delay is 1.01 days. The average target price bias is 23.7%, which is consistent with the average observed

in Bradshaw, Brown, and Huang [2013] for roughly the same period. Average one-year-ahead and two-

year-ahead annual EPS forecast bias relative to realized EPS (variables Fcast_Bias) is 0.42% and 0.997%

of current price, respectively. The average analyst in our sample has 7.5 years of experience and is

20Untabulated principal component analysis produces one Unpleasant_Weather factor with an Eigenvalue greater than one.21 We consider “psycho-physical” effects of unpleasant weather on individuals’ behaviors to fall under the umbrella of “psychological” effects (e.g., Steers and Rhodes [1978]; Markham and Markham [2005]). An example of a “psycho-physical” effect is an employee choosing not to go to work on a rainy day because he/she does not enjoy getting damp on the walk to the office, rather than because of any real physical impediment to traveling to the office. 22 The NOAA’s Storm Events Database includes events that meet any of the following criteria: “(a) The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce; (b) rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area; and (c) other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.” See http://www.ncdc.noaa.gov/stormevents/. 23 Sample sizes for Make_Rec and Make_Tprice are reduced because, as discussed, the analyst must have an outstanding recommendation and target price, respectively, to be included in these analyses.

14

employed by a brokerage firm with 77.6 analysts or analyst teams. 16.7% of our firm-quarter-analyst

observations relate to an “All Star” analyst. Lastly, our sample firms are large with an average share price

of $35 and market capitalization of $9.4 billion (not adjusted for inflation).

The average untransformed cloud cover, rainfall, and wind realizations in our sample are 4.8

oktas, 0.12 millimeters per hour, and 8.3 miles per hour, respectively. The transformed weather variables

have a mean and standard deviation of roughly zero and one, with the slight differences due to

winsorization. The rightmost column of Panel B tabulates the residual standard deviation in the dependent

variables after they are orthoganalized to the analyst, firm-quarter, and city fixed effects used in our main

regression model below. In most cases the fixed effects remove less than 50% of the variation in the

dependent variables. As expected, Table 1, Panel C shows that Cloud, Rain, and Wind are positively

correlated, supporting the use of Unpleasant_Weather in our empirical estimations. Panel C also provides

basic univariate evidence that unpleasant weather is associated with muted responses to earnings news in

that Unpleasant_Weather is negatively correlated with Make_Fcast, Make_Rec, and Make_Tprice and

positive correlated with Fcast_Delay. However, the correlations between Unpleasant_Weather and our

six pessimism variables are mixed in terms of both signs and significance.

3. Analysis of weather-induced moods on analyst behavior

3.1 Empirical specification

We test the effects of weather-induced mood on analyst activity and pessimism using the

following OLS model:24

Outputi,j,q = β1Weatheri,q + ΣβkControlsi,q + ΣβkAnalysti + ΣβkFirm_Qtrj,q+ ΣβkCityi,j,q + ε (1)

Output is one of our measures of activity or pessimism for analyst i following firm j’s earnings

announcement for quarter q and Weather is either Unpleasant_Weather or Cloud in analyst i's city during

24 Several of our independent variables are indicators and, thus, could alternatively be evaluated with a binary response model. We use OLS to accommodate our large, multi-dimensional fixed effects structure and avoid the incidental parameters problem.

15

the measurement window.25 Analyst, Firm_Qtr, and City are analyst, firm-quarter, and city fixed effects,

and singleton observations (i.e., analysts, firm-quarters, or cities with only one observation in the

available sample) are dropped prior to estimation to avoid biasing the regression standard errors (Correia

[2015]).26 The fixed effect structure removes all temporally constant analyst characteristics, location

characteristics, and characteristics of the firm and its earnings announcement in quarter q. The remaining

variation consists of non-constant conditions relating to analyst i while he/she responds to firm j’s

earnings announcement in quarter q. The coefficient of interest is β1. Predicted signs for each Output

measure are presented in the tables discussed below.

Controls include the non-constant, analyst-specific and location-specific characteristics

Severe_Weather, Allstar, Analyst_Exp, and Broker_Size, as defined in Section 2. We include

Severe_Weather to control for physically disruptive weather and increase confidence that our

Unpleasant_Weather variable captures the effect of weather-induced mood. Although severe weather can

also have psychological effects on individuals’ moods and decision-making (Westefeld, Less, Ansley, and

Sook Yi [2006]; Watt and Difrancescantonio [2012]), we err on the side of caution by attributing all

effects of severe weather as physical rather than emotional. Results are qualitatively unchanged if we drop

Severe_Weather. Because analysts’ incentives change after Regulation FD and the Global Settlement

(Bagnoli, Watts, and Zhang [2008]), we also include an interaction between Allstar and a binary variable

for the period after 2001, variable Post2001 (the Post2001 main effect is absorbed by the firm-quarter

fixed effects). The remaining Controls vary depending on the dependent variable; regressions relating to

buy/hold/sell recommendations control for Rec_Before, and regressions relating to target prices control

25 As noted, we do not include all three of Cloud, Rain, and Wind in a single model due to issues of multicollinearity. We tabulate results for Cloud alone for robustness and comparisons to existing weather-related finance research. 26 Mathematically, dropping singletons has no impact on the coefficient estimates. Untabulated results show that dropping singleton observations has almost no impact on the reported t-statistics (i.e., in most cases the t-stat changes by < 0.01).

16

for Tprice_Before. Standard errors are clustered by earnings announcement date to correct for cross-

sectionally correlated residuals.27

3.2 Analyst activity results

Table 2, Panel A presents select coefficients from estimations of equation (1) for analyst activity

using our combined Unpleasant_Weather measure. Each column focuses on a different aspect of analyst

activity, with samples sizes varying depending on availability of the dependent variable as well as due to

dropping singleton observations. Unpleasant_Weather is significantly associated with all four measures

of analyst activity in the predicted directions. Specifically, we find that the probabilities of an analyst

making an earnings forecast, a buy/hold/sell recommendation, or target price recommendation are

negatively associated with unpleasant local weather conditions after the earnings announcement (first

three columns). We also find that forecast delays are positively associated with unpleasant local weather

conditions after the earnings announcement (fourth column).

Given the unconditional average Make_Fcast of 0.469, the results in column (1) indicate that a

one-unit (i.e., roughly one standard deviation) increase in Unpleasant_Weather is associated with a

(0.0189/0.469 =) 4.0% relative decrease in the likelihood of an analyst releasing an EPS forecast.28 Or,

moving 3.3 units from the 5th to 95th percentile of Unpleasant_Weather equates to a 13.3% relative

decrease in the likelihood of releasing an EPS forecast. Similarly, moving from the 5th to 95th percentiles

of Unpleasant_Weather is associated with a 17.9% relative decrease in the likelihood of a buy/hold/sell

recommendation, a 9.4% relative decrease in the likelihood of making a target price recommendation, and

a 7.7% relative increase in forecasting delay.

A few other relations also warrant discussion. First, the presence of severe weather has a large,

negative impact on analyst activity. Second, Allstar analysts tend to be more active through 2001, but

27 Untabulated tests with standard errors clustered by date and firm produce qualitatively unchanged results for the coefficients of interest, as do tests with standard errors clustered by date and analyst. 28 As an example in context, a one-unit increase in Unpleasant_Weather from roughly -2.5 to -1.5 (i.e., starting from a very nice day) reflects an average increase in cloud cover of 1.3 octiles (from 0.2 octiles to 1.5 octiles), 0.006 millimeters of rain (from 0.000 mm to 0.006 mm), and 0.8 mile-per-hour faster wind speed (5.7 mph to 6.6 mph).

17

these differences reverse after 2001. Third, analysts working at larger brokers are more likely to issue a

forecast and are faster at issuing forecasts when they do. Fourth, more experienced analysts are less likely

to make an EPS forecast and slower at issuing a forecast. Finally, analysts with higher pre-earnings

announcement target prices are less likely to update their target prices after the earnings announcement.

Panel B presents estimates of equation (1) using Cloud in lieu of Unpleasant_Weather. All results

are qualitatively unchanged from Panel A. In sum, the results in Table 2 document robust negative

associations between unpleasant weather and analyst activity.

3.3 Analyst pessimism results

Table 3, Panel A presents select coefficients from estimations of equation (1) for our analyst

pessimism measures using Unpleasant_Weather. Each column in the table focuses on a different analyst

forecast attribute, with sample sizes again varying depending upon data availability. These estimations

reveal Unpleasant_Weather is unassociated with recommendation downgrades or one-year-ahead

forecasts (columns (1) through (3)), but significantly and negatively associated with longer-term forecasts

and target price revisions (columns (4) through (6)). The presence of weather-induced biases in two-year

ahead and target price revisions is consistent with longer-horizon forecasts being more sensitive to analyst

judgments than short-horizon forecasts, as found in Lin and McNichols [1998]. In terms of magnitudes, a

roughly one standard deviation increase in Unpleasant_Weather is associated with a pessimistic bias in

two-year-ahead forecasts 0.0114% of price, or moving from the 5th to 95th percentiles of

Unpleasant_Weather is associated with a pessimistic bias of roughly (3.3 * 0.0114 =) 0.0376% of price.

Relative to the average share price of $34.91, this bias equates to roughly $0.013 per share. Moving from

the 5th to 95th percentile of Unpleasant_Weather is associated with a target price bias of roughly (0.36% *

3.3=) 1.2% of price. Replacing Unpleasant_Weather with Cloud (Panel B) yields weaker results, although

the negative coefficients on the two-year-ahead forecast biases remain significant at 10% or better. In sum,

the results in Table 3 are consistent with unpleasant weather inducing analyst pessimism.

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Unlike the analyst activity tests, there is no evidence that Severe_Weather has any impact on

forecast biases. This difference is likely due to severe weather creating physical barriers and frictions

capable of impeding activity, without necessarily generating the type of subconscious psychological

biases associated with pessimism. We find some evidence that All-star analysts are optimistically biased

prior to 2002, but these effects reverse after Regulation FD and the Global Settlement. Similarly, we find

some evidence that experienced analysts are more pessimistic, but not across all forecasting horizons.

3.4 Additional analyses and robustness tests

This section further explores the effects of unpleasant weather on analyst behavior. For

parsimony, we discuss only results using Unpleasant_Weather and do not tabulate control variables.

3.4.1 Additional analyses: Accuracy of updated forecasts

We next examine unsigned analyst forecast errors, conditional upon prevailing weather

conditions, for two reasons. First, although weather-induced apathy, lethargy, and reduced cognitive

capacity appear to cause analysts to be less active and slower to respond to earnings announcements,

these same effects could cause analysts to issue less accurate forecasts, resulting in larger unsigned

forecast errors. Second, although weather-induced pessimism appears to induce negative forecast biases,

psychology evidence also indicates that pessimism can cause individuals to think more critically or, vice-

versa, optimism fosters heuristic thinking (Sinclair and Mark [1995]). Thus, the forecast delays we

attribute to reduced activity could instead reflect pessimistic analysts performing more careful analyses. If

so, we should observe more accurate forecasts in the presence of unpleasant weather.

