Sell-Side Analysts’ Responses to Mutual Fund Flow-Driven Mispricing*

Johan Sulaeman

Southern Methodist University sulaeman@smu.edu

Kelsey D. Wei

University of Texas at Dallas kelsey.wei@utdallas.edu

May 16, 2012

* We thank Larry Brown, Narasimhan Jegadeesh, Wei Jiang, Mozaffar Khan, Chris Malloy, Darius Miller, Mandy Tham, Kumar Venkataraman, and participants at the 2011 Penn State Analyst Research Conference, the 2011 China International Conference in Finance, and the 2011 Financial Economics and Accounting Conference for helpful discussions and valuable comments. We also thank the Financial Markets Research Center at Vanderbilt University for generously providing its Market Microstructure dataset. Sulaeman is at the Cox School of Business, Southern Methodist University, sulaeman@smu.edu and (214) 768-8284. Wei is at the Naveen Jindal School of Management, University of Texas at Dallas, kelsey.wei@utdallas.edu and (972) 883-5978.

Sell-Side Analysts’ Responses to Mutual Fund Flow-Driven Mispricing

Abstract

This paper examines the unique role sell-side analysts play in speeding up the price correction process following mutual fund flow-driven mispricing. We find that some analysts persistently issue price-correcting recommendation changes on stocks subject to extreme flow-driven trading pressure. At the same time, they make little or no change to their concurrent earnings forecasts for the stocks, suggesting that the recommendation changes are not driven by changes in stock fundamentals. Their ability to detect flow-driven mispricing appears to be related to their superior research skill: these analysts make more accurate earnings forecasts and more informative recommendation changes for the average stock they cover, regardless of whether it is subject to flow-driven mispricing or not. Through their recommendation revisions, these skillful analysts play a unique and important role in facilitating liquidity provision and stabilizing the financial market. While stocks under flow-driven trading pressure typically suffer significant liquidity deterioration, those receiving price-correcting recommendation revisions experience faster and greater liquidity improvement. Consequently, these stocks also experience accelerated price correction. This role of analyst research in identifying mispricing and accelerating the price adjustment is more pronounced for stocks with lower institutional ownerships or greater information uncertainty.

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Recent studies show that concentrated mutual fund trades due to extreme and correlated

capital flows can exert significant price pressure on overlapped fund holdings, often causing their

prices to deviate considerably from fundamental values for prolonged periods (Coval and

Stafford, 2007; Frazzini and Lamont, 2008; Lou, 2011). In this study, we examine the unique

role that sell-side analysts could play in promoting liquidity provision and speeding up the price

correction process following this type of liquidity shock.

We focus on equity analysts because the extant literature extensively shows that analyst

research has investment value beyond common investment signals, suggesting that analysts have

better knowledge concerning the valuation fundamentals of the stocks they cover than the

average investor and thus are better at detecting potential mispricing.1,2 While analysts cannot

provide liquidity directly when they recognize flow-driven mispricing, their stock

recommendation changes can prompt investors who were uncertain regarding the potential

mispricing to subsequently trade against the mispricing. Therefore analysts can play an indirect

but important role in improving liquidity provision and speeding up the price correction process.

Using sell-side analyst forecasts and recommendations during 1993–2006, we find that

there exists a group of analysts who persistently upgrade stocks subject to outflow-driven

underpricing and downgrade stocks subject to inflow-driven overpricing. We also find that their

responses to flow-driven mispricing cannot be explained by the alternative explanation that they

1 See, for example, Womack (1996) and Jegadeesh et al. (2004) for the investment value of analyst research. Another line of research documents the potential sources of this investment value, e.g., proximity to firm headquarters (Malloy, 2005) or social connection with corporate managers (Cohen, Frazzini, and Malloy, 2008). 2 Consistent with this view, Brav, Lehavy, and Michaely (2005) use analysts’ expected rate of returns to identify priced risk factors from mispricing.

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simply follow a contrarian revision strategy: These “skillful” analysts generally respond

positively to past stock returns when revising their recommendations, consistent with prior

evidence of average analysts’ revision behavior (Bradshaw, 2004; Jegadeesh et al., 2004).

Since fund flow-driven mispricing is due to the need for immediate trading at the mutual

fund level rather than new fundamentals-related information on the underlying fund holdings, it

should be largely exogenous to changes in firm fundamentals. Therefore we hypothesize that

skillful analysts’ recommendation changes for stocks subject to flow-driven mispricing may not

be accompanied by significant changes in their cash flow estimates.3 Indeed, we find that skillful

analysts’ concurrent earnings forecast revisions for stocks subject to flow-driven mispricing are

significantly smaller in magnitude than those for other stocks with concurrent recommendation

changes or those of other analysts concerning the same stock. Therefore the observed

recommendation changes appear to be primarily driven by the divergence between skillful

analysts’ valuations of the firm and the market valuation.

It is important to note that our findings do not necessarily imply that skillful analysts

have the ability to analyze mutual fund flows and trades and thus recognize flow-driven

mispricing per se. They may have obtained private information regarding mutual fund flows and

trades from, for example, affiliated brokers handling trades for mutual fund clients. If this is the

case, skillful analysts’ superior performance should be largely limited only to stocks subject to

flow-driven mispricing. Alternatively, it may be the case that these analysts generally possess

3 This hypothesis is in interesting contrast to the evidence of Kecskes, Michaely, and Womack (2010) and Brown and Huang (2010) that recommendation revisions are usually more informative when accompanied by corroborating earnings forecast revisions.

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superior research ability and better understand the true intrinsic value of the firms they cover

(e.g., through their fundamental analysis and interactions with company insiders). They will

revise their recommendations if the observed market price deviates from their intrinsic value

estimate of the firm, whether driven by mutual fund trading pressure or not. Under this scenario,

skillful analysts may be superior in other aspects of their research and thus able to issue more

informative investment recommendations, whether triggered by mispricing (flow driven or not)

or changes in firm fundamentals.

Our analysis of analysts’ other research attributes—that is, their earnings forecasts and

recommendations—suggests that their ability to identify flow-driven mispricing is likely to be

driven by their overall research skill. In particular, skillful analysts’ quarterly earnings forecasts

are significantly more accurate than those of their peers. Moreover, their recommendation

changes are associated with greater abnormal returns. These findings remain robust when we

exclude stocks that are subject to flow-driven trading pressure or control for potentially

contrarian analyst styles. Therefore the ability of skillful analysts to detect flow-related

mispricing appears to be skill driven.

If analyst opinions can influence investor trading, skillful analysts’ responses to flow-

driven mispricing should accelerate the price correction process. Our analysis of market liquidity

indicates that both outflow-driven sale stocks (i.e., fire sales) and inflow-driven purchase stocks

(i.e., fire purchases) experience a general decline in market liquidity in the form of higher

Amihud (2002) illiquidity ratio and effective trading spreads. However, those receiving price-

correcting recommendation revisions experience greater improvements of market liquidity in

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subsequent quarters. This evidence suggests that analyst revisions could be instrumental in

promoting liquidity provision.

Consistent with the observed liquidity improvement as a result of analyst revisions, we

find that during the quarter (t) immediately following fire purchases, fire-purchase stocks

downgraded by analysts underperform those not downgraded by approximately 1.21 percentage

points in terms of Daniel et al. (1997) characteristic-adjusted abnormal returns (henceforth

DGTW abnormal returns). Since these stocks experience quicker price corrections immediately

following mutual fund flow-driven trading, the magnitude of their return reversals in subsequent

quarters is much smaller. Specifically, fire-purchase stocks not initially downgraded experience

DGTW abnormal returns of 3.53 percentage points lower during subsequent quarters (t + 3

through t + 8) relative to fire-purchase stocks downgraded in t. This suggests that their price

correction takes much longer, possibly due to the delay in liquidity provision in the absence of

analyst revisions. Similar albeit slightly weaker differences in the speed of price adjustment are

also observed among fire-sale stocks.

Furthermore, our cross-sectional analyses indicate that the extent to which analyst

revisions can accelerate the price adjustment depends on the presence of other sophisticated

investors and the degree of uncertainty surrounding the mispricing. Specifically, it is more

pronounced among stocks with lower institutional ownership or greater analyst forecast

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dispersion. 4 Therefore analyst recommendation revisions seem to play a distinct role in

facilitating liquidity provision and helping stabilize the financial market.

To rule out the possibility that analyst revisions are correlated with stock-specific factors

that also drive fund flows, we analyze a subset of fire-sale and fire-purchase stocks whose prior

performance is in the opposite direction of the flows of funds trading them. More precisely, we

focus only on those stocks that have positive (negative) returns before they are subject to fire

sales (fire purchases). This refined flow-driven mispricing definition essentially excludes cases

where firm-specific information may drive stock returns, fund flows, and analyst

recommendation changes simultaneously. Under this much stricter definition of flow-driven

mispricing, we again find that analyst recommendation changes effectively speed up the price

correction process.

In summary, we find that a group of skillful sell-side analysts plays a significant role in

alleviating mutual fund flow-induced trading pressure. This finding complements existing

evidence on the trading responses of other informed market participants, such as hedge funds and

corporate insiders, to similar liquidity events (Ali et al., 2010; Chen et al., 2010; Shive and Yun,

2011). Unlike many of these potential arbitrageurs, who are often constrained by limits of

arbitrage as they either face investment restrictions or rely on outside financing that could be

correlated with mutual fund flows, sell-side analysts face fewer such constraints because no

4 Sadka and Scherbina (2007) show that mispricing tends to arise when a stock has a high level of analyst disagreement and low liquidity.

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direct trading is involved.5 Moreover, analysts can respond in a timelier manner than other

potential liquidity providers since they do not face any explicit restrictions on when they can

revise their recommendations. As such, our study highlights an indirect yet important force that

helps restore market efficiency.

Our paper also contributes to the literature on equity analysts’ role in discovering new

information versus processing existing information to identify mispricing (Ivkovic and

Jegadeesh, 2004; Asquith et al., 2005; Chen et al., 2010). Although some studies attempt to

address analysts’ ability to process existing information, their findings could be consistent with

alternative interpretations.6 This is partly due to the difficulty in isolating the divergence of an

analyst’s valuation on a firm from the market valuation that is orthogonal to innovations in firm

fundamentals. In contrast, we consider mutual fund flow-driven pressure as an exogenous source

of stock mispricing, making our findings less susceptible to alternative interpretations. In

addition, unlike other types of temporary deviations of prices from fundamentals that last for

only a short period (e.g., the post-earnings announcement drift), fund flow-driven mispricing

often lasts 12–18 months before it is fully corrected (Coval and Stafford, 2007; Lou, 2011). As

such, our analyses have greater power to test analysts’ ability to identify and correct mispricing.

5 For example, Ben-David et al. (2011) show that, rather than provide liquidity, hedge funds significantly reduced their equity holdings during the 2008 financial crisis. 6 For example, while Chen et al. (2010) document significant market reaction to analyst reports issued immediately following earnings announcements, suggestive of analysts’ information interpretation role, the finding could be consistent with analyst reports containing information about firm risk that is not reflected in earnings announcements.

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I. Data and Methodology

A. Measuring Flow-Forced Mutual Fund Trades

In order to examine price pressure due to mutual fund trades driven by extreme capital

flows, we construct a stock-level flow-driven price pressure measure following Coval and

Stafford (2007). We collect fund information from two databases. First, Thomson Reuters

provides quarterly “snapshots” of portfolio holdings for U.S. equity mutual funds during 1993–

2006, which we use to infer fund purchases and sales by examining changes in quarterly

holdings.7 Quarterly portfolio holdings are adjusted for stock splits and dividends using the end-

of-quarter cumulative adjustment factor from the Center for Research in Security Prices (CRSP).

We exclude all trades by international funds, municipal bond funds, “bond and preferred” funds,

sector funds and index funds to focus on actively managed domestic equity funds. Second, the

CRSP Survivorship Bias Free Mutual Fund Database provides fund returns and total net asset

value. We link these two databases using the MFLINKS dataset provided by the Wharton

Research Data Services.

We compute quarterly fund flows as the change in total net assets during the quarter,

adjusted for investment returns (assuming flows occur at the end of each quarter). Since the

different share classes of a fund represent claims to the same underlying portfolio and therefore

do not differ in their underlying trades, we combine all the share classes of each fund when

computing flows. We calculate fund returns as the weighted average of returns across all share

7 For funds not reporting at the end of a given quarter, we assume that they follow a buy-and-hold strategy and carry forward (for a maximum of three months) their most recent holdings to calculate trades during the following quarter.

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classes of a fund, with the weight being the beginning-of-quarter total net asset (TNA) value.8 A

fund is considered to experience extreme flows if it has flows above the 90th percentile or below

the 10th percentile among all funds during the quarter.

Next we aggregate the inflow-induced purchases and outflow-induced sales of each stock

across all funds in a given quarter. We then normalize the trade imbalance by the stock’s shares

outstanding at the beginning of the quarter. The flow-induced trading measure of a stock is

defined as

=∑ max 0, ∆ | > 90 . −∑ max 0,−∆ | < 10 .ℎ

(1)

where ΔHoldingsjit is the change in fund j’s holding of stock i in quarter t and Flowjt is the capital

flow for fund j in quarter t. We require a stock to be owned by at least five funds to compute its

flow-driven trading pressure. Essentially, Forced measures the degree to which a stock’s trading

is accounted for by mutual funds experiencing significant inflows or outflows. Throughout the

paper, stocks with Forced above the 90th percentile (below the 10th percentile) among all stocks

during the quarter are considered fire-purchase (fire-sale) stocks.

Similar to earlier studies on mutual fund flow-driven trading pressure (Coval and

Stafford, 2007; Khan et al., 2011; Lou, 2011), in unreported analyses we find that fire-sale (fire-

8 We winsorize quarterly flows at the top and the bottom 2.5 percentiles to minimize the impact of outliers in fund TNA due to mergers and splits. Our results remain qualitatively unchanged if we exclude these extreme observations.

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purchase) stocks in our sample experience significantly negative (positive) abnormal returns in

the event quarter. Furthermore, initial price pressure effects persist for about six months before

starting to reverse. Overall, the capital market seems to take quite a while to fully correct this

type of mispricing since stock prices do not fully recover until almost two years after the

liquidity event. In addition, similar to Coval and Stafford (2007) and Khan et al. (2011), we find

that inflow-driven purchases cause a similarly large price pressure effect as outflow-driven

sales.9

B. Measuring Analyst Activities

We collect data on individual analyst recommendations during 1993–2006 from the

I/B/E/S detailed recommendation database. All of our analyses focus on analysts who cover at

least one stock experiencing a fire purchase or a fire sale during the quarter. As reported in Table

I, the median analyst in our sample covers six stocks and has about three years’ (13 quarters’)

experience. There are considerable variations in the distributions of these analysts: Analysts at

the 95th percentile of the distribution cover three times as many stocks (19 stocks) and have four

times as much experience (57 quarters) as the median analyst. In addition, typical analysts do not

issue a recommendation revision every quarter. Summary statistics on analysts’ affiliated

brokerage houses are also reported in Table I.

Table II reports the average number of stocks experiencing fire sales (fire purchases)

each quarter and the fraction of these stocks that are subsequently upgraded or downgraded by at

least one analyst, or remain unrevised. When computing analyst revisions, we consider a 9 These results are available upon requests.

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previously issued recommendation as active for 180 days since the issue date or the last date it

was reconfirmed. We exclude a small number of firm–quarters where the fire-sale (fire-

purchase) stock already carries a unanimous “strong buy” (“strong sell”) recommendation since

no analysts can further revise their recommendation revisions in the direction of price

correction.10 As reported in Table II, in the quarter following the fire sale (fire purchase), about

27% (28%) of fire-sale (fire-purchase) stocks receive a recommendation upgrade (downgrade)

from at least one equity analyst. This fraction is similar if we allow analysts two quarters to

respond to the flow-driven trading pressure.

Panel B of Table II shows that an average analyst in our sample covers one to two stocks

subject to the flow-driven trading pressure each quarter. Panel C reports the fraction of analysts

who make at least one revision to correct the flow-driven mispricing. Conditional on covering at

least one fire-sale (fire-purchase) stock not already having an outstanding strong buy (strong sell)

recommendation, about 9% (10%) of analysts issue a recommendation upgrade (downgrade).

This proportion is similar to the proportion of Institutional Investor “all-star” analysts examined

in other studies.11

II. Identifying Skillful Analysts

Throughout the paper we designate an analyst as “skillful” in a specific quarter t if she

issues at least one price-correcting recommendation revision following fire-sale or fire-purchase

events (i.e., an upgrade following a fire sale or a downgrade following a fire purchase) 10 Our results remain qualitatively and quantitatively unchanged if we retain these stocks in the sample. 11 For example, Fang and Yasuda (2009) report that the proportion of all-star analysts in their sample is 10.48%.

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occurring in any of the preceding two quarters (t - 1 or t - 2).12 Occasionally, skillful analysts

may not revise their recommendations on all stocks classified as experiencing flow-driven

trading pressure. In unreported analyses, we find that fire-sale (fire-purchase) stocks

subsequently upgraded (downgraded) by skillful analysts experience at least three times greater

trading pressure (in terms of the magnitude of the Forced measure) than those not revised. Given

the discrete nature of analyst recommendations, it is not surprising that analysts revise their

recommendations only on stocks that are considerably mispriced.

Since analysts may change their stock recommendations for a variety of reasons, to

consider them skillful in identifying flow-driven mispricing, it is crucial to examine (1) whether

they merely revise recommendations in response to significant price movements as a result of the

flow-induced trading pressure; (2) whether their recommendation revisions are driven by

information that is orthogonal to changes in firm fundamentals; (3) whether their actions persist

over time; and (4) whether their price-correcting recommendation changes are attributable to

skill or other information channels. We address these issues in detail in the next four subsections.

A. Recommendation Changes and Past Stock Returns

Previous studies indicate that past stock returns are an important determinant of analyst

recommendations. Since fire sales and fire purchases often result in extreme returns, a contrarian

recommendation style could also lead to price-correcting recommendation changes following

such events. To investigate this alternative explanation for skillful analysts’ actions, we compare

12 We allow analysts two quarters to respond to flow-driven trading pressure to account for potential persistence in fund flows and the resulting flow-driven mispricing. Our findings are very similar if we consider only flow-driven trading events in the prior quarter.

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the likelihood of a price-correcting recommendation revision by skillful versus other analysts,

controlling for past returns.

Specifically, Table III reports parameter estimates from regressing the likelihood of

upgrading (Panel A) or downgrading (Panel B) by individual analysts on an indicator variable

indicating analysts designated as skillful in the prior quarter (Skillful Analyst), flow-induced

purchase (Forced_Buy) or sale (Forced_Sell) indicators, cumulative returns during the same

period flow-induced trading is measured (Return), and their interaction terms with the skillful

analyst indicator. We control for each analyst’s outstanding recommendation in the prior quarter

by including five indicator variables for each possible level of recommendation (i.e., from strong

buy to strong sell). All analyses include time fixed effects. Since certain analysts could

inherently be more optimistic or pessimistic than others, we adjust standard errors for analyst-

level clustering when computing t-statistics to account for serial correlations among individual

analysts’ recommendation changes.

Consistent with prior findings that analyst opinions are positively related to stock returns

(e.g., Bradshaw, 2004; Jegadeesh et al., 2004), the likelihood of analyst upgrading

(downgrading) is significantly higher for stocks experiencing higher (lower) returns in the prior

two quarters. Interestingly, while stocks subject to flow-forced sales (purchases) do not have a

significantly higher likelihood of being upgraded (downgraded) by the average analyst, they are

significantly more likely to receive a price-correcting recommendation from skillful analysts.

Specifically, Forced_Sell*Skillful Analyst and Forced_Buy*Skillful Analyst are significantly

positive in the upgrade and downgrade regressions, respectively. In addition, skillful analysts do

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not appear to have a contrarian recommendation style. In fact, they are, on average, slightly more

likely to react positively to past returns when changing their recommendations, as indicated by

the significantly positive (negative) interaction term, Return*Skillful Analyst, in the upgrade

(downgrade) regression. This evidence suggests that skillful analysts have the ability to

differentiate extreme returns from mispricing and that their responses to flow-driven mispricing

are distinct from a simple contrarian revision style.

B. Recommendation Changes and Concurrent Earnings Forecast Revisions

To lend further credence to our conjecture that skillful analysts’ recommendation

revisions for stocks under flow-driven trading pressure are motivated by perceived mispricing,

we analyze their concurrent cash flow estimates for those firms to rule out the possibility that

their actions are instead driven by new information regarding future cash flows.

Kecskes, Michaely, and Womack (2010) and Brown and Huang (2010) document that

recommendation revisions are often accompanied by earnings forecast revisions. Moreover, they

argue that analyst recommendations accompanied by earnings forecast revisions in the same

direction could be more informative because they are more credible and less subject to

behavioral and incentive biases. In contrast, if skillful analysts recognize flow-driven mispricing,

then we would not expect them to significantly revise earnings forecasts when revising the

recommendations of firms subject to flow-induced price pressure. This is because the flow-

driven price pressure is due to changes in financial condition at the fund level rather than new

firm-level information about fund holdings. Thus the related price movements should result in

little or no change in analyst earnings forecasts. To test this hypothesis, we compare concurrent

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earnings forecast revisions by analysts who issue price-correcting recommendation revisions for

fire-sale and fire-purchase stocks with those for other stocks revised by them or those by other

analysts concerning the same stock.

We obtain one-year-ahead earnings forecast data from the I/B/E/S detail history file.

Each quarter, for stocks receiving at least one upgrade or downgrade, we take the most recent

outstanding earnings forecasts by individual analysts and compute each analyst’s forecast

revision as the change from quarter t - 1 to quarter t. If an analyst does not revise his or her

earnings forecast during this period, we consider the revision to be zero as long as the

outstanding forecast was issued within the past 180 days.13

Since earnings forecast revisions can also be related to various factors such as firm

characteristics, analyst characteristics, and market conditions, we employ the following

multivariate regression model:

|Earnings Forecast Revision|ijt = f (Price-Correctionijt-1, AnalystCharit-1, FirmCharjt-1) (2)

where |Earnings Forecast Revision|ijt is the absolute value of the earnings forecast revision of

analyst i on stock j in quarter t, normalized by the standard deviation of all earnings forecasts on

stock j issued in quarter t concerning the same fiscal year, and Price-Correction is an indicator

variable that takes the value of one for a forecast revision issued by an analyst making a

recommendation revision that is in the direction of price correction during the quarter. We 13 A potential issue with examining quarterly forecast revisions is that forecasts issued in quarters t - 1 and t may correspond to different fiscal years if the firm announces its annual earnings in quarter t. To avoid this issue, for forecast revisions that straddle two consecutive fiscal years, we follow Hong, Lee, and Swaminathan (2003) to calculate the revision as the difference between the one-year-ahead earnings forecast issued in quarter t and the two-year-ahead earnings forecast issued in quarter t - 1.

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include quarter fixed effects to control for time variations in earnings forecast revisions and

adjust standard errors for firm-level clustering when computing t-statistics.

Here AnalystChar is a vector of analyst characteristics (Tenure, # of Stocks Covered,

Large Broker, and Prestigious Broker), while FirmChar is a vector of firm characteristics

(Return Volatility, Return Skewness, Stock Beta, and Firm Size) measured in the year prior to the

revision quarter. Specifically, since analysts new to the profession may make bolder forecasts in

order to stand out, we control for analyst experience as proxied by the number of quarters since

an analyst’s first appearance in the I/B/E/S database (Tenure) and the number of stocks covered

by the analyst the previous year (# of Stocks Covered). We also control for characteristics of the

brokerage house with which an analyst is affiliated: Large Broker is an indicator variable that

takes the value of one if an analyst works for a brokerage house employing more than 50

analysts in the previous year and zero otherwise; Prestigious Broker is an indicator variable that

takes the value of one if an analyst works for one of the top 15 brokerage houses in equity

research according to Institutional Investor magazine and zero otherwise.14

The size of forecast revisions may also be related to firm characteristics. We expect

earnings forecast revisions to be larger in magnitude if the firm’s performance is more volatile or

uncertain. Therefore we control for Return Volatility, Return Skewness, Stock Beta, and Firm

Size.15 We also include the number of forecasts in each specific firm-quarter as an independent

14 We follow Section A4 of Kecskes, Michaely, and Womack (2010) to identify prestigious brokers. 15 We measure Return Volatility as the standard deviation of the residuals from annual Carhart (1997) four-factor model regressions over the one-year period immediately preceding the revision quarter, Return Skewness as the skewness of the regression residuals, Stock Beta as the coefficient estimate of the market factor from the same four-factor model, and Firm Size as the logged market capitalization of the stock at the beginning of the revision quarter.

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variable to account for potential bias in the normalized forecast revisions (i.e., the noise in the

standard deviation of forecasts in the denominator may be affected by the number of forecasts).

Table IV reports the result of this regression analysis. Model (1) includes only the main

variable of interest, Price Correction. The parameter estimate for this variable is significantly

negative (-1.8944, t = -8.90), supporting our hypothesis that concurrent earnings forecast

revisions issued by skillful analysts who make recommendation revisions in the direction of

price correction tend to be much smaller in magnitude than those accompanying other

recommendation revisions.

We introduce analyst and firm characteristics as control variables in the next two models.

The results for Models (2) and (3) show that analysts with longer tenure or those covering more

stocks tend to make smaller revisions. The magnitude of forecast revisions tends to be larger for

smaller firms and firms with higher return volatilities or lower market betas. Despite the control

of analyst and firm characteristics, the parameter estimate for our main variable of interest,

Price-Correction, remains significantly negative.16

In summary, the significantly smaller magnitude of concurrent forecast revisions by

analysts who make price-correcting recommendation changes toward fire-sale or fire-purchase

stocks strongly supports our hypothesis that skillful analysts’ price-correcting recommendation

changes are primarily driven by perceived mispricing, as opposed to changes in firm

fundamentals.

16 In an unreported analysis, we examine the likelihood of recommendation changes falling around earnings announcement dates. We do not find any significant differences between fire-sale and fire-purchase stocks and others. Therefore it is unlikely that the recommendation changes of fire-sale and fire-purchase stocks are driven by recent earnings news.

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C. Do Skillful Analysts Have the Persistent Ability to Detect Mispricing?

To ensure that skillful analysts’ identification of flow-driven mispricing is not due to

chance, we analyze the persistence of the skillful analyst designation over time by examining the

probability of a particular analyst being designated as skillful conditional on whether the analyst

was designated as such in the preceding period. Specifically, we examine the percentage of

skillful versus “non-skillful” analysts in the current quarter who are classified as skillful in each

of the subsequent four quarters. Table V reports the difference and odds ratio during quarters

t + 1 through t + 4 between the two groups. Analysts who cover no stocks experiencing flow-

driven trading pressure in a particular quarter are excluded from the analysis for that quarter.

The result in Table V indicates that analysts designated as skillful in quarter t are about

2.58 times more likely to be designated as skillful in quarter t + 1 according to the odds ratio,

compared with analysts not designated as such in quarter t. This effect is quite persistent since

analysts designated as skillful in quarter t are more than twice as likely to be designated as

skillful as other analysts in any of the following four quarters. Differences in odds ratio between

these two groups of analysts are highly significant at the 1% level according to χ2 tests

(unreported). The persistence of the skillful analyst status suggests that skillful analysts do not

simply issue price-correcting recommendations by chance.

D. Overall Research Performance of Skillful Analysts

Our results so far show that skillful analysts demonstrate a persistent ability to identify

flow-driven mispricing. An important issue is the source of such ability. One possibility is that

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some analysts habitually engage in a negative feedback strategy and thus upgrade a stock when

they observe price declines following outflow-driven downward price pressure or downgrade the

stock following inflow-driven upward price pressure. Our evidence in Table III suggests that this

is unlikely to be the case given that skillful analysts typically react positively to past returns.

Another possibility is that these analysts may have obtained private information concerning

flow-driven trading specifically and revise their recommendations accordingly. For example, an

analyst from a large investment bank may be tipped off about flow-forced trading by affiliated

brokers handling trades for their fund clients or even directly from affiliated mutual funds

involved in flow-forced trading. Lastly, some analysts may be more skillful in analyzing

information regarding the fundamental value of stocks and are thus better at identifying

mispricing, regardless of whether it is flow induced or not.

Although all of these potential explanations could be consistent with analysts issuing

price-correcting revisions following flow-driven mispricing, only the skill-based explanation

predicts the unconditional superior performance of skillful analysts. That is, only if skillful

analysts have greater overall research ability in collecting private information and/or processing

public information will they be able to outperform other analysts on the average stock they

cover, regardless of whether or not it is subject to flow-driven mispricing. In this subsection we

examine whether analysts who issue price-correcting recommendation revisions have better

research performance in general. Specifically, we examine whether these analysts issue more

accurate earnings forecasts and provide more informative recommendation revisions.

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We first examine the relative accuracy of skillful analysts’ earnings forecasts. We use

two proxies to measure relative accuracy: (1) the absolute difference between an analyst’s

forecast and the actual reported earnings, normalized by the standard deviation of all earnings

forecasts for the same fiscal year-end, and (2) the average percentile ranking of forecast errors

among all analysts covering the same stock during the same period. The latter is the inverse of

the Accuracy Score measure of Hong and Kubik (2003). We normalize this measure so that it is

centered at zero and ranges from -50 to +50. Each quarter, we obtain individual analysts’ most

recent one-year-ahead earnings forecasts from the I/B/E/S detail history file and compute the

forecast error as the absolute difference between an analyst’s earnings forecast and the reported

earnings.17 A more accurate analyst should have lower normalized forecast errors and a lower

percentile of forecast errors.

Since prior studies show that analyst forecast accuracy is related to analyst and firm

characteristics, we conduct a multivariate analysis in Table VI where our forecast accuracy

measures are regressed on a dummy variable indicating skillful analysts and a set of control

variables. In particular, to account for the possibility that analysts who revise their

recommendations in response to past stock returns in a contrarian way (rather than revising

recommendations to correct perceived mispricing) tend to be more accurate, we include an

indicator variable, Contrarian. It takes the value of one for analysts who issue at least one

recommendation downgrade (upgrade) in quarter t for stocks ranked in the top (bottom) return

decile in the preceding two quarters and zero otherwise. Other control variables include analyst

17 Our results are very similar if we examine the forecast error of analysts’ one-quarter-ahead earnings forecasts.

20

tenure (Tenure), the number of stocks covered by the analyst (# of Stocks Covered) in the

previous year, the Large Broker dummy, the Prestigious Broker dummy, Return Volatility,

Return Skewness, Stock Beta, Firm Size, and the number of analyst forecasts (Number of

Forecasts) in each specific firm–quarter. Lastly, to control for variations in forecast errors over

time, we include quarter fixed effects. The t-statistics are computed with heteroskedasticity-

robust standard errors clustered by firm.

The estimates in Panel A of Table VI show that, after controlling for analyst and firm

characteristics, skillful analysts have significantly smaller forecast errors relative to their peers

using both measures of forecast accuracy. In particular, Panel A shows that the average

normalized forecast error of skillful analysts in quarter t is lower than that of non-skillful

analysts by about 1.72 percentage points. In terms of economic magnitude, this difference is

more than 50% larger than the effect on forecast errors of an analyst’s affiliation with a large

brokerage house (1.10 percentage points). This result suggests that skillful analysts are not only

better at identifying mispricing, but also better at forecasting firms’ cash flows in general.

To ensure that our finding on the forecast accuracy of skillful analysts is not driven by

their superior performance on stocks that are subject to flow-driven mispricing in the preceding

two quarters (t - 2 or t - 1), we repeat the analysis after excluding these stocks. The result

reported in Panel B of Table VI shows that our main findings remain qualitatively and

quantitatively similar. Therefore skillful analysts have greater forecasting ability for the average

stock they cover, regardless of whether it is mispriced or not.

21

In Table VII we examine whether skillful analysts make more informative

recommendation revisions. In particular, we compare the market-adjusted abnormal returns for

recommendation revisions issued by skillful analysts against those issued by other analysts

against all stocks. We report coefficient estimates from regressing the cumulative market-

adjusted returns during the three-day [-1,1] or six-day [0,5] window surrounding

recommendation revisions on a dummy variable indicating recommendation changes made by

skillful analysts and a battery of control variables.

To account for potential differences in performance that may be attributed to analyst

characteristics other than the ability to detect mispricing, we control for the same set of analyst

characteristics used in Table VI, including the contrarian analyst dummy indicating analysts with

a contrarian recommendation revision style. We also control for the effect of past returns of the

revised stock. Specifically, Momentum is the stock’s percentile ranking of return momentum,

measured as raw returns during the 12-month period ending in the month before the

recommendation revision month. The variable Previous Week’s Return is the raw return during

the week ending two days prior to the recommendation revision day. Since Kecskes, Michaely,

and Womack (2010) and Brown and Huang (2010) show that recommendation changes that are

accompanied by corroborating earnings forecast revisions are more informative and influential,

we also include a Concurrent Forecast Revisions dummy variable set to one for a

recommendation revision issued on the same day as an earnings forecast revision and zero

otherwise. Finally, we include time fixed effects and use heteroskedasticity-robust standard

errors clustered by firm when computing t-statistics.

22

Panels A and B of Table VII present the results for recommendation upgrades and

downgrades, respectively. Column 1 of Panel A shows that recommendation upgrades made by

skillful analysts generate abnormal returns that are 38 basis points higher during the three-day

window compared to those of other analysts. Similarly, Column 1 of Panel B shows that these

analysts’ recommendation downgrades generate abnormal returns that are about 36 basis points

lower than those of other analysts during the same window. In contrast, revisions made by

contrarian analysts who merely upgrade stocks with low past returns or downgrade stock with

high past returns do not earn any significant abnormal returns. These findings are robust when

we use an alternative six-day [0, 5] return measurement window. Furthermore, the results in the

last two columns of Panels A and B indicate that excluding stocks subject to flow-driven

mispricing does not change the inference of these analyses. Therefore analysts who are better at

identifying mispriced stocks also provide more informative stock recommendations. This finding

further bolsters the skill-based explanation for skillful analysts’ price-correcting recommendation

changes.

III. The Role of Analysts in Correcting Flow-Driven Mispricing

A. Analyst Recommendation Revisions and Liquidity Improvement

Having established the ability of certain analysts to recognize flow-induced mispricing,

we now examine the extent to which their price-correcting recommendations affect the price

correction process. Coval and Stafford (2007) propose that the speed of price correction

following mutual fund flow-driven trading pressure depends on the degree of liquidity provision

23

by other market participants. Given the uncertainty regarding the exact cause of large price

movements following mutual fund flow-driven trading pressure, potential liquidity providers are

more likely to be sophisticated investors who recognize the deviation of transaction prices from

fundamental firm values. For example, Ali et al. (2010) and Zhang (2010) show that company

insiders and certain fund managers exploit this type of mispricing by trading against flow-

constrained mutual funds.

On the other hand, it is not entirely clear which types of sophisticated market participants

are able or willing to act quickly to provide liquidity, given their investment objectives and

investment constraints. First, institutional investors such as pension funds and mutual funds are

generally constrained by narrow investment mandates (Almazan et al., 2004). Therefore they

may not be able to take advantage of investment opportunities outside of their existing mandates.

Second, while other institutional investors such as hedge funds may not face such investment

constraints, they are nonetheless constrained by limits of arbitrage due to correlated capital

flows, margin calls, and risk limits (Ben-David et al., 2011). In addition, they may be more

interested in front-running mutual funds that experience extreme flows, given the predatory

nature of their trading.18 Similarly, corporate insiders may not be able to provide liquidity

quickly because of the widespread existence of blackout windows and diversification concerns.19

In contrast, although equity analysts cannot directly provide liquidity, they are in a more

flexible position to help promote liquidity provision via their recommendation revisions. That is,

18 Indeed, Chen et al. (2010) and Shive and Yun (2011) find evidence consistent with hedge funds selling ahead of distressed mutual funds and earning significant abnormal returns at their expense. 19 Bettis et al. (2000) document that 78% of their sample firms have explicit blackout periods during which insiders are prohibited from trading.

24

analyst recommendation revisions issued in response to flow-driven trading pressure can induce

trading in the direction of price correction. As such, these revisions can prompt investors who

were unclear about the exact source of the large price movements following flow-driven fund

trading to subsequently participate in liquidity provision. Therefore we hypothesize that timely

recommendation revisions following flow-driven mispricing can help improve liquidity

provision.

Although we cannot directly observe the market demand and supply of stocks under price

pressure, several studies show that the funding liquidity of traders is often closely related to the

market liquidity of the securities they trade (e.g., Brunnermeier and Pedersen, 2009; Aragon and

Strahan, 2011). Therefore, we examine whether analyst revisions are related to greater

improvement of market liquidity for fire-sale and fire-purchase stocks.

Specifically, we compare changes in the market liquidity during the quarters surrounding

fire sales or fire purchases between stocks revised by analysts in the direction of price correction

and those that are not, using both a low-frequency and a high-frequency liquidity measure. The

former is the Amihud (2002) illiquidity ratio calculated as the absolute daily return divided by

the dollar trading volume (number of shares traded multiplied by end-of-day stock price) in

million dollars, averaged over a quarter. 20 The latter is the effective spread derived from

transaction level data as the effective half-spread divided by price, where the effective half-

20 Following the existing literature, we exclude stocks with a price lower than $5 or with less than 30 daily return observations during the quarter to reduce the noise in this illiquidity measure. Since NYSE/AMEX and NASDAQ report trading volume differently, we divide the trading volume of NASDAQ stocks by 2 when computing their Amihud measure.

25

spread is defined as | − |. We calculate the daily trade size-weighted average of this

measure for each stock and then take the quarterly (equal-weighted) average of the daily

averages. To construct this variable we use the market microstructure summary dataset from the

Financial Markets Research Center, which consolidates trade-level data from TAQ.21

We estimate a multivariate regression of changes in market liquidity from quarters (t - 1, t

- 2) to quarter t or quarters (t + 1, t + 2) on dummy variables indicating stocks subject to flow-

induced purchases (Forced_Buy) versus sales (Forced_Sell) during quarters (t - 1, t - 2),

consensus analyst upgrades (Upgrade) versus downgrades (Downgrade) during quarter t, and

their interaction terms. This analysis is designed to capture the marginal effect of

recommendation revisions on changes in market liquidity around flow-driven trading pressure

events while controlling for the unconditional standalone effect of analyst revisions on changes

of liquidity. To account for the effect of firm characteristics that are likely to be related to

changes in stock liquidity, we included firm size (Firm Size), market-to-book ratio (M/B ratio),

stock price (Price) and daily stock return volatility (Return Volatility) as control variables. Size is

the logarithm of the firm’s market capitalization, M/B is the market-to-book ratio as of the prior

year, Price is measured as the logarithm of the end-of-quarter stock price, Return Volatility is the

standard deviation of stock returns in the preceding year. To ensure that our finding on liquidity

changes is not driven by the effect of analyst revisions on stocks simply experiencing extreme

returns, we further include interaction terms between the cumulative return (Past Returns) during

21 We employ an intraday measure of liquidity in an attempt to obtain a more accurate representation of market liquidity. We use the effective spread rather than the quoted spread to account for the possibility that transactions occur outside of the quoted spread.

26

the trading pressure quarters (t-2, t-1) and the upgrade and downgrade dummies. Lastly, we

control for time fixed effects in all regression models and compute t-statistics based upon robust

standard errors adjusted for firm-level clustering.

The result in Table VIII shows that stocks subject to flow-induced trading pressure, in

general, exhibit continued decline in their market liquidity during the subsequent quarters and

this effect is often more persistent for fire-sale stocks. However, they benefit particularly from

price-correcting analyst recommendation changes beyond the standalone effect of analyst

revisions on changes of stock liquidity. For example, while a typical stock subject to inflow-

induced buying pressure continues to experience deterioration of liquidity for several quarters,

those that are downgraded by analysts during quarter t start to experience a significant immediate

improvement in their market liquidity in quarters t or t + 1 through t + 2, as evidenced by the

significant decline in their Amihud (2002) illiquidity measure and their effective spread.

Similarly, upgraded fire-sale stocks experience greater liquidity recovery relative to non-

upgraded ones, although the effect is only observed from a significant reduction in their effective

spread. These findings are distinct from changes in liquidity associated with analyst revisions on

stocks simply experiencing extreme returns.

Therefore price-correcting analyst revisions have a distinct effect on the liquidity

improvement of stocks subject to flow-driven mispricing. This evidence is consistent with our

hypothesis that analysts are instrumental in inducing liquidity provision from bystanders in the

market, who might be hesitant to trade against flow-constrained funds without observing clear

signals of mispricing.

27

B. Analysts’ Recommendation Revisions and the Speed of Subsequent Price Correction

Since analyst recommendation revisions seem to improve stock liquidity following flow-

induced trading pressure, we hypothesize that stocks subject to flow-driven price pressure

ultimately experience a faster price recovery when skillful analysts revise their recommendations

in the direction of price correction. Specifically, we track stock returns in the period following

mutual fund flow-driven fire sales and fire purchases and then compare the price recovery

patterns between those stocks that receive consensus recommendation revisions in the direction

of price correction and those that do not. Since upgraded stocks are generally likely to have

higher abnormal returns while downgraded stocks are likely to have lower abnormal returns, we

conduct this analysis in a multivariate setting, controlling for the standalone effect of analyst

revisions on returns. This allows us to better tease out the marginal effect of analyst revisions in

speeding up the price adjustment of mispriced stocks.

In Table IX, we regress DGTW (1997) characteristic-adjusted abnormal returns on

dummy variables indicating fire sales or fire purchases, consensus upgrades or downgrades, and

their interaction terms.22 In addition, we control for the cumulative stock return during the two

quarters when fire sales and purchases are measured and its interaction terms with analyst

upgrades and downgrades. We also include quarter fixed effects in all analyses and report t-

statistics computed using standard errors adjusted for firm-level clustering.

Column (1) of Table IX shows that stocks subject to outflow-driven mutual fund sales in

quarter t - 2 or t - 1 experience insignificant returns in quarter t while stocks subject to inflow- 22 Since we analyze DGTW (1997) characteristic-adjusted abnormal returns, there is no need to control for common return-predictive stock characteristics such as size, book-to-market ratio, and momentum.

28

driven purchases still have positive abnormal returns. That is, price correction appears to be

faster for the former group of stocks. This is probably unsurprising since it would be easier for

potential liquidity providers to buy undervalued stocks than to sell overvalued stocks, given the

short-sale constraint. Regarding the standalone effect of analyst revisions, Column (1) indicates

that consensus upgrades are associated with significantly positive abnormal returns while

consensus downgrades are associated with significantly negative abnormal returns.

The interactions of Forced_Sell with Upgrade and Forced_Buy with Downgrade capture

the effect of analyst recommendation changes on the price correction process following mutual

fund flow-driven trades. Indeed, the parameter estimates indicate that upgraded fire-sale stocks

experience a stronger price correction in the quarter immediately following the fire-sale event

relative to those not upgraded, as indicated by the higher DGTW characteristic-adjusted

abnormal returns for the former group during quarter t. Similarly, downgraded fire-purchase

stocks earn significantly lower returns compared to those fire-purchase stocks not downgraded.

These findings suggest that, even after controlling for the direct effect of analyst

recommendation changes on stock returns, analyst upgrades (downgrades) of fire-sale (fire-

purchase) stocks seem to have significant incremental effects on the price correction process—

presumably because of the signaling effect of analyst revisions that prompts liquidity provisions

from other market participants.

In Column (2) of Table IX we further control for the interaction terms of past returns with

analyst upgrades and downgrades, respectively. These additional controls ensure that our tests

capture the effect of analyst revisions on the price correction process instead of merely their

29

effect on stocks experiencing extreme past returns. The inferences we obtain from Column (1)

remain unchanged, suggesting that skillful analysts’ price-correcting recommendation changes

have a standalone effect on the returns of stocks subject to flow-driven mispricing.

To further examine whether analyst recommendation revisions indeed help accelerate the

price correction process, we alternatively analyze whether unrevised fire-sale and fire-purchase

stocks experience greater (delayed) return reversals in subsequent quarters. Prior studies show

that mutual fund flow-induced price pressure exhibits short-term persistence (perhaps due to

persistence in fund flows).23 In addition, Table VIII indicates that fire sale or fire purchase stocks

unrevised by analysts continue to suffer deterioration of liquidity in subsequent quarters. We thus

skip the two quarters immediately following the analyst revision quarter because we may not

observe any significant return reversals for unrevised stocks until several quarters later. Instead,

our analysis focuses on the comparison of abnormal returns during quarters t + 3 through t + 8

between revised and unrevised stocks.

Column (4) of Table IX shows that mutual fund flow-forced trading is negatively

correlated with future abnormal returns during quarters t + 3 to t + 8, confirming prior findings

that mutual fund flow-driven trades cause significant return reversals. More importantly, we find

that the interaction between Forced_Buy and Downgrade is significantly positive. Essentially,

when the incidence of mutual fund inflow-driven purchases is coupled with a consensus

downgrade, subsequent abnormal returns of the stock are less negative (by 3.53 percentage

23 In particular, Edmans, Goldstein, and Jiang (2009) document that return reversals associated with outflow-induced selling pressure do not start until about six months after the initial trading pressure. Lou (2011) finds that return reversals take even longer to emerge among his sample stocks.

30

points), suggesting that timely downgrades of an overpriced stock help reduce subsequent

negative return reversals. In other words, the price correction process takes significantly longer

for non-downgraded stocks, potentially due to the lack of liquidity provision in the absence of

the signaling effect of analyst revisions. Interestingly, neither Past Return*Upgrade nor Past

Return*Downgrade is significant in Column (4). Therefore for stocks experiencing extreme

returns, but not necessarily mispricing, there is no price correction effect from analyst revisions

during subsequent quarters.

Note that the interaction term between Forced_Sell and Upgrade, although having the

expected sign, is not statistically significant and is smaller in magnitude than the interaction term

between Forced_Buy and Downgrade. This difference could potentially be driven by several

factors. First, compared to downgrades, previous studies document that analyst upgrades are less

informative and have more short-lived effects on stock returns (Womack, 1996; Jegadeesh et al.,

2004), and thus may have a weaker impact on investor behavior. Second, many institutional

investors face short-sale constraints and may not be able to trade against mutual fund inflow-

driven purchases. As a result, analysts’ roles in promoting liquidity provision may be more

critical when the stock is subject to upward as opposed to downward price pressure. In the next

two subsections, we further examine other circumstances where analysts’ role in correcting

mispricing could be more pronounced.

31

C. The Effect of Institutional Ownership

The speed of price adjustment following flow-driven mispricing may depend on the size

of the potential pool of liquidity providers. Since institutional investors may be more informed

about intrinsic firm value than individual investors and can trade on a large scale to potentially

serve as the counterparty for flow-constrained mutual funds, we expect analyst recommendation

revisions to have a more pronounced effect on the price correction process for low institutional

ownership stocks. In other words, analysts can potentially act as “substitutes” for institutional

investors to facilitate price correction. To test this hypothesis, we divide our sample stocks each

quarter into high and low institutional ownership groups based on their (lagged) levels of

institutional ownership and then compare the effect of analyst revisions on the speed of price

correction between these two groups. We measure a firm’s institutional ownership (IO) as the

fraction of the firm’s shares held by institutional investors (obtained from the Thomson Reuters

Institutional Holdings (13F) database). Stocks whose institutional ownerships are ranked in the

top tercile during the quarter are considered high institutional ownership stocks, while the

remaining stocks are included in the low institutional ownership group.

In the first two columns of Table X, we separately analyze the price correction process

for high versus low institutional ownership stocks. The results suggest that the effect of analyst

revisions in aiding the price correction process is mostly concentrated in stocks with relatively

lower institutional ownership, stocks that are likely to have a smaller pool of potential liquidity

providers. Specifically, the interaction terms between Forced_Sell and Upgrade and between

Forced_Buy and Downgrade are both statistically significant with the expected signs among

32

stocks with low institutional ownership, but insignificant among stocks with high institutional

ownership. The F-tests show that the differences in these interaction terms between high and low

institutional ownership stocks are statistically significant at the 1% level.

D. The Effect of Information Asymmetry

One of the main reasons that liquidity provision is constrained immediately following a

stock’s concentrated flow-driven mutual fund trading is that there is uncertainty regarding the

stock’s fundamental value and thus the related mispricing. The signaling effect of analyst

revisions would therefore be more critical for stocks with greater valuation uncertainty.

Furthermore, Loh and Stulz (2010) show that analyst recommendation revisions are usually more

influential on the prices of stocks that have a more uncertain information environment. Thus we

expect that analysts’ role in identifying potential mispricing and facilitating price adjustments

should be more pronounced for stocks with greater information asymmetry.

To test this hypothesis we use the dispersion of analyst earnings forecasts to proxy for

information asymmetry and examine how it affects the marginal effect of analyst

recommendations on the price correction process following flow-driven mispricing. We measure

analyst earnings forecast dispersion as the standard deviation of individual analysts’ most recent

one-year-ahead earnings forecasts (for the current fiscal year) during the quarter, normalized by

the four-quarter-lagged stock price. 24 We require that each stock must have at least three

24 We normalize the standard deviation of earnings forecasts with the four-quarter-lagged stock price to avoid the confounding effect of abnormal price movements around flow-driven purchases and sales of the stock.

33

forecasts (from different analysts) during the quarter in order to compute its forecast dispersion

measure.

In the last two columns of Table X, we divide our sample of stocks into two groups based

on the dispersion of their analyst earnings forecasts as of quarter t - 1. Stocks with earnings

forecast dispersion ranked in the top tercile during the quarter along with stocks with missing

forecast dispersion are considered high information uncertainty stocks. The latter stocks either

have no active analyst coverage or are covered by too few analysts, both cases associated with

greater information uncertainty. The remaining stocks are classified into the low information

uncertainty group. We then separately estimate the effect of analyst recommendation revisions

on the price correction process for the high and low analyst dispersion groups.

When comparing across these two groups of firms with different information

environments, we find that the interaction term between Forced_Sell and Upgrade is

significantly negative while that between Forced_Buy and Downgrade is significantly positive

among stocks with high information uncertainty. In contrast, neither interaction term is

significant for the low information uncertainty group. The differences in the two interaction

terms between high and low uncertainty stocks are both significant at the 1% level.

In summary, the results in this section suggest that analysts play an important role in

correcting mispricing via the signaling effect of their recommendation revisions. This role

becomes more pronounced when other potentially informed market participants are less likely to

participate in the price correction process and when there is greater uncertainty regarding the

mispricing.

34

IV. Robustness Analyses

Flow-driven mispricing is largely exogenous to firm fundamentals because stocks heavily

bought or sold by funds subject to heavy capital flows experience significant immediate price

impacts but subsequent price reversals. In contrast, stocks subject to widespread trading by

unconstrained mutual funds do not experience similar reversals (Coval and Stafford, 2007;

Mozaffar, Kogan, and Serafeim, 2011). In addition, it is unlikely that individual stocks’ past

performance would be responsible for the relation between flow-forced fund trading and

subsequent price pressure because funds experiencing extreme outflows (inflows) do not

necessarily sell (buy) their poor-performing (well-performing) holdings disproportionally.25

To further address the concern that stock fundamentals, rather than exogenous flow-

forced trading, are responsible for both fund flows and the subsequent returns of fund holdings,

we limit our attention to the subset of fire-sale or fire-purchase stocks that are unlikely to be

responsible for extreme fund flows and thus the related flow-driven fund trading. Specifically,

we focus on fire-sale (fire-purchase) stocks that do not experience negative (positive) returns in

the quarter immediately preceding the outflow-induced (inflow-induced) fund selling (buying).

Since these stocks’ performance is in the opposite direction of the subsequent fund flows, it is

unlikely that changes in their underlying valuation fundamentals are responsible for the extreme

flows of the funds holding them. Rather, their observed large price movements subsequent to

25 Coval and Stafford (2007) show that funds’ tendency to eliminate poorly performing positions is not affected by their flows. Furthermore, they show that funds experiencing extreme inflows are actually more likely to reduce their top-performing positions, compared to funds experiencing fire sales.

35

flow-driven trading are more likely to be attributed to temporary trading pressure when they are

just bought or sold proportionally by funds experiencing flow shocks.

In Table XI, we repeat our analyses of the price correction process for this subset of

stocks subject to flow-driven mispricing. Panel A shows the results for all stocks while Panel B

compares the results between stocks with different levels of institutional ownership or

information uncertainty. Among this subset of fire-sale and fire-purchase stocks whose prior

performance has little impact on the extreme flows of the funds holding them, we again find that

analyst revisions play a significant role in speeding up their price correction process. The

economic and statistical significance of the analyst effect is very similar to that based upon the

full sample (see Table IX). Moreover, once again we find that this role played by analysts in

improving price efficiency is more pronounced among stocks with lower institutional ownership

or greater informational uncertainty.26

V. Conclusion

This paper examines analyst recommendation changes on stocks that experience mutual

fund flow-driven trading pressure. We find that a group of skillful sell-side equity analysts

persistently identifies these mispriced stocks and revises their recommendations accordingly,

even though they do not normally have a contrarian revision style. At the same time, their

concurrent forecast revisions on these stocks are significantly smaller in magnitude than those of

26 In unreported analyses, we also re-run our earlier analyses (e.g., the probabilities of upgrades/downgrades and earnings forecast revisions) using this stricter definition of flow-driven trading pressure. Our findings are qualitatively and quantitatively similar to what are reported in the paper.

36

other stocks or of other analysts concerning the same stock, suggesting that skillful analysts’

recommendations are not driven by new information regarding future firm cash flows. Further,

we find that analysts with superior ability in identifying flow-driven mispricing also seem to

have superior overall research performance. Specifically, they produce more accurate earnings

forecasts and more influential recommendation revisions for their covered stocks, even for stocks

that are not subject to flow-driven mispricing. These findings suggest that analysts’ ability to

detect flow-driven mispricing is skill driven.

If recommendation revisions issued by these skillful analysts for stocks under flow-

driven trading pressure send a signal to the market about mispricing, they may prompt more

investors who were uninformed or uncertain regarding the mispricing to participate in liquidity

provision, which will in turn accelerate the price adjustment process. We find evidence

supporting this hypothesis. Specifically, we find that these price-correcting revisions lead to

greater improvement of market liquidity and speedier price correction for stocks subject to flow-

driven mispricing. More importantly, we show that skillful analysts play a more pronounced role

in improving price efficiency when the market is less likely to recognize or correct flow-driven

mispricing, that is, when firms have lower institutional ownership or their information

environment is more uncertain. In general, our results highlight the unique role sell-side analysts

can play in helping stabilize the financial market, as compared to other sophisticated market

participants.

37

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39

Table I: Summary Statistics of Analyst Characteristics

This table reports summary statistics for equity analysts covering stocks subject to flow-driven trading pressure. All characteristics are measured at the beginning of the quarter when flow-driven mutual fund trading pressure is measured. Tenure is the number of quarters since an analyst’s first appearance in the I/B/E/S database. # of Stocks Covered is the average number of stocks covered by each analyst each quarter. # of Recommendation Revisions is the average number of recommendation revisions made by each analyst each quarter. Broker Size is the number of analysts working for the analyst’s affiliated brokerage house. Large Broker is an indicator variable that takes the value of 1 if an analyst works for a brokerage house employing more than 50 other analysts, and 0 otherwise. Prestigious Broker is an indicator variable that takes the value of 1 if an analyst works for a prestigious brokerage house according to Kecskes, Michaely and Womack (2010), and 0 otherwise.

Mean Stdev P5 P25 Median P75 P95

Tenure (in Quarters) 18.97 17.90 1 5 13 27 57 # of Stocks Covered 7.67 6.57 1 3 6 11 19 # of Recommendation Revisions 0.59 1.53 0 0 0 1 3 Broker Size 43.16 44.65 3 11 28 56 137 Large Broker 0.29 Prestigious Broker 0.33

40

Table II: Analyst Coverage of Stocks Experiencing Extreme Flow-Driven Trading Pressure This table reports summary statistics on stocks experiencing extreme mutual fund flow-driven trading pressure and on analysts covering these stocks. Panel A reports the average number of stocks that experience extreme flow-driven trading pressure during a quarter, the average number of analysts covering each such stock, and the fraction of such stocks that have been upgraded, downgraded, or remain unrevised in quarter t. Panel B reports analysts’ average coverage of stocks experiencing flow-driven trading pressure. Panel C reports the fraction of analysts who issue at least one recommendation revision in the direction of price correction in quarter t (conditional on covering at least one stock experiencing extreme flow-driven trading pressure).

Panel A: Stocks Experiencing Flow-Driven Trading Pressure

Fire-Sale/Purchase

Measurement Period: t-1 t-1:t-2

Number of Fire-Sale Stocks with Analyst Coverage 252 460 … Average Number of Analysts Covering 8.52 8.50 … Fraction Upgraded by >1 Analyst 0.27 0.26 … Fraction Downgraded by >1 Analyst 0.33 0.32 … Fraction with No Revision 0.51 0.52

Number of Fire-Purchase Stocks with Analyst Coverage 265 474 … Average Number of Analysts Covering 6.54 6.66 … Fraction Upgraded by >1 Analyst 0.24 0.23 … Fraction Downgraded by >1 Analyst 0.28 0.29 … Fraction with No Revision 0.56 0.55

Panel B: Analyst Coverage of Stocks Experiencing Flow-Driven Trading Pressure

Fire-Sale/Purchase

Measurement Period:

Average # of Stocks per Analyst-Quarter: t-1 t-1:t-2

… Fire-Sale Stocks 0.55 0.96 … Fire-Purchase Stocks 0.57 1.02 … Fire-Sale/Purchase Stocks 1.16 1.92

Panel C: Fraction of Analysts Issuing Price-Correcting Recommendation Revisions

Fire-Sale/Purchase

Measurement Period:

Conditional on Covering > 1 Stocks under Flow-Driven Pressure: t-1 t-1:t-2

… Fraction Upgrading > 1 Fire-Sale Stock(s) 0.09 0.11 … Fraction Downgrading > 1 Fire-Purchase Stock(s) 0.10 0.12 … Fraction Upgrading > 1 FS Stock(s) or Downgrading > 1 FP Stock(s) 0.12 0.15

41

Table III: Flow-Driven Trading Pressure, Stock Returns, and Recommendation Revisions

This table estimates linear probability models of analyst upgrades and downgrades. The dependent variable is an indicator variable for recommendation upgrades (Panel A) or recommendation downgrades (Panel B) in quarter t. Skillful Analyst takes the value of 1 if an analyst issues at least one recommendation revision in the direction of price correction (i.e., an upgrade following a fire sales or a downgrade following a fire purchases) in quarter t-1 on stocks that have experienced extreme flow-driven mispricing during any of the preceding two quarters, and 0 otherwise. Forced_Sell (Forced_Buy) takes the value of 1 if the stock experiences extreme outflow- (inflow-) driven trading pressure in quarters (t-2, t-1), and 0 otherwise; Return denotes cumulative stock returns during quarters (t-2, t-1). We do not report intercepts since all models include indicator variables for each possible lagged level of analyst recommendations (from strong buy to strong sell) and time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the analyst level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Recommendation Upgrade

Dependent Variable: Prob (Upgrade)

Skillful Analyst -0.0084 0.0158 (-0.06) (0.11)

Forced_Sell -0.0019 -0.0063 (-0.03) (-0.10)

Return 1.3298*** 1.1277*** (14.01) (11.55)

Forced_Sell*Skillful Analyst 1.7848*** 1.7451*** (10.39) (10.09) Return* Skillful Analyst 1.0247*** (4.36)

“Strong Buy” -1.6671*** -1.6626*** (-10.34) (-10.31)

“Buy” 2.1491*** 2.1552*** (12.95) (12.98)

“Hold” 5.5081*** 5.5156*** (32.14) (32.17)

“Sell” 11.9260*** 11.9379*** (36.98) (37.01)

“Strong Sell” 15.8796*** 15.8880*** (22.86) (22.87)

Time FE

Number of Observations 1,141,541 1,141,541 R2 0.077 0.077

42

Table III: Flow-Driven Trading Pressure, Stock Returns, and Recommendation Revisions (Continued)

Panel B: Recommendation Downgrade

Dependent Variable: Prob (Downgrade) Skillful Analyst 0.0525 0.0670 (0.26) (0.33)

Forced_Buy 0.0595 0.0552 (0.70) (0.65)

Return -2.8924*** -2.7811*** (-24.71) (-22.32)

Forced_Buy* Skillful Analyst 1.9681*** 1.9854*** (9.55) (9.44)

Return* Skillful Analyst -0.5642** (-1.90)

“Strong Buy” 11.2516*** 11.2500*** (53.20) (53.19)

“Buy” 7.6420*** 7.6394*** (41.69) (41.68)

“Hold” 1.0447*** 1.0414*** (6.35) (6.33)

“Sell” -0.7434*** -0.7491*** (-4.20) (-4.23)

“Strong Sell” -0.8945*** -0.8983*** (-5.14) (-5.17)

Time FE

Number of Observations 1,141,100 1,141,100 R2 0.099 0.099

43

Table IV: Earnings Forecast Revisions of Stocks Experiencing Flow-Driven Trading Pressure

This table examines earnings forecast revisions of analysts that also issue recommendation revisions in the same quarter. The dependent variable is the absolute value of the change in earnings forecasts divided by the standard deviation of all earnings forecasts issued for the same fiscal year end during the quarter (in percentage). Price-Correction is an indicator variable that takes the value of 1 for forecast revisions that accompany either (1) recommendation upgrades on stocks that have experienced fire sales in any of the preceding two quarters, or (2) recommendation downgrades on stocks that have experienced fire purchases in any of the preceding two quarters. Tenure is defined as the number of quarters since an analyst’s first appearance in the I/B/E/S database. # of Stocks Covered is the number of stocks covered by each analyst in the previous year. Large Broker is an indicator variable that takes the value of 1 if an analyst works for a brokerage house employing more than 50 analysts in the previous year, and 0 otherwise. Prestigious Broker is an indicator variable that takes the value of 1 if an analyst works for a prestigious brokerage house, and 0 otherwise. Firm characteristics are measured in the year prior to the quarter of forecast revisions. Return characteristics are obtained from rolling annual firm-level Carhart (1997) four-factor regressions. Return Volatility is the standard deviation of the regression residuals; Return Skewness is the skewness of these residuals; Stock Beta is the coefficient estimate of the market factor from the same four-factor model regression. Firm Size is the logarithm of the stock’s market capitalization at the end of the preceding quarter. Finally, # of Forecasts is the number of earnings forecasts issued for the same fiscal year end during the quarter. We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent Variable: |ΔEstimate|/Standard Deviation of Estimates

(1) (2) (3) Independent Variables Price-Correction -1.8944*** -2.0325*** -2.8857*** (-8.90) (-9.47) (-12.73)

Tenure -0.0197*** -0.0029 (-4.16) (-0.66) # of Stocks Covered -0.0931*** -0.0411*** (-7.24) (-3.46) Large Broker -0.3797* -0.3757* (-1.72) (-1.74) Prestigious Broker -1.7502*** 0.2339

(-8.81) (1.20)

Return Volatility 0.2735*** (7.29)

Return Skewness -0.0566 (-0.27)

Stock Beta -0.0166* (-1.66)

Firm Size -1.9366*** (-20.03)

# of Forecasts -0.0080*** (-3.34)

Time FE Number of Observations 43,173 43,173 43,173 R2 0.018 0.022 0.078

44

Table V: The Persistence of “Skillful” Analyst Status This table examines the persistence of equity analysts’ ability to identify extreme flow-driven mispricing. In each quarter t, an analyst is classified as “skillful” if he/she issues at least one recommendation revision in the direction of price correction (i.e., an upgrade following a fire sale or a downgrade following a fire purchase) on stocks subject to flow-driven mispricing during any of the two preceding quarters (t-1 or t-2). The percentage of analysts identified as “skillful” versus “non-skillful” in quarter t that are classified as “skillful” in each of the subsequent four quarters is reported. We also report the differences and odds ratios between these two groups. All numbers are reported in percentages, except for t-statistics and odds ratios. t-statistics (in parentheses) for the difference between “skillful” and “non-skillful” analysts are calculated using the time-series of quarterly differences. All differences are statistically significant at the 1% level according to χ2 tests.

Transitional Probability

“Skillful” Analyst in Quarter:

t+1 t+2 t+3 t+4

Skillful Analysts in Quarter t 24.43 23.16 22.14 20.85

Non-Skillful Analysts in Quarter t 9.46 10.05 10.48 10.65 Skillful minus Non-Skillful 14.97 13.11 11.66 10.20

(25.17) (23.68) (17.22) (16.85)

Odds Ratio (Skillful/Non-Skillful) 2.58 2.30 2.11 1.96

45

Table VI: The Performance of Skillful Analysts’ Earnings Forecasts This table compares the forecast accuracy between “skillful” and “non-skillful” analysts. An analyst is classified as “skillful” if he/she issues at least one recommendation revision in the direction of price correction (i.e., an upgrade following a fire sale or a downgrade following a fire purchase) in quarter t on stocks that have experienced extreme flow-driven mispricing during quarter t-2 or t-1. We use two proxies for relative earnings forecast accuracy in quarter t: (1) the forecast error normalized by the standard deviation of the stock’s all outstanding earnings forecasts for the same fiscal year end, and (2) the forecast error percentile for the stock. In Panel A, we report the results of multivariate regressions of normalized forecast errors and error percentiles on a dummy variable indicating skillful analyst, and analyst and firm characteristics as control variables. In Panel B, we report results of the same analyses using a subsample of stocks excluding those stocks that experience extreme flow-driven mispricing during quarters (t-2, t-1). Contrarian is a dummy variable that takes the value of 1 for analysts who issue at least one recommendation downgrade (upgrade) in quarter t for stocks ranked in the top (bottom) return decile in the preceding two quarters, and 0 otherwise. Other analyst characteristics (Tenure, # of Stocks Covered, Large Broker, and Prestigious Broker) and firm characteristics (Return Volatility, Return Skewness, Stock Beta, Firm Size and # of Forecasts) are defined in Table V. We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: All Firms Dependent Variable: Normalized Forecast Error (x100) Error Percentile

Skillful Analyst -1.7374*** -1.7214*** -0.3102*** -0.3181*** (-4.49) (-4.44) (-2.82) (-2.89)

Contrarian -0.8178* -0.8665** -0.2326* -0.2418** (-1.88) (-1.98) (-1.90) (-1.97)

Tenure 0.0135* 0.0142* 0.0086*** 0.0089*** (1.66) (1.75) (3.82) (3.94)

# of Stocks Covered -0.0446** -0.0443** -0.0217*** -0.0209*** (-2.50) (-2.48) (-4.62) (-4.44)

Large Broker -1.1447*** -1.0997*** -0.3020** -0.3040** (-2.79) (-2.67) (-2.56) (-2.57)

Prestigious Broker 0.7149* 0.6400* 0.0054 0.0287 (1.92) (1.71) (0.05) (0.27)

Return Volatility 0.0233 0.0137*** (0.74) (2.80)

Return Skewness 0.4191** 0.0721** (2.17) (2.38)

Stock Beta -0.0140 -0.0047** (-0.74) (-2.42)

Firm Size -0.0007 0.0001 (-0.13) (0.04)

# of Forecasts 0.1130*** -0.0221*** (4.45) (-6.31)

Time FE Number of Observations 641,850 641,850 641,850 641,850 R2 0.003 0.003 0.000 0.000

46

Table VI: The Performance of Skillful Analysts’ Earnings Forecasts (Continued)

Panel B: Excluding Firms under Flow-Driven Trading Pressure Dependent Variable: Normalized Forecast Error (x100) Error Percentile

Skillful Analyst -1.6669*** -1.6548*** -0.2525** -0.2602** (-3.89) (-3.86) (-2.07) (-2.14)

Contrarian -0.9114* -0.9770** -0.3107** -0.3194** (-1.93) (-2.06) (-2.33) (-2.39)

Tenure 0.0074 0.0083 0.0073*** 0.0077*** (0.85) (0.94) (3.03) (3.15)

# of Stocks Covered -0.0449** -0.0433** -0.0207*** -0.0200*** (-2.43) (-2.34) (-4.21) (-4.04)

Large Broker -1.0975** -1.0516** -0.2484** -0.2512** (-2.55) (-2.43) (-1.99) (-2.01)

Prestigious Broker 0.6122 0.5564 -0.0434 -0.0211 (1.57) (1.42) (-0.39) (-0.19)

Return Volatility 0.0406 0.0155*** (1.17) (2.83)

Return Skewness 0.2744 0.0420 (1.32) (1.28)

Stock Beta -0.0087 -0.0040 (-0.51) (-1.35)

Firm Size 0.0007 0.0004 (0.14) (0.34)

# of Forecasts 0.0979*** -0.0221*** (3.77) (-5.94)

Time FE Number of Observations 550,398 550,398 550,398 550,398 R2 0.003 0.003 0.000 0.000

47

Table VII: The Performance of Skillful Analysts’ Recommendation Revisions

This table compares the three-day [-1,1] and six-day [0,5] market-adjusted abnormal returns around recommendation revisions made by “skillful” vs. “non-skillful” analysts. We examine upgrades (Panel A) and downgrades (Panel B) separately. In Columns (1) and (2) of each panel, we regress abnormal stock returns around recommendation revisions on a dummy variable that is equal to 1 for recommendation revisions made by “skillful” analysts, and 0 otherwise, as well as control variables including the contrarian analyst indicator (Contrarian), analyst characteristics (Tenure, # of Stocks Covered, Large Broker, and Prestigious Broker) as defined in Table V, stock characteristics (Firm Size, Momentum, and Previous Week’s Return), and Concurrent Forecast Revisions. Contrarian is a dummy variable that takes the value of 1 for analysts who issue at least one recommendation downgrade (upgrade) in quarter t for stocks that are ranked in the top (bottom) return decile in the preceding two quarters, and 0 otherwise. Firm Size is the logarithm of the stock’s market capitalization at the end of the preceding quarter. Momentum is the stock’s percentile ranking of return momentum measured as the raw returns during the twelve months ending in the month before the recommendation month. Previous Week’s Return is the raw returns during the trading week ending two days before the recommendation day. Concurrent Forecast Revisions is an indicator variable that takes the value of 1 for recommendation revisions issued on the same day as an earnings forecast revision and 0 otherwise. In Columns (3) and (4), we report results for the same analyses using a subsample of stocks excluding those stocks experiencing extreme flow-driven mispricing during quarters (t-2, t-1). We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Recommendation Upgrades Abnormal Returns around Recommendation Revisions in %

Sample: All Firms Excluding Firms under Flow-Driven Trading Pressure

Window: [-1,1] [0,5] [-1,1] [0,5]

Skillful Analyst 0.3830*** 0.3288*** 0.3439*** 0.2839** (3.56) (2.95) (3.11) (2.49)

Contrarian 0.1435 0.0891 0.0830 0.0046 (1.18) (0.75) (0.68) (0.04)

Tenure -0.0001 0.0008 -0.0001 0.0013 (-0.08) (0.41) (-0.08) (0.63)

# of Stocks Covered -0.0179*** -0.0077** -0.0185*** -0.0085** (-5.55) (-2.09) (-5.68) (-2.28)

Large Broker 0.9870*** 0.9875*** 1.0313*** 0.9737*** (8.58) (8.31) (8.71) (8.00)

Prestigious Broker 0.5089*** 0.2552** 0.4809*** 0.2816** (4.63) (2.28) (4.31) (2.45)

Firm Size -0.5235*** -0.5190*** -0.5159*** -0.5073*** (-17.43) (-17.44) (-16.76) (-16.61)

Momentum -2.0988*** -1.0659*** -2.0124*** -1.0479*** (-11.10) (-5.43) (-10.24) (-5.05)

Previous Week’s Return -0.7091 -3.3291*** -0.8843 -2.7527*** (-0.73) (-3.73) (-0.83) (-2.84)

Concurrent Forecast Revisions 0.5680*** 0.5805*** 0.5860*** 0.5262*** (6.57) (6.52) (6.54) (5.65)

Time FE Number of Observations 50,374 50,374 45,120 45,120 R2 0.037 0.034 0.040 0.033

48

Table VII: The Performance of Skillful Analysts’ Recommendation Revisions (Continued)

Panel B: Recommendation Downgrades Abnormal Returns around Recommendation Revisions in %

Sample: All Firms Excluding Firms Under Flow-Driven Trading Pressure

Window: [-1,1] [0,5] [-1,1] [0,5]

Skillful Analyst -0.3648*** -0.2709** -0.3007** -0.2584* (-2.73) (-2.14) (-2.16) (-1.96)

Contrarian -0.2145 -0.0809 -0.1628 -0.0170 (-0.94) (-0.35) (-0.69) (-0.07)

Tenure 0.0062** 0.0040 0.0056** 0.0025 (2.51) (1.64) (2.20) (0.96)

# of Stocks Covered 0.0387*** 0.0229*** 0.0359*** 0.0225*** (8.63) (5.16) (7.90) (4.99)

Large Broker -1.0263*** -1.0380*** -0.9844*** -1.0557*** (-7.19) (-7.42) (-6.64) (-7.18)

Prestigious Broker -0.4377*** -0.4724*** -0.3979*** -0.3886*** (-2.99) (-3.36) (-2.65) (-2.69)

Firm Size 0.4713*** 0.4991*** 0.4591*** 0.5049*** (13.13) (14.89) (13.01) (15.14)

Momentum 3.5284*** 2.6138*** 3.2874*** 2.5023*** (13.34) (10.21) (11.90) (9.27)

Previous Week’s Return 5.7873*** 0.9792 4.5348*** -0.0996 (5.68) (1.09) (4.19) (-0.10)

Concurrent Forecast Revisions -2.9676*** -1.8705*** -2.7230*** -1.7162*** (-23.62) (-15.51) (-21.13) (-13.83)

Time FE Number of Observations 62,783 62,778 55,540 55,535 R2 0.062 0.041 0.056 0.040

49

Table VIII: Recommendation Revisions and Market Liquidity around Flow-Driven Trading Pressure

This table examines changes in market liquidity surrounding extreme flow-driven mispricing. Our illiquidity measures are the Amihud (2002) illiquidity measure and the effective spread. The Amihud illiquidity measure is computed as the absolute daily return divided by the dollar trading volume (number of shares traded multiplied by end-of-day stock price) in millions of dollars, averaged over a quarter. The effective spread is defined as the effective half-spread divided by price. Each quarter, we calculate the daily trade size-weighted average of this measure, and then take the quarterly average of the daily averages. The dependent variables are changes in these illiquidity measures between the two quarters in which fire sales/purchases are measured (t-2, t-1), and the analyst revision quarter (t) or the subsequent two quarters (t+1 and t+2). We include the following control variables: Firm Size measured as the logarithm of market capitalization, M/B Ratio measured as the market-to-book ratio in the prior year, Price measured as the logarithm of stock price, Return Volatility measured as the standard deviation of stock return in the prior, and Past Returns measured as the cumulative return during the trading pressure quarters (t-2, t-1). We do not report intercepts since all models include time fixed effects. The t-statistics (in parentheses) are calculated using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Dependent Variable: Change in Amihud Measure,

relative to (t-2:t-1) Change in Effective Spread (in %),

relative to (t-2:t-1) t (t+1:t+2) t (t+1:t+2) Upgrade -0.0961 -0.1200** -0.0077*** -0.0123***

(-1.31) (-1.98) (-8.11) (-9.62) Downgrade -0.0409 0.2036*** -0.0018** -0.0082***

(-0.76) (3.59) (-2.16) (-7.27) Forced_Sell 0.1706** 0.2181*** 0.0062*** 0.0101***

(2.00) (2.68) (4.96) (5.47) Forced_Buy 0.1054 0.2163** 0.0039 0.0125***

(0.98) (2.01) (1.44) (3.40) Forced_Sell*Upgrade 0.1047 -0.1450 -0.0072*** -0.0062**

(0.35) (-0.54) (-2.75) (-2.50) Forced_Buy*Downgrade -0.4593** -0.3924* -0.0070*** -0.0055**

(-2.00) (-1.75) (-2.65) (-2.37) Firm Size -0.0275 -0.1337*** -0.0003 -0.0006

(-1.18) (-4.34) (-1.19) (-1.19) M/B Ratio -0.1035*** -0.1922*** -0.0046*** -0.0111***

(-5.24) (-8.21) (-12.29) (-16.99)

Price -0.0162 0.0998*** -0.0000 -0.0000 (-0.48) (2.71) (-0.05) (-0.07)

Return Volatility 1.1842*** 3.0361*** 0.0138*** 0.0657*** (3.17) (6.41) (2.96) (8.43)

Past Returns -1.4960*** -1.3923*** -0.0971*** -0.1262*** (-13.71) (-12.10) (-9.08) (-8.31)

Past Returns*Upgrade -0.0753 0.0432 0.0100** 0.0122** (-0.30) (0.18) (2.04) (1.97)

Past Returns*Downgrade -0.1852 -0.6981*** 0.0257 0.0365 (-0.94) (-3.02) (0.06) (0.61)

Time FE Number of Observations 72,027 62,593 82,560 82,560 R2 0.020 0.034 0.2158 0.1954

50

Table IX: The Price Correction Effect of Recommendation Revisions

This table regresses DGTW (1997) characteristic-adjusted abnormal returns on dummy variables indicating flow-driven trading pressure, analyst revisions, and their interaction terms. The dependent variable in these regressions is abnormal returns (in percentages) during quarter t or quarters (t+3, t+8). Forced_Sell (Forced_Buy) takes the value of 1 if the stock experiences extreme outflow- (inflow-) driven trading pressure in quarters (t-2, t-1), and 0 otherwise. Upgrade (Downgrade) takes the value of 1 if the stock receives a consensus upgrade (downgrade) in quarter t, and 0 otherwise. All models control for cumulative returns (Past Returns) during the trading pressure quarters (t-2, t-1). We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Abnormal Returns

Quarter t Cumulative Abnormal Returns

Quarters [t+3:t+8] Upgrade 3.5036*** 3.7109*** 0.4443 0.2581

(19.18) (19.75) (0.69) (0.39)

Downgrade -2.9286*** -2.8394*** 0.6374 0.7409 (-16.12) (-15.39) (1.12) (1.22)

Forced_Sell -0.0167 -0.0040 2.7685*** 2.7560*** (-0.07) (-0.02) (2.64) (2.62)

Forced_Buy 0.6672*** 0.6536*** -2.3443** -1.3650 (2.83) (2.77) (-2.27) (-1.30)

Forced_Sell*Upgrade 1.0417* 1.0575* -1.3873 -1.3007 (1.80) (1.85) (-0.82) (-0.77)

Forced_Buy*Downgrade -1.2053** -1.1519** 3.5283* 3.6462** (-2.21) (-2.11) (1.83) (1.97)

Past Returns 0.7753** 1.6054*** -4.3254*** -4.4778*** (1.99) (3.42) (-3.95) (-3.34)

Past Returns*Upgrade -2.9838*** 2.6027 (-3.02) (0.94)

Past Returns*Downgrade -1.4883 -1.6961 (-1.61) (-0.63)

Time FE Number of Observations 113,880 113,880 113,880 113,880 R2 0.017 0.017 0.0010 0.0010

51

Table X: Cross-Sectional Variations in the Price Correction Effect of Recommendation Revisions

This table regresses DGTW (1997) characteristic-adjusted abnormal returns on dummy variables indicating flow-driven trading pressure, analyst revisions, and their interaction terms, separately for stocks with different levels of institutional ownership (Columns 1 and 2) or information uncertainty (Columns 3 and 4). Stocks with quarterly institutional ownership ranked in the top tercile are considered as high IO firms. Stocks with quarterly earnings forecast dispersion ranked in the top tercile or with missing forecast dispersion are considered as high information uncertainty stocks. All models control for cumulative stock returns (Past Returns) during quarters (t-2, t-1). We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Dependent Variable: Cumulative DGTW-Adjusted Returns: Quarters [t+3:t+8]

(1) (2) (3) (4) Subsample:

Low IO High IO

Low Uncertainty

High Uncertainty

Upgrade 0.9378 -0.4867 -0.6528 0.6073 (1.07) (-0.51) (-0.70) (0.72)

Downgrade 1.5622* -0.4043 -0.1762 1.1301 (1.81) (-0.53) (-0.21) (1.41)

Forced_Sell 6.1501*** 1.3696 3.6552** 2.3272* (3.83) (1.08) (2.52) (1.82)

Forced_Buy -1.6144 0.2211 1.7309 -2.4860** (-1.12) (0.15) (0.93) (-2.02)

Forced_Sell*Upgrade -4.5059* 1.8084 1.7574 -3.9004** (-1.68) (0.84) (0.70) (-2.34)

Forced_Buy*Downgrade 8.5857** -0.2780 0.9922 5.4407*** (2.09) (-0.13) (0.31) (2.65)

Past Returns -5.5238*** -2.0298 -6.3644*** -3.9903*** (-3.46) (-0.93) (-2.48) (-2.61)

Past Returns*Upgrade 6.5948* -3.7735 6.7481 1.4609 (1.71) (-1.01) (1.10) (0.46)

Past Returns*Downgrade -0.1827 -3.8770 1.1744 -2.6672 (-0.05) (-1.02) (0.23) (-0.85)

Time FE

Number of Observations 43,153 70,614 36,213 77,675 R2 0.015 0.021 0.030 0.014

52

Table XI: Robustness Checks of the Price Correction Effect of Recommendation Revisions

This table repeats the analyses in Tables IX and X using alternative definitions of Forced_Sell and Forced_Buy that exclude fire-sale and fire-purchase stocks whose prior performance is in the same direction as extreme fund flows. (+)Ret/Forced_Sell [(‒)Ret/Forced_Buy] takes the value of 1 if the stock experiences extreme outflow- (inflow-) driven trading pressure in quarters (t-2, t-1) and has a positive (negative) abnormal return in quarter t-3, and 0 otherwise. Upgrade (Downgrade) takes the value of 1 if the stock receives a consensus upgrade (downgrade) in quarter t, and 0 otherwise. All models control for abnormal cumulative stock returns (Past Returns) during the trading pressure quarters (t-2, t-1). Panel A reports the results for the full sample while Panel B reports those for the subsamples of high versus low institutional ownership or information uncertainty groups. We do not report intercepts since all models include time fixed effects. t-statistics reported in parentheses are computed using standard errors clustered at the firm level. *, **, and *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Full Sample Dependent Variable: Abnormal Returns

Quarter t Cumulative Abnormal Returns

Quarters [t+3:t+8] Upgrade 3.5799*** 3.7853*** 0.3490 0.1637

(19.87) (20.41) (0.56) (0.25)

Downgrade -2.9822*** -2.8887*** 0.7404 0.8513 (-16.68) (-15.92) (1.32) (1.41)

(+)Ret/Forced_Sell -0.1485 -0.1360 2.3353** 2.3212** (-0.55) (-0.51) (2.15) (2.13)

(‒)Ret/Forced_Buy 1.2919*** 1.2834*** -2.1356* -2.1601* (4.44) (4.41) (-1.74) (-1.76)

(+)Ret/ Forced_Sell*Upgrade 1.0605* 1.0521* -1.0280 -0.9379 (1.91) (1.78) (-0.51) (-0.47)

(‒)Ret /Forced_Buy*Downgrade -1.2751* -1.2203* 4.5856** 4.7286** (-1.93) (-1.85) (2.53) (2.58)

Past Returns 0.7745* 1.6160*** -4.4165*** -4.5628*** (1.99) (3.44) (-4.00) (-3.38)

Past Returns*Upgrade -3.0110*** 2.6367 (-3.05) (0.95)

Past Returns*Downgrade -1.5239 -1.7636 (-1.64) (-0.66)

Time FE Number of Observations 113,880 113,880 113,880 113,880 R2 0.017 0.017 0.010 0.010

53

Table XI: Robustness Checks of the Price Correction Effect of Recommendation Revisions

(Continued)

Panel B: Subsample Analysis

Dependent Variable: Cumulative DGTW-Adjusted Returns: Quarters [t+3:t+8] (1) (2) (3) (4)

Subsample: Low IO High IO

Low

Uncertainty High

Uncertainty Upgrade 0.8136 -0.5026 -0.6866 0.4970

(0.94) (-0.54) (-0.75) (0.60)

Downgrade 1.7750** -0.4222 -0.1655 1.2914 (2.07) (-0.55) (-0.19) (1.64)

(+)Ret/Forced_Sell 5.4043*** 1.0268 2.4534* 2.2926 (3.09) (0.83) (1.68) (1.63)

(‒)Ret/Forced_Buy -2.2283 -0.8584 -1.1777 -2.5316* (-1.23) (-0.52) (-0.68) (-1.72)

(+)Ret/ Forced_Sell*Upgrade -4.7684* 1.6035 1.0241 -4.0988** (-1.86) (1.07) (1.01) (-2.17)

(‒)Ret /Forced_Buy*Downgrade 10.8105** -0.1937 1.6396 5.2460** (2.53) (-0.08) (0.73) (2.42)

Past Returns -5.7170*** -1.9757 -6.2819** -4.1398*** (-3.57) (-0.89) (-2.43) (-2.70)

Past Returns*Upgrade 6.6571* -3.7772 6.7389 1.5053 (1.73) (-1.01) (1.10) (0.48)

Past Returns*Downgrade -0.2809 -3.8559 0.9636 -2.6870 (-0.08) (-1.02) (0.19) (-0.86)

Time FE Number of Observations 113,880 113,880 113,880 113,880 R2 0.015 0.021 0.031 0.014