The 52-week high stock price and analyst recommendation ... · The 52-week high stock price and...

The 52-week high stock price and analyst recommendation revisions

Fengfei Li* Chen Lin† Tse-Chun Lin‡

Faculty of Business and Economics, University of Hong Kong

Abstract

We investigate whether 52-week high stock prices serve as reference points for analyst

recommendation revisions. We find that analysts are more likely to downgrade stocks when the

prices approach the 52-week high levels. This anchoring effect is stronger for stock with higher

information asymmetry but moderated by analysts’ reputation, work experience, and educational

background. We also find that a strategy that takes long positions in stocks approaching 52-week

high generates positive returns, and a strategy that shorts stocks with recommendation

downgrades is less profitable for the downgrades near 52-week high than for other downgrades.

Moreover, the downgraded firms with prices near 52-week high subsequently experience less

negative earnings forecast revisions, compared with other downgraded firms. Collectively, our

results indicate that downgrades near 52-week high are partially attributable to anchoring

heuristic and less informative. Overall, our paper provides new evidence on the determinants of

analyst recommendation revisions from a behavioral perspective.

JEL classification: G14; G24; D82; M41

Keywords: 52-week high; reference point; analyst recommendation; information environment

We would like to thank Sumit Agarwal, Rui Albuquerque, Jarrad Harford, Byoung-Hyoun Hwang, Ji-Chai Lin, Hai

Lu, Gideon Saar, Yupana Wiwattanakantang, Ayako Yasuda, and participants at the Korea University, University of

Hong Kong, National Chengchi University, 2015 Taiwan Finance Symposium on Corporate Finance, and 2015

China International Conference in Finance for their comments. We thank Lauren Cohen and Lily Fang for making

analyst education data available to us, and thank Jay Ritter for sharing star analyst data. Any remaining errors are

ours. * Email: [email protected], Tel: (852) 3917-8637, Fax: (852) 2548-1152. † Email: [email protected], Tel: (852) 3917-7793, Fax: (852) 2548-1152. ‡ Email: [email protected], Tel: (852) 2857-8503, Fax: (852) 2548-1152.

mailto:[email protected]



The 52-week high stock price and analyst recommendation revisions

August 2016

Abstract

We investigate whether 52-week high stock prices serve as reference points for analyst

recommendation revisions. We find that analysts are more likely to downgrade stocks when the

prices approach the 52-week high levels. This anchoring effect is stronger for stock with higher

information asymmetry but moderated by analysts’ reputation, work experience, and educational

background. We also find that a strategy that takes long positions in stocks approaching 52-week

high generates positive returns, and a strategy that shorts stocks with recommendation

downgrades is less profitable for the downgrades near 52-week high than for other downgrades.

Moreover, the downgraded firms with prices near 52-week high subsequently experience less

negative earnings forecast revisions, compared with other downgraded firms. Collectively, our

results indicate that downgrades near 52-week high are partially attributable to anchoring

heuristic and less informative. Overall, our paper provides new evidence on the determinants of

analyst recommendation revisions from a behavioral perspective.

JEL classification: G14; G24; D82; M41

Keywords: 52-week high; reference point; anchoring heuristic, analyst recommendation revisions;

information environment

1

“We are downgrading CNK from 2*/Above Average to 3*/Average for the simple reason that the

stock has performed in line with the definition of our 2*/AA rating — up 27% since upgrading

CNK back on June 18 — and is now floating near a 52-week high.”

— David Miller wrote in his report for Cinemark Holdings (CNK) on Dec 14, 2009

1. Introduction

Financial analysts have been argued to serve as an important information intermediary in

capital markets and key players in shaping corporate information environment (e.g., Lang and

Lundholm, 1996; Healy and Palepu, 2001; Frankel, Kothari, and Weber, 2006; Beyer, Cohen,

Lys, and Walther, 2010).1 As the information produced by analysts is essential for investors to

evaluate the return potential of investment opportunities and monitor the use of their committed

capital, it is important to understand the potential sources of biases of analyst recommendations.

The existing literature focuses mainly on the incentive biases caused by various types of

conflicts of interest that impede an analyst’s ability to function as an objective information

intermediary and producer (e.g. Michaely and Womack, 1999; O’Brien, McNichols, and Lin,

2005; Chen and Matsumoto, 2006; Kolasinski and Kothari, 2008; Firth, Lin, Liu, and Xuan,

2013). 2 In contrast, little is known about the potential behavioral biases of analyst

1 Beyer et al. (2010) evaluate the relative contribution of voluntary disclosures, analyst forecasts, and mandatory

disclosures to the information reflected in security prices and they find that analyst forecasts account for 22% of the

information provision. Related to this, analyst recommendation revisions are shown to contain valuable information

for investment (e.g., Jegadeesh, Kim, Krische, and Lee, 2004; Boni and Womack, 2006; Green, 2006). Compared to

the other market participants, financial analysts are equipped with substantial financial and industry knowledge and

they track corporate performance on a regular and continuous basis (Yu, 2008). Moreover, they interact with

management directly in conference calls and visit companies and plants to collect additional information (Brown,

Call, Clement, and Sharp, 2015). 2 Previous studies have examined conflicts of interest faced by analysts stemming from investment-banking business

relationship (e.g., Michaely and Womack, 1999; O’Brien, McNichols, and Lin, 2005; Kolasinski and Kothari, 2008);

access to management-provided information (e.g., Chen and Matsumoto, 2006), and commission fee pressure from

institutional investors (e.g., Firth, Lin, Liu, and Xuan, 2013).

2

recommendations.3

In this study, we explore the relation between 52-week high stock price and analyst

recommendation revisions. We focus on the 52-week high because it is widely reported and

discussed in financial news outlets such as the Wall Street Journal, Reuters, Bloomberg, and

Yahoo Finance and is salient to various market participants and investors.4 Moreover, during

1993-2013, about 10.9% of 2,072,612 sell-side analyst reports on the U.S. companies in

Thomson One mention the 52-week high.5 This is not surprising as the existing studies have

shown that 52-week high affects decisions made by managers, employees, and shareholders

within a company. Hence, if the 52-week high price serves as a reference point for analysts as

well, this anchoring effect would have non-trivial implications due to their role as information

intermediary. Furthermore, we focus on recommendation revisions as prior studies have shown

that the revisions are more informative than the recommendation levels (e.g., Womack, 1996;

Francis and Soffer, 1997).

Using I/B/E/S sell-side analyst recommendation revisions (upgrades, downgrades, and

reiterations) for U.S. stocks from 1993 to 2013, we find that a dummy variable indicating stock

3 Mokoaleli-Mokoteli, Taffler, and Agarwal (2009) show that analysts’ new buy stock recommendations are

associated with overoptimism bias and representativeness bias in terms of the “good company, good stock”

syndrome. Jegadeesh and Kim (2010) find that sell-side analysts tend to herd when issuing stock recommendations.

Bradshaw (2004) and Barniv, Hope, Myring, and Thomas (2009) provide evidence that analyst recommendations

are positively related with heuristic-based valuation (i.e., price-to-earnings to growth ratio and long-term earnings

growth). Michaely and Womack (1999) mention that underwriter analysts might anchor their recommendations at a

stock’s offering price. However, they do not provide empirical evidence. 4 Previous research has shown that the 52-week high price serves as a reference point for the decisions of many

market participants, including corporate managers (Baker, Pan, and Wurgler, 2012), employees (Heath, Huddart,

and Lang, 1999), options traders (Poteshman and Serbin, 2003), and stock traders (George and Hwang, 2004;

Huddart, Lang, and Yetman, 2009). See Baker and Wugler (2013) for a more comprehensive review on the literature

related to reference points and anchoring. In addition, Ben-Rephael, Da, and Israelsen (2016) find that closeness to

52-week high is positively related to abnormal institutional investor attention based on news-searching and news-

reading activity for specific stocks on Bloomberg as well as abnormal retail investor attention. 5 From Nov 1, 1993 to Dec 31, 2013, Thomson One database has a total of 2,258,370 sell-side analyst reports on the

U.S. companies. There are 2,072,612 text-searchable analyst reports (others are images and unsearchable), among

which 225,208 reports mention 52-week high after searching by keywords of 52-week high, or 52-wk high (the first

keyword accounts for 85%). In addition, there are 1,169,279 analyst reports mentioning 52-week high in terms of

52-week range after searching by keywords of 52-week range or 52-wk range.

3

price approaching 52-week high positively predicts recommendation downgrades based on logit

regressions. The approaching 52-week high dummy equals one if the stock price at day t−1

before the revision date t is within 5% below the 52-week high.6 For firms with stock prices near

the 52-week high, the odds of being downgraded are 32.7% higher than that for other firms. The

results are robust after controlling for other known determinants of analyst recommendations as

in Jegadeesh, Kim, Krische, and Lee (2004) and Loh and Stulz (2011). This finding can be

attributed to analysts’ belief that the stock has limited upside potential as its price is close to the

“resistance level” and its valuation becomes less attractive as there is little margin for error. The

52-week high price thus serves as a reference point for analyst recommendation decisions. The

economic magnitude of this anchoring effect is also substantial, compared with other known

incentive biases. For example, Kolasinski and Kothari (2008) show that acquirer affiliation

increases the odds of a recommendation upgrade of the acquirer by 30.2% around the M&A

deals.

Hirshleifer (2001) argues that greater uncertainty and a lack of accurate information about

firm value lead to more psychological biases. Consistent with this argument, we find that the

anchoring effect is stronger for firms with greater information asymmetry, such as firms with

smaller size, lower analyst coverage, younger age, higher idiosyncratic volatility, higher

probability of informed trading (PIN), higher absolute discretionary accruals, and lower

earnings forecast accuracy. Our results indicate that analysts are more likely to be affected by

the 52-week high anchoring when making recommendation revisions for firms that are more

6 In Figure 2, approaching 52-week high dummy (Approach52) equals 1 if stock price at day t−1 is within x%

(where x takes values from 3% to 50%) below the 52-week high. The coefficients of Approach52 are decreasing and

become insignificant after 20%. We also use the ratio of price at day t−1 relative to the 52-week high as another

proxy for nearness to the 52-week high (see, e.g., Baker, Pan and Wurgler, 2012), and we find consistent results as

reported in Appendix. As we focus on cases where stock price is sufficiently close to the 52-week high, the

advantage of approaching 52-week high dummy is that it can directly capture this effect, we choose to use it for our

main analyses.

4

difficult to value because of higher information asymmetry.