We measure accuracy based on the logged absolute errors in analysts’ earnings forecasts (variable

Fcast_Error) and target prices (variable Tprice_Error) relative to future realized values. Specifically,

these variables are calculated as the logged absolute values of Fcast_Bias and Tprice_Bias, although

results are qualitatively unchanged if unlogged values are used. Results in Table 4 find no significant

association between unpleasant weather and forecast errors. The absence of an effect of weather on

19

unsigned forecast errors suggests that slower, mood-affected analysts who issue forecasts are no more or

less accurate than forecast-producing analysts experiencing pleasant weather.

3.4.2 Additional analyses: Asymmetric responses to good versus bad earnings surprises

Weather-induced negative mood may cause market participants to over-react to negative news

events but have little to no effect on neutral or positive events (Kahneman and Tversky [1979], Edmans,

Garcia, and Norli [2007]). To search for this asymmetric effect, we re-examine the relations between

unpleasant weather and analyst behaviors conditional upon the sign of the firm’s unexpected earnings.

Specifically, we re-estimate equation (1) but replace Unpleasant_Weather with two separate variables:

Unpleasant_Weather_UE_Pos and Unpleasant_Weather_UE_Neg. The former (latter) variable takes the

value of Unpleasant_Weather when unexpected earnings >= 0 (unexpected earnings < 0), and is zero

otherwise. If unpleasant weather causes analysts to overreact to negative earnings surprises, then

Unpleasant_Weather_UE_Neg should have the dominant effect in our OLS estimations.

Results in Panel A of Table 5 find negative relations between analyst activity and both

Unpleasant_Weather_UE_Pos and Unpleasant_Weather_UE_Neg, although the coefficients are

insignificant in two of eight cases. F-tests confirm that the coefficients are jointly significant in all tests,

and tests of coefficient differences are all insignificant. Turning to analyst pessimism estimations in Panel

B, although not all of the test coefficients load significantly, we again fail to find consistent evidence of

differences in the coefficient magnitudes for good versus bad news. In sum, we find little evidence of an

asymmetric effect conditional on the sign of the recent earnings news.

3.4.3 Robustness test: Further controlling for potential physical effects

One concern is that our research design is capturing a physical effect of weather rather than a

weather-induced mood effect. We control for physical effects in in our main specification by including

Severe_Weather, and our use of winsorized values and logged weather variables further reduces concerns

that our results are driven by outliers. However, it is possible that light rainfall has a physical effect on

20

analyst output that is not adequately controlled in our main models. Because cloud and wind have less of

a physical presence than rain, we construct a new unpleasant weather variable, Cloud_Wind, which is the

first principal component of combining just Cloud and Wind. Rain is included as an extra control

variable under the conservative assumption that any effect of rain is due to physical effects. Panels A and

B of Table 6 present partial results of these updated models. The coefficients on Cloud_Wind are

qualitatively unchanged from the Unpleasant_Weather coefficients in Tables 2 and 3, with the exception

of the Tprice_Bias pessimism test that is no longer significant. Rain is marginally significant in three of

the ten models, and Severe_Weather remains significant in all four analyst activity tests. Untabulated

results replacing Cloud_Wind with just Cloud are qualitatively similar to those reported in Table 6, except

that the Fcast_Bias_y2 is not significant.29 We interpret these data as finding no evidence that a physical

rain effect is responsible for our results.

3.4.4 Robustness test: Alternative measurement window of analyst behavior

Our tests focus on analyst forecasting behavior occurring within trading days [0, 2] relative to an

earnings announcement. Section 2 discusses this research design choice but also notes that our use of a

relatively short measurement window may systematically eliminate slower analysts. To determine

whether the “short” window affects our inferences, we re-estimate our main tests using a longer

measurement window spanning one calendar week after the earnings announcement date (i.e., calendar

days [0, 7]). Untabulated results are qualitatively similar to those in Tables 2 and 3.

3.4.5 Robustness test: Analyst behavior and weather in New York City

29 Untabulated analysis also examines the effects of daily snowfall, as sourced from the NOAA’s Global Historical Climatology Network-Daily database. Significant snowfalls likely to cause physical disruptions are captured in our Severe_Weather variable and are, therefore, controlled in all tests. While light snowfall and corresponding cold temperatures likely have emotional effects and could therefore be included in Unpleasant_Weather, we do not focus on temperature-related effects for the reasons discussed in Section 2. Untabulated robustness tests including snow as an assumed control for physical effects in our Cloud_Wind model produce results that are qualitatively unchanged from those in Table 6.

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Because 53% of our analysts reside in New York City, our analyst results may be driven by NYC

weather. We test this possibility by re-estimating our analyst activity tests interacting

Unpleasant_Weather with an indicator for NYC analysts. Untabulated results find that the

Unpleasant_Weather main effects are qualitatively unchanged, indicating that the main relation between

weather and analysts’ decision-making is not limited to or driven by analysts in NYC.

3.4.6 Robustness test: Measurement of unpleasant weather

Although the vast majority of psychology research investigates the link between mood and

realized weather, it is possible that individuals’ moods may be more affected by unexpected weather

conditions. We calculate “abnormal” unpleasant weather, Unpleasant_Weather_Abn, as the observed

Unpleasant_Weather minus the principal component of combining Cloud, Rain, and Wind observed in the

same week over the trailing seven years. We then re-estimate equations (1) using

Unpleasant_Weather_Abn in lieu of Unpleasant_Weather. In untabulated tests we find that the

Unpleasant_Weather_Abn coefficients are reduced in both magnitudes and significance from those in

Tables 2 and 3. This attenuation, combined with a positive correlation between Unpleasant_Weather and

Unpleasant_Weather_Abn of roughly 61%, suggests that actual weather conditions are responsible for the

analyst behavior observed.

Our tests also assume that analyst behavior is influenced primarily by weather conditions

experienced concurrently over our three-day measurement window. However, weather-induced

depression may be more severe after prolonged unpleasant weather, or analyst behavior may be

influenced by weather conditions that precede the earnings announcement. Extending the weather

measurement window to include the one-week period preceding the earnings announcement produces

weaker results (untabulated), indicating that adding the pre-earnings announcement weather likely

increases noise rather than improving model specification.

3.4.7 Additional analyses: Cross-sectional variation in analyst behaviors

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The effects of unpleasant weather on analyst activity may vary cross-sectionally depending on

analyst- and firm-level characteristics. First, large brokerages likely have greater resources to compensate

for weather-affected analysts, as well as more extensive review policies to mitigate the impact of

individual analyst behaviors. Second, analyst teams and review processes may be stronger when covering

larger firms because larger firms are more visible and have potentially greater reputation and economic

consequences. Third, the relations between Unpleasant_Weather and analyst output may differ for more

experienced analysts, although the directional prediction is less clear. Experienced analysts may be less

susceptible to behavioral influences, but are also likely more senior and less subject to oversight, in which

case their output might be more susceptible to behavioral issues. Fourth, analysts’ incentives for

providing fast and accurate updates may be stronger following firms’ year-end earnings announcements.

We examine these cross-sectional predictions by interacting each of Broker_Size, MVE, Analyst_Exp, and

an indicator for fourth fiscal quarter with Unpleasant_Weather. Untabulated results show that the

Unpleasant_Weather main effects are qualitatively unchanged when the interactions are included, but that

there is little consistent evidence that the effects vary cross-sectionally with broker size, firm size, analyst

experience, or a fourth quarter indicator.

4. Market pricing tests

The behaviors of equity analysts likely resemble those of sophisticated market participants in

general. Because we find that weather affects the mood and behavior of analysts, weather could

potentially impact the mood and behavior of a sufficiently large number of investors to generate an

observable effect on equilibrium market prices. Prior research finds evidence consistent with weather-

induced pessimism causing investors to temporarily value firms more negatively. Our analyst-level

pessimism tests support this conclusion. In this section, we investigate whether inactivity due to weather-

induced moods affects the speed or completeness of the market’s response to new information. Our

predicted pricing effect is both distinct from, and complementary to, existing research on how weather-

induced pessimism affects equilibrium market prices.

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Our predictions about weather-induced mood, inactivity, and market pricing are closely related to

the “limited attention” literature in finance and accounting. The theory underlying this literature typically

assumes that market prices reflect a weighted average of risk-averse investors’ beliefs about firm value,

and that investors have limited capacity to process information in a timely manner. If a sufficient number

of investors face attention constraints and either trade with a delay or trade on incomplete information,

then these inattentive investors can impede the market price adjustment process.30 Empirically, the

delayed price adjustment manifests as smaller stock returns per unit of earnings surprise at the time of the

earnings announcement (i.e., smaller ERCs), followed by larger stock price drift as the initial

underpricing corrects over the subsequent quarter (i.e., larger PEAD).

Similar to the limited attention literature, we predict that short-window ERCs will be smaller and

PEAD will be larger if weather-induced mood causes investors to either abstain from trading or to trade

on an incomplete information set during the earnings announcement window. Rather than attempting to

estimate a weighted average weather experienced by all investors trading in a stock, we follow previous

research on weather and stock returns and investigate our market pricing prediction using weather in

NYC, which is the location of the highest concentration of sophisticated capital market participants in the

U.S. The primary tension in our prediction is that market participants (including those outside of NYC

and potentially experiencing better weather) have an economic incentive to arbitrage weather-induced

mispricing. As noted in the Introduction, we stress that our pricing tests lack the identification strength of

our analyst-based tests but are provided as an initial inquiry into the pricing effects of investor inactivity

due to weather-induced moods.

4.1 Market pricing tests: Sample construction and variable measurements

Table 7, Panel A details our sample construction for our market pricing test. We intersect

Compustat, IBES, and CRSP to obtain an initial sample of earnings announcements for the years 1990

30 As discussed by Hirshleifer and Teoh [2003], even if attentive investors can identify mispricing, mispricing can persist in equilibrium if those attentive investors are limited in the amount of risk they are willing to bear.

24

through 2013, retaining observations where the earnings announcement dates are the same in Compustat

and IBES.31 We retain only ordinary common shares of U.S. public companies (identified via CRSP share

codes 10 and 11) and drop observations with insufficient data to calculate unexpected earnings or

required return metrics (UE, AR and AR_PEAD, defined below). To reduce the effects of outliers and

errors, we drop observations with quarter-end price below $1, earnings surprise in excess of price, or

earnings announcement dates that are before or more than one year after the fiscal quarter-end. Finally,

we drop observations missing weather data or the control variables discussed below, leaving a sample of

193,208 firm-quarter observations.

Our first set of market tests (labeled “ERC tests”) examine the mapping of unexpected earnings

news into abnormal returns (AR) over a three-day “ERC period” starting with the firm’s earnings

announcement (i.e., days [0, 2]).32 AR is calculated as the firm’s buy-and-hold return less the value-

weighted return of a portfolio of firms matched on quintiles of size and book-to-market, multiplied by 100

to be in basis points.33 Our second set of market tests (labeled “PEAD tests”) examine abnormal returns

generated over days [3, 75] relative to the firm’s earnings announcement (AR_PEAD). We use a PEAD

measurement window spanning 75 days because Bernard and Thomas [1990] find that a significant

portion of the PEAD following quarter q occurs in the short window around the earnings announcement

for quarter q+1; in our sample, the use of a 75-day PEAD window allows us to capture approximately

90% of q+1 earnings announcements.34 Unexpected earnings are calculated as the IBES actual EPS

realization less the most recent median consensus forecast prior to the earnings announcement, scaled by

31 Our sample begins in 1990 due to limited IBES coverage prior to that date. 32 Our three-day ERC measurement window is chosen to mimic the timing used in our analyst tests. Untabulated tests show that our results are qualitatively unchanged using a two-day [0, 1] earnings announcement window. 33 Quintile cut-offs and portfolio returns are sourced from Ken French’s website (November 2015). Portfolio assignments are based on market value as of the end of June preceding the earnings announcement, and book-to-market as of the trailing December. As shown in Panel A of Table 7, using simple market-adjusted returns instead of portfolio-adjusted returns increases the sample size by 6,308 observations, but untabulated results produce qualitatively unchanged results. Using portfolios based on size deciles and nested book-to-market quintiles as of the month-end prior to the earnings announcement also produce qualitatively unchanged results (untabulated). 34 Within our sample, the proportion of returns in the three-day period around the earnings announcement for quarter q+1 relative to the total return in the PEAD window is roughly 0.44, indicate that an average of 44% of the PEAD-period return is realized upon the subsequent earnings announcement. Robustness tests discussed below examine price corrections in the short-window around the subsequent quarter’s earnings announcement, and also examine PEAD windows spanning 50 to 100 days. Our results are qualitatively unchanged.

25

quarter-end price. To reduce the effects of stale and outlier forecasts, we require that the consensus is no

more than 100 days old and is based on at least two analysts’ estimates. Similar to Hirshleifer, et al.

[2009] and DellaVigna and Pollet [2009], we sort unexpected earnings into deciles to construct the

variable UE.

Weather data for our market tests are obtained and assembled via the same process used for our

analyst activity tests. We construct a daily time series of NYC weather by retaining weather data from the

closest available NOAA weather station from the central latitude and longitude coordinates of the

NYSE’s zip code. Cloud_NYC, Wind_NYC, and Rain_NYC are the logged and standardized average

hourly cloud cover, wind speed, and rainfall observed over the ERC measurement window, and

Unpleasant_Weather_NYC is the first principal component of Cloud_NYC, Rain_NYC, and Wind_NYC.

Table 7, Panel B presents descriptive statistics. All variables are further defined in the Appendix.

Untransformed versions of certain variables are provided for information purposes, but transformed

specifications are used in all tests.

4.2 Empirical analysis: Market pricing tests

4.2.1 Separate ERC and PEAD regressions

Our first tests are based on separate ERC and PEAD regressions, estimated using OLS:

AR = α1(UE)+ α2(UE*Weather) + Σαk(Controls) + Σαk(UE*Controls) + Σαk(Date) + ε (2a)

AR_PEAD =β1(UE)+ β2(UE*Weather)+ Σβk(Controls)+ Σβk(UE*Controls) )+ Σβk(Date)+µ (2b)

AR, AR_PEAD, UE, and Weather are as previously defined. Controls include severe weather

(Severe_Weather), firm market value (MVE), earnings persistence (Persist), earnings volatility (Volatility),

institutional ownership (InstOwn), stock market beta (Beta), book-to-market (BTM), lag between the

quarter-end and earnings announcement date (RepLag), decile ranking of the number of firms announcing

earnings that day (Busy), an indicator for negative earnings (Loss), and an indicator for the fourth fiscal

quarter (FQ4), an indicator for Friday earnings announcements (Friday), and indicators for weeks 1

26

through 52 to control for seasonal trends (w1-w52). Date fixed effects control for unobserved

characteristics of each earnings announcement date and also absorb the main effects of Weather, Friday,

Busy, and w1-w52 since these variables do not vary across firms within a date.35 All control variables are

de-meaned before calculating the interactions with UE such that the main effects can be interpreted at the

sample averages. Standard errors are clustered by date to correct for cross-sectionally correlated

residuals.36 The coefficients α1 and β1 are the main ERC and PEAD coefficients, respectively, when

Unpleasant_Weather_NYC equals 0 (i.e., at the sample average). The coefficients α1 and β1 are expected

to be positive, consistent with the well-documented positive relations between UE and both the initial

price response and subsequent price drift to earnings news. The coefficients α2 and β2 estimate the

differences in ERC and PEAD for increasingly unpleasant weather. Our main hypothesis predicts α2 < 0

and β2 > 0, which would be consistent with unpleasant weather in NYC causing delayed market price

responses to earnings news.

In addition to estimating these models using the full sample, we also estimate these models after

splitting the sample roughly in half by time, namely pre-2005 and post-2004. We present these separate

estimations for two reasons. First, our analyst-level analysis uses data that ends in 2004, so partitioning

allows us to document the magnitude of these pricing effects in the time period contemporaneous with

our analyst-level sample. Second, Green, Hand, and Soliman [2011] and Chordia, Subrahmanyam, and

Tong [2014] find that earnings pricing anomalies attenuate or disappear due to increased liquidity and

lower trading costs in recent years, and Chakrabarty, Moulton, and Wang [2015] find that attention-based

mispricing (i.e., reduced ERCs and greater PEAD, like we examine here) is mitigated by high-frequency,

algorithmic trading in 2008 and 2009. Thus, partitioning the sample at roughly the median date allows us

to investigate whether any weather-based mispricing dissipates in the latter half of our sample. 35 To be clear, Date are indicators for each earnings announcement date, such as 20 January 2010. Date absorb the main effects of Week but not the Week*UE interactions, the latter of which control for seasonal trends in the ERC and PEAD slope coefficients. 36 We do not cluster by firm because we do not expect that serial correlation affects both the dependent variable and independent variables of interest. Consistent with this assumption, untabulated tests find that clustering by both date and firm produce virtually unchanged t-statistics on the variables of interest, nor are the double-clustered t-statistics uniformly higher or lower than those reported herein.

27

Table 8, Panel A presents select coefficients from estimations of equations (2a) and (2b) using

Unpleasant_Weather_NYC. Coefficients on our control variables are untabulated for parsimony. The

estimations presented in the first and second sets of columns document the influence of weather on ERCs

and PEAD, respectively. Consistent with our predictions of lower ERCs on unpleasant weather days, our

full sample estimations (without and with controls in columns (1) and (2), respectively) document

significant negative coefficients on the interaction term UE*Unpleasant_Weather_NYC, consistent with a

muted response to earnings news in the presence of unpleasant weather. Focusing on PEAD, our full

sample estimations (in columns (5) and (6)) document significantly positive coefficients on the interaction

term UE*Unpleasant_Weather_NYC. This positive relation is consistent with greater PEAD following

those earnings announcements associated with unpleasant weather. Finally, as predicted, for both

estimations results are substantially stronger in the first half of the sample (columns (3) and (7) versus

columns (4) and (8)), with an insignificant PEAD result in the post-2004 sample.

For completeness, Panel B presents select coefficients and t-statistics from estimations of

equations (2a) and (2b) using Cloud_NYC. The Cloud_NYC-based results tend to be weaker but

qualitatively unchanged from those in Panel A.

In sum, our market analyses document both ERC and PEAD evidence consistent with unpleasant

weather in NYC causing delays in market participants’ reactions to earnings news. The presence of

weather-induced aggregate pricing effects around earnings announcement dates supports the notion that

weather impedes the processing of earnings news. However, this market effect seems to have been

attenuated over time.

4.2.2 Combined ERC and PEAD test

A drawback of measuring PEAD over days [3, 75] is that, in addition to capturing the post

earnings announcement price correction, the long measurement window introduces noise due to price

movements that are unrelated to the prior quarter’s earnings news. Moreover, prior research highlights

the challenges associated with measuring risk-adjust returns and performing statistical tests using long run

28

return metrics. To alleviate these measurement concerns and provide a more precise test of our PEAD

prediction, we perform a “short-window” combined ERC and PEAD test that simultaneously examines

both underpricing due to weather at quarter q as well as the correction of weather-related underpricing

from the previous quarter. This methodology is built on evidence that a significant portion of mispricing

at quarter q’s earnings announcement is expected to be corrected when earnings are re-examined upon the

announcement for quarter q+1 (Bernard and Thomas [1990]). Specifically, we run the following OLS

regression:

ARq =α0 +α1(UEq) + α2(UEq* Weatherq) +β1(UEq-1) + β2(Weatherq-1) + β3(UEq-1* Weatherq-1) + Σαk(Controlsq) + Σαk(UEq*Controlsq) + Σαk(Dateq) + ε

(3)

ARq is the three-day abnormal return at the earnings announcement for quarter q, α1 measures the

ERC for quarter q, and α2 measures the incremental difference in quarter q’s ERC when accompanied by

increasingly unpleasant weather. As previously, α2 is expected to be negative. Coefficients β1 through β3

relate to the prior quarter’s earnings surprise and weather. To the extent that unpleasant weather impaired

the pricing of last quarter’s earnings news, we expect β3 on (UEq-1* Weatherq-1) to be positive as prices

correct during the current quarter’s earnings announcement for last quarter’s under-reaction.

Table 9, Panel A presents select coefficients from estimating model (4) in the full, early, and late

sample using Unpleasant_Weather_NYC. Sample sizes are reduced because we require consecutive

quarters’ data. As earlier, the significantly negative coefficient on (UEq*Unpleasant_Weather_NYCq) is

consistent with unpleasant weather in the current period attenuating the market reaction to this period’s

earnings news. After controlling for contemporaneous news and weather conditions, the significantly

positive coefficient on the interaction term (UEq-1*Unpleasant_Weather_NYCq-1) is consistent with

weather-induced underpricing from quarter q-1 correcting in quarter q.37 Unlike the previous PEAD

model, we also find evidence of significant weather-related PEAD in the post-2004 period. Estimations

replacing Unpleasant_Weather_NYC with Cloud_NYC (Panel B) produce qualitatively unchanged results,

37 The significant negative coefficients on UEq-1 are consistent with Ball and Bartov [1996] who find a negative association between earnings announcement returns and lagged earnings once both current and lagged earnings are included in the same ERC model.

29

except the PEAD result in the post-2004 sub-period is only marginally significant. In sum, these results

are consistent with inactivity due to weather-induced negative moods delaying the equilibrium price

response to earnings announcements.

4.2.3 Volume tests

Weather-induced inactivity may also cause investors to avoid or delay trading on new

information, causing lower trading volumes at earnings announcements when the weather is unpleasant.38

We measure abnormal trading volume (Abnormal_Volume) as the firm’s average daily shares traded

during the ERC window divided by the total shares outstanding, minus the firm’s trailing average over

days [-70, -5].39 We then estimate variations of the following OLS model:

Abnormal_Volume = α1(Weather) + Σαk(Controls) + ε (4)

Controls include both signed and absolute unexpected earnings (UE, and UE_ABS), the controls from the

previous pricing models, and, in select models, abnormal market volume (Abnormal_Market_Volume).

The coefficient α1 captures the effects of unpleasant weather on abnormal trading volume during the

earnings announcement window.

The first three columns of Table 10, Panel A present results estimating equation (4) for

Unpleasant_Weather_NYC using the full sample; the fourth and fifth columns present estimations for the

pre-2005 and post-2004 periods. Columns (1) and (2) document a negative relation between unpleasant

weather and trading volume, without and with control variables. Column (3) adds

Abnormal_Market_Volume and the results are qualitatively unchanged. Columns (4) and (5) indicate that

volume is decreasing with Unpleasant_Weather in both the earlier and later halves of our sample, but that

the coefficient magnitude and statistical significance are greater in the pre-2004 period. Estimations

38 Whereas lower volume would be consistent with weather-induced moods causing market participants to refrain from trading, the lack of an association would not be inconsistent with our pricing evidence. If inattentive investors continue to trade on non-updated information, then we would observe a delayed price response and higher trading volume due to dispersion of beliefs between attentive and inattentive investors. 39 As discussed in Bamber, Barron, and Stevens [2011], there is no agreed-upon measure of trading volume around earnings announcements. We use the percentage of shares traded as our volume measure, rather than dollar volume, to avoid endogeneity between weather and the firm’s share price and to give equal weighting to smaller firms.

30

replacing Unpleasant_Weather_NYC with Cloud_NYC (Panel B) produce qualitatively unchanged results.

Together, these volume tests support our hypothesis that weather-induced negative moods impede market

responses to earnings announcements.

4.2.4 Robustness test: ERCs following positive versus negative earnings surprises

A concern is that our evidence of reduced ERCs on unpleasant weather days is due to market

participants asymmetrically reacting to earnings announcements, such that they underreact to positive

news but have a complete or overreaction to negative news. Such behavior could be consistent with

weather-induced pessimism rather than an inactivity explanation. Results in Table 11 repeat the ERC tests

from Table 8 but splitting UE into two variables: UE_Pos and UE_Neg. The former (latter) takes the

value of UE for earnings surprises greater than or equal to zero (less than zero), and zero otherwise. The

interactions between UE_Pos * Unpleasant_Weather_NYC are significantly negative in all estimations,

consistent with underreactions to good earnings news in the presence of unpleasant weather. Importantly,

the interactions between UE_Neg * Unpleasant_Weather_NYC are also all negative, consistent with

underreactions (not overreactions) to bad earnings news when the weather is unpleasant. T-tests at the

bottom of the Panel find no evidence of differences in the magnitudes of the interactions. Untabulated

results using Cloud_NYC instead of Unpleasant_Weather_NYC also fail to find evidence of asymmetric

responses to positive versus negative news.

4.2.5 Additional robustness tests and analyses

Additional, untabulated tests confirm that our market-pricing results are generally unchanged in

the following scenarios: the ERC window is shortened to days [0,1]; the PEAD window ends on a variety

of increments between 50 and 100 days after quarter-end; Rain_NYC is included as an additional control

variable for physical effects; and the models include firm fixed effects and interactions with UE.

5. Conclusion

31

We develop and investigate a new set of predictions about how weather-induced negative moods

decrease activity levels, thereby impeding market participants’ responses to earnings news. We also

reexamine an existing prediction about the effects of weather-induced pessimism on market participants’

stock assessments. Our large-sample tests exploit intra-national variation in analysts’ locations and

responses to a common information signal, supporting the existence of a causal link between local

weather and both the efficiency and biasedness of individual market participants’ responses to earnings

announcements. Our market tests provide initial evidence consistent with unpleasant weather

systematically affecting the efficiency of the price formation process around a key information event, as

evidenced by reduced ERCs and greater PEAD.

We view our contribution as three-fold. First, we provide compelling evidence that the economic

decision-making process of professional analysts, and likely other sophisticated market participants, is

affected by weather-induced mood. Second, we contribute to the limited attention literature by

investigating a new behavioral mechanism by which inattention can arise and affect market pricing.

Finally, while both our predictions and empirical setting are fundamentally different from existing

research on the weather and market prices, our evidence that weather-induced mood affects sophisticated

market participants’ behaviors lends empirical credence to prior research on how weather-induced

pessimism affects aggregate market pricing.

We believe that our analyst-based research design is capable of capturing the consequences of our

predicted biases and, therefore, our tests have the power to cast serious doubt on our main hypothesis of

how unpleasant weather impedes the flow of information in capital markets. Instead of rejecting our main

predictions, our tests have generated novel results consistent with unpleasant weather impeding the

processing of earnings news. We encourage future research to examine whether the individual effects

documented in this paper exist in other settings or can be explained by alternative mechanisms. With

respect to our pricing evidence, we caution that our research design does not allows to make causal

statements, and future work is needed to better understand how weather-induced moods’ affect the pricing

mechanism. Such additional research includes understanding the forces that prevent arbitrage from

32

unraveling these bias, and the creation of a broader framework that can help reconcile our documented

inactivity effects (i.e., underreaction to both positive and negative information), with evidence

documenting an asymmetric over-reaction to news depending upon investor mood (e.g., Gulen and

Hwang [2012]; Mian and Sankaraguruswamy [2012]; Autore, Bergsma, and Jiang [2015]).

33

References

ABDEL-KHALIK, A. R. and J. ESPEJO. “Expectations Data and the Predictive Value of Interim Reporting.” Journal of Accounting Research 16 (1978): 1-13.

APA. Diagnostic and Statistical Manual of Mental Disorders. Washington, D.C.: American Psychiatric Association, 1994.

AUTORE, D. M.; K. BERGSMA and D. JIANG. "The Pre-holiday Corporate Announcement Effect." Unpublished paper, 2015.

BAGNOLI, M.; S. G. WATTS and Y. ZHANG. “Reg-FD and the competitiveness of all-star analysts.” Journal of Accounting and Public Policy 27 (2008): 295-316.

BALL, R. and E. BARTOV. “How naive is the stock market's use of earnings information?” Journal of Accounting and Economics 21 (1996): 319-337.

BAMBER, L. S.; O. E. BARRON and D. E. STEVENS. “Trading Volume Around Earnings Announcements and Other Financial Reports: Theory, Research Design, Empirical Evidence, and Directions for Future Research*.” Contemporary Accounting Research 28 (2011): 431-471.

BERNARD, V. L. and J. K. THOMAS. “Evidence that stock prices do not fully reflect the implications of current earnings for future earnings.” Journal of Accounting and Economics 13 (1990): 305-340.

BERTRAND, M.; E. DUFLO and S. MULLAINATHAN. “How Much Should We Trust Differences-In-Differences Estimates?” The Quarterly Journal of Economics 119 (2004): 249-275.

BRADSHAW, M. "Analysts' Forecasts: What Do We Know after Decades of Work?" Unpublished paper, 2011.

BRADSHAW, M.; L. BROWN and K. HUANG. “Do sell-side analysts exhibit differential target price forecasting ability?” Review of Accounting Studies 18 (2013): 930-955.

BUSHEE, B. J. and H. L. FRIEDMAN. “Disclosure Standards and the Sensitivity of Returns Mood.” Review of Financial Studies Forthcoming (2015).

BUSSE, M. R.; D. G. POPE; J. C. POPE and J. SILVA-RISSO. “The Psychological Effect of Weather on Car Purchases.” The Quarterly Journal of Economics (2014).

CHAKRABARTY, B.; P. C. MOULTON and X. WANG. "Attention Effects in a High-Frequency World." Unpublished paper, 2015.

CHANG, S.-C.; S.-S. CHEN; R. K. CHOU and Y.-H. LIN. “Weather and intraday patterns in stock returns and trading activity.” Journal of Banking & Finance 32 (2008): 1754-1766.

CHORDIA, T.; A. SUBRAHMANYAM and Q. TONG. “Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?” Journal of Accounting and Economics 58 (2014): 41-58.

CLARK, L. A. and D. WATSON. “Mood and the mundane: Relations between daily life events and self-reported mood.” Journal of Personality and Social Psychology 54 (1988): 296-308.

COHEN, L. and A. FRAZZINI. “Economic Links and Predictable Returns.” The Journal of Finance 63 (2008): 1977-2011.

COLEMAN, D. F. and N. V. SCHAEFER. “Weather and Absenteeism.” Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 7 (1990): 35-42.

CONLIN, M.; T. O'DONOGHUE and T. J. VOGELSANG. “Projection Bias in Catalog Orders.” The American Economic Review 97 (2007): 1217-1249.

34

CONNOLLY, M. “Here Comes the Rain Again: Weather and the Intertemporal Substitution of Leisure.” Journal of Labor Economics 26 (2008): 73-100.

CORREIA, S. "Singletons, Cluster-Robust Standard Errors and Fixed Effects: A Bad Mix." Unpublished paper, 2015.

CROTHERS, T. D. “Effects of Weather on Scientific Work.” Science 23 (1894): 92-92.

CUNNINGHAM, M. R. “Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan.” Journal of Personality and Social Psychology 37 (1979): 1947-1956.

DE SILVA, D. G.; R. A. J. POWNALL and L. WOLK. “Does the sun ‘shine’ on art prices?” Journal of Economic Behavior & Organization 82 (2012): 167-178.

DEHAAN, E.; T. SHEVLIN and J. THORNOCK. “Market (in)attention and the strategic scheduling and timing of earnings announcements.” Journal of Accounting and Economics 60 (2015): 36-55.

DELLAVIGNA, S. and J. M. POLLET. “Investor Inattention and Friday Earnings Announcements.” The Journal of Finance 64 (2009): 709-749.

DENISSEN, J. J. A.; L. BUTALID; L. PENKE and M. A. G. VAN AKEN. “The effects of weather on daily mood: A multilevel approach.” Emotion 8 (2008): 662-667.

DOLVIN, S. D.; M. K. PYLES and Q. WU. “Analysts Get SAD Too: The Effect of Seasonal Affective Disorder on Stock Analysts' Earnings Estimates.” Journal of Behavioral Finance 10 (2009): 214-225.

EDMANS, A.; D. GARCIA and O. NORLI. “Sports Sentiment and Stock Returns.” The Journal of Finance 62 (2007): 1967-1998.

ENGELBERG, J. E. and C. A. PARSONS. “The Causal Impact of Media in Financial Markets.” The Journal of Finance 66 (2011): 67-97.

FRANCIS, J. and L. SOFFER. “The Relative Informativeness of Analysts' Stock Recommendations and Earnings Forecast Revisions.” Journal of Accounting Research 35 (1997): 193-211.

GIVOLY, D. and J. LAKONISHOK. “The information content of financial analysts' forecasts of earnings: Some evidence on semi-strong inefficiency.” Journal of Accounting and Economics 1 (1979): 165-185.

GOETZMANN, W. N.; D. KIM; A. KUMAR and Q. WANG. “Weather-Induced Mood, Institutional Investors, and Stock Returns.” Review of Financial Studies 28 (2015): 73-111.

GREEN, J.; J. R. M. HAND and M. T. SOLIMAN. “Going, Going, Gone? The Apparent Demise of the Accruals Anomaly.” Management Science 57 (2011): 797-816.

GULEN, H. and B.-H. HWANG. "Daily Stock Market Swings and Investor Reaction to Firm-Specific News." Unpublished paper, 2012.

HEALY, P. M. and K. G. PALEPU. “Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature.” Journal of Accounting and Economics 31 (2001): 405-440.

HIRSHLEIFER, D.; S. S. LIM and S. H. TEOH. “Driven to Distraction: Extraneous Events and Underreaction to Earnings News.” The Journal of Finance 64 (2009): 2289-2325.

HIRSHLEIFER, D.; S. S. LIM and S. H. TEOH. “Limited Investor Attention and Stock Market Misreactions to Accounting Information.” Review of Asset Pricing Studies 1 (2011): 35-73.

HIRSHLEIFER, D. and T. SHUMWAY. “Good Day Sunshine: Stock Returns and the Weather.” The Journal of Finance 58 (2003): 1009-1032.

35

HIRSHLEIFER, D. and S. H. TEOH. “Limited attention, information disclosure, and financial reporting.” Journal of Accounting and Economics 36 (2003): 337-386.

HONG, H. and J. C. STEIN. “A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets.” The Journal of Finance 54 (1999): 2143-2184.

HOWARTH, E. and M. S. HOFFMAN. “A multidimensional approach to the relationship between mood and weather.” British Journal of Psychology 75 (1984): 15-23.

KAHNEMAN, D. and A. TVERSKY. “Prospect Theory: An Analysis of Decision under Risk.” Econometrica 47 (1979): 263-291.

KELLER, M. C.; B. L. FREDRICKSON; O. YBARRA; S. CÔTÉ; K. JOHNSON; J. MIKELS; A. CONWAY and T. WAGER. “A Warm Heart and a Clear Head: The Contingent Effects of Weather on Mood and Cognition.” Psychological Science 16 (2005): 724-731.

LAMARE, J. R. “Mobilization and Voter Turnout: Should Canvassers Worry about the Weather?” PS: Political Science & Politics 46 (2013): 580-586.

LEMON, J. S. “Psychic Effects of the Weather.” The American Journal of Psychology 6 (1894): 277-279.

LIN, H.-W. and M. F. MCNICHOLS. “Underwriting relationships, analysts' earnings forecasts and investment recommendations.” Journal of Accounting and Economics 25 (1998): 101-127.

LOUGHRAN, T. and P. SCHULTZ. “Weather, Stock Returns, and the Impact of Localized Trading Behavior.” Journal of Financial and Quantitative Analysis 39 (2004): 343-364.

LYS, T. and S. SOHN. “The association between revisions of financial analysts' earnings forecasts and security-price changes.” Journal of Accounting and Economics 13 (1990): 341-363.

MADSEN, J. "Consequences of Scheduled Earnings Announcements: Evidence from Customer-Supplier Relationships." Unpublished paper, 2015.

MARKHAM, S. and I. MARKHAM. “Biometeorological effects on worker absenteeism.” International Journal of Biometeorology 49 (2005): 317-324.

MIAN, G. M. and S. SANKARAGURUSWAMY. “Investor Sentiment and Stock Market Response to Earnings News.” The Accounting Review 87 (2012): 1357-1384.

NIESSNER, M. "Strategic Disclosure Timing and Insider Trading." Unpublished paper, 2015.

NOVY-MARX, R. “Predicting anomaly performance with politics, the weather, global warming, sunspots, and the stars.” Journal of Financial Economics 112 (2014): 137-146.

RICHARDSON, S.; İ. TUNA and P. WYSOCKI. “Accounting anomalies and fundamental analysis: A review of recent research advances.” Journal of Accounting and Economics 50 (2010): 410-454.

ROBERTS, M. R. and T. M. WHITED 'Chapter 7 - Endogeneity in Empirical Corporate Finance1,' in Handbook of the Economics of Finance, edited by M. H. George M. Constantinides and M. S. Rene: Elsevier, 2013: 493-572.

SANDERS, J. L. and M. S. BRIZZOLARA. “Relationships between Weather and Mood.” The Journal of General Psychology 107 (1982): 155-156.

SAUNDERS, E. M., JR. “Stock Prices and Wall Street Weather.” The American Economic Review 83 (1993): 1337-1345.

SCHWARZ, N. and G. L. CLORE. “Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states.” Journal of Personality and Social Psychology 45 (1983): 513-523.

36

SHON, J. J. and P. ZHOU. “Are Earnings Surprises Interpreted More Optimistically on Very Sunny Days? Behavioral Bias in Interpreting Accounting Information.” Journal of Accounting, Auditing & Finance 24 (2009): 211-232.

SINCLAIR, R. C. and M. M. MARK. “The effects of mood state on judgemental accuracy: Processing strategy as a mechanism.” Cognition and Emotion 9 (1995): 417-438.

SMITH, R. and G. BRADLEY. “The Influence of Thermal Conditions on Teachers’ Work and Student Performance.” Journal of Educational Administration 32 (1994): 34-42.

STEERS, R. M. and S. R. RHODES. “Major influences on employee attendance: A process model.” Journal of Applied Psychology 63 (1978): 391-407.

STICKEL, S. E. “The timing of and incentives for annual earnings forecasts near interim earnings announcements.” Journal of Accounting and Economics 11 (1989): 275-292.

TIETJEN, G. H. and D. F. KRIPKE. “Suicides in California (1968–1977): Absence of seasonality in Los Angeles and Sacramento counties.” Psychiatry Research 53 (1994): 161-172.

WATSON, D. Mood and temperament. New York: Guilford Press, 2000.

WATT, M. and S. DIFRANCESCANTONIO. “Who's Afraid of the Big Bad Wind? Origins of Severe Weather Phobia.” Journal of Psychopathology & Behavioral Assessment 34 (2012): 440-450.

WESTEFELD, J. S.; A. LESS; T. ANSLEY and H. SOOK YI. “Severe Weather Phobia.” American Meteorological Society 87 (2006): 747-749.

WRIGHT, W. F. and G. H. BOWER. “Mood effects on subjective probability assessment.” Organizational Behavior and Human Decision Processes 52 (1992): 276-291.

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Appendix: Variable Specifications

Panel A: Analyst tests Weather variables are measured from the date of the earnings announcement through the date of the analysts’ first recommendation, target price, or annual EPS forecast release, released within days [0, 2] of the earnings announcement. For analysts that take no actions, weather is measured for the full three-day period starting with the earnings announcement. Non-binary variables are winsorized at the 1st and 99th percentile.

Measure Description Allstar Indicator variable if the analyst is designated as an “all-star” analyst in Nelson’s Directory during

the year of the earnings announcement. Analyst_Exp Analyst experience. Calculated as the number of years between the analyst’s first EPS forecast in

IBES and the firm’s earnings announcement, logged. Broker_Size Size of the analyst’s brokerage firm. Calculated as the logged count of the unique analyst codes in

the IBES detail file who announce an EPS forecast during the year of the firm’s earnings announcement.

Cloud Average hourly cloud cover. Measured in octiles of sky coverage, logged, and standardized Cloud_Wind First principal component of combining Cloud and Wind. Fcast_Bias_v2y Annual EPS forecast for year y less the outstanding consensus as of the previous day, scaled by

price, multiplied by 100. Positive (negative) values are consistent with optimism (pessimism). Fcast_Biasy Annual EPS forecast for year y less actual EPS, scaled by price, multiplied by 100. Positive

(negative) values are consistent with optimism (pessimism). Fcast_Delay Logged number of days between the earnings announcement and when the analyst makes his/her

first annual EPS forecast announcement. Restricted to analysts making a forecast over days [0,2]. Fcast_Errory Logged absolute value of Fcast_Biasy. Larger values are consistent with greater analyst error. Make_Fcast Binary variable equal to one if the analyst issues an annual EPS forecast within the three-day

window starting with the earnings announcement. Make_Rec Binary variable equal to one if the analyst makes a buy/hold/sell recommendation within the three-

day window starting with the earnings announcement. Restricted to analysts who made a recommendation within the previous year.

Make_Tprice Binary variable equal to one if the analyst issues a target price recommendation within the three-day window starting with the earnings announcement. Restricted to analysts who issued a target price within the previous year.

MVE Firm’s market value of equity on the day prior to the earnings announcement, in millions. Logged when used in regression specifications.

Post2001 Indicator variable for earnings announcements occurring after 2001. Rain Average hourly rainfall. Measured in millimeters, logged, and standardized Rec_Before The level of the analyst’s buys/hold/sell recommendation prior to the earnings announcement.

Integer values coded from 0 for “sell” to 4 for “strong buy.” Rec_Chg Difference between an analyst’s buy/hold/sell recommendation after the earnings announcement

and the analyst’s buy/hold/sell recommendation before the earnings announcement. Positive (negative) values are consistent with optimism (pessimism).

Severe_Weather Binary variable equal to one if any of the following NOAA Storm Events Database event codes take place in the analyst’s county during the weather measurement window: “Flash Flood,” “Flood,” “Thunderstorm Wind,” “Lightening,” “Tornado,” “Funnel Cloud,” “Hail,” “Heavy Rain,” “Debris Flow,” “Dust Devil,” “Frost/Freeze,” “High Surf,” “High Wind,” “Storm Surge/Tide,” “Strong Wind,” “Wildfire,” and “Winter Weather.”

Tprice_Before The analyst’s outstanding target price prior to the earnings announcement, scaled by price two days prior to the earnings announcement.

Tprice_Bias 12-month target price less the actual stock price 12 months in the future, scaled by price as of the target price announcement date. Positive (negative) values are consistent with optimism (pessimism).

Tprice_Error Logged absolute value of Tprice_Bias. Larger values are consistent with greater analyst error. UE_Neg_Binary Indicator variable for negative earnings surprise in the preceding earnings announcement.

Earnings surprise is calculated as actual EPS minus the median analyst consensus, where the consensus is not more than 100 days old and based on a minimum of two analyst estimates.

38

UE_Pos_Binary Indicator variable for zero or positive earnings surprise in the preceding earnings announcement. Earnings surprise is calculated as actual EPS minus the median analyst consensus, where the consensus is not more than 100 days old and based on a minimum of two analyst estimates.

Unpleasant_Weather First principal component of combining Cloud, Rain, and Wind. Wind Average hourly wind speed. Measured in miles per hour, logged, and standardized

Panel B: Market response test variables The ERC window in all tests is days [0, 2] relative to the earnings announcement. The PEAD window is days [3, 75]. Non-binary variables are winsorized at the 1st and 99th percentile.

Variable Description Abnormal_Market_ Volume

Average Abnormal_Volume for all CRSP shares.

Abnormal_Volume Abnormal trading volume. Calculated as the average daily shares traded divided by the total outstanding shares during the ERC window, minus the average volume over days -70 through -5.

Analysts Number of individual analyst forecasts included in the IBES consensus. Variable “numest.” AR Buy-and-hold portfolio-adjusted return measured from the earnings announcement through two

trading days following the earnings announcement. The portfolio value-weighted returns are based on quintiles of market value and book-to-market, as sourced from Ken French’s website. Multiplied by 100 to be in percentage points.

AR_P Buy-and-hold portfolio-adjusted return measured from the day after the AR window through 75 days following the earnings announcement. Multiplied by 100 to be in percentage points.

Beta Stock market beta, calculated over days [-252, -5] relative to the earnings announcement. BTM Book value of common equity divided by market value of equity as of the end of the quarter. Busy Decile ranking of all earnings announcements on Compustat on the day of the firm’s earnings

announcement. Sorted into deciles by year within sample. Cloud_NYC Average hourly cloud cover over the ERC window. Measured in octiles of sky coverage, logged,

and standardized. Date Indicators for each earnings announcement date. FQ4 Indicator for the fourth fiscal period Friday Indicator for Friday earnings announcements InstOwn Institutional ownership, as sourced from the Thomson 13f database. Loss Indicator variable for IBES EPS < 0 MVE Firm size, calculated as log of market value of equity as of the end of the fiscal quarter. Persist Earnings persistence, calculated as the AR(1) coefficient of regressing current earnings on prior

year’s earnings in the same quarter, calculated over trailing four years. Rain_NYC Average hourly rainfall over the ERC window. Measured in millimeters, logged, and

standardized. Replag Reporting lag, calculated as the logged number of days between quarter-end and the earnings

announcement. Severe_Weather Binary variable equal to one if any land-based severe weather events from the NOAA Storm

Events Database take place in New York City during the ERC window. UE Decile of unexpected earnings, calculated as actual EPS per IBES minus the most recent

consensus, scaled by price as of the end of the fiscal quarter. The most recent consensus must be no older than 100 days and based on a minimum of two individual analyst estimates.

UE_Neg Variable equal to UE for unexpected earnings < 0, and zero otherwise. UE_Pos Variable equal to UE for unexpected earnings >= 0, and zero otherwise. Unpleasant_Weather_NYC

First principal component of combining Cloud, Rain, and Wind, calculated over the ERC window.

Volatility Earnings volatility, standard deviation of seasonal difference in EPS, calculated over trailing four years.

Wind_NYC Average hourly wind speed over the ERC window. Measured in miles per hour, logged, and standardized.

w1-w52 Indicators for each of weeks 1 through 52, in which the earnings announcement occurs.

39

Table 1: Descriptive Statistics Panel A presents descriptive data on our sample selection. The initial sample includes firm-quarter observations with available data in Compustat, CRSP, and I/B/E/S that have the same earnings announcement date in both Compustat and I/B/E/S. Data availability limits the analyst tests to 1997 – 2004. Firm-quarter-analyst observations consist of all analysts who issue at least one annual forecast in both the year prior and subsequent to the earnings announcement. Panel B presents summary statistics on our analyst test variables. See the Appendix for variable definitions. Unlogged values are provided for information but logged specifications are used in regression tests. Panel C presents Pearson correlation coefficients between our main weather and primary analyst activity dependent variables. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Sample selection procedures for analyst activity analyses Observations Firm-quarters: 1997 – 2004 141,712 Firm-quarter-analyst observations 873,362 Less: missing from the IBES Translation File -45,875 Less: analysts following fewer than two firms or four firm-quarters -4,751 Less: firm-quarters with fewer than two analysts -17,837 Less: missing analyst locations -141,223 Less: analysts outside the U.S. - 18,609 Less: missing weather data -9,090 Final firm-quarter-analyst sample 635,826 Unique firm-quarters 94,469 Unique analysts 5,456 Unique cities 139 Average analysts per firm-quarter, excluding singletons 7.6 Average unique cities per firm-quarter, excluding singletons 3.8

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Table 1: Descriptive Statistics (Continued) Panel B: Summary statistics

N Mean 25 Pct. Median 75 Pct. Std. Dev. Residual Std. Dev.

Dependent variables Make_Fcast 635,826 0.469 0.000 0.000 1.000 0.499 0.371 Make_Rec 375,914 0.053 0.000 0.000 0.000 0.225 0.196 Make_Tprice 238,287 0.260 0.000 0.000 1.000 0.438 0.356 Fcast_Delay (unlogged) 298,053 1.011 1.000 1.000 1.000 0.600 0.413 Fcast_Delay 298,053 0.647 0.693 0.693 0.693 0.335 0.234 Rec_Chg 375,914 -0.004 0.000 0.000 0.000 0.212 0.185 Fcast_Bias_y1 254,617 0.420 -0.317 -0.020 0.519 2.660 0.647 Fcast_Bias_v2_y1 258,599 -0.265 -0.294 0.000 0.178 1.509 1.067 Fcast_Bias_y2 179,546 0.997 -0.793 0.218 2.105 5.135 0.670 Fcast_Bias_v2_y2 183,445 -0.340 -0.488 0.000 0.236 1.777 0.996 Tprice_Bias 59,854 0.237 -0.163 0.112 0.464 0.883 0.169 Weather variables Cloud (untransformed) 635,826 4.812 3.495 5.000 6.417 2.044 Cloud 635,826 0.000 -0.361 0.255 0.707 1.000 Rain (untransformed) 635,826 0.121 0.000 0.004 0.108 0.260 Rain 635,826 -0.010 -0.508 -0.486 0.029 0.941 Wind (untransformed) 635,826 8.336 6.245 7.867 10.055 2.957 Wind 635,826 0.003 -0.613 -0.002 0.665 0.967 Unpleasant_Weather 635,826 -0.003 -0.525 0.012 0.504 0.967 Control & Other Variables

Severe_Weather 635,826 0.051 0.000 0.000 0.000 0.219 Allstar 635,826 0.167 0.000 0.000 0.000 0.373 Broker_Size (unlogged) 635,555 77.64 21.00 55.00 112.00 75.20 Broker_Size 635,555 3.851 3.045 4.007 4.718 1.103 Analyst_Exp (unlogged) 635,826 7.465 2.764 5.885 11.510 5.619 Analyst_Exp 635,826 1.623 1.017 1.772 2.443 1.004 Share Price 635,686 34.91 15.20 26.67 41.75 477.06 MVE 635,686 9,355 505 1,728 6,549 23,098

Panel C: Select Pearson correlation coefficients

1. Cloud 2. Rain 3. Wind 4. Unpleasant_Weather 1. Cloud 1 2. Rain 0.283*** 1 3. Wind 0.138*** 0.054*** 1 4. Unpleasant_Weather 0.792*** 0.712*** 0.445*** 1 5. Make_Rec -0.017*** -0.011*** -0.001 -0.016*** 6. Make_Tprice -0.031*** -0.019*** -0.005** -0.030*** 7. Make_Fcast -0.107*** -0.052*** -0.036*** -0.103*** 8. Fcast_Delay 0.133*** 0.050*** 0.067*** 0.131*** 9. Rec_Chg -0.001 0.000 0.000 0.000 10. Fcast_Bias_y1 0.017*** -0.006*** 0.002 0.008*** 11. Fcast_Bias_y2 0.029*** -0.006** 0.006** 0.018*** 12. Fcast_Bias_V2_y1 -0.011*** 0.009*** -0.002 -0.004** 13. Fcast_Bias_V2_y2 -0.013*** 0.010*** -0.002 -0.004* 14. Tprice_Bias 0.039*** -0.011*** 0.012*** 0.024***

41

Table 2: Influence of Unpleasant Weather on Analyst Activity

This table presents select coefficients from various estimations of the following OLS model:

Output = β1Weather + ΣβkControls + ΣβkAnalyst + ΣβkFirm_Qtr + ΣβkCity + ε Output is one of our four proxies for analyst activity. Panel A presents regressions using the combined variable Unpleasant_Weather. Panel B presents regressions using the weather variable Cloud (standardized into units of standard deviation). Analyst, firm-quarter, and city fixed effects are untabulated. Observations with singleton fixed effects are dropped prior to estimation. See Appendix for variable definitions. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Unpleasant_Weather combined measure

HA (1) (2) (3) HA (4) Dependent variable: Make_Fcast Make_Rec Make_Tprice Fcast_Delay Unpleasant_Weather - -0.0189 -0.0029 -0.0074 + 0.0235 [-4.81]*** [-3.69]*** [-3.48]*** [5.85]*** Severe_Weather -0.0757 -0.0147 -0.0377 0.0708 [-7.69]*** [-5.75]*** [-3.77]*** [7.40]*** Allstar 0.0359 0.0088 0.0307 0.0002 [9.54]*** [3.87]*** [4.65]*** [0.06] Allstar * Post2001 -0.0449 -0.0083 -0.0220 0.0138 [-9.58]*** [-2.87]*** [-3.15]*** [3.40]*** Broker_Size 0.0147 -0.0013 0.0056 -0.0109 [6.27]*** [-1.03] [1.39] [-4.72]*** Analyst_Exp -0.0157 -0.0025 -0.0030 0.0063 [-7.01]*** [-1.69]* [-0.67] [2.71]*** Rec_Before -0.0012 [-1.62] Tprice_Before -0.0138 [-7.90]*** Fixed Effects Yes Yes Yes Yes N 628,347 358,260 224,806 279,604 Adjusted R-squared 0.3531 0.0490 0.1771 0.3925

Panel B: Cloud (controls untabulated) HA (1) (2) (3) HA (4) Dependent Variable: Make_Fcast Make_Rec Make_Tprice Fcast_Delay Cloud - -0.0177 -0.0022 -0.0067 + 0.0218 [-5.64]*** [-2.91]*** [-3.62]*** [6.20]*** Control Variables Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes N 628,347 358,260 224,806 279,604 Adjusted R-squared 0.3530 0.0490 0.1770 0.3926

42

Table 3: Influence of Unpleasant Weather on Analyst Pessimism This table presents select coefficients from various estimations of the following OLS model:

Output = β1Weather + ΣβkControls + ΣβkAnalyst + ΣβkFirm_Qtr + ΣβkCity + ε Output is one of our proxies for analyst pessimism. Panel A presents coefficients from regressions using the combined Unpleasant_Weather variable. Panel B presents regressions using the weather variable Cloud (standardized into units of standard deviation). Analyst, firm-quarter, and city fixed effects are untabulated. Observations with singleton fixed effects are dropped prior to estimation. See Appendix for variable definitions. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Unpleasant_Weather combined measure (1) (2) (3) (4) (5) (6) HA Rec_Chg Fcast_Bias

_y1 Fcast_Bias

_v2_y1 Fcast_Bias

_y2 Fcast_Bias

_v2_y2 Tprice_Bias

Unpleasant_Weather - 0.0001 -0.0018 -0.0027 -0.0114 -0.0116 -0.0036 [0.15] [-1.01] [-1.22] [-2.72]*** [-2.88]*** [-2.46]** Severe_Weather -0.0008 -0.0027 -0.0063 -0.0131 -0.0079 -0.0032 [-0.36] [-0.27] [-0.55] [-0.59] [-0.42] [-0.35] Allstar 0.0009 0.0162 0.0132 0.0393 0.0384 0.0208 [0.45] [1.78]* [1.43] [2.05]** [2.18]** [2.17]** Allstar * Post2001 -0.0023 -0.0297 -0.0248 -0.0627 -0.0431 -0.0128 [-0.89] [-2.55]** [-2.12]** [-2.79]*** [-2.02]** [-1.33] Broker_Size -0.0033 -0.0069 -0.0000 -0.0179 -0.0102 0.0089 [-2.76]*** [-1.16] [-0.00] [-1.35] [-0.87] [1.81]* Analyst_Exp -0.0013 -0.0075 -0.0078 -0.0292 -0.0351 0.0019 [-0.89] [-1.11] [-1.13] [-1.98]** [-2.75]*** [0.30] Rec_Before -0.0497 [-65.80]*** Tprice_Before 0.2248 [13.55]*** Fcast_Delay -0.0297 0.0842 -0.0166 0.1308 [-4.00]*** [8.43]*** [-1.25] [8.38]*** Fixed Effects Yes Yes Yes Yes Yes Yes N 358,260 236,980 240,793 162,735 167,194 45,016 Adjusted R-squared 0.0795 0.9186 0.7323 0.9383 0.5626 0.9472 Panel B: Cloud (controls untabulated) (1) (2) (3) (4) (5) (6) HA Rec_Chg Fcast_Bias

_y1 Fcast_Bias

_v2_y1 Fcast_Bias

_y2 Fcast_Bias

_v2_y2 Tprice_Bias

Cloud - 0.0001 -0.0017 -0.0026 -0.0072 -0.0103 -0.0019 [0.13] [-0.97] [-1.26] [-1.77]* [-2.75]*** [-1.50] Controls Yes Yes Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes Yes Yes N 358,260 236,980 240,793 162,735 167,194 45,016 Adjusted R-squared 0.0795 0.9186 0.7323 0.9383 0.5626 0.9472

43

Table 4: Influence of Unpleasant Weather on Analyst Accuracy This table presents select coefficients from various estimations of the following OLS model:

Output = β1Weather + ΣβkControls + ΣβkAnalyst + ΣβkFirm_Qtr + ΣβkCity + ε Output is one of our proxies for analyst accuracy. Panel A presents coefficients from regressions using the combined Unpleasant_Weather variable. Analyst, firm-quarter, and city fixed effects are untabulated. Observations with singleton fixed effects are dropped prior to estimation. See Appendix for variable definitions. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) HA Fcast_Error_y1 Fcast_Error_y2 Tprice_Error Unpleasant_Weather ? 0.0006 0.0005 -0.0008 [1.14] [0.69] [-0.16] Severe_Weather -0.0008 -0.0021 0.0262 [-0.32] [-0.57] [1.07] Allstar 0.0028 0.0013 0.0463 [1.29] [0.39] [1.55] Allstar * Post2001 -0.0035 -0.0028 -0.0423 [-1.37] [-0.76] [-1.36] Broker_Size -0.0017 -0.0045 -0.0184 [-1.17] [-2.04]** [-1.03] Analyst_Exp -0.0000 -0.0003 -0.0545 [-0.01] [-0.15] [-2.55]** Fcast_Delay -0.0121 -0.0082 [-6.87]*** [-3.78]*** Tprice_Before 0.0864 [7.16]*** Fixed Effects Yes Yes Yes N 236,980 162,735 45,016 Adjusted R-squared 0.9212 0.9246 0.6684

44

Table 5: Analyst Behavior after Positive Versus Negative News This table presents select coefficients from various estimations of the following OLS model:

Output = β1Unpleasant_Weather_UE_Pos + β2 Unpleasant_Weather_UE_Neg+ ΣβkControls + ΣβkAnalyst

+ ΣβkFirm_Qtr + ΣβkCity + ε Output is one of our proxies for analyst activity or pessimism. Unpleasant_Weather_UE_Pos is equal to Unpleasant_Weather in cases where the recent earnings surprise was greater than or equal to zero, and zero otherwise. Unpleasant_Weather_UE_Neg is equal to Unpleasant_Weather in cases with negative earnings surprise, and zero otherwise. Panel A (B) presents regressions of analyst activity (pessimism). Fixed effects are untabulated. Observations with singleton fixed effects are dropped prior to estimation. See Appendix for variable definitions. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Analyst activity tests (partial results reported)

HA (1) (2) (3) HA (4) Dependent variable: Make_Fcast Make_Rec Make_Tprice Fcast_Delay Unpl._Weather_UE_Pos - -0.0178 -0.0028 -0.0083 + 0.0239 [-4.57]*** [-3.23]*** [-3.68]*** [5.91]*** Unpl._Weather_UE_Neg - -0.0192 -0.0020 -0.0044 + 0.0242 [-4.74]*** [-1.51] [-1.54] [5.85]*** Controls Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes N 608,148 350,496 222,286 277,776 Adjusted R-squared 0.3492 0.0496 0.1771 0.3915 F-Test of joint sig. [12.03]*** [5.54]*** [6.98]*** [19.20]*** Tests of Coeff. Differences (Unpl._Weather_UE_Pos) – (Unpl._Weather_UE_Neg)

-0.0014 0.0008 0.0039

0.0003

[0.58] [0.60] [1.22] [0.13] Panel B: Analyst pessimism tests (partial results reported) (1) (2) (3) (4) (5) (6) HA Rec_Chg Fcast_Bias

_y1 Fcast_Bias

_v2_y1 Fcast_Bias

_y2 Fcast_Bias

_v2_y2 Tprice_Bias

Unpl._Weather_UE_Pos - 0.0002 -0.0033 -0.0075 -0.0047 -0.0098 -0.0050 [0.25] [-1.74]* [-3.12]*** [-1.04] [-2.30]** [-2.88]*** Unpl._Weather_UE_Neg - 0.0003 0.0038 0.0083 -0.0271 -0.0209 -0.0060 [0.26] [0.68] [1.33] [-2.09]** [-1.81]* [-1.73]* Controls Yes Yes Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes Yes Yes N 358,260 236,980 240,793 162,735 167,194 45,016 Adjusted R-squared 0.0796 0.9186 0.7323 0.9383 0.5626 0.9472 F-Test of joint sig. [0.06] [1.74] [5.78]*** [2.6]* [4.06]** [5.08]*** Tests of Coeff. Differences (Unpl._Weather_UE_Pos) – (Unpl._Weather_UE_Neg)

0.0001 0.0071 0.0158 -0.0224 -0.0111 -0.0010

[0.08] [1.20] [2.37]** [-1.66]* [-0.92] [-0.26]

45

Table 6: Further Controlling for Potential Physical Effects This table presents select coefficients from various estimations of the following OLS model:

Output = β1Weather + ΣβkControls + ΣβkAnalyst + ΣβkFirm_Qtr + ΣβkCity + ε Output is one of our proxies for analyst activity or pessimism. Panel A (B) presents analysis of our activity (pessimism) dependent variables. Analyst, firm-quarter, and city fixed effects are untabulated. Observations with singleton fixed effects are dropped prior to estimation. See Appendix for variable definitions. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Analyst activity tests (partial results reported)

HA (1) (2) (3) HA (4) Dependent variable: Make_Fcast Make_Rec Make_Tprice Fcast_Delay Cloud_Wind - -0.0151 -0.0024 -0.0070 + 0.0206 [-4.40]*** [-2.99]*** [-3.31]*** [5.60]*** Rain -0.0078 -0.0013 -0.0023 0.0073 [-1.76]* [-1.61] [-0.92] [1.74]* Severe_Weather -0.0785 -0.0149 -0.0397 0.0777 [-7.74]*** [-5.70]*** [-3.93]*** [8.25]*** Controls Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes N 628,347 358,260 224,806 279,604 Adjusted R-squared 0.3530 0.0490 0.1771 0.3924

Panel B: Analyst pessimism tests (partial results reported) (1) (2) (3) (4) (5) (6) HA Rec_Chg Fcast_Bias

_y1 Fcast_Bias

_v2_y1 Fcast_Bias

_y2 Fcast_Bias

_v2_y2 Tprice_Bias

Cloud_Wind - -0.0004 -0.0007 -0.0010 -0.0114 -0.0123 -0.0022 [-0.59] [-0.35] [-0.44] [-2.45]** [-2.85]*** [-1.51] Rain 0.0005 -0.0011 -0.0018 -0.0025 -0.0008 -0.0029 [0.82] [-0.50] [-0.75] [-0.58] [-0.19] [-1.72]* Severe_Weather -0.0012 -0.0027 -0.0061 -0.0176 -0.0144 -0.0025 [-0.55] [-0.27] [-0.52] [-0.78] [-0.76] [-0.27] Controls Yes Yes Yes Yes Yes Yes Fixed Effects Yes Yes Yes Yes Yes Yes N 358,260 236,980 240,793 162,735 167,194 45,016 Adjusted R-squared 0.0795 0.9186 0.7323 0.9383 0.5626 0.9472

46

Table 7: Market Pricing Analysis: Descriptive Statistics Panel A presents descriptive data on our sample selection procedures. The initial samples include firm-quarter observations with available data in Compustat, CRSP, and I/B/E/S, and that have the same earnings announcement date in both Compustat and I/B/E/S. The market pricing tests span 1990 through 2013. Panel B presents summary statistics on our market test variables. See appendix for variable definitions. Unlogged values are provided for information but, where indicated, logged specifications are used in regression tests. Panel C presents Pearson correlation coefficients between the weather variables and primary dependent variables. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Market pricing tests Observations Firm-quarters with validated earnings announcement dates: 1990-2013 327,530 Drop: CRSP share codes other than 10 and 11 (ordinary, common, US shares) -17,439 Drop: missing UE or market-adjusted stock returns -102,606 Drop: price < $1 or unexpected earnings > price -1,269 Drop: earnings announcements before or more than one-year after quarter-end -109 Drop: missing control variables -4,870 Drop: missing weather data -1,729 Drop: missing portfolio-adjusted AR or AR_P -6,308 Final firm-quarter sample 193,200

Panel B: Summary statistics

Mean 25 Pct. Median 75 Pct. Std. Dev. Dependent variables AR 0.045 -3.994 -0.009 4.119 8.373 AR_PEAD 0.050 -12.370 -0.961 10.732 22.629 Weather variables Cloud_NYC (untransformed) 4.464 3.056 4.583 5.917 1.911 Cloud_NYC -0.001 -0.500 0.230 0.719 0.993 Rain_NYC (untransformed) 0.133 0.000 0.022 0.165 0.239 Rain_NYC -0.008 -0.608 -0.489 0.241 0.961 Wind_NYC (untransformed) 8.285 6.133 7.740 10.017 2.932 Wind_NYC -0.001 -0.688 -0.041 0.697 0.986 Unpleasant_Weather_NYC -0.003 -0.579 0.085 0.657 0.981 Other Variables UE (not in deciles) -0.0008 -0.0009 0.0003 0.0018 0.0114 MVE (unlogged) 3,808 249 730 2,421 10,137 MVE 6.729 5.518 6.594 7.792 1.667 BTM 0.544 0.270 0.455 0.707 0.417 RepLag (unlogged) 30.0 22.0 28.0 36.0 12.0 Replag 3.327 3.091 3.332 3.584 0.380 Analysts (unlogged) 7.72 3.00 6.00 10.00 5.79 Analysts 1.783 1.099 1.792 2.303 0.727 Persist 0.332 -0.048 0.232 0.706 0.609 Volatility 0.427 0.094 0.200 0.445 0.681 Beta 1.064 0.659 1.011 1.403 0.563 InstOwn 0.591 0.398 0.613 0.791 0.249 FQ4 0.251 0.000 0.000 1.000 0.434 Loss 0.179 0.000 0.000 0.000 0.384 Friday 0.062 0.000 0.000 0.000 0.242 Busy 4.432 2.000 4.000 7.000 2.849 Severe_Weather 0.042 0.000 0.000 0.000 0.200

47

Table 8: Influence of Unpleasant Weather on the Market Pricing of Earnings News This table presents select coefficients from various estimations of the following OLS models:

AR or AR_PEAD =α0 +α1(UE) + α2(UE*Weather) + Σαk(Controls) + Σαk(UE*Controls) + Σαk(Date) + ε Panel A (B) presents regressions using our combined weather measure Unpleasant_Weather_NYC (Cloud_NYC). Columns (1)-(4) estimate the AR model without and with controls. Columns (5)-(8) estimate the AR_PEAD model without and with controls. Both models are estimated using the full sample, and subsamples partitioned pre-2005/post-2004. Untabulated controls in columns (2) through (4) include Severe_Weather, MVE, Persist, Volatility, InstOwn, Beta, BTM, Loss, FQ4, Friday, Busy, Replag, and w1-w52. All variables are de-meaned before calculating the interactions. Date fixed effects are untabulated. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Unpleasant Weather in New York City

ERC Tests (model 2a) PEAD Tests (model 2b) HA (1) (2) (3) (4) HA (5) (6) (7) (8) Sample Period: Full Full Pre-2005 Post-2004 Full Full Pre-2005 Post-2004 UE 0.8205 0.8217 0.7286 0.9422 0.2803 0.2182 0.2858 0.1180 [90.42]*** [101.15]*** [56.20]*** [69.44]*** [12.31]*** [9.72]*** [7.35]*** [3.51]*** UE*Unpleasant_Weather_NYC - -0.0888 -0.0662 -0.0747 -0.0442 + 0.0632 0.0532 0.1422 -0.0030 [-9.34]*** [-7.57]*** [-5.12]*** [-4.01]*** [2.75]*** [2.29]** [3.30]*** [-0.11] Controls & Interactions No Yes Yes Yes No Yes Yes Yes Date fixed effects Yes Yes Yes Yes Yes Yes Yes Yes N 193,200 193,200 103,307 89,893 193,200 193,200 103,307 89,893 Adjusted R-squared 0.0842 0.0978 0.0639 0.1369 0.0169 0.0214 0.0238 0.0185

Panel B: Cloud Cover in New York City

ERC Tests (model 2a) PEAD Tests (model 2b) HA (1) (2) (3) (4) HA (5) (6) (7) (8) Sample Period: Full Full Pre-2005 Post-2004 Full Full Pre-2005 Post-2004 UE 0.8207 0.8208 0.7254 0.9406 0.2801 0.2188 0.2848 0.1177 [90.56]*** [101.08]*** [56.22]*** [68.82]*** [12.30]*** [9.78]*** [7.32]*** [3.48]*** UE * Cloud_NYC - -0.0848 -0.0550 -0.0356 -0.0374 + 0.0723 0.0476 0.1085 -0.0030 [-8.44]*** [-5.96]*** [-2.44]** [-3.15]*** [3.03]*** [2.00]** [2.43]** [-0.11] Controls & Interactions No Yes Yes Yes No Yes Yes Yes Date fixed effects Yes Yes Yes Yes Yes Yes Yes Yes N 193,200 193,200 103,307 89,893 193,200 193,200 103,307 89,893 Adjusted R-squared 0.0841 0.0976 0.0636 0.1368 0.0169 0.0214 0.0238 0.0185

48

Table 9: PEAD Price Corrections at the Subsequent Earnings Announcement This table presents select coefficients from various estimations of the following OLS model:

ARq = α0 +α1(UEq) + α2(UEq* Weatherq) +β1(UEq-1) + β2(Weatherq-1) + β3(UEq-1* Weatherq-1) + Σαk(Controlsq) + Σαk(UEq*Controlsq) + Σαk(Dateq) + ε

Panel A (B) presents regressions using our combined weather measure Unpleasant_Weather_NYC (Cloud_NYC). In these estimations, the subscript q (q-1) denotes the current (previous) quarterly earnings announcement period realization for the respective variable. Column (1) is estimated using the full sample. Columns (2) and (3) are estimated using subsamples partitioned pre-2005/post-2004. Untabulated controls in all models include Severe_Weather, MVE, Persist, Volatility, InstOwn, Beta, BTM, Loss, FQ4, Busy, Friday, Replag, and w1-w52. All variables are de-meaned before calculating the interactions such that the main effects can be interpreted at the sample averages. Date fixed effects are untabulated. Sample sizes are reduced from Table 8 due to requiring two consecutive quarters’ data. Standard errors are clustered by date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Unpleasant Weather in New York City

HA (1) (2) (3) Full Pre-2005 Post-2004 UEq 0.9087 0.8109 1.0043 [99.22]*** [52.69]*** [69.26]*** UEq * Unpleasant_Weather_NYCq - -0.0624 -0.0695 -0.0489 [-6.47]*** [-3.78]*** [-4.28]*** UEq-1 -0.2441 -0.2075 -0.2651 [-29.20]*** [-15.84]*** [-24.64]*** Unpleasant_Weather_NYCq-1 -0.0099 0.0231 -0.0271 [-0.34] [0.45] [-0.76] UEq-1 * Unpleasant_Weather_NYCq-1 + 0.0273 0.0419 0.0190 [3.39]*** [2.95]*** [1.98]** Controls and Interactions Yes Yes Yes Date fixed effects Yes Yes Yes N 160,571 76,461 84,110 Adjusted R-sq 0.1096 0.0682 0.1480

Panel B: Cloud Cover in New York City

HA (1) (2) (3) Full Pre-2005 Post-2004 UEq 0.9088 0.8121 1.0027 [99.16]*** [52.78]*** [68.82]*** UEq * Cloud_NYCq - -0.0545 -0.0381 -0.0406 [-5.37]*** [-2.13]** [-3.27]*** UEq-1 -0.2444 -0.2108 -0.2643 [-29.14]*** [-15.87]*** [-24.39]*** Cloud_NYCq-1 -0.0151 0.0047 -0.0251 [-0.52] [0.09] [-0.72] UEq-1 * Cloud_NYCq-1 + 0.0252 0.0338 0.0172 [2.93]*** [2.15]** [1.67]* Controls and Interactions Yes Yes Yes Date fixed effects Yes Yes Yes N 160,571 76,461 84,110 Adjusted R-sq 0.1094 0.0680 0.1479

49

Table 10: Influence of Unpleasant Weather on Trading Volume This table presents select coefficients from various estimations of the following OLS model:

Abnormal_Volume = α1(Weather) + Σαk(Controls) + ε

In these estimations, the dependent variable is Abnormal Volume, the firm’s share turnover during the earnings announcement period [0, 2] minus normal trading volume in the trailing days [-75, -5]. Panel A (B) presents regressions using our combined weather measure Unpleasant_Weather_NYC (Cloud_NYC). Untabulated controls include signed and absolute UE deciles, Severe_Weather, MVE, Persist, Volatility, InstOwn, Beta, BTM, Loss, FQ4, Replag, Friday, Busy, and w1-w52. Columns (3)-(6) also include an additional control for the average abnormal volume for all CRSP firms. Controls and fixed effects are untabulated for brevity. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively. Panel A: Unpleasant Weather in New York City

HA (1) (2) (3) (4) (5) Sample Period: Full Sample Full Sample Full Sample Pre-2004 Post-2005 Unpleasant_Weather_NYC - -0.0692 -0.0524 -0.0455 -0.0659 -0.0267 [-10.54]*** [-9.95]*** [-9.74]*** [-9.77]*** [-4.10]*** Control variables No Yes Yes Yes Yes Abn. Market Volume No No Yes Yes Yes N 193,198 193,198 193,198 103,306 89,892 Adjusted R-sq. 0.0028 0.1286 0.1315 0.1061 0.1357

Panel B: Cloud Cover in New York City

HA (1) (2) (3) (4) (5) Sample Period: Full Sample Full Sample Full Sample Pre-2004 Post-2005 Cloud_NYC - -0.0833 -0.0428 -0.0336 -0.0222 -0.0285 [-13.24]*** [-8.48]*** [-7.65]*** [-3.24]*** [-4.60]*** Control variables No Yes Yes Yes Yes Abn. Market Volume No No Yes Yes Yes N 193,198 193,198 193,198 103,306 89,892 Adjusted R-sq. 0.0041 0.1282 0.1311 0.1046 0.1358

50

Table 11: ERC Tests – by Positive versus Negative Earnings Surprise This table presents select coefficients from various estimations of the following OLS model:

AR =α0 +α1(UE_Pos) + α2(UE_Neg) + α3(UE_Pos*Unpleasant_Weather_NYC) + α5(UE_Neg* Unpleasant_Weather_NYC) + Σαk(Controls) + Σαk(UE*Controls) + Σαk(Date) + ε

The variable UE_Pos (UE_Neg) takes the value of UE for earnings surprises greater than or equal to zero (less than zero), and zero otherwise. All other variables are unchanged from the ERC model in Table 8. Controls are untabulated for brevity. Standard errors are clustered by earnings announcement date. The superscripts ***, **, * indicate two-tailed statistical significance at the 1%, 5%, and 10% level, respectively.

ERC Tests HA (1) (2) (3) (4) Sample Period: Full Full Pre-2005 Post-2004 UE_Pos 0.8130 0.8192 0.7286 0.9370 [82.55]*** [89.17]*** [51.65]*** [61.12]*** UE_Neg 0.7444 0.8017 0.7291 0.8999 [22.51]*** [23.87]*** [15.99]*** [17.50]*** UE_Pos*Unpleasant_Weather_NYC - -0.0857 -0.0673 -0.0773 -0.0462 [-6.49]*** [-5.33]*** [-3.51]*** [-2.92]*** UE_Neg*Unpleasant_Weather_NYC - -0.0925 -0.0649 -0.0716 -0.0418 [-5.71]*** [-4.15]*** [-3.01]*** [-2.05]** Controls & Interactions No Yes Yes Yes Date fixed effects Yes Yes Yes Yes N 193,200 193,200 103,307 89,893 Adjusted R-squared 0.0842 0.0978 0.0639 0.1369 F-Test of joint sig. [43.88]*** [29.05]*** [13.08]*** [8.29]*** Tests of Coeff. Differences (UE_Pos*Unpleasant_Weather_NYC) – (UE_Neg*Unpleasant_Weather_NYC)

-0.0068 0.0024 0.0057 0.0044

[-0.30] [0.11] [0.16] [0.15]