Learning through Difficult Times: Evidence from Financial Analysts

June, 2019

Abstract: We document that financial analysts who experienced industry shocks over theircareer (experienced analysts) make more accurate earnings forecasts and more informative
recommendation changes. The effect is unlikely to be explained by improved access tomanagement as we find a stronger effect of industry shock experience after Regulation Fair
Disclosure. Exogenous coverage termination of experienced analysts have real impacts on
affected firms’ information environment. Overall, the evidence suggests that analysts canacquire industry expertise by learning through difficult times.
Key words: Industry shocks; Experience; Forecast accuracy; Analysts

11. Introduction
Sell-side financial analysts are prominent information intermediaries in capital markets.
They engage in private information search, perform prospective analysis aimed at forecastinga firm’s future earnings and cash flows, and conduct retrospective analysis that interprets past
events. Regulators and other market participants view analysts’ activities and the competition
among analysts as enhancing the informational efficiency of security prices. Given the valuable
service analysts provide to market participants, numerous studies have been devoted to what
determines the quality of analyst research. On the one hand, academic studies show that a
number of innate attributes and external factors including analysts’ ability, general and firmspecific experience, incentives, task complexity, the prestige of their brokerage house, andaccess to firm management are related to their forecasting performance (Mikhail et al., 1997;
Clement, 1999; Malloy, 2005; Cohen, Frazzini, and Malloy, 2010). On the other hand,practitioners in the survey of Institutional Investor (II) indicate that one of the most important
attributes of a good analyst is the in-depth industry knowledge.1 Consistent with II’s survey
evidence, Bradley, Gokkaya, and Liu (2017) find that analysts produce better forecasts andmore informative recommendations on firms related to their preanalyst work experience.
However, related industry work experience may not be the only way for an analyst toacquire industry expertise. Given the importance of industry expertise in determining their
output and career outcomes, analysts should have strong incentives to gain industry knowledge
through other channels. In this paper, we study whether analysts can also gain industry

1

Each October, II releases its annual all-star analyst rankings, which polls buy-side institutions and ranks the top

sell-side analysts in each industry. In addition to a list of top analysts, II provides information on the qualities that
respondents view as most important. Industry knowledge has been consistently ranked the most important trait.
2

experience through industry shocks and how such experience relates to their subsequent
performance. Our conjecture is motivated by Arrow (1962)’s seminal work on learning by
doing, who states that learning can only take place through the attempt to solve a problem andtherefore only takes place during activity. Kempf et al. (2017) provide evidence supporting thislearning by doing argument, as they find that a fund manager who has experienced industry
shocks become more skilled subsequently.
To test the idea, we follow Kempf et al. (2017) and define a shock for a given industryquarter if the value-weighted industry return is ranked in the bottom four across all 48 FamaFrench industries in that quarter. We then identify experienced analysts as those who haveexperienced industry shocks over their career. We use large negative industry returns to define
industry shocks for several reasons. First, studies find that analysts tend to exert more effort
and investors pay more attention to analyst research in bad times (Loh and Stulz, 2018). Thissuggests that learning will be most effective in bad times when high-quality feedback is readily
available. Second, a large literature in psychology documents that bad events have a greater
impact on individuals than comparable good events. For example, Baumeister et al. (2001)
made the following observations when arguing “Bad is stronger than good”:
The greater power of bad events over good ones is found in everyday events, major life
events (e.g., trauma), close relationship outcomes, social network patterns, interpersonal
interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more
impact than good ones, and bad information is processed more thoroughly than good.
Based on this measure, we examine how analysts’ performance is related to their industry
shock experience. Using a comprehensive sample of 1,978,053 earnings forecasts on 6,172
3

firms over the period of 1983 to 2015, we find that the relative forecast error of analysts withindustry shock experience is significantly lower than that of inexperienced analysts. In ourempirical specification, we control for various analyst and forecast characteristics that areassociated with forecast accuracy as documented by previous studies, including general andfirm-specific forecasting experience, brokerage size, and the timeliness and frequency of
forecasts. More importantly, we also include analyst by quarter and firm by quarter fixed effects,
so our identification comes from variation across firms within the same analyst’s portfolio in
the same time and variation across different analysts’ forecasts on the same firm in the sametime. We are able to do this because (1) for an analyst covering more than one industry (70%of analysts), she may have shock experience for firms in one industry but not for firms in
another industry, even when she covers both sets of firms at the same time; (2) for firms withmultiple analysts following, some analysts will have industry shock experience while others
do not. After controlling for the paired fixed effects and an extensive list of controls, we findforecasts issued by analysts with industry shock experience is 0.89% more accurate than thosemade by inexperienced analysts. The effect is economically sizeable, as analysts need about
3.7 more years of firm-specific experience to achieve the same level of improvement in forecast
accuracy.
After documenting that experienced analysts improve their forecast accuracy, it is natural
to ask how this improvement in performance is achieved. One potential channel could be thatanalysts are able to cultivate a good relationship with firm managers in a difficult industry
environment, and hence enjoy better access to management subsequently. 2 If this channel

2

Cohen et al. (2010) find that analysts gain comparative information advantages through educational ties with4

dominates, the result should be much weaker after the passage of Regulation Fair Disclosure(Reg FD), which prohibits firm managers from selectively disclosing material nonpublic
information to analysts. However, we find the impact of industry shock experience on forecast
accuracy becomes even stronger after Reg FD, suggesting that improved access to firmmanagement is unlikely to explain our findings. Rather, the stronger effect observed in the postReg FD period suggests that experienced analysts who have a better understanding and indepth knowledge of the industry now have an even larger comparative advantage relative to
inexperienced analysts who cannot get access to private information as easily as before.
Next, we investigate whether market participants can properly value analysts’ industry
shock experience by examining market reactions to recommendation changes issued byexperienced analysts versus inexperienced analysts. The results show that experienced analysts’
upward and downward recommendation changes elicit stronger market reactions than those of
inexperienced analysts, after controlling for analyst*quarter and firm*quarter fixed effects.
For instance, upward (downward) recommendation changes issued by experienced analysts areassociated with 0.22% (0.41%) higher (lower) 3-day announcement returns compared torecommendations issued by inexperienced analysts. As a benchmark, the median 3-day
announcement returns for upward (downward) recommendation changes is 1.60% (-1.48%).
These results suggest that market indeed places more weight on experienced analyst forecasts
than on inexperienced analyst forecasts.
We further examine whether experienced analysts have any real impacts on firms’

senior officers and board members of firms that they cover, thus they outperform on stock recommendations.
Green et al. (2014) state that access to management is an important source of analysts’ informational advantage
and generally leads to more informative analyst research.
5

information environment using the exogenous termination of analyst coverage due to brokerageclosures. Following Kelly and Ljungqvist (2012), we identify 55 brokerage closure events
during 2003 and 2012, which leads to a reduction in analyst coverage that is exogenous to thecovered firms’ characteristics. Consistent with Kelly and Ljungqvist (2012), we confirm thatfirms losing analyst coverage experience a significant increase in information asymmetry, a
reduction in stock liquidity, and lower stock returns during the event window. More importantly,
these effects are significantly greater for losses of experienced analysts compared to losses of
inexperienced analysts. For example, the cumulative abnormal returns (CAR) over the 3-day
window around exogenous termination of analyst coverage is on average 0.94% lower for theloss of experienced analysts compared to the loss of inexperienced analysts. This difference
persists for the entire trading month, implying that coverage termination of experienced analyst
exerts more negative impacts for affected firms’ information environment than losses of
inexperienced analysts.
Our final test concerns what motivates analysts to learn through industry shocks andimprove performance. One possibility is that those analysts who successfully navigate throughindustry shocks have better future career outcomes. Consistent with this conjecture, we findanalysts gaining experience during industry shocks are 46% more likely to become an II allstar analyst.
Our study contributes to the literature in several ways. First, our paper identifies a novel
and important source of analyst skill acquired through industry shocks. As we construct theexperience measure based on industry shocks, our measure is naturally different from thegeneral and firm-specific forecasting experience that increase linearly with time and are the6focus of earlier studies (Clement, 1999; Gilson et al., 2001). In addition, we find analysts’
industry shock experience is valued by market participants and can affect the information
environment of covered firms. Our paper thus extends the work of Bradley, Gokkaya and, Liu(2017) by showing that industry expertise can be gained by learning through difficult times in
addition to preanalyst work experience.
Second, our study contributes to the growing literature on how past experience shapes
economic agents’ current preference and beliefs. Malmendier and Nagel (2011) show thatexperiences of macroeconomic shocks have long-term effects on retail investors’ risk
preferences. Kempf et al. (2017) document that industry experience is a first-order driver ofprofessional investors’ skills. Our paper emphasizes the importance and relevance ofexperience gained from past industry shocks for sell-side analysts, another important market
participant. An additional advantage of using analyst as a setting to test the effect of experienceis that we can measure the quality of analysts’ output using forecast accuracy, as compared tofund managers whose performance can only be inferred from noisy returns.
The rest of the paper proceeds as follows. Section 2 describes the data and provides
descriptive statistics. Section 3 reports the main empirical results regarding the impact ofindustry experience on analyst forecast accuracy and market reactions to recommendation
changes. In section 4, we examine the real economic impacts of analyst industry experience onfirms’ information environment. In section 5, we conduct additional analyses and robustness
tests. The last section concludes.

7

2. Data and Summary Statistics
2.1 Data
Our sample construction begins with the Institutional Brokers’ Estimate System (I/B/E/S)
database, from which we obtain individual analyst’s quarterly earnings forecasts and firm’s
actual earnings per share (EPS) from 1983 to 2015. We remove analysts coded as anonymousby I/B/E/S because it is not possible to track their forecasts. We exclude stale forecasts that are
more than 120 days old due to their low information content. To avoid confounding effects
from earnings announcement, we also exclude forecasts that are issued less than three days
before the earnings announcement day. We keep only observations with a non-missing valueof the actual EPS in order to calculate forecast error.
We then merge the analyst forecast sample with CRSP/Compustat to get stock price andaccounting information of firms covered by each analyst. Each stock covered by any analysts
is assigned to a corresponding Fama-French 48 industry portfolio based on the 4-digit
Compustat SIC code. We use CRSP SIC codes if Compustat SIC codes are not available. To
construct a quarterly measure of industry experience (SExp), we convert monthly 48 industry
portfolio returns to quarterly returns.3 We remove observations with missing variables for ourbaseline regression. These filtering criteria result in a sample of 14,086 analysts issuing
1,978,053 quarterly earnings forecasts.
To examine stock market reactions to analysts’ recommendation changes, we obtain dataof stock recommendations from I/B/E/S Recommendation History data set, which contains therecommendations of individual analysts with ratings ranging from 1 (strong buy) to 5 (strong3

source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
8sell). Recommendation changes are computed as the current rating minus the prior rating by
the same analyst. Analysts coded as anonymous by I/B/E/S are excluded. Reiterations of earlier
recommendations are dropped because of their lower information content. Following Loh andStulz (2011), we exclude recommendations that fall in the three-day window around quarterly
earnings announcement dates (obtained from Compustat). We also exclude recommendation
changes in which multiple analysts issued a recommendation on the same day. The resulting
sample contains 57,989 recommendation changes.
To identify exogenous coverage termination of analysts, we use 55 brokerage closure
events between 2003 and 2012 that affect at least two analysts as stated in Appendix I of Cen
et al. (2016). The criteria used to identify brokerage closures are as follows: (1) I/B/E/S stop
file has received termination notifications of EPS forecasts sent by the brokerage firm andclustered notifications are required to ensure that all stocks under coverage by the broker are
terminated at the same time; (2) the brokerage firm never issues anything in I/B/E/S Detail
Files for both earnings forecasts and recommendations afterwards; and (3) the brokerage
firm/research division is out of business by double checking information from Bloomberg
Businessweek, Capital IQ, Factiva and FINRA broker check database.
Finally, we hand collect Institutional Investor all-star analyst lists from 1983 to 2011 inorder to examine how industry experience affects analysts’ career outcomes.

2.2 Variable Constructions
2.2.1 Experience Based on Industry Shocks
To construct the industry shock experience measure, we first define an industry as
9experiencing a shock in a quarter if the value-weighted return of the industry is ranked among
the bottom four across all 48 Fama-French industry portfolios in that quarter (i.e., IS = 1). Next,
we compute the experience of an analyst for an industry in a given quarter (𝐸𝑥𝑝𝑎,𝑞,𝑖 ) by adding
up the incidences of shocks the analyst has experienced in that industry since she first started
covering any stocks in that industry:
𝐸𝑥𝑝𝑎,𝑖,𝑞 = ∑ 𝐼𝑆𝑖,𝜏 ,

(1)

𝜏<𝑞

where a indexes analysts, q indexes quarters, and IS denotes a shock in industry i in quarter 𝜏.
Then we create a dummy variable based on whether or not an analyst has experienced any
industry shocks. Specifically, the dummy variable equals one if an analyst has experienced atleast one shock in industry i up to current quarter q:
𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 = 𝐼[𝐸𝑥𝑝𝑎,𝑖,𝑞 > 0].

(2)

Note that the industry shock experience constructed in this way refers to experience gainedin economic downturns, which is different from experience gained from pure passage of time.
In other words, our experience measure is not a linear function of time. Rather, it depends onan analyst’s exposure to particular shocks or business cycles of covered industries.
2.2.2 Analyst Performance Measures
We use several metrics to measure analyst performance. The first and most direct measureis the earnings forecast accuracy. The second measure is based on price reactions to analyst
recommendation changes. The third category concerns the impacts of analysts on firms’
information environment. We describe each of these measures in detail below.
Following the literature (Malloy 2005; Green et al. 2014: Bradley et al. 2017), we measureearnings forecast accuracy using proportional mean absolute forecast error (PMAFE)10

constructed by Clement (1999). Specifically, PMAFE is defined as:̅̅̅̅̅̅̅̅̅
𝑃𝑀𝐴𝐹𝐸𝑎,𝑓,𝑞 = (𝐴𝐹𝐸𝑎,𝑓,𝑞 − ̅̅̅̅̅̅̅̅̅
𝐴𝐹𝐸𝑓,𝑞 )/𝐴𝐹𝐸
𝑓,𝑞

(3)

where 𝐴𝐹𝐸𝑎,𝑓,𝑞 is the absolute forecast error for analyst a’s forecast of firm f in quarter q, and̅̅̅̅̅̅̅̅̅
𝐴𝐹𝐸𝑓,𝑞 is the mean absolute forecast error for firm f in quarter q. The numerator compares
analyst a’s absolute forecast error to the average absolute forecast error of other analysts
following the same stock during the same period and the denominator aims to reduce
heteroscedasticity. Thus, 𝑃𝑀𝐴𝐹𝐸𝑎,𝑓,𝑞 evaluates analyst a’s forecast accuracy relative to all
analysts covering firm f in quarter q and controls for heterogeneities across companies, time,and industries (Ke and Yu, 2006). The lower the value of PMAFE, the more accurate theforecast. Negative values of PMAFE represent better than average forecast performance while
positive values of PMAFE represent worse than average performance.
We measure the informativeness of analyst research using the market reaction torecommendation changes in the three-day event window [0, +2], where day 0 is theannouncement date of the recommendation change. We compute the n-day buy-and-holdcumulative abnormal return (CAR) following event i as:
𝑛

𝑛

𝐷𝐺𝑇𝑊
𝐶𝐴𝑅𝑖 = ∏(1 + 𝑅𝑖𝑡 ) − ∏(1 + 𝑅𝑖𝑡
)
𝑡=0

(4)

𝑡=0

𝐷𝐺𝑇𝑊
where 𝑅𝑖𝑡 is the raw return of stock i on day t, and 𝑅𝑖𝑡
is the return on day t of abenchmark portfolio with the same size, book-to-market, and momentum characteristics as
stock i (Daniel et al., 1997). Stocks are held in the event portfolio either until the end of holding
period or until the analyst changes her recommendation, whichever comes first for all holding
periods.
To quantify a firm’s information environment and stock liquidity, we use bid-ask spreads,
11Amihud illiquidity measures (Amihud, 2002), and stale returns measure (Lesmond et al., 1999).
Bid-ask spread is computed as (ask–bid)/[(ask+bid)/2] using daily closing bid and ask datafrom CRSP. Amihud illiquidity is calculated as the natural log of one plus the ratio of theabsolute stock return to the dollar trading volume and multiple by 106. Stale returns measure is
the percentage of trading days with zero or missing returns in CRSP.
2.2.3 Control Variables
Mikhail et al. (1997) find that analyst experience is an important determinant of forecast
accuracy. We include general experience (GExp), which measures the number of years sincean analyst first began to issue earnings forecast on any stock, and firm-specific experience(FExp), which measures the number of years an analyst has covered a specific firm. Clement(1999) documents that resources available to the analyst, forecast characteristics, and portfolio
complexity also matter for analyst performance. Hence, we use brokerage size (BrSz),
measured as the total number of analysts working at the brokerage firm of the forecasting
analyst, to capture resources available to analysts. As for forecast characteristics, we control
for forecast timeliness and analyst effort. Timeliness is proxied by the number of calendar days
between the forecast issue date and the earnings announcement date (Age) (Clement, 1999),
and analyst effort is proxied by the number of forecasts issued by an analyst for a particularfirm during the year ending five days before the current forecast (Fre) (Green et al., 2014).
Finally, we use the number of firms followed by an analyst in a quarter (FmFlw) to represent
portfolio complexity.

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2.3 Summary Statistics
Table 1 presents the summary statistics of key variables, with Panel A for dependent
variables, Panel B for analyst characteristics, and Panel C for other control variables used in
our analyses. Appendix 1 provides detailed definitions of all variables. Continuous variables
are winsorized at the 1% and 99% percentiles to reduce the impact of outliers. Due to thedifferences in sample construction, size, and period, the number of observations related to eachdependent variable varies. Panel C is based on the sample of recommendation changes, which
consists of 57,989 firm-quarter observations from 1993 to 2013.
As reported in Panel A, the average PMAFE is -9.53% and the market reacts torecommendation upgrades (downgrades) with an average CAR [0, 2] of 2.20% (-1.99%). An
analyst makes 1.70 forecasts for each firm each quarter on average. As shown in Panel B, 36%of analysts have experienced industry shocks. On average, an analyst in our sample has
provided forecasts for 8.10 years and covered each firm in her/his portfolio for 9.90 quarters.
An average brokerage house has 60 sell-side analysts making earnings forecasts. The average
number of days between forecasts and earnings announcements is 67.48. An average analyst
issues 1.36 forecasts for a particular firm during the quarter ending five days before the current
forecast and covers 13 firms each quarter. All of these values are similar to existing studies (DeFranco and Zhou, 2009; Bradley et al., 2017). Summary statistics of other control variables areshown in Panel C and they are largely in line with Green et al. (2014).

3. Main Results
In this section, we first examine the forecast performance of experienced analysts
13compared to inexperienced analysts. We then use the Regulation Fair Disclosure setting toevaluate the underlying mechanisms through which industry shock experience improves
analyst performance. Lastly, we study whether investors value analysts’ industry experienceby examining market reactions to recommendation changes.

3.1 Industry Shock Experience and Forecast Accuracy
To test whether analysts experiencing industry shocks issue more accurate forecast, we
employ the following baseline regression:𝑖
𝑃𝑀𝐴𝐹𝐸𝑎,𝑓,𝑞= 𝛽1 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 + 𝛽2 𝑋𝑎,𝑓,𝑞 + 𝜆𝑎,𝑞 + 𝜆𝑓,𝑞 + 𝜀𝑎,𝑓,𝑞(5)

where 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 is an indicator equal to 1 if analyst a has ever experienced shocks in industry
i by quarter q, 𝑋𝑎,𝑓,𝑞 is a vector of analyst-firm-quarter level control variables known to
influence forecast accuracy. It includes general and firm-specific experience, brokerage size,
age and frequency of forecast, and the number of firms followed by analyst a. 𝜆𝑎,𝑞 and 𝜆𝑓,𝑞represent analyst× quarter fixed effects and firm× quarter fixed effects, respectively. These
fixed effects ensure that we are identifying variation in forecast error across stocks covered by
the same analyst at the same time and variation across different analysts’ forecasts issued on
the same firm at the same time. Standard errors are double-clustered at the analyst and firmlevel. Our main coefficient of interest is 𝛽1 , which captures the impact of industry shock
experience on forecast accuracy.
Table 2 reports the regression results. Column (1) does not control for any fixed effects.
SExp has a negative and statistically significant coefficient of -0.514 (t = -4.45), suggestingthat experiencing industry shocks improves analyst forecast accuracy. Column (2) controls for14

firm×quarter fixed effects that absorb time-varying firm characteristics. The coefficient on
SExp becomes -0.583 and remains highly significant (t = -4.32). Column (3) further includes
analyst × quarter fixed effects that control for time-varying analyst characteristics. The
coefficient is still significantly negative while the magnitude becomes even larger (-0.889, t =4.24). Therefore, all three models indicate that earnings forecasts issued by experienced
analysts are significantly more accurate than those by inexperienced analysts. These results arealso economically significant. For example, in column (3), the coefficient of -0.889 implies
that analysts with industry shock experience produce 0.89% more accurate earnings forecast
than those issued by inexperienced analysts. As a comparison, analysts need about 3.7 years
more of firm-specific experience to achieve the same level of forecast accuracy improvement.
The coefficients of control variables are in line with the prior literature (Mikhail et al.,
1997; Clement, 1999; Green et al., 2014; Bradley et al., 2017). Both general experience andfirm-specific experience of analysts reduce forecast errors. Analysts who work for a larger
brokerage house are likely to possess more resources and result in better forecast accuracy. The
older the forecast, the higher the forecast error, suggesting that stale forecasts are lessinformative. Forecast frequency is positively related to forecast accuracy. Lastly, analysts who
have a complex portfolio (i.e., those who cover more firms) have inferior forecast performance.
Besides baseline regressions, we also examine whether industry experience gained
through past shocks is more valuable during the subsequent industry shock periods. We test
this conjecture by including an interaction term between industry shock experience and adummy variable IS indicating industry shock period. Columns (4) to (6) of Table 2 present theresults. All the coefficients on interaction term SExp×IS are negative and significant,15

suggesting that the outperformance of experienced analysts relative to inexperienced analysts
is particularly pronounced in industry downturns. For example, column (6) shows thatconditional on being in an industry shock period, analysts with industry shock experience issue
forecasts that are on average 1.16% more accurate. Notably, all coefficients on SExp remain
significantly negative, suggesting that analysts’ industry shock experience improves forecast
accuracy not only during bad times, but in other normal periods as well.

3.2 How Does Industry Shock Experience Translate into Better Performance?
Results from the previous section show that forecasts issued by experienced analysts aresuperior to those issued by inexperienced analysts. The natural question to ask is how does
industry shock experience translate into better forecast performance? One plausible channel isthat analysts gain more in-depth knowledge of an industry during industry downturns. Loh andStulz (2018) document that greater uncertainty during bad times causes investors to rely more
on analyst research, which should incentivize analysts to spend more effort, collect more
fundamental information and seek a better understanding of the industry. As a result, theseexperienced analysts later on become more capable in analyzing covered firms’ industry trends,
competitive threats, positioning within the industry, the impact of regulatory risk, etc.
Prior studies document that access to firm management leads to more informative researchby analysts. Cohen et al. (2010) find that analysts with educational ties to senior officers ofcovered firms issue recommendations that are more informative. Similarly, Green et al. (2014)
find that access to firm management at broker-hosted investor conference is an importantsource of analysts’ information advantage. Thus, another possible channel is that analysts are16

able to cultivate a good relationship with firm managers in a difficult industry environment,
perhaps due to the importance of analyst support in such periods. The good relationshipbetween analysts and management formed in industry downturns may facilitate the flow ofprivate information and improve forecast performance for analysts in subsequent periods.
To disentangle these two channels, we utilize the setting of Regulation Fair Disclosure(Reg FD). Reg FD, passed by the SEC on October 23, 2000, prohibits disclosures of material
nonpublic information to selective parties including sell-side analysts. Prior studies show that
Reg FD is effective in curbing the private information flow between managers and analysts(Cohen et al. 2010; Koch, Lefanowicz, and Robinson 2012). If the improved forecast accuracyof experienced analysts is mainly due to access to private information, we should expect theeffect of industry shock experience to be much weaker or even disappear in the post-Reg FD
period. However, if experienced analysts have better forecast performance because of theirsuperior industry knowledge gained in economic downturns, the effect should persist in the
post-Reg FD period. We use the following regression model to conduct the test:𝑖
𝑃𝑀𝐴𝐹𝐸𝑎,𝑓,𝑞= 𝛽1 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 + 𝛽2 Reg FD + 𝛽3 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 × 𝑅𝑒𝑔 𝐹𝐷(6)+𝛽4 𝑋𝑎,𝑓,𝑞 + 𝜆𝑎,𝑞 + 𝜆𝑓,𝑞 + 𝜀𝑎,𝑓,𝑞
Our variable of interest is the interaction between industry shock experience dummy SExp andthe indicator Reg FD, which indicates the post-Reg FD periods. The channel of private
information flow would predict a significantly positive 𝛽3 .
Table 3 reports the results. We find that the coefficients on SExp×Reg FD are negativeand significant, regardless of which specification is used. The negative coefficient on SExp×
Reg FD means the impact of industry shock experience on forecast accuracy becomes even17stronger after Reg FD, suggesting that improved access to private information is unlikely to
explain our findings. Rather, the result suggests that experienced analysts who have a superior
understanding and in-depth knowledge of the industry now have an even larger comparativeadvantage relative to inexperienced analysts who cannot get access to private information as
easily as before.
Our second test of the underlying mechanisms focuses on analyst effort. If experienced
analysts make better forecast mainly due to their superior industry knowledge, they should be
more active in revising forecasts. On the other hand, the channel of private information does
not predict an increase in analyst effort. To test, we use the number of forecasts issued by ananalyst to proxy for analyst effort. Formally, our model is as follows:𝑖
𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑎,𝑓,𝑞= 𝛽1 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 + 𝛽2 𝑋𝑎,𝑓,𝑞 + 𝜆𝑎,𝑞 + 𝜆𝑓,𝑞 + 𝜀𝑎,𝑓,𝑞(7)

The dependent variable Activity is measured as log (1+ number of forecasts issued by an analyst
per firm-quarter). Table 4 reports the regression results. Column (1) does not include any fixed
effects, column (2) controls for firm × quarter fixed effects, and column (3) controls foranalyst×quarter fixed effects and firm×quarter fixed effects. For all regression specifications,the coefficient on SExp is positive and statistically significant, suggesting that analysts withindustry shock experience exert more effort to improve their forecasts, perhaps due to their
better understanding of industry dynamics.

3.3 Market Reactions to Recommendation Changes
Having established that analysts with industry experience make better forecasts, we next
investigate whether the market can recognize the more valuable research by experienced
18

analysts. If investors highly value analysts’ industry expertise as indicated by II’s survey,
market reaction to recommendation changes issued by analysts with industry shock experienceis likely to be more pronounced.
Similar to Green et al. (2014), we consider the direction of recommendation changes aswell as their magnitudes when examining the price impact of recommendation changes. We
first define a recommendation change as an upgrade (a downgrade) if the revised
recommendation is more favorable (unfavorable). Then we explore the effect of industry shock
experience on the informativeness of recommendations by regressing the three-day buy-andhold cumulative abnormal return CAR[0,+2] on the industry shock experience indicator (SExp)
and the absolute value of recommendation changes (RecChange).4 The regression model also
includes control variables from equation (6), analyst×quarter fixed effects, and firm×quarter
fixed effects. We estimate the following panel regression:𝑖
𝐶𝐴𝑅𝑎,𝑓,𝑞= 𝛽1 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑞 + 𝛽2 𝑅𝑒𝑐𝐶ℎ𝑎𝑛𝑔𝑒𝑎,𝑓,𝑞 + 𝛽3 𝑋𝑎,𝑓,𝑞 + 𝜆𝑎,𝑞 + 𝜆𝑓,𝑞 + 𝜀𝑎,𝑓,𝑞 (8)Table 5 presents the results. Columns (1) to (3) in Panel A show the results for market
reactions to recommendation upgrades. We find that market reactions to experienced analysts’
upward recommendation changes are indeed more pronounced. The coefficient on SExp incolumn (3) implies that market reactions to recommendation upgrades issued by experienced
analysts are 0.22% greater than those issued by inexperienced analysts. As expected, thecoefficient on RecChange is significantly positive, suggesting that larger absolute
recommendation changes lead to significantly higher market reactions. Other control variables

4

We winsorize CAR[0,+2] at the 1st and 99th percentiles to reduce the impact of firm-specific events not captured

by our filters. Our results are also robust to using non-winsorized returns.
19are of signs in line with the literature (Green et al., 2014; Bradley et al., 2017).
Columns (4) to (6) in Panel A report the results for market reactions to recommendation
downgrades. Similar to the findings for upgrades, market reactions to recommendation
downgrades issued by experienced analysts are about 0.4% stronger than those issued byinexperienced analysts.
To ensure the robustness of our findings, we re-estimate equation (8) by pooling thesample of recommendation upgrades and downgrades together. We first multiply cumulative
abnormal returns by -1 for downgrades and then include an upgrade dummy variable to accountfor the fact that upgrades are likely to be more informative than downgrades (Green et al.,2014). The results presented in Panel B confirm that recommendations issued by experienced
analysts are more informative. Specifically, column (3) shows that the recommendations issued
by analysts with industry shock experience elicit 0.32% higher announcement returns.
To sum up, the findings in Table 5 suggest that market participants place greater weighton recommendations issued by analysts with industry shock experience.

4. Analyst Industry Shock Experience and Firms’ Information Environment
In this section, we examine the real impacts of experienced analysts on the financial
market. Given they mainly act as an information intermediary, analysts should play aparticularly important role in shaping firms’ information environment. Several recent papers
use analyst coverage termination resulting from brokerage mergers and closures as quasinatural experiments and find affected firms face significantly higher information asymmetry
and cost of capital after coverage termination (Kelly and Ljungqvist, 2012; Derrien and20

Kecskes, 2013). Given our findings that analysts with industry shock experience produce more
informative research, it is reasonable to expect that losses of experienced analysts should havea greater impact on firms’ information environment than losses of inexperienced analysts.
To that end, we identify 55 brokerages that stopped releasing analyst forecasts between
2003 and 2012 using the I/B/E/S Stopped Estimation File and applying the filters in Cen et al.
(2016). We focus on brokerage closures only because in our setting, the brokerage house could
choose which analysts to keep in the case of brokerage mergers, so the termination decisions
become endogenous. Appendix 2 presents a complete list of brokerage closure events. The 55
brokerage firms employed 628 analysts (not including junior analysts without coverage
responsibilities), so an average brokerage closure involves 11 analysts, and an average analyst
covers 8 stocks, consistent with findings in Kelly and Ljungqvist (2012).
Using a difference-in-differences approach (DiD) approach, we compare the change of
information environment of treatment firms and that of control firms. We measure firms’
information environment using bid-ask spread, Amihud illiquidity (Amihud, 2002), and stale
return measure (Lesmond et al., 1999). Treatment firms are those that lose at least one analyst
due to a brokerage closure. The criteria for selecting control firms follow Bradley et al. (2017).
We require that candidate control firms be in the same size and book-to-market quintile in thepreceding June, be covered by one or more sell-side analysts in the year before the broker event,and not experience a coverage termination in the year before or after the brokerage closure. We
retain control firms that have the smallest difference in the number of analysts compared tocorresponding treatment firms affected by brokerage closures.
Table 6 reports the results. For each information environment measure, we compute its
21

change during a 3-month and a 6-month window. The column “Losing analysts” shows themean DiD estimates for the full sample of firms that experienced the loss of analyst coverage.
Consistent with the findings of Kelly and Ljungqvist (2012), analyst coverage terminations
increase information asymmetry of affected firms.
In the next two columns, we separate the sample of treatment firms into two groups basedon the exogenous termination of coverage by experienced analysts (Losing experienced
analysts) and inexperienced analysts (Losing inexperienced analysts). For firms losing
experienced analysts, all changes of information asymmetry measures are significant. The
mean change of bid-ask spread, Amihud illiquidity and missing/zero return days in a 3-monthwindow are 0.07%, 1.32%, and 0.72%, respectively. For firms losing inexperienced analysts,
the magnitudes of changes are much smaller, both economically and statistically. Column“Difference” tests the difference in DiD between these two groups. The differences for all
measures of information asymmetry are positive and significant, suggesting that firms losing
experienced analysts due to brokerage closures suffer a higher degree of information
asymmetry, compared with firms losing inexperienced analysts.
Theories of asymmetric information models predict that higher information asymmetry
leads to higher expected return required by investors and negative market reactions to coveragetermination announcement. Accordingly, we examine whether drops in coverage by
experienced analysts evoke more negative price reaction. We use cumulative abnormal returns
over various windows (CAR) to measure the price impact of coverage termination (Kelly and
Ljungqvist, 2012; Bradley et al., 2017).
Table 7 presents the results. Columns “Losing Analysts” show the results for the full22

sample. Consistent with the literature, we find a loss of analyst coverage results in a significant
decline in stock prices on average. For example, CAR[-1,+1], CAR[-1,+3], and CAR[-1,+5]
around coverage termination are -0.597%, -0.708% and -1.091%. Using the market model andthe Fama-French three-factor model to calculate CARs generates similar results. These returns
are not transitory over the first trading month as the CAR[+5, +22] is not statistically different
from zero. More importantly, when we separate the sample of treatment firms into two groups,
the price decline is more pronounced for firms losing experienced analysts. For instance,
average CARs over the [−1,+1] period are an economically and statistically significant −1.196%
for loss of experienced analysts compared to -0.258% for loss of inexperienced analysts. The
difference in mean DiD of -0.938% is highly significant as shown in the last column.
Taken together, results in this section suggest that a loss of experienced analysts have more
pronounced impacts on firms’ information environment and stock prices compared to a loss of
inexperienced analysts.

5. Addition Tests and Robustness Checks
5.1 Analyst Industry Experience and Career Outcomes
In this section, we examine whether analysts with industry shock experience are rewarded
by brighter career prospects. Answer to this question could help inform us what motivate
analysts to learn through difficult times. One type of favorable career outcome that isparticularly relevant to analysts is all-star analyst status. Each October, Institutional Investor
publishes an annual list of the top-three analysts and runners-up by covered industry based ona survey of buy-side investors (Groysberg et al., 2011). “All-star” rankings are widely viewed23

as the most prestigious status for analysts and star analysts earn 61% more than other analysts(Groysberg et al., 2011). Thus, analysts should have strong incentives to be included in theserankings.
We run a logistic regression and estimate the effect of industry shock experience on thelikelihood of becoming an II all-star analyst, conditional on all-star status in the previous year
and analyst characteristics as in other models (lagged by one year). The logit model takes thefollowing form:
𝑆𝑡𝑎𝑟𝑎,𝑓,𝑦 = 𝛽1 𝑆𝐸𝑥𝑝𝑎,𝑖,𝑦−1 + 𝛽2 𝑋𝑎,𝑓,𝑦−1 + 𝛽3 𝑆𝑡𝑎𝑟𝑎,𝑓,𝑦−1 + 𝜆𝑓,𝑦 + 𝜀𝑎,𝑓,𝑦(9)

where 𝑆𝑡𝑎𝑟𝑎,𝑓,𝑦 is a dummy variable equal to one if analyst a covering firm f is an all-star
analyst in year y.
Table 8 reports the results. Column (1) does not include any fixed effects while column(2) controls for firm×year fixed effects.5 The coefficients on SExp in both specifications are
positive and significant, suggesting that analysts with industry shock experience are more likely
to become all-star analysts than inexperienced analysts. Economically, column (2) shows theodds ratio is 1.46, suggesting that the odds of being an II all-star analyst are 46% higher forexperienced analysts than for inexperienced analysts. Signs of control variables are consistent
with the literature. General experience, firm-specific experience, and forecast frequency are
positively associated with the probability of being an all-star analyst. Analysts employed bylarger brokerage houses are more likely to become all-star analysts (Bradley et al., 2017). Beingan all-star analyst in the prior year significantly increases the probability of being an all-star in5

We cannot control for analyst*year fixed effects as the dependent variable all-start status is defined at analyst-

year level.
24the current year.

5.2 Robustness Checks
While our main results use industry definitions based on Fama-French 48 industry
classifications, one may be concerned about the robustness of our results using alternative
industry classifications. To address this concern, we reclassify firms covered by analysts into
industry group using Global Industry Classification System (GICS), recalculate our variablesof interest, and re-examine our baseline results (equations (5) and (6)).
The results are reported in Table 9. Consistent with our baseline findings, all coefficientson SExp continue to be significantly negative, suggesting that the effect of industry experienceis robust to alternative industry classification. In columns (4) to (6), we additionally control forthe interaction between industry experience and post-Reg FD indicator (SExp×Reg FD). Again,
we find that the impact of industry experience on forecast performance becomes stronger after
Reg FD. We conclude that our findings are not contaminated by the potential noise in industry
classification.

6. Conclusion
Learning from doing matters for sell-side analysts. Using a sample of analyst earnings
forecasts from 1983 to 2015, we find that earnings forecasts issued by analysts with industry
shock experience are more accurate than those issued by inexperienced analysts, after
controlling for general and firm-specific forecasting experience and other factors that
potentially explain the variations of forecast accuracy. This effect becomes stronger after Reg
25

FD, suggesting that superior industry knowledge, rather than access to private information, isthe main channel underlying our findings. We also find that experienced analysts’ upward anddownward recommendation revisions lead to stronger market reactions than those of
inexperienced analysts, consistent with these analysts’ recommendations being more
informative. Further findings reveal that experienced analysts have a significant impact onfirms’ information environment and stock prices, and they are more likely to become
Institutional Investor all-stars. Overall, our results suggest that analysts can acquire industry
expertise by learning through difficult times.

26

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29

Appendix 1 Variable Definitions
Variable

Definition

Panel A: Dependent Variables
PMAFE

The proportional mean absolute forecast error calculated as the difference between
the absolute forecast error (AFE) (in $) for an analyst on a firm and the mean absoluteforecast error (MAFE) for the firm scaled by the mean absolute forecast error for thefirm.

CAR (%)DGTW value-weighted market-adjusted cumulative abnormal return.

Market CAR (%)

Cumulative abnormal returns based on the market model. The market model factor
loadings are estimated over a one-year preevent window ending 11 days before thetermination of analyst coverage.

FF (1993) CAR (%)

Cumulative abnormal returns based on the Fama-French three-factor model. The
Fama-French three-factor model factor loadings are estimated over a one-year
preevent window ending 11 days before the termination of analyst coverage.

Activitylog (1+an analyst’s number of forecasts per firm-quarter)

Bid-ask spread (%)

100 * (ask–bid)/[(ask+bid)/2] using daily closing bid and ask data

Amihud illiquidity (%)

log(1+ absolute stock return/(dollar trading volume/106))Missing/zero ret days (%)

The percentage of trading days with zero or missing returns in the corresponding time
window.

Panel B: Analyst Characteristics
Shock experience (SExp)

Dummy variable equal to 1 if the forecast is issued by an analyst with shock-related
forecast experience, and 0 otherwise.

Genral experience (GExp)

Number of years since an analyst first issued an earnings forecast (for any firm)

Firm-specific experience (FExp)

Number of quarters since an analyst first provided earnings forecast for a firm.

Brokerage size (BrSz)

Total number of analysts working at the brokerage firm of the forecasting analyst.

Forecast age (Age)

Number of calendar days between the forecast issue date and the earnings
announcement date.

Forecast frequency (Fre)

Number of forecasts issued by an analyst for a particular firm during the quarter
ending five days before the current forecast.

Firms followed (FmFlw)

Number of firms followed by an analyst in a quarter.

Panel C: Other Control Variables
IS

Dummy variable equal to 1 if the value-weighted industry return is ranked among thebottom 4 across all 48 Fama-French industries in a quarter and 0 otherwise.

Reg FD

Dummy variable equal to 1 after the passage of Reg FD and 0 otherwise

UpgradeDummy variable equal to 1 if the research is favorable (e.g., a recommendation
change from hold to buy or an upward revised earnings forecast) 0 otherwise.

Downgrade

Dummy variable equal to 1 if the research is unfavorable and 0 otherwise.

Abs(Rec change) (RecChange)

Absolute value of the recommendation change. For example, going from a hold (=3)
to a strong buy (=1) would have a value of two.

30

Appendix 2 Lists of Brokerage Closures
Our sample includes all brokerage closure events between 2003 and 2012. We use the following three screening criteria to
identify broker closure events (Cen et al., 2016): (1) The brokerage sends end EPS estimate notifications to I/B/E/S stop file.
Brokerage firms terminate coverage of certain stocks now and then, we only include those stop notifications sent by thebrokerage when the rest stocks under coverage were terminated at one time in order to avoid possible misspecification; (2)The brokerage firm never issues earnings forecasts or recommendations in I/B/E/S Detail EPS File and Recommendation Fileafterwards; (3) Information from Bloomberg Businessweek, Capital IQ, Factiva and FINRA broker check database confirmsthat the brokerage firm is out of business.
ID

Date

Brokerage Name

ID

Date

Brokerage Name1

Apr-03

Commerce Capital Markets

29

Jun-07

Prudential Equity Group2

Jul-03

The Chapman Company

30

Jul-07First Dallas Securities

3

Sep-03

Bluefire Research31

Oct-07

Cathay Financial

4

Jan-04

Cantillon Co.

32

Dec-07

Aperion Group5

Jan-04Semi-Equity Partners

33

Apr-08Henley Company

6Jan-04Hudson River Analytics

34

Oct-08

Coker & Palmer Inc.

7

Feb-04Montauk Capital Markets

35

Nov-08JSA Research

8Mar-04

Royalist Research36

Feb-09Stanford Group Company

9Oct-04

Schwab Soundview Capital markets

37

Apr-09Dutton Associates10

Nov-04

Whitaker Securities

38

Nov-07

Nollenberger Capital

11

Mar-05JB Hanauer Co.

39

Jun-09Wasserman Associates

12Mar-05HD Brous Co.

40

Oct-09Utendahl Capital Partners

13

May-05

Tradition Asiel Securities

41

Dec-09The Robins Group14

Jun-05

Terra Nova Institutional42

Dec-09Ragen Mackenzie

15

Jun-05

IRG Research43

Feb-10FTN Equity Capital Markets

16

Aug-05Wells Fargo Securities

44

Feb-10Pali Research17

Sep-05

Granite Financial Group45

Jun-10Jesup & Lamont Securities

18

Jan-06Southwest Securities

46

Aug-11Signal Hill Group19

Mar-06

Halpern Capital47

Aug-11

Broadpoint Capital

20

Mar-06

Arabella Securities

48

Jan-12

Wealth Monitors

21

May-06

Variant Research Corp49

Feb-12

Kaufman Bros

22

Aug-06

Foresight Research Solution

50

Mar-12Collins Stewart

23

Sep-06

New York Global Securities

51

Apr-12Morgan Joseph Co.

24

Sep-06

Moors & Cabot Capital52

Jun-12

Auriga USA

25

Oct-06

Infinium Securities

53

Jul-12

Pritchard Capital Partners

26

Dec-06

Miller Johnson Steichen Kinnard

54

Oct-12Thinkequity27

Mar-07

DE Investment Research55

Dec-12

Avian Securities

28

Apr-07

Cohen Company

31

Table 1 Summary Statistics
This table reports summary statistics, with Panel A for the dependent variables, Panel B for analyst characteristics, and PanelC for other control variables used in our analyses. Analyst data are from I/B/E/S from 1983 to 2015, firm characteristics arefrom Compustat, and stock price data are from CRSP. The number of observations used in different analyses varies. Panel C
is based on the sample for recommendation changes, which consists of 57,989 firm-quarter observations from 1993 to 2013.
See Appendix 1 for a detailed description of these variables.
Variables

N

Mean

p25

Medianp75

Std. Dev.

PMAFE (%)

1,978,053-9.53-51.24-10.7820

61.59

CAR[0,+2] Upgrade (%)

27,771

2.20

-0.64

1.60

4.41

5.05

CAR[0,+2] Downgrade (%)

30,218

-1.99

-4.27

-1.48

0.77

5.56

Activity

1,576,392

0.95

0.69

0.69

1.10

0.29

SExp

1,978,053

0.36

0

0

1

0.49

GExp

1,978,053

8.10

3

7

12

6.14

FExp

1,978,053

9.90

3

7

14

9.50

BrSz

1,978,053

59.84

24

51

91

42.24

Age

1,978,053

67.48

50

71

90

25.90

Fre

1,978,053

1.36

1

1

2

0.64

FmFlw

1,978,053

13.34

9

12

17

7.19

IS

1,978,053

0.07

0

0

0

0.25

RegFD

1,978,053

0.68

0

1

1

0.47

Upgrade57,989

0.48

0

0

1

0.50

RecChange

57,989

0.06

-1

1

1

1.46

Panel A: Dependent Variables

Panel B: Analyst Characteristics

Panel C: Other Control Variables

32

Table 2 Industry Shock Experience and Forecast Accuracy
This table reports regression results for analyst earnings forecast error. The dependent variable is the proportional mean
absolute forecast error (PMAFE). Standard errors are double-clustered at the analyst and firm level. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% level, respectively.
SExp

(1)

(2)

(3)

(4)

(5)

(6)

-0.514***-0.583***-0.889***-0.431***-0.461***-0.754***(-4.45)(-4.32)(-4.24)(-3.60)(-2.68)(-4.03)-0.729**-0.364**-0.403**(-2.05)(-1.99)(-2.56)-0.044***-0.050**-1.009

SExp×IS
GExp-0.042***(-3.83)(-3.30)(-1.90)(-3.94)(-2.35)(-1.03)FExp-0.061***-0.049***-0.060***-0.062***-0.105***-0.068***(-8.82)(-6.69)(-6.85)(-8.93)(-12.76)(-7.11)-0.018***-0.020***-0.026*-0.017***-0.011***-0.037**(-13.08)(-14.01)(-1.68)(-12.93)(-7.06)(-2.16)0.176***

0.195***

0.203***

0.176***

0.178***

0.180***(92.05)(91.12)(81.20)(91.83)(72.29)(62.42)-11.409***-10.490***-9.673***-11.408***-7.447***-5.977***(-140.50)(-126.69)(-104.89)(-139.68)(-80.91)(-57.64)0.202***

0.131***

0.263

0.201***

0.076***

0.048(24.59)(13.68)(0.04)(24.37)(7.15)(0.00)

0.351

0.258

0.565

(1.17)(0.40)(0.73)

Firm×quarter

Analyst×quarter

BrSz
Age
Fre
FmFlw

-0.039***-0.877*

IS
Intercept
Fixed-6.279***-6.238***(-28.42)(-28.05)

None

Firm×quarter

effects

Analyst×quarter

None

Firm×quarter

Firm×quarterN

1,978,053

1,978,053

1,978,053

1,978,053

1,978,053

1,978,053

R2

0.034

0.165

0.179

0.034

0.166

0.179

0.118

0.132

0.118

0.131

Adj. R2

33

Table 3 Regulation FD and the Impact of Industry Shock Experience
This table reports the results from regressions of analyst earnings forecast error on the interaction between industry experienceand a dummy indicating the post-Regulation FD period. The dependent variable is the proportional mean absolute forecast
error (PMAFE). Reg FD indicates periods after the passage of Regulation Fair Disclosure. Standard errors are double-clusteredat the analyst and firm level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
SExp
Reg FD

(1)

(2)

(3)

-1.751***-1.420***-0.684**(-9.53)(-4.47)(-2.08)0.573*---0.645***-0.052***-0.049**(-7.30)(-4.17)(-2.09)-0.069***-0.057*-0.325*(-6.09)(-1.69)(-1.88)-0.061***-0.106***-0.069***(-8.84)(-12.45)(-6.95)-0.018***-0.012***-0.040**(-13.56)(-7.60)(-2.23)0.171***

0.177***

0.177***(88.30)(68.09)(57.92)-11.518***-6.574***-4.962***(-142.02)(-69.34)(-46.41)0.205***

0.072***

0.853(25.02)(6.57)(0.05)

Firm×quarter

Analyst×quarter(1.67)
SExp×Reg FD
GExp
FExp
BrSz
Age
Fre
FmFlw
Intercept-5.994***(-26.32)

Fixed effects

None

Firm×quarterN

1,978,053

1,978,053

1,978,053

R2

0.034

0.165

0.180

0.118

0.132

Adj. R2

34

Table 4 Industry Shock Experience and Analyst Effort
This table reports regression results for analyst effort. The dependent variable is analyst effort, defined as log (1+number offorecasts issued by an analyst per firm-quarter). Standard errors are double-clustered at the analyst and firm level. *, **, and ***indicate statistical significance at the 10%, 5%, and 1% level, respectively.
(1)

(2)

(3)

0.007***

0.006***

0.006***(5.91)(5.17)(4.11)-0.001***-0.001***-0.001(-8.74)(-16.11)(-0.04)FExp

0.002***

0.001***

0.001***(5.00)(19.65)(15.91)BrSz

0.015***

0.017***

0.013***(3.24)(3.57)(2.90)-0.005***-0.138***-0.129***(-12.47)(-16.18)(-23.53)0.019***

0.092***

0.102***(32.87)(14.78)(15.56)-0.070***-0.099***-0.120***(-8.95)(-5.41)(-5.12)

Firm×quarter

Analyst×quarter

SExp
GExp

Age
Fre
FmFlw
Intercept

0.734***(3.24)

Fixed effects

None

Firm×quarter
N

1,115,475

1,115,475

1,115,475

R2

0.075

0.386

0.576

0.282

0.408

Adj. R2

35

Table 5 Industry Shock Experience and Market Reactions to Recommendation Changes
This table reports regression results of market reactions to analysts’ recommendation changes. The dependent variable is the
cumulative DGTW-adjusted abnormal return during the [0, 2] window around the announcement of a recommendation changeby an analyst for a firm in a quarter. RecChange is the absolute value of the difference between an analyst’s revised
recommendation in a quarter and the recommendation in the previous quarter. Panel A splits the sample into two subsamples
where analysts issue upgrade recommendation and downgrade recommendation. Panel B uses the pooled sample in which the
cumulative abnormal returns of downgrades are multiplied by -1. Standard errors are double-clustered at the analyst and firmlevel. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
Panel A

UpgradeDowngrade

(1)

(2)

(3)

(4)

(5)

(6)

SExp

0.206***

0.198***

0.217***-0.403***-0.399***-0.405***(3.06)(2.67)(3.41)(-5.69)(-3.11)(-5.30)

RecChange

0.442***

0.353***

0.698***-0.531***-0.684***-0.512***(7.36)(6.83)(3.70)(-8.19)(-7.34)(-4.52)

0.003

0.005

0.003

-0.009-0.027-0.010(0.47)(0.19)(0.43)(-1.35)(-0.47)(-1.48)0.015***

0.013***

0.014***-0.022***-0.023***-0.043***(4.18)(3.27)(3.85)(-6.08)(8.25)(-6.16)0.008***

0.005***

0.012***-0.007***-0.010**-0.018***(11.14)(9.36)(10.03)(-9.38)(-6.37)(-10.72)

0.012***

0.015***

0.011***-0.006***-0.005***-0.006***(8.82)(3.29)(2.79)(-4.30)(-4.42)(-4.60)

0.012

0.037

0.054

-0.284***-0.205***-0.249**(0.27)(1.12)(1.05)(-6.11)(-6.53)(-2.52)-0.011**-0.001**-0.012**0.010*

0.013**

0.021**(-2.17)(-2.02)(-2.31)(1.92)(2.01)(2.26)

Firm×quarter

Analyst×quarter

GExp
FExp
BrSz
Age
Fre
FmFlw
Intercept
Fixed effects

0.053***-0.088(3.30)(-0.50)None

Firm×quarter

Analyst×quarter

None

Firm×quarter

Firm×quarter

N

27,771

27,771

27,771

30,21830,21830,218

R2

0.110

0.681

0.790

0.109

0.647

0.759

0.380

0.585

0.318

0.518

Adj. R236

Panel B
SExp
Upgrade
RecChange
GExp
FExp
BrSz
Age
Fre
FmFlw
Intercept

Pooled
(1)

(2)

(3)

0.308***

0.296***

0.317***(6.19)(5.42)(5.33)

0.219***

0.205***

0.179***(4.96)(3.01)(2.84)

0.490***

0.431***

0.466***(10.82)(9.51)(10.13)

0.003

0.009

0.110

(0.70)(0.27)(0.83)

0.018***

0.007***

0.055***(7.06)(6.28)(4.23)

0.008***

0.011***

0.009***(14.07)(13.49)(14.35)0.009***

0.007***

0.005***(8.91)(6.15)(8.32)

0.152***

0.231**

0.204**(4.75)(2.22)(1.97)-0.011***-0.006***-0.035***(-2.83)(-3.19)(-3.00)

Firm×quarter

Analyst×quarter

*

0.024

(1.70)
Fixed effects

None

Firm×quarter
N

57,98957,98957,989

R2

0.114

0.665

0.812

0.382

0.541

Adj. R2

37

Table 6 Brokerage Closures, Loss of Experienced Analysts, and Information Environment
This table reports results on the effect of exogenous brokerage closure on changes in firms’ information asymmetry.
Information asymmetry is measured by three- and six-month bid-ask spread, Amihud illiquidity, and percentage of days withmissing or zero returns. The first column, “Losing analysts”, shows the cross-sectional means of difference-in-differences(DiD) for the full sample of treatment firms losing analysts due to brokerage closures. Column (2) ((3)), “Losing experienced
analysts” (“Losing inexperienced analysts”), reports results for a sample of firms losing experienced analysts (inexperienced
analysts) due to brokerage closures. Column (4) reports the differences between the two groups. Control firms are matched totreatment firms using the Daniel et al. (1997) algorithm based on the Fama and French (1993) pricing factors and analyst
coverage as in Kelly and Ljungqvist (2012). *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.
Difference-in-Differences
Losing Analysts

Variable (%)
Bid-ask spread

LosingLosingExperienced

Inexperienced

Analysts

Analysts

Difference

N

Mean

N

Mean

N

Mean

Mean

4,993

0.032**

1,877

0.065***

3,116

0.020*

0.044**

4,993

0.041***

1,877

0.092***

3,116

0.025*

0.068***

4,993

0.487*

1,877

1.315***

3,116

0.026

1.289***

4,993

0.506

1,877

1.384***

3,116

0.031*

1.352**

4,993

0.264

1,877

0.719***

3,116

0.006

0.713**

4,993

0.193

1,877

0.578**

3,116

0.004

0.574*(three-month window)
Bid-ask spread(six-month window)
Amihud illiquidity(three-month window)
Amihud illiquidity(six-month window)
Missing/zero ret days
(three-month window)
Missing/zero ret days
(six-month window)

38

Table 7 Brokerage Closures, Loss of Experienced Analysts, and Stock Returns
This table reports the mean difference-in-differences market reaction to a loss of analyst coverage arising from exogenous
brokerage closure. Abnormal returns are calculated based on market return adjustment, market model, and Fama-French 3factor model, respectively. The first column, “Losing analysts”, reports the means of DiD for the full sample of treatment firms
losing analysts due to brokerage closures. Column (2) ((3)), “Losing experienced analysts” (“Losing inexperienced analysts”),
reports results for a sample of firms losing experienced analysts (inexperienced analysts) due to brokerage closures. Column(4) reports the differences between these two groups. Control firms are matched to treatment firms using the Daniel et al. (1997)
algorithm based on the Fama and French (1993) pricing factors and analyst coverage as in Kelly and Ljungqvist (2012). *, **,and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
Difference-in-Differences
Losing Analysts

Losing Experienced

LosingAnalysts

Inexperienced

Difference

Analysts
Variable (%)

N

Mean

N

Mean

N

Mean

Mean

CAR[-1, +1]4,993-0.597**1,877-1.196***

3,116-0.258*-0.938***

CAR[-1, +3]

4,993-0.708***1,877-1.457***3,116-0.261*-1.196***

CAR[-1, +5]

4,993-1.091***1,877-2.274***3,116-0.350*-1.925**

CAR[+5, +22]

4,993

0.046

1,877

-0.532

3,116

0.392

-0.924Market CAR[-1, +1]4,993-0.623***1,877-1.219***3,116-0.276-0.943***Market CAR[-1, +3]

4,993-0.812***

1,877-1.644***3,116-0.298*-1.345***Market CAR[-1, +5]

4,993-1.003***1,877-2.249***3,116-0.306-1.943***Market CAR[+5, +22]

4,993

0.015

1,877

-0.437

3,116

0.286

-0.723

FF CAR[-1, +1]

4,993-0.618***1,877-1.205***

3,116-0.260*-0.944***

FF CAR[-1, +3]

4,993-0.775**1,877-1.638***3,116-0.291*-1.347***FF CAR[-1, +5]

4,993-0.964**1,877-2.188***3,116-0.293-1.895***FF CAR[+5, +22]

4,993

-0.002

1,877-0.379

3,116

0.225

-0.60439

Table 8 Industry Shock Experience and All-star Analyst Status
This table presents logistic regression results for the effect of industry experience on the probability of being an all-star analyst.
The dependent variable is a binary variable equal to one if the analyst is listed as an all-star analyst in the current year’s Octoberissue of Institutional Investor magazine and zero otherwise. All control variables are lagged by one year. The sample is from1983 to 2011. Standard errors are double-clustered at the analyst and firm level. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% level, respectively.
(1)

(2)

0.181***

0.389***(6.64)(23.97)GExp

0.061***

0.075***(30.06)(30.08)FExp

0.036***

0.033***(27.37)(21.43)1.811***

2.260***(80.42)(82.48)0.100***

0.142***(6.74)(3.40)-0.131***-0.086***(-5.93)(-2.98)Lag (Star)

0.111***

0.143***(11.29)(4.95)

Intercept-10.788***

SExp

BrSz
Fre
FmFlw

(-92.99)
Fixed effects

None

Firm×yearN376,809376,809

Pseudo R2

0.258

0.281

40

Table 9 Robustness using GICS Industry Classification
This table presents regression results for analyst earnings forecast errors on industry shock experience. Industry classifications
are based on the Global Industry Classification System (GICS). The dependent variable is the proportional mean absoluteforecast error (PMAFE). Reg FD indicates periods after the passage of Regulation Fair Disclosure. Standard errors are doubleclustered at the analyst and firm level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
SExp

(1)

(2)

(3)

(4)

(5)

(6)

-0.584***-0.516***-0.808***-2.241***-1.883***-1.150***(-3.39)(-2.77)(-2.65)(-7.43)(-4.14)(-3.39)0.899***---0.907***-0.176***-0.338***(-5.90)(-3.68)(-3.36)

Reg FD(3.74)
SExp×Reg FD
GExp
FExp
BrSz
Age
Fre
FmFlw
Intercept
Fixed effects-0.045***-0.058***-0.292**-0.077***-0.045***-0.097***(-2.88)(-3.65)(-2.00)(-4.77)(-2.59)(-2.86)-0.044***-0.028***-0.054***-0.043***-0.072***-0.047***(-4.74)(-3.02)(-4.68)(-4.68)(-7.24)(-3.61)-0.019***-0.022***-0.025-0.020***-0.012***-0.044**(-9.84)(-10.97)(-0.96)(-10.24)(-5.66)(-2.51)0.182***

0.197***

0.198***

0.176***

0.181***

0.171***(63.77)(64.34)(51.45)(61.07)(51.06)(38.45)-10.493***-9.768***-8.857***-10.643***-6.954***-5.211***(-91.01)(-84.86)(-64.17)(-92.39)(-54.60)(-33.65)0.205***

0.142***

0.494*

0.206***

0.074***

0.948*(15.90)(10.16)(1.82)(16.02)(4.66)(1.93)

Firm×quarter

Analyst×quarter-7.736***-7.505***(-23.40)(-21.63)

None

Firm×quarter

Analyst×quarter

None

Firm×quarter

Firm×quarter

N

1,685,332

1,685,332

1,685,332

1,685,332

1,685,332

1,685,332

R2

0.027

0.156

0.251

0.027

0.159

0.264

0.127

0.165

0.127

0.168

Adj. R241