Moreover, it is well documented that there exists a positive relationship between analyst

reputation and research quality (e.g., Stickel, 1992; Leone and Wu, 2007; Fang and Yasuda,

2009, 2013). Hence, star analysts might rely less on the anchoring heuristic for their

recommendation decisions. Following previous studies (e.g., Fang and Yasuda, 2009; Liu and

Ritter, 2011), star analysts are defined based on the Institutional Investor magazine’s annual list

of All-star analysts. We find that star analysts are less affected by the 52-week high anchoring,

possibly because of their superior abilities. This anchoring effect is also less pronounced for

analysts who work in top brokerage firms and analysts who graduate from Ivy League schools

(or also holding a MBA degree). The evidence suggests that skill difference among analysts exist.

Further, consistent with George and Hwang (2004), we find that a strategy that takes a long

position in stocks whose current prices are close to their 52-week high generates significantly

positive returns, which indicates that analysts’ downgrade decisions near 52-week high are

biased by the anchoring heuristic. Prior research shows that downgrades are associated with

negative subsequent abnormal returns on average (e.g., Jegadeesh and Kim, 2010; Loh and Stulz,

2011). We thus also examine an investment strategy that involves shorting stocks with

recommendation downgrades and find that this strategy is less profitable for the downgrades near

52-week high than for the other downgrades within the same firm. Kim and Verrecchia (1991)

point out that price change is increasing in the precision of the announced information. The

results suggest that the downgrades near 52-week high are less informative and at least partially

driven by the anchoring heuristic, which compromises analysts’ objectivity.

We also provide another corroborative evidence from analysts’ earnings forecast revisions

around their downgrades. If analysts’ downgrades for stocks with price approaching 52-week

5

high are more associated with the anchoring effect rather than with negative anticipation about

the firm value, we should observe less downward earnings forecast revisions for these

downgraded firms.7 Our results are consistent with this prediction.

To better understand the anchoring effect of 52-week high on analysts’ recommendation

decisions, we investigate how each downgrade category contributes to the overall effect. We find

that the most frequent downgrades near 52-week high are from Buy to Hold, while very few

recommendations are downgraded to Sell. Further subsample analysis indicates that the positive

correlation between 52-week high dummy and downgrade is stronger for firms with a prior

active rating of Buy or Hold. This provides evidence that our main result is not driven primarily

by the Strong Buy rated firms being downgraded to lower categories, as these firms can only be

downgraded or maintain their current ratings.

Finally, we perform a variety of robustness tests. First, we control for other potential anchors

such as the analyst’s target price, historical high price, and other less commonly cited prices (65-

week high and 39-week high).8 The evidence from pairwise horse-race regressions indicates that

the 52-week high is the dominating anchor. Second, we find consistent results when using

different econometric methods (probit, linear probit, OLS, and ordered logit models), excluding

reiterations, and changing model specifications with firm, analyst, year or industry fixed effects.

Third, we adopt alternative measures of nearness to the 52-week high. The results from all these

tests are in line with our main hypothesis.

Our paper relates to several strands of literature. First, we contribute to the small but

7 Bradshaw (2004) argues that presumably earnings forecasts are linked to a stock’s value while recommendations

are based on the comparison between value and current stock price. However, recommendations do not appear to

capture a stock’s intrinsic value relative to current price and are more related with heuristic valuation based on price-

to-earnings to growth ratio (PEG) and long-term earnings growth (LTG). Barniv et al. (2009) also find the positive

relation between recommendations and heuristic valuation (PEG and LTG) before and after Regulation FD. Our

results are consistent with these studies. 8 Because the target price data starts from 1999, and a large number of analysts do not disclose target prices in their

reports (e.g., Bradshaw, 2002). We control for target price in robustness checks but not in our main regressions.

6

growing literature on the behavioral biases of analyst recommendations (e.g., Bradshaw, 2004;

Barniv, Hope, Myring, and Thomas, 2009; Mokoaleli-Mokoteli, Taffler, and Agarwal, 2009;

Jegadeesh and Kim, 2010) by showing that the 52-week high stock price acts as a reference point

for analyst recommendation decisions. 9 Second, this paper adds to the literature on analyst

information production. Beyer, Cohen, Lys, and Walther (2010) illustrate that analyst reporting

decisions play a key role in shaping firms’ information environment. The information produced

by analysts mainly derives from two sources: interpretation of public information and discovery

of private information (e.g., Ivkovic and Jegadeesh, 2004; Chen, Cheng, and Lo, 2010). Our

results suggest that analysts also rely on heuristics, like anchoring on a salient nominal price

level, to make recommendation decisions. Third, this study also enriches the literature on 52-

week high. The 52-week high has been shown to affect decisions by managers, employees, and

shareholders (e.g., Heath, Huddart, and Lang, 1999; George and Hwang, 2004; Baker, Pan, and

Wurgler, 2012). We provide evidence that the 52-week high also affects the recommendation

decisions of financial analysts—the information intermediaries whose recommendations could

potentially affect investment decisions of numerous investors.

Our paper is also related to two concurrent studies by Birru (2015) and George, Hwang,

and Li (2015). Birru (2015) proposes two behavioral biases (expectational errors and

underreaction to news) induced by 52-week high and tests these biases based on how investors

respond to earnings news and how analysts set their target prices. George, Hwang, and Li (2015)

9 As our focus is on the anchoring effect and analyst recommendation revisions, this paper is also linked to another

strand of the literature on analyst earnings forecast bias. Besides the incentives-based explanations, most of existing

studies focus on cognitive bias to explain analysts’ overreaction to earnings information (see Kothari (2001) and

Ramnath, Rock, and Shane (2008) for an overview). This cognitive bias is consistent with representativeness

heuristic and self-deception biases like overconfidence, overoptimism, and cognitive dissonance (e.g., Friesen and

Weller, 2006). In addition, other explanations on analyst earnings forecast bias are also offered such as anchoring

effect towards the industry norm (Cen, Hilary, and Wei, 2013), conservatism (Elliott, Philbrick, and Wiedman,

1995), the affect heuristic (Antoniou, Kumar, and Maligkris, 2015), and behavioral response mechanisms like pro

forma reporting (Andersson and Hellman, 2007).

7

also study how investors respond to earnings news when stock price is approaching 52-week

high. Our paper differs from theirs in that we provide a new perspective on the determinants of

analyst recommendation revisions. We also provide comprehensive evidence on what types of

analysts and firms are more affected by the 52-week high anchoring effect.

The remainder of the paper is organized as follows. Section 2 develops the hypotheses.

Section 3 describes the data and provides summary statistics. Section 4 discusses empirical

results for the three hypotheses. Section 5 presents supporting evidence. Section 6 shows

additional tests and robustness checks. Section 7 concludes.

2. Hypothesis development

Tversky and Kahneman (1974) show that people often rely on heuristics to predict values.

Heuristics simplify the complex tasks of evaluation. However, heuristics also lead to biases in

judgments under uncertainty. One of the prominent heuristics is anchoring, meaning that

individuals rely on irrelevant but salient anchors to form beliefs. Consistent with the anchoring

theory, 52-week high has been documented as an important reference point for various market

participants (e.g., Heath, Huddart, and Lang, 1999; Poteshman and Serbin, 2003; George and

Hwang, 2004; Baker, Pan, and Wurgler, 2012; Driessen, Lin, and Van Hemert, 2013). Based on

the anchoring theory and the aforementioned studies, we investigate how the 52-week high price

affects analyst recommendation revisions. When stock price is approaching the 52-week high,

referred as “resistance level” by practitioners, analysts might believe that the stock has limited

upside potential and little margin for error. The stock’s valuation thus becomes less attractive.

Consequently, analysts might downgrade their recommendations for the stock, leading to our

main hypothesis:

8

H1: Firms with stock prices approaching 52-week high are more likely to be downgraded by

analysts.

This hypothesis extends the existing literature by proposing that, in addition to the known

determinants of analyst recommendation revisions, the 52-week high reference price plays a

significant role in analyst recommendation decisions.

Hirshleifer (2001) points out that for stocks with greater uncertainty, a lack of accurate

feedback about their fundamentals leaves more room for psychological biases. Therefore, the

misvaluation effects would be stronger among firms with more uncertainty or poor information

environment. Analysts might have difficulties to provide accurate valuation of firms with higher

information asymmetry and thus rely more on heuristics for their recommendation decisions. We

thus propose our second hypothesis as follows:

H2: When firms are more difficult to value under higher information asymmetry, analysts

rely more on the 52-week high reference price for their recommendation revisions.

Hypothesis 2 predicts that firms with greater information asymmetry have higher odds of

being downgraded when their stock prices are approaching the 52-week high.

The existing studies show that analysts’ abilities and characteristics can explain some

differences in their research output. For example, Stickel (1992) shows that analysts on the

Institutional Investor All-American Research Team supply more accurate earnings forecasts than

other analysts. Consistently, Leone and Wu (2007) argue that Institutional Investor ranking is

9

helpful to identify high quality analysts, and the ranked analysts have superior abilities.

Furthermore, Loh and Stulz (2011) find that recommendation changes issued by star analysts are

more likely to be influential. Fang and Yasuda (2013) also show that there is a positive relation

between star status and investment value of stock recommendations. These findings suggest that

star analysts have superior abilities in stock valuation. Hence, if the 52-week high price indeed

serves as an anchor for analyst recommendation decisions, we expect that star analysts are less

affected by this anchoring heuristic. Hence, we propose our third hypothesis as follows:

H3: As star analysts have superior abilities, their recommendation revisions are less

affected by the 52-week high reference price.

3. Data and summary statistics

3.1 Sample construction

Analyst recommendation data are obtained from the I/B/E/S detailed recommendation file

from November 1993 to December 2013. I/B/E/S codes recommendations using a five-point

scale from 1 (strong buy) to 5 (sell). To make the interpretation of the results more intuitive, we

reverse analyst ratings from I/B/E/S so that a higher score corresponds to more favorable

recommendation (i.e., 5 for Strong Buy, 4 for Buy, 3 for Hold, 2 for Underperform, and 1 for

Sell). We focus on individual analyst recommendation revisions as previous studies suggest that

recommendation revisions are more informative than recommendation levels (e.g., Womack,

1996; Francis and Soffer, 1997).

Following the literature (e.g., Chen and Matsumoto, 2006; Howe, Unlu, and Yan, 2009;

Jegadeesh and Kim, 2010; Loh and Stulz, 2011; Bradshaw, Brown, and Huang, 2013), we

10

calculate recommendation revision as the current recommendation minus the most recent

previous active recommendation issued by the same analyst for the same firm. By construction,

each recommendation revision takes a value between −4 and 4 and can be classified as

downgrade, upgrade, or reiteration. A recommendation is considered to be active for up to 12

months after it is issued or until the day that the recommendation has been recorded in I/B/E/S

stopped file.10 We impose the 12-month criterion to filter out stale recommendations (e.g.,

Jegadeesh et al., 2004; Barber, Lehavy, McNichols, and Trueman, 2006). Our sample excludes

initiations (first-time recommendation) and re-initiations (i.e., a new recommendation issued by

an analyst on a stock for which there exists no prior active recommendation). We then merge

recommendation data with stock return data from CRSP and accounting data from Compustat,

and further require firms to have the 52-week high stock prices.

Our final sample consists of 214,691 analyst recommendation revisions made by 10,841

analysts for 10,219 U.S. firms over the period 1993-2013, including 88,579 downgrades (41.3%),

81,404 upgrades (37.9%), and 44,708 reiterations (20.8%). Our sample size and

recommendation composition are comparable with those of other studies (e.g., Howe, Unlu, and

Yan, 2009; Jegadeesh and Kim, 2010; Loh and Stulz, 2011).11 The sample size for the tests that

involve star analysts and analysts’ educational backgrounds varies based on the availability of

the data. All-star analyst data are from the October issue of Institutional Investor magazine and

available during 1993-2008. Analyst education data are from Cohen, Frazzini, and Malloy (2010)

and available for 2,014 analysts during 1993-2013.

10 I/B/E/S provides the announcement date and review date for each recommendation. Announcement date is the

date that the recommendation was reported. Review date is the date that the most recent recommendation was

confirmed by I/B/E/S with the contributor. I/B/E/S assumes a recommendation to be stopped if it is not updated or

confirmed in 180 days after the review date. 11 For example, in Howe, Unlu, and Yan (2009), their revised recommendation sample (including 170,522

recommendation revisions) consists of 44.5% downgrades, 36.5% upgrades, and 19% reiterations during the period

1994-2006.

11

3.2 Variable measurement

To measure the nearness to the 52-week high, we use an approaching 52-week high dummy

that equals 1 if the stock price at day t−1 relative to the recommendation revision date t is within

5% below the 52-week high (i.e., 0.95 < price at day t−1/52-week high < 1, where the price ratio

is adjusted for stock splits and stock dividends). Similar as in Heath, Huddart, and Lang (1999),

the 52-week high stock price is the maximum stock price over a 52-week period ending one

month prior to the recommendation revision date. To focus on the first time the stock price

approaches the 52-week high, we exclude cases where stock prices break through the 52-week

high within one month before recommendation revisions (e.g., Driessen, Lin, and Van Hemert,

2013). In untabulated results, our findings are robust if we include these cases. We set

approaching 52-week high dummy equal to zero for firms that issue stock splits (or stock

dividends) within one month before the revision to exclude their potential influences on the 52-

week high effect.12

Following the literature, we adopt seven measures of high information asymmetry: small

firm size, low analyst coverage, young firm age, high idiosyncratic volatility, high probability of

informed trading (PIN), high absolute discretionary accruals (e.g., Bhattacharya, Ecker, Olsson,

and Schipper, 2012), and lower earnings forecast accuracy (e.g., Lang and Lundholm, 1996).

We use the method of Stambaugh, Yu, and Yuan (2015) to construct a composite rank that

combines these information asymmetry proxies. Specifically, firms are ranked each year based

on each of the seven information asymmetry measures. The higher the rank, the greater the

relative degree of information asymmetry. A firm’s composite rank is the arithmetic average of

its ranking percentile for each of available information asymmetry measures. Firms with the

12 A stock split will change the price, the 52-week range, the consensus price target, and other price-related items. It

can create some havoc on many quote systems that do not adjust all the data simultaneously and immediately.

Nevertheless, our results remain consistent without this exclusion.

12

highest information asymmetry receive the highest composite rank. We define a dummy

variable, High composite rank, that equals 1 if the firm’s composite rank is above the yearly

cross-sectional median.

To capture analyst quality, we use a dummy variable, Star, which equals 1 if the analyst is

ranked as an All-star (top three) in the October issue of Institutional Investor magazine in year

t−1. We also explore other analyst characteristics such as working and educational backgrounds,

as these factors proxy for quality and connection of analysts (e.g., Gottesman and Morey, 2006;

Cohen, Frazzini, and Malloy, 2010). Specifically, we construct three additional dummy variables:

TopExp, Ivy, and IvyMBA. TopExp equals 1 for analyst with experience above the cross-sectional

median and working in top brokerage firms in year t−1. Following previous studies (e.g., Hong

and Kubik, 2003; Fang and Yasuda, 2009; Liu and Ritter, 2011), brokerage firm reputation is

measured by Carter-Manaster rank.13 Brokerage firms with the highest Carter-Manaster score of

9 are defined as the top brokerage firms. Ivy equals 1 if the analyst has graduated from an Ivy

League school; IvyMBA equals 1 if the analyst has graduated from an Ivy League school and also

has a MBA degree. The correlation between Star and TopExp, Ivy, IvyMBA is 0.377, 0.143, and

0.136, respectively.14 The correlation between Ivy and IvyMBA is 0.710. All the correlations are

positive and significant with p-value of less than 0.001.

3.3 Descriptive statistics and correlations

In Table 1, Panel A reports summary statistics of the main variables used in the regressions.

The median of current rating is 3 (Hold) and the median of prior rating is 4 (Buy). The average

recommendation change across our whole sample is −0.046, with a high standard deviation of

13 Carter-Manaster rank is obtained from Jay Ritter’s website. http://bear.warrington.ufl.edu/ritter/ipodata.htm 14 The correlation between top brokerage firm dummy and Star is 0.292, which is lower than the correlation of 0.377

between Star and TopExp. Thus we choose TopExp as a proxy for Star. The results are similar if we only consider

top brokerage firm.

13

1.306. The mean of approaching 52-week high dummy is 0.045, which means that about 4.5%

recommendation changes are made after the stock prices at day t−1 approaching 52-week high.

The mean value of Star, TopExp, Ivy, and IvyMBA is 8.2%, 12.7%, 22.9%, and 13%, respectively.

[TABLE 1 ABOUT HERE]

Panel B of Table 1 presents Pearson correlation between main variables. The downgrade

dummy is positively associated with approaching 52-week high dummy, but negatively

correlated with the three measures of price run-up. Furthermore, price run-ups are positively

correlated with approaching 52-week high dummy, suggesting the importance of controlling for

price run-ups when analyzing the 52-week high effect.

[FIGURE 1 ABOUT HERE]

For illustration of our main hypothesis, we provide an example of analyst recommendation

downgrade after stock price at day t−1 approaching 52-week high (within 5% below the 52-week

high). Figure 1 presents that the stock price of eBay approached the 52-week high on September

10th, 2004, and the analyst downgraded the recommendation from Strong Buy to Hold on

September 13th, 2004.

4. Empirical Results

In this section, we investigate the relation between the 52-week high stock price and analyst

recommendation revisions. We then examine how firm and analyst characteristics would

influence the anchoring effect.

4.1 Estimation model and variables

14

Because recommendation changes are discrete and ordinal, we adopt logit regression.15

Based on Jegadeesh et al. (2004), we add the approaching 52-week high dummy into the

determinants of recommendation changes. Specifically, we run the following logit regression:

Downgradej,t = β0 + β1 Approach52j,t−1 + δ′Xj + Industry FE + Year FE + εj,t (1)

where subscript j and t denote the recommendation revision j issued on day t. The dependent

variable Downgrade is a dummy that equals 1 for recommendation downgrades and 0 for

upgrades and reiterations.16 Approach52 is the approaching 52-week high dummy that equals 1 if

the stock price at day t−1 is within 5% below the 52-week high.

Similar as in Jegadeesh et al. (2004), the vector X includes a set of control variables that are

relevant to analyst recommendation changes. When the stock price is near the 52-week high, the

firm has experienced a substantial price run-up recently. The price momentum effect is captured

by four variables: Runupt−5, t−1, Runupt−21, t−6, Runupm−6, m−2, and Runupm−12, m−7, which are the

cumulative returns over different horizons before the revision date.17 The earnings momentum

effect is controlled by including Earnings forecast revisions and standardized unexpected

earnings (SUE). Furthermore, we consider valuation indicators such as Size, book-to-market

(B/M), Earnings to price, and Turnover, growth indicators like Long-term growth forecast and

Sales growth, and fundamental indicators like Accruals and Capital expenditure. Besides, we

control for further factors related to analyst recommendation decisions in Loh and Stulz (2011),

including Idiosyncratic volatility, Institutional ownership, Analyst coverage, Analyst dispersion,

15 For robustness check, we also use alternative models including probit model, ordered logit model, linear probit

model, and OLS regressions with firm, analyst, year or industry fixed effects, as shown in Table 9. 16 When we exclude reiterations, the results are consistent and reported in Panel B of Table 9. 17 The results are similar when the run-ups are adjusted by CRSP value-weighted market returns.

15

and Analyst experience. The detailed definitions of all variables are provided in Appendix A.

Finally, we include Fama-French 48 industry fixed effects and year fixed effects. We report the

coefficients from logit regressions (i.e., log odds ratios), but interpret them in terms of odds

ratios (exponentiated coefficients). Standard errors are two-way clustered by firm and year to

adjust for cross-sectional and time-series correlations (Petersen, 2009).

4.2 Baseline regression results

Table 2 shows the regression results from estimation of equation (1). The approaching 52-

week high dummy (Approach52) is significantly positive, which means that analysts are more

likely to downgrade stocks with prices approaching the 52-week high. The coefficient of

Approach52 ranges from 0.090 (z-statistic = 3.97) in column 1 to 0.283 (z-statistic = 4.75) in

column 5, the corresponding odds ratio ranges from 1.094 (e0.090) to 1.327 (e0.283). In terms of

economic magnitude, the coefficient in column 5 implies that the odds of being downgraded by

analysts are about 32.7% higher for firms with stock prices approaching the 52-week high than

that for other firms.18

Our interpretation of the results is that analysts might believe the company has limited

upside and little margin for error when its stock price is near the resistance level. The economic

magnitude of this anchoring bias is comparable to the incentive bias found in previous research.

For example, Kolasinski and Kothari (2008) show that acquirer affiliation induces an increase

of 30.2% in the odds of recommendation upgrade of the acquirer around the M&A deals. In sum,

our findings are consistent with Hypothesis 1 that analysts use the 52-week high as a reference

18 As indicated in Table 1, our sample mean probability of a downgrade is 41.3% (or an odds ratio of 0.704). If the

odds of being downgraded for a stock were equal to the sample mean, then an odds ratio of 1.327 associated with

approaching 52-week high (Approach52=1) implies that the odds ratio of being downgraded would increase to 0.934

(or a probability of 48.3%). This means that the increase in downgrade probability is about 17% when stock price is

approaching 52-week high.

16

point for their recommendation decisions and tend to downgrade their recommendations when

stock prices approach the 52-week high.


The coefficients of price run-ups are significantly negative (except for Runupt−21, t−6). For

example, in column 2, the coefficient of Runupt−5, t−1 is −1.166 (z-statistic = −3.50), then the odds

ratio associated with a 10% increase in Runupt−5, t−1 is 0.890 (e−1.166 × 0.1), which means that the

odds of being downgraded drop by 11% with a 10% increase in run-up over the past five days.

This result suggests that analysts are less likely to downgrade firms with recent price run-ups.

However, when stock price is close to its 52-week high, the probability of being downgraded

increases considerably.

[FIGURE 2 ABOUT HERE]

In our baseline case, we use 5 percent below the 52-week high for measuring the nearness to

the 52-week high. In Figure 2, we adopt various thresholds for the nearness of current price to

the 52-week high from 3 to 50 percent. We plot the coefficients of Approach52 from the

estimation of equation (1) with all the controls. The coefficients are significantly positive for 3 to

20 percent and insignificant from 21 percent onwards. Figure 2 clearly indicates that the

probability of being downgraded increases with the nearness of current price to the 52-week

high.19

One might argue that the increase in downgrade probability is due to the anticipated lower

future performance after stock price approaching the 52-week high. However, George and

Hwang (2004) show that stocks with current prices near the 52-week high perform better in the

19 We also perform a falsification test based on 1000 simulations. For each of the simulations, a pseudo 52-week

high price is randomly selected from the stock prices (excluding the actual 52-week high price) during the previous

52-weeks and 30 days before the analyst recommendation revision date. In untabulated results, the average

coefficient of approaching pseudo 52-week high dummy (5%) is -0.042 from estimation of equation (1) with all the

controls. This result further supports that our finding for Hypothesis 1 is not a statistical fluke.

17

subsequent months. In Appendix Table 2, we confirm their results using buy-and-hold abnormal

returns. The evidence indicates that analysts’ downgrades near 52-week high are less related to

the negative expectation of firm value, which will be further verified in Section 5.

4.3 The influence of information asymmetry on the anchoring effect of 52-week high

For firms with higher information asymmetry, analysts may face more difficulties in either

acquiring or interpreting information, which might impair their abilities to provide valuable

recommendations. In this case, we expect that analysts rely more on heuristics for their

recommendation decisions. As discussed in Section 3.2, we use seven measures of information

asymmetry to construct a composite rank. To examine whether the relation between the 52-week

high price and recommendation downgrade is affected by information asymmetry, we add an

interaction term of approaching 52-week high dummy with a high information asymmetry

dummy into equation (1):

Downgradej,t = β0 + β1 Approach52j,t−1 + β2 Approach52j,t−1 × Information asymmetryj

+ β3 Information asymmetryj + δ′Xj + Industry FE + Year FE + εj,t (2)

where subscript j and t refer to the recommendation revision j issued on day t, and Information

asymmetry represents one of the eight variables of high information asymmetry and the

composite rank. The interaction coefficient β2 gauges the effect of approaching 52-week high on

recommendation downgrade under high information asymmetry.20

20 Previous studies show that interpreting interaction effects in terms of marginal effects in non-linear models is

difficult because the marginal effects differ for each observation (e.g., Ai and Norton, 2003; Greene, 2010). Buis

(2010) and Kolasinski and Siegel (2010) point out that the exponentiated interaction term coefficient in logit models

has a straightforward and meaningful interpretation by presenting effects as multiplicative effects (e.g., odds ratio).

The multiplicative effects control for the differences between the groups in baseline odds. Since our purpose is to

examine whether the effect of approaching 52-week high on recommendation downgrade varies with high

18


Table 3 presents the regression results based on equation (2). In columns 1-8, the proxies for

high information asymmetry are: high composite rank, small firm size, low analyst coverage,

young age, high idiosyncratic volatility, high PIN, high absolute discretionary accruals, and low

earnings forecast accuracy, respectively. Each of these dummies equals 1 if the firm’s

information asymmetry is higher than the yearly cross-sectional median. The coefficients of

interaction terms are significantly positive, which means that the 52-week high anchoring effect

is stronger for firms with greater information asymmetry. For example, in column 3, the

coefficient of interaction term is 0.147 (z-statistic = 2.57). The effect of approaching 52-week

high for firms with lower analyst coverage is 15.84% (e0.147 − 1) higher than the effect for firms

with higher analyst coverage. This set of results supports Hypothesis 2 that analysts are more

likely to anchor on the 52-week high when making recommendation decisions under high

uncertainty.

4.4 The anchoring effect of 52-week high and analyst characteristics

In this subsection, we explore whether the anchoring effect of 52-week high varies among

analysts with different characteristics like abilities and reputation. Specifically, does the 52-week

high reference price affect star analysts’ recommendation decisions? To examine the impact of

analyst characteristics, we add an interaction term of approaching 52-week high dummy with

analyst characteristic dummy into equation (1):

Downgradej,t = β0 + β1 Approach52j,t−1 + β2 Approach52j,t−1 × Analyst characteristicj

information asymmetry, not to assess the absolute marginal effect of high information asymmetry, it is appropriate

to just rely on the interaction terms to draw inferences. The direction of high information asymmetry’s influence is

the same as the sign of the interaction term coefficient (see Kolasinski and Siegel (2010)). In addition, in unreported

results, the direction of interaction coefficient is the same when we run a linear probability model instead of logit

model.

19

+ β3 Analyst characteristicj + δ′Xj + Industry FE + Year FE + εj,t (3)

where subscript j and t represent the recommendation revision j issued on day t, and Analyst

characteristic is one of the four dummies that indicate the characteristic of the analyst who

issues the recommendation revision. As described in Section 3.2, the four dummy variables are:

Star, TopExp, Ivy, and IvyMBA.


Table 4 reports the results estimated from equation (3). In columns 1-4, the coefficients of

approaching 52-week high dummy are significantly positive, while all the interaction terms are

significantly negative. In column 1, the coefficient of interaction term is −0.250 (z-statistic =

−2.91), which shows that the anchoring effect of 52-week high is 22.1% (e−0.250 − 1) lower for

star analysts than that for non-star analysts. Our findings are consistent with the prediction of

Hypothesis 3 that star analysts are less affected by the 52-week high reference price. Moreover,

in columns 2-4, the results show that experienced analysts working in top brokerage firms and

analysts who have graduated from Ivy League schools (or also hold a MBA degree) rely less on

the 52-week high anchoring heuristic. But we do not find significant results based on only

brokerage reputation or MBA degree. Our results are also consistent with previous findings that

star analysts have superior abilities, and skill differences exist among analysts (e.g., Leone and

Wu, 2007; Fang and Yasuda, 2009, 2013).

5. Supporting evidence

In this section, we examine the profits from trading strategies associated with the 52-week

high or recommendation downgrades, and analyze earnings forecast revisions around

20

recommendation downgrades near 52-week high to provide supporting evidence for the

anchoring heuristic interpretation.

5.1 Trading strategy based on the nearness to the 52-week high

George and Hwang (2004) find that stocks whose current prices are near 52-week high tend

to earn high future returns. Li and Yu (2012) also show that nearness to the Dow 52-week high

positively predicts future aggregate market returns. Based on these studies, we examine a

strategy of taking a long position in stocks approaching 52-week high, by using all firms with

common shares (share code of 10 or 11) listed on the NYSE, AMEX, or NASDAQ in CRSP.

Specifically, we form calendar-time portfolios of stocks with a ratio of stock price to the 52-

week high (adjusted for stock splits and stock dividends) larger than 0.95 at the end of month t−1,

and hold the portfolios for 3, 6, or 12 months following the portfolio formation date. The

portfolios are then rebalanced monthly. As a result, we have monthly portfolio returns (equal-

weighted (EW) or value-weighted (VW) by market capitalization at the end of month t−1) from

November 1993 to December 2013 (same as our analyst sample period). We then regress the

monthly portfolio excess returns (by subtracting the one-month Treasury bill rate from raw

returns) on the three Fama and French (1993) factors and a fourth momentum factor suggested

by Carhart (1997) as well as two new factors—profitability and investment (Fama and French,

2015).


In Table 5, Panel A reports the average monthly raw returns (in percentages) and risk-

adjusted abnormal returns (alphas) to the 52-week high portfolios. The EW portfolio returns are

significantly positive. For example, for 6-month holding period, average monthly raw return is

1.38% (t-statistic = 4.03) and Fama-French 5-factor alpha is 0.34% (t-statistic = 2.98) per month.

21

For VW portfolios, the results are slightly weaker. This is consistent with Li and Yu (2012) that

smaller stocks are more subject to mispricing due to anchoring and also in line with our previous

result in Table 3 that analysts rely more on heuristics for smaller stocks that are more difficult to

value. All these results, along with evidence in previous studies, show that nearness to the 52-

week high can positively predict future returns, which suggests that analysts’ downgrade

decisions when stock prices are close to the 52-week high are biased by the anchoring heuristic.

5.2 Trading strategy associated with recommendation downgrades

Previous studies document that the subsequent returns of downgraded stocks are

significantly negative (e.g., Jegadeesh and Kim, 2010; Loh and Stulz, 2011). Although the

market reacts significantly on the revision day, stock prices continue to drift in the same

direction of recommendation revisions over the next three to six months (e.g., Womack, 1996;

Jegadeesh and Kim, 2010). Kim and Verrecchia (1991) argue that price change increases with

the precision of the announced information. Since some downgrades are at least partially due to

the anchoring heuristic instead of information, an investment strategy of taking a short position

in a stock when it receives a recommendation downgrade should be less profitable for

downgrades associated with heuristic than for the other downgrades. We use the following

procedure to measure the differences in profits between shorting a stock with a downgrade near

52-week high and shorting the same stock with a downgrade not approaching 52-week high.

First, we divide the total of 88,579 downgrades into two groups: 4,041 downgrades having

price close to the 52-week high (Approach52=1) and 84,538 other downgrades (Approach52=0).

We require firms to have both types of downgrades, which yields a total of 2,064 firms. Next, we

compute the average abnormal returns (ARs) separately for taking these two types of

downgrades and calculate the difference of the two average ARs for each firm. We then derive

22

the arithmetic mean (both EW and VW) of these differences of ARs across 2,064 firms. Thus,

the average differences in profits by shorting these two types of downgrades within the same

firm are the negative values of the average differences of ARs.

Following Jegadeesh and Kim (2010) and Loh and Stulz (2011), we compute T-day

abnormal returns ARt,t+T from recommendation revision day t to t+T (where T=1, 21, 63, or 126,

and day 0 is the next trading day for recommendation revisions made after the market close or on

non-trading days). We use two measures of ARs: market-adjusted ARs (by subtracting the

contemporaneous compounded CRSP value-weighted market returns) and DGTW-adjusted

(Daniel, Grinblatt, Titman, and Wermers, 1997) ARs (by subtracting the return on a benchmark

portfolio with the same size, B/M, and momentum characteristics as the stock).

Panel B of Table 5 reports the average difference in profits between shorting downgrades

near 52-week high and shorting other downgrades within the same firm. We find that the average

difference in profits are significantly negative (except that the difference based on market-

adjusted 6-month ARs is insignificant). In other words, an investment strategy that involves

shorting downgraded stocks (e.g., Boni and Womack, 2006) is less profitable for downgrades

near 52-week high, compared with the other downgrades. In untabulated tests, we keep only

downgrades to unfavorable recommendations (Hold, Sell, or Underperform) and find similar

results. These findings suggest that downgrades near 52-week high contain less information and

are at least partially associated with the 52-week high anchoring.

5.3 Earnings forecast revisions around recommendation downgrades

If downgrades near 52-week high are more attributable to the anchoring effect and less

related to subsequent firm value, we should observe less downward earnings forecast revisions

for these downgrades. To test this conjecture, we use earnings forecasts for the next fiscal quarter

23

(I/B/E/S forecast period indicator (FPI) = 7) and the next fiscal year (FPI=2).21

We first look at earnings forecasts issued on the same day t that the analyst downgrades the

stock. Earnings forecast revision is calculated as the current forecast minus the prior forecast

issued by the analyst for the same firm and the same forecast period. We use three dummy

variables for different categories of forecast revisions: Negative revision dummy (Positive

revision dummy) equals 1 if the current earnings forecast is lower (higher) than the prior earnings

forecast; No revision dummy equals 1 if the forecast remains unchanged. We calculate the mean

value of these dummies for each group of downgrades separately and then test the differences of

mean values between these two groups. Further, we do the same analysis using earnings

forecasts issued within a five-day window (t−5, t+5) or a ten-day window (t−10, t+10) around

recommendation downgrades.


In Table 6, Panel A reports earnings forecasts for the next fiscal quarter. The results show

that the mean value of Negative revision dummy for downgrades near 52-week high is

significantly lower, compared with the other downgrades. For example, based on earnings

forecasts issued on day t, the mean value of Negative revision dummy is 0.176 for downgrades

near 52-week high and 0.314 for other downgrades. The mean difference is −0.138 and

statistically significant based on z-test (z-statistic= −10.78). In addition, the mean values of

Positive revision dummy and No revision dummy for downgrades near 52-week high are

significantly larger, suggesting that analysts’ downgrades near 52-week high are not so much due

to negative firm value as to anchoring heuristic based on 52-week high. Panel B shows similar

results of earnings forecasts for the next fiscal year.

21 The untabulated results for the current fiscal quarter (FPI=6) and the current fiscal year (FPI=1) are similar.

24

In Appendix Table 1, we also find that the future performance (Tobin's q and ROA) is better

for firms that are downgraded near 52-week high than for other downgraded firms. Overall, the

results from trading strategies and earnings forecast revisions provide supporting evidence for

our main hypothesis and suggest that downgrades near 52-week high are at least partially

because of the anchoring effect.

6. Additional tests and robustness checks

In this section, we first explore the categories of recommendation downgrades when stock

prices approach the 52-week high and also conduct subsample analysis by prior recommendation

levels. We then examine the time trend of the 52-week high anchoring effect. Last, we perform a

battery of robustness checks for our main findings.

6.1 Recommendation downgrade categories

To better understand the anchoring effect of 52-week high on analysts’ recommendation

decisions, we examine how each downgrade category contributes to the overall results. We first

categorize 4,041 downgrades with stock price near 52-week high based on the prior and current

recommendation levels. In Panel A of Table 7, the frequency distribution shows that the top

category is Buy downgraded to Hold, accounting for 38.9% of downgrades near 52-week high. 22


We further conduct subsample analysis by prior recommendation levels. As shown in Panel

B, the coefficients of Approach52 are 0.302 (z-statistic= 4.58) and 0.287 (z-statistic= 2.76) for

the subsamples with prior recommendation levels of Buy and Hold, respectively, which are

22 The frequency also shows that very few analysts downgrade stocks with prices approaching 52-week high to Sell,

which provides evidence against the possibility that analysts are biased because they intend to cater to investors.

Specifically, if analysts realize that investors have a greater propensity to sell winner stocks (i.e., the disposition

effect), analysts might cater to investors’ beliefs and downgrade their recommendations to Sell. Hence, our findings

are inconsistent with this catering story.

25

larger than 0.176 (z-statistic= 2.05) for the subsample with prior recommendation of Strong Buy.

The results suggest that the anchoring effect of 52-week high is stronger for firms that have a

Buy or Hold recommendation. In addition, in column 5, we use another subsample excluding

extreme prior recommendation levels of Strong Buy and Sell and find significant anchoring

effect. This result also alleviates the concern that our main finding is driven by downgrades from

Strong Buy recommended firms, as these firms can only be downgraded or maintain their current

ratings.

6.2 Robustness tests

First, we examine whether the 52-week high effect is influenced by the target price by

running a horse race regression with an additional approaching target price dummy in equation

(1). This dummy variable equals 1 if stock price at day t−1 is within 5% (or 10%) below the

target price issued by the analyst who revises the recommendation. Panel A of Table 8 shows

that the 52-week high is the dominant anchor, but approaching target price does not have

predictability on recommendation downgrades. The result indicates that while 52-week high may

potentially influence the target price set by analyst as well (Birru, 2015), approaching target price

does not drive our main results. In Panel B, we run similar regressions using historical high and

other stock price highs that are less commonly cited such as 65-week high and 39-week high,

and also find the dominant effect of the 52-week high. These results provide supportive evidence

that the 52-week high plays a special role in analysts’ research.


One might ask whether the predictability of 52-week high on recommendation downgrades

could be driven by the econometric model selection. To address this concern, we implement

additional tests by linear probability model and OLS regressions with firm, analyst, and year

26

fixed effects. Moreover, we also adopt probit model and ordered logit model. For probit and

linear probability models where the dependent variable is downgrade dummy, the coefficients of

Approach52 are significantly positive as reported in Table 9. For OLS and ordered logit models

where the dependent variable is recommendation change, the coefficients of Approach52 are

significantly negative, which means that there is an inverse relation between approaching 52-

week high and recommendation change. Finally, in Panel B, we redo all these analyses based on

the subsample excluding reiterations. The results from all these tests are consistent with our main

hypothesis.


In robustness tests reported in Appendix B, we use stock price at day t−3 or (t−3, t−1) to

define the approaching 52-week high dummy, and also use 4% or 6% to measure the nearness to

the 52-week high. Consistently, all the coefficients of Approach52 are significantly positive

(reported in Appendix Table 2). Combined with Figure 2, the results suggest that our main

finding is robust to different definitions of nearness to the 52-week high.

Furthermore, we use another measure for the nearness to 52-week high based on previous

studies (e.g., Baker, Pan, and Wurgler, 2012), the ratio of price at day t−1 to the 52-week high.

As analyst recommendations are shown to be momentum chasing, we add a square term of the

price ratio to capture the non-linear relationships between analyst recommendation change and

the price ratio. As we have found earlier, analysts tend not to downgrade stocks with price run-

ups, but more likely to downgrade when stock price is approaching the resistance level. This can

be captured by a positive (negative) coefficient on the price ratio (square term of the price ratio).

Indeed, the result from OLS regression shows an inverted U-shaped relation between

27

recommendation change and the price ratio. The result based on logit regression is consistent

(reported in Appendix Table 3).

We also find robust results based on a subsample with available analyst education data

(reported in Appendix Table 4). In untabulated results, we conduct analyses for subsample

periods and find that the 52-week high effect exists before and after 2003. In addition, we also

investigate whether the 52-week low price also serves as a reference point in analyst

recommendation decisions. However, we do not find significant predictability of approaching

52-week low on analyst recommendation revisions (shown in Appendix Table 5).

Finally, we add two more control variables that are not included in previous studies

(Jegadeesh et al., 2004; Loh and Stulz, 2011): (i) the cumulative S&P 500 index returns during

the past 52 weeks (or the ratio of S&P 500 index level at day t−1 to the highest index level in the

past 52 weeks, or cumulative CRSP value-weighted market returns during the past 52 weeks); (ii)

the previous recommendation level from the analyst. The results (untabulated) are qualitatively

and quantitatively similar.23 In sum, all the robustness tests are in line with our main hypothesis.

7. Conclusion

This paper explores the effect of the 52-week high stock price on analyst recommendation

revisions. We find that analysts use the 52-week high as a reference point and tend to downgrade

stocks with price approaching the 52-week high. This is due to analysts’ belief that when stock

price approaches the so-called resistance level, the stock has limited upside and little margin for

error. The anchoring effect of 52-week high is more pronounced for firms with higher

information asymmetry and mitigated by the reputation, work experience, and educational

23 The coefficients of market returns are positive and insignificant, while the coefficient of the previous

recommendation level is significantly positive.

28

background of analysts.

Furthermore, we find that a strategy of taking a long position in stocks with current prices

close to the 52-week high generates positive returns, and a strategy that involves taking a short

position in stocks with recommendation downgrades is less profitable for downgrades near 52-

week high than for the other downgrades for the same stock. In addition, firms with downgrades

near 52-week high subsequently experience less downward earnings forecast revisions. These

results suggest that downgrades near 52-week high are at least partially attributable to the

anchoring heuristic and less associated with firm fundamentals.

In sum, our paper provides new evidence on the determinants of analyst recommendations

from a behavioral perspective. The economic magnitude of this behavioral bias is comparable

with other known incentive biases found in the literature, which indicates that the 52-week high

anchoring is an important source of bias in analyst research that might compromises analysts’

ability to function objectively as information intermediary and provider.

29

References

Ai, C., Norton, E.C., 2003. Interaction terms in logit and probit models. Economics Letters 80,

123-129.

Andersson, P., Hellman, N., 2007. Does pro forma reporting bias analyst forecasts? European

Accounting Review 16, 277-298.

Antoniou, C., Kumar, A., Maligkris, A., 2015. The affect heuristic and analysts' earnings

forecasts. Working paper. University of Warwick, University of Miami and University of

Warwick.

Baker, M., Pan, X., Wurgler, J., 2012. The effect of reference point prices on mergers and

acquisitions. Journal of Financial Economics 106, 49-71.

Baker, M., Wurgler, J., 2013. Behavioral corporate finance: An updated survey. Handbook of the

Economics of Finance, edited by George Constantanides, Milton Harris, and Rene Stulz,

Chapter 5, 2: 357-424. New York, NY: Elsevier.

Barber, B.M., Lehavy, R., McNichols, M., Trueman, B., 2006. Buys, holds, and sells: The

distribution of investment banks' stock ratings and the implications for the profitability of

analysts' recommendations. Journal of Accounting and Economics 41, 87-117.

Barniv, R., Hope, O.-K., Myring, M.J., Thomas, W.B., 2009. Do analysts practice what they

preach and should investors listen? Effects of recent regulations. Accounting Review 84,

1015-1039.

Ben-Rephael, A., Da, Z. and Israelsen, R.D., 2016. It depends on where you search: A

comparison of institutional and retail attention. Working paper. Indiana University,

University of Notre Dame and Indiana University.

30

Beyer, A., Cohen, D.A., Lys, T.Z., Walther, B.R., 2010. The financial reporting environment:

Review of the recent literature. Journal of Accounting and Economics 50, 296-343.

Bhattacharya, N., Ecker, F., Olsson, P.M., Schipper, K., 2012. Direct and mediated associations

among earnings quality, information asymmetry, and the cost of equity. Accounting Review

87, 449-482.

Birru, J., 2015. Psychological barriers, expectational errors, and underreaction to news. Working

paper. The Ohio State University.

Boni, L., Womack, K.L., 2006. Analysts, industries, and price momentum. Journal of Financial

and Quantitative Analysis 41, 85-109.

Bradshaw, M.T., 2002. The use of target prices to justify sell-side analysts' stock

recommendations. Accounting Horizons 16, 27-41.

Bradshaw, M.T., 2004. How do analysts use their earnings forecasts in generating stock

recommendations? Accounting Review 79, 25-50.

Bradshaw, M.T., Brown, L.D., Huang, K., 2013. Do sell-side analysts exhibit differential target

price forecasting ability? Review of Accounting Studies 18, 930-955.

Brown, L.D., Call, A.C., Clement, M.B., Sharp, N.Y., 2015. Inside the “Black Box” of sell-side

financial analysts. Journal of Accounting Research 53, 1-47.

Buis, M.L., 2010. Stata tip 87: Interpretation of interactions in non-linear models. The Stata

Journal 10, 305-308.

Carhart, M.M., 1997. On persistence in mutual fund performance. Journal of finance 52, 57-82.

Cen, L., Hilary, G., Wei, K.C.J., 2013. The role of anchoring bias in the equity market: Evidence

from analysts’ earnings forecasts and stock returns. Journal of Financial and Quantitative

Analysis 48, 47-76.

31

Chen, S., Matsumoto, D.A., 2006. Favorable versus unfavorable recommendations: The impact

on analyst access to management-provided information. Journal of Accounting Research 44,

657-689.

Chen, X., Cheng, Q., Lo, K., 2010. On the relationship between analyst reports and corporate

disclosures: Exploring the roles of information discovery and interpretation. Journal of

Accounting and Economics 49, 206-226.

Cohen, L., Frazzini, A., Malloy, C., 2010. Sell-side school ties. Journal of Finance 65, 1409-

1437.

Daniel, K., Grinblatt, M., Titman, S., Wermers, R., 1997. Measuring mutual fund performance

with characteristic-based benchmarks. Journal of Finance 52, 1035-1058.

Driessen, J., Lin, T.C., Van Hemert, O., 2013. How the 52-week high and low affect option-

implied volatilities and stock return moments. Review of Finance 17, 369-401.

Elliot, J.A., Philbrick, D.R., Wiedman, C.I., 1995. Evidence from archival data on the relation

between security analysts' forecast errors and prior forecast revisions. Contemporary

Accounting Research 11, 919-938.

Fama, E.F., French, K.R., 1993, Common risk factors in the returns on stocks and bonds, Journal

of Financial Economics 33, 3-56.

Fama, E.F., French, K.R., 1997. Industry costs of equity. Journal of Financial Economics 43,

153-193.

Fama, E.F., French, K.R., 2015, A five-factor asset pricing model, Journal of Financial

Economics 116, 1-22.

Fang, L., Yasuda, A., 2009. The effectiveness of reputation as a disciplinary mechanism in sell-

side research. Review of Financial Studies 22, 3735-3777.

32

Fang, L.H., Yasuda, A., 2013. Are stars' opinions worth more? The relation between analyst

reputation and recommendation values. Journal of Financial Services Research 46, 235-269.

Firth, M., Lin, C., Liu, P., Xuan, Y., 2013. The client is king: Do mutual fund relationships bias

analyst recommendations? Journal of Accounting Research 51, 165-200.

Francis, J., Soffer, L., 1997. The relative informativeness of analysts' stock recommendations

and earnings forecast revisions. Journal of Accounting Research 35, 193-211.

Frankel, R., Kothari, S.P., Weber, J., 2006. Determinants of the informativeness of analyst

research. Journal of Accounting and Economics 41, 29-54.

Friesen, G., Weller, P.A., 2006. Quantifying cognitive biases in analyst earnings forecasts.

Journal of Financial Markets 9, 333-365.

George, T.J., Hwang, C.-Y., 2004. The 52-week high and momentum investing. Journal of

Finance 59, 2145-2176.

George, T.J., Hwang, C.-Y., Li, Y., 2015. Anchoring, the 52-week high and post earnings

announcement drift. Working paper. University of Houston, Nanyang Technological

University and Loughborough University.

Gottesman, A.A., Morey, M.R., 2006. Manager education and mutual fund performance. Journal

of Empirical Finance 13, 145-182.

Green, T.C., 2006. The value of client access to analyst recommendations. Journal of Financial

and Quantitative Analysis 41, 1-24.

Greene, W., 2010. Testing hypotheses about interaction terms in nonlinear models. Economics

Letters 107, 291-296.

33

Healy, P.M., Palepu, K.G., 2001. Information asymmetry, corporate disclosure, and the capital

markets: A review of the empirical disclosure literature. Journal of Accounting and


Heath, C., Huddart, S., Lang, M., 1999. Psychological factors and stock option exercise.

Quarterly Journal of Economics 114, 601-627.

Hirshleifer, D., 2001. Investor psychology and asset pricing. Journal of Finance 56, 1533-1597.

Hong, H., Kubik, J.D., 2003. Analyzing the analysts: Career concerns and biased earnings

forecasts. Journal of Finance 58, 313-351.

Howe, J.S., Unlu, E., Yan, X.S., 2009. The predictive content of aggregate analyst

recommendations. Journal of Accounting Research 47, 799-821.

Huddart, S., Lang, M., Yetman, M.H., 2009. Volume and price patterns around a stock's 52-week

highs and lows: Theory and evidence. Management Science 55, 16-31.

Ivković, Z., Jegadeesh, N., 2004. The timing and value of forecast and recommendation revisions.

Journal of Financial Economics 73, 433-463.

Jegadeesh, N., Kim, J., Krische, S.D., Lee, C.M.C., 2004. Analyzing the analysts: When do

recommendations add value? Journal of Finance 59, 1083-1124.

Jegadeesh, N., Kim, W., 2010. Do analysts herd? An analysis of recommendations and market

reactions. Review of Financial Studies 23, 901-937.

Kim, O., Verrecchia, R.E., 1991. Trading volume and price reactions to public announcements.

Journal of Accounting Research 29, 302-321.

Kolasinski, A.C., Kothari, S.P., 2008. Investment banking and analyst objectivity: Evidence from

analysts affiliated with mergers and acquisitions advisors. Journal of Financial and

Quantitative Analysis 43, 817-842.

34

Kolasinski, A.C., Siegel, A.F., 2010. On the economic meaning of interaction term coefficients

in non-linear binary response regression models. Working paper. Texas A&M University

and University of Washington.

Kothari, S.P., 2001. Capital markets research in accounting. Journal of Accounting and


Lang, M.H., Lundholm, R.J., 1996. Corporate disclosure policy and analyst behavior.

Accounting Review 71, 467-492.

Leone, A., Wu, J.S., 2007. What does it take to become a superstar? Evidence from institutional

investor rankings of financial analysts. Working paper. University of Miami and University

of Rochester.

Li, J., Yu, J., 2012. Investor attention, psychological anchors, and stock return predictability.

Journal of Financial Economics 104, 401-419.

Liu, X., Ritter, J.R., 2011. Local underwriter oligopolies and IPO underpricing. Journal of

Financial Economics 102, 579-601.

Loh, R.K., Stulz, R.M., 2011. When are analyst recommendation changes influential? Review of

Financial Studies 24, 593-627.

Michaely, R., Womack, K.L., 1999. Conflict of interest and the credibility of underwriter analyst

recommendations. Review of Financial Studies 12, 653-686.

Mokoaleli-Mokoteli, T., Taffler, R.J., Agarwal, V., 2009. Behavioural bias and conflicts of

interest in analyst stock recommendations. Journal of Business Finance & Accounting 36,

384-418.

O'Brien, P.C., McNichols, M.F., Lin, H.-W., 2005. Analyst impartiality and investment banking

relationships. Journal of Accounting Research 43, 623-650.

35

Petersen, M.A., 2009. Estimating standard errors in finance panel data sets: Comparing

approaches. Review of Financial Studies 22, 435-480.

Poteshman, A.M., Serbin, V., 2003. Clearly irrational financial market behavior: Evidence from

the early exercise of exchange traded stock options. Journal of Finance 58, 37-70.

Ramnath, S., Rock, S., Shane, P., 2008. The financial analyst forecasting literature: A taxonomy

with suggestions for further research. International Journal of Forecasting 24, 34-75.

Stambaugh, R.F., Yu, J., Yuan, Y., 2015. Arbitrage asymmetry and the idiosyncratic volatility

puzzle. Journal of Finance 70, 1903-1948.

Stickel, S.E., 1992. Reputation and performance among security analysts. Journal of Finance 47,

1811-1836.

Tversky, A., Kahneman, D., 1974. Judgment under uncertainty: Heuristics and biases. Science

185, 1124-1131.

Womack, K.L., 1996. Do brokerage analysts' recommendations have investment value? Journal

of Finance 51, 137-167.

Yu, F., 2008. Analyst coverage and earnings management. Journal of Financial Economics 88,

245-271.

36

Table 1

Summary Statistics

The sample consists of 214,691 analyst recommendation changes from 10,841 analysts for 10,219 unique firms during the period

from November 1993 to December 2013. Analyst rating from I/B/E/S is reversed so that higher rating means more favorable

recommendation. Analyst rating ranges from 1 to 5, indicating sell, underperform, hold, buy and strong buy, respectively.

Recommendation change is calculated as the current rating minus the prior rating for the stock by the same analyst and takes a

value between −4 and 4. Downgrade is a dummy that equals 1 for recommendation downgrades and 0 for reiterations and

upgrades. Approach52 is an approaching 52-week high dummy, which equals 1 if the stock price at day t−1 is within 5% below

the 52-week high (i.e. (1−0.05) × 52-week high < price at t−1< 52-week high). Price/52-week high is the ratio of stock price at

day t−1 divided by 52-week high. Runupt−5, t−1 is the cumulative return from day t−5 to t−1. Runupt−21, t−6 is the cumulative return

from day t−21 to t−6. Runupm−6, m−2 is the cumulative return from day t−126 to t−22. Runupm−12, m−7 is the cumulative return from

day t−252 to t−127. All variables are defined in Appendix A. All continuous variables (except Price/52-week high) are

winsorized at the 0.25th and 99.75th percentiles to eliminate outliers. Panel A reports the descriptive statistics and Panel B

presents the Pearson correlation matrix. Correlations that are statistically significant at the 5% level or less are in bold.

Panel A: Descriptive statistics

Mean Std. Q1 Median Q3 N

Current rating 3.570 0.993 3 3 4 214,691

Prior rating 3.616 0.998 3 4 4 214,691

Recommendation change -0.046 1.306 -1 0 1 214,691

Downgrade 0.413 0.492 0 0 1 214,691

Approach52 0.045 0.208 0 0 0 214,691

Price/52-week high 0.660 0.229 0.503 0.705 0.848 214,691

Runupt−5, t−1 -0.007 0.103 -0.047 -0.003 0.036 214,691

Runupt−21, t−6 -0.012 0.141 -0.079 -0.010 0.054 214,691

Runupm−6, m−2 -0.028 0.327 -0.206 -0.034 0.122 214,691

Runupm−12, m−7 0.058 0.419 -0.175 0.023 0.222 214,691

Earnings forecast revisions -0.019 0.272 -0.009 0.000 0.007 212,621

SUE -0.055 1.299 -0.820 0.000 0.603 205,546

Size 7.340 1.799 6.093 7.265 8.539 214,691

B/M 0.838 2.806 0.268 0.452 0.731 211,537

Earnings to price -0.032 0.409 0.006 0.042 0.068 211,939

Turnover 0.011 0.011 0.004 0.007 0.013 214,691

Accruals -0.003 0.070 -0.033 -0.007 0.022 181,659

Capital expenditure 0.048 0.089 0.008 0.027 0.064 203,584

Sales growth 1.326 0.983 1.005 1.115 1.301 209,250

Long-term growth forecast 17.543 11.408 10.600 15.000 21.670 204,333

Idiosyncratic volatility 0.028 0.018 0.016 0.024 0.035 214,691

Institutional ownership 0.643 0.275 0.464 0.676 0.839 214,691

Analyst coverage 9.727 7.772 4 8 14 214,691

Analyst dispersion 0.278 1.164 0.031 0.077 0.188 195,320

Analyst experience 1.554 0.709 1.099 1.609 2.079 214,691

Age 18.864 18.018 6 12 25 214,691

PIN 0.138 0.076 0.088 0.124 0.173 192,985

Discretionary accruals 0.297 1.936 0.028 0.072 0.187 178,629

Star 0.082 0.274 0 0 0 181,784

TopExp 0.127 0.333 0 0 0 214,691

Ivy 0.229 0.420 0 0 0 55,938

IvyMBA 0.130 0.336 0 0 0 55,938

37

Panel B: Pearson correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

Downgrade (1)

Approach52 (2) 0.002

Runupt−5, t−1 (3) -0.057 0.093

Runupt−21, t−6 (4) 0.001 0.070 0.059

Runupm−6, m−2 (5) -0.099 0.117 -0.008 -0.072

Runupm−12, m−7 (6) -0.015 0.033 -0.039 -0.055 -0.038

Forecast revisions (7) -0.005 0.020 -0.011 -0.010 0.094 0.081

SUE (8) -0.045 0.086 0.028 0.013 0.145 0.160 0.101

Size (9) -0.061 0.069 0.006 -0.009 0.142 0.065 0.064 0.078

B/M (10) 0.002 -0.003 0.017 0.019 -0.048 -0.055 -0.036 -0.029 -0.162

Earnings to price (11) -0.020 0.038 -0.003 -0.003 0.152 0.176 0.168 0.096 0.184 -0.057

Turnover (12) 0.015 -0.059 -0.007 -0.027 -0.057 0.030 -0.068 -0.026 0.053 0.059 -0.120

Accruals (13) 0.025 -0.013 -0.025 -0.020 -0.015 0.067 0.025 0.060 -0.064 -0.029 0.124 -0.026

Capital expenditure (14) 0.017 -0.024 -0.014 -0.014 -0.041 0.026 -0.015 -0.027 -0.033 0.011 0.008 0.034 -0.049

Sales growth (15) 0.031 -0.024 -0.046 -0.062 -0.050 0.083 0.011 0.054 -0.108 -0.021 -0.037 0.054 0.068 0.100

LTG forecast (16) 0.054 -0.068 -0.065 -0.073 -0.070 0.113 0.013 0.021 -0.212 -0.060 -0.096 0.101 0.038 0.110 0.365

Idio. volatility (17) 0.093 -0.139 -0.064 -0.066 -0.289 -0.064 -0.129 -0.145 -0.425 0.021 -0.396 0.264 -0.010 0.077 0.215 0.436

Inst. ownership (18) -0.025 0.017 0.000 0.004 0.044 0.022 0.012 0.041 0.236 0.018 0.091 0.323 -0.021 -0.054 -0.126 -0.107 -0.195

Analyst coverage (19) -0.016 -0.006 0.014 0.006 -0.029 -0.038 0.006 -0.016 0.587 -0.099 0.017 0.269 -0.082 0.062 -0.053 -0.036 -0.101 0.214

Analyst dispersion (20) 0.016 -0.022 -0.019 -0.037 -0.077 -0.041 -0.355 -0.064 -0.052 0.038 -0.169 0.125 -0.022 0.016 0.058 0.030 0.139 -0.019 -0.007

Analyst experience (21) -0.030 0.014 0.011 0.014 0.005 -0.046 -0.011 0.002 0.139 -0.029 -0.008 0.107 -0.055 -0.055 -0.083 -0.129 -0.084 0.170 0.067 0.013

38

Table 2

The Effect of 52-Week High on Analyst Recommendation Downgrade

This table shows the predictability of approaching 52-week high on recommendation downgrade. The logit regression is:

Downgradej,t = β0 + β1 Approach52j,t−1 + δ′Xj + Industry FE + Year FE + εj,t,

where subscript j and t denote the recommendation revision j issued on day t. The dependent variable is a downgrade dummy,

which equals 1 for downgrades and 0 for reiterations and upgrades. Approach52 equals 1 if stock price at day t−1 is within 5%

below the 52-week high. All variables are defined in Appendix A. All regressions control for year and Fama-French 48 industry

fixed effects. Numbers in parentheses are z-statistics based on two-way clustered standard errors by firm and year. ***, **, and *

denote the significance levels of 1%, 5%, and 10%, respectively.

(1) (2) (3) (4) (5)

Approach52 0.090*** 0.254*** 0.266*** 0.280*** 0.283***

(3.97) (4.97) (5.16) (4.51) (4.75)

Runupt−5, t−1 -1.166*** -1.092*** -1.220*** -1.142***

(-3.50) (-3.25) (-3.75) (-3.46)

Runupt−21, t−6 -0.070 0.009 -0.079 -0.021

(-0.36) (0.04) (-0.41) (-0.10)

Runupm−6, m−2 -0.644*** -0.609*** -0.580*** -0.543***

(-13.76) (-12.09) (-11.21) (-11.53)

Runupm−12, m−7 -0.135*** -0.101*** -0.119*** -0.128***

(-5.08) (-3.68) (-3.35) (-3.65)

Earnings forecast revisions

0.049** 0.075*** 0.111***

(2.38) (3.03) (3.79)

SUE

-0.045*** -0.039*** -0.035***

(-7.78) (-6.49) (-6.02)

Size

-0.044*** -0.030***

(-7.70) (-2.99)

B/M

-0.005* -0.006*

(-1.90) (-1.94)

Earnings to price

0.059 0.099**

(1.45) (2.14)

Turnover

5.265*** 3.476***

(7.39) (5.03)

Accruals

0.655*** 0.671***

(7.28) (7.23)

Capital expenditure

0.251*** 0.213**

(3.23) (2.48)

Sales growth

0.038*** 0.032**

(3.47) (2.45)

Long-term growth forecast

0.234*** 0.093

(3.13) (1.12)

Idiosyncratic volatility

4.173***

(3.58)

Institutional ownership

0.027

(0.77)

Analyst coverage

0.016

(0.11)

Analyst dispersion

0.011**

(2.15)

Analyst experience -0.048***

(-2.71)

Intercept -0.487*** -0.501*** -0.492*** -0.325*** -0.408***

(-9.23) (-11.68) (-8.26) (-7.83) (-5.35)

Year fixed effect Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes

Observations 214,691 214,691 203,689 162,302 152,422

Pseudo R2 0.005 0.014 0.014 0.018 0.019

39

Table 3

The Effect of 52-Week High on Recommendation Downgrade and Information Asymmetry

This table shows the effect of 52-week high on recommendation downgrade under high information asymmetry. The logit regression is as follows:

Downgradej,t = β0 + β1 Approach52j,t−1 + β2 Approach52j,t−1 × Information asymmetryj + β3 Information asymmetryj + δ′Xj + Industry FE + Year FE + εj,t,

where subscript j and t denote the recommendation revision j issued on day t. The dependent variable is the downgrade dummy. Approach52 equals 1 if the stock price at day t−1

is within 5% below the 52-week high. Approach52 × Info. asymmetry is the interaction of approaching 52-week high dummy with high information asymmetry dummy. Firms are

ranked each year based on each of the seven information asymmetry measures, including firm size, analyst coverage, firm age, idiosyncratic volatility, probability of informed

trading (PIN), absolute discretionary accrual (absDCA), and earnings forecast accuracy. The higher the rank, the greater the relative degree of information asymmetry. A firm’s

composite rank of information asymmetry is the arithmetic average of its ranking percentile for each of the available information asymmetry measures. Firms with the highest

information asymmetry receive the highest composite rank. In column 1, High composite rank equals 1 if the firm’s composite rank is above the yearly cross-sectional median rank.

In columns 2-8, the dummy variables for high information asymmetry are Small_Size, Low_Coverage, Young_Age, High_Idio, High_PIN, High_absDCA, and Low_Accuracy,

respectively. Low_Accuracy is based on the average earnings forecast accuracy in the previous year. Forecast accuracy is the absolute value of earnings forecast error, which is

computed as actual earnings per share (EPS) minus the median forecasted EPS, scaled by stock price measured at the end of the fiscal quarter to which EPS relates. The

regressions include all the control variables and year and Fama-French 48 industry fixed effects, but those coefficients are not reported. All variables are defined in Appendix A.

Numbers in parentheses are z-statistics based on two-way clustered standard errors by firm and year. ***, **, and * denote the significance levels of 1%, 5%, and 10%,

respectively.

Dependent variable: Downgrade dummy

Information asymmetry High composite rank Small_Size Low_Coverage Young_Age High_Idio. High_PIN High_absDCA Low_Accuracy

(1) (2) (3) (4) (5) (6) (7) (8)

Approach52 0.225*** 0.229*** 0.208*** 0.201*** 0.191*** 0.231*** 0.218*** 0.240***

(3.79) (3.48) (3.44) (3.45) (3.81) (3.07) (3.33) (4.52)

Approach52 × Info. asymmetry 0.198*** 0.147** 0.147** 0.196*** 0.406*** 0.136** 0.142** 0.125**

(3.47) (2.34) (2.57) (2.98) (5.00) (2.04) (2.24) (2.02)

Information asymmetry 0.014 0.041** 0.026 -0.028* 0.006 -0.001 0.026* 0.024*

(0.71) (2.38) (1.02) (-1.76) (0.23) (-0.07) (1.71) (1.91)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes

Observations 152,422 152,422 152,422 152,422 152,422 137,270 150,453 152,422

Pseudo R2 0.019 0.019 0.019 0.019 0.019 0.020 0.019 0.019

40

Table 4

The Effect of 52-Week High on Recommendation Downgrade and Analyst Characteristics

This table shows the effect of 52-week high on recommendation revision decisions of analysts with different characteristics. The

estimated logit regression is:

Downgradej,t = β0 + β1 Approach52j,t−1 + β2 Approach52j,t−1 × Analyst characteristicj + β3 Analyst characteristicj

+ δ′Xj + Industry FE + Year FE + εj,t, where subscript j and t denote the recommendation revision j issued on day t. The dependent variable is the downgrade dummy.

Analyst characteristics include Star, TopExp, Ivy and IvyMBA. Star is a dummy variable that equals 1 if the analyst was an all-

star (top3) in year t−1 based on the October issue of Institutional Investor magazine. TopExp is a dummy for experienced analyst

from top brokerage firm, which equals 1 if the analyst worked in a top brokerage firm with a highest Carter-Manaster rank of 9

and the analyst’s experience was above cross-sectional median in year t−1. Ivy equals 1 if the analyst has ever attended an Ivy

League school. IvyMBA equals 1 if the analyst has ever attended an Ivy League school and also has a MBA degree. Approach52

equals 1 if price at day t−1 is within 5% below the 52-week high. Approach52 × Analyst characteristic is the interaction of

approaching 52-week high dummy with analyst characteristic dummy. The regressions include all the control variables and year

and Fama-French 48 industry fixed effects, but those coefficients are not reported. All variables are defined in Appendix A.

Numbers in parentheses are z-statistics based on two-way clustered standard errors by firm and year. ***, **, and * denote the

significance levels of 1%, 5%, and 10%, respectively.

Dependent variable: Downgrade dummy

Analyst characteristic Star TopExp Ivy IvyMBA

(1) (2) (3) (4)

Approach52 0.259*** 0.309*** 0.346*** 0.347***

(4.36) (4.69) (4.00) (3.87)

Approach52 × Analyst characteristic -0.250*** -0.242*** -0.192** -0.337**

(-2.91) (-2.61) (-2.42) (-2.37)

Analyst characteristic -0.140* -0.083 -0.011 0.039*

(-1.94) (-1.25) (-0.35) (1.65)

Controls Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes

Observations 128,756 152,422 40,194 40,194

Pseudo R2 0.021 0.019 0.021 0.021

41

Table 5

Profits from the 52-Week High Strategy & the Strategy of Shorting Stocks with Recommendation Downgrades

Panel A reports the average monthly portfolio returns (in percentages) for the 52-week high strategy of taking a long position in stocks whose current prices are close to the 52-

week high. To be included in the portfolio for month t (formed at the end of month t−1), a stock must have a 52-week high ratio (Price/52-week high) larger than 0.95 at the end of

month t−1. Price/52-week high is defined as the ratio of the stock price at the end of month t−1 to the maximum stock price in the past 52-weeks ending in month t−1 (adjusted for

stock splits and stock dividends). The portfolios are then rebalanced monthly. The portfolio holding period is 3 months, 6 months, and 12 months following the portfolio formation

date. The average raw return and risk adjusted abnormal return (alpha) (equal-weighted and value-weighted by market capitalization at the end of month t−1) based on the Fama-

French (1993) three-factor model, the Carhart (1997) four-factor model, and the Fama-French (2015) five-factor model are reported. The sample induces all CRSP firms listed on

the NYSE, AMEX, or NASDAQ that have common shares (a CRSP share code of 10 or 11). The sample period is from November 1993 through December 2013. Numbers in

parentheses are t-statistics based on Newey-West (1987) standard errors. Panel B reports the average difference in profits to a strategy that takes a short position in stocks with

downgrades near 52-week high as compared with a strategy that takes a short position in other downgrades within the same firm. First, the total of 88,579 downgrades in our

sample is divided into two groups: 4,041 downgrades having price close to the 52-week high (Approach52=1) and 84,538 other downgrades (Approach52=0). Each firm is

required to have both types of downgrades, which yields a total of 2,064 firms. Next, the average abnormal returns (ARs) is computed separately for these two types of downgrades,

and the difference of the two average ARs is calculated for each firm. Finally, the average differences in profits by shorting these two types of downgrades within the same firm are

the negative values of the arithmetic mean (EW or VW by average firm value at t−22 associated with downgrades) of these differences of ARs across 2,064 firms. AR(0,1) is the

two-day cumulative abnormal returns from recommendation revision day t to t+1. For recommendation revisions made after the market close or on non-trading days, day 0 is the

next trading day. AR(0,21), AR(0,63) and AR(0,126) are 1-month, 3-month and 6-month ARs. ARs are adjusted by subtracting the contemporaneous compounded CRSP value-

weighted market returns or the returns on a benchmark portfolio with the same size, B/M, and momentum characteristics as the stock (Daniel, Grinblatt, Titman, and Wermers

1997). ***, **, and * denote the significance levels of 1%, 5%, and 10%, respectively.

Panel A: Profits from the 52-week high strategy of taking a long position in stocks approaching 52-week high

Equal-Weighted Value-Weighted

Holding period Raw return FF 3-factor alpha Carhart 4-factor alpha FF 5-factor alpha

Raw return FF 3-factor alpha Carhart 4-factor alpha FF 5-factor alpha

3 1.42*** 0.58*** 0.40*** 0.47***

0.92*** 0.22** -0.001 0.09

(4.41) (5.11) (4.11) (3.72)

(3.37) (2.49) (-0.02) (0.80)

6 1.38*** 0.47*** 0.30*** 0.34***

0.91*** 0.18** -0.01 0.04

(4.03) (4.26) (3.08) (2.98)

(3.16) (2.27) (-0.23) (0.45)

12 1.24*** 0.28*** 0.26*** 0.25***

0.87*** 0.09* 0.01 0.02

(3.50) (3.22) (2.76) (2.62)

(3.02) (1.92) (0.11) (0.41)

Panel B: Average difference in profits by shorting downgrades near 52-week high vs. other downgrades within the same firm

AR(0,1) AR(0,21) AR(0,63) AR(0,126)

No. of firms Weight Mkt-adj. DGTW-adj. Mkt-adj. DGTW-adj. Mkt-adj. DGTW-adj. Mkt-adj. DGTW-adj.

Average difference EW -0.98%*** -0.95%*** -1.36%*** -1.25%*** -0.90%** -1.38%*** -0.16% -1.25%* 2,064

Average difference VW -0.66%*** -0.72%*** -0.79%*** -0.95%*** -1.14%*** -1.75%*** -1.34%*** -2.26%***

42

Table 6

Earnings Forecast Revisions around Recommendation Downgrades

This table shows analysts’ earnings forecast revisions around their recommendation downgrades. All the recommendation

downgrades from our main sample are divided into two groups: 4,041 recommendation downgrades for firms with stock prices at

day t−1 approaching 52-week high (Approach52=1) and 84,538 downgrades for firms with stock prices at day t−1 not

approaching 52-week high (Approach52=0). For each group, we form three categories of earnings forecasts that are issued by the

same analyst who downgrades the stock on day t: 1) earnings forecasts issued on day t; 2) earnings forecasts issued within a five-

day window (t−5, t+5); 3) earnings forecasts issued within a ten-day window (t−10, t+10). Negative revision dummy (Positive

revision dummy) equals 1 if the current earnings forecast is lower (higher) than the prior active earnings forecast. No revision

dummy equals 1 if the forecast remains unchanged. The mean value of each of these three dummies is calculated within each

earnings forecasts category for each group of recommendation downgrades. N is the number of earnings forecast revisions. Panel

A and Panel B report earnings forecast revisions for the next fiscal quarter (I/B/E/S forecast period indicator (FPI) = 7) and for

the next fiscal year (FPI=2), respectively. Difference is the difference of the mean value of each dummy variable between two

groups. The z-test for the difference between two means is reported in the last column.

Panel A: Earnings forecast revisions for the next fiscal quarter

Subsamples of recommendation downgrades

Approach52=1 Approach52=0

Forecast issue date Dummy variable Mean N Mean N Difference z-statistic

day t Negative revision dummy 0.176 164 0.314 8,161 -0.138 -10.78

Positive revision dummy 0.087 81

0.047 1,212 0.040 4.32

No revision dummy 0.737 687

0.639 16,619 0.098 6.63

(t−5, t+5) Negative revision dummy 0.157 193

0.297 10,003 -0.140 -13.16


0.049 1,654 0.045 5.36


0.654 22,060 0.095 7.54


0.280 10,999 -0.130 -13.61


0.052 2,031 0.041 5.36

No revision dummy 0.757 1,132 0.668 26,228 0.089 7.85

Panel B: Earnings forecast revisions for the next fiscal year

day t Negative revision dummy 0.494 675 0.696 24,953 -0.202 -14.68


0.111 3,980 0.147 12.32


0.193 6,917 0.054 4.59


0.677 30,329 -0.220 -18.06


0.124 5,550 0.165 15.01


0.199 8,892 0.055 5.15


0.661 32,891 -0.229 -20.25


0.136 6,762 0.168 16.15

No revision dummy 0.263 525 0.203 10,080 0.060 6.03

43

Table 7

Recommendation Downgrade Categories and Subsample Tests by Prior Recommendation Levels

Panel A reports the categories and frequency of downgrades near 52-week high. The sample consists of 4,041 recommendation

downgrades for firms with stock prices at day t−1 approaching the 52-week high (i.e., Approach52 (5%) =1). Frequency is the

number of downgrades. Panel B shows the subsample tests by the prior recommendation levels (excluding Sell). For columns 1-4,

the total sample of 214,691 recommendation changes are divided into subsamples by the prior recommendation levels. Column 5

uses the total sample excluding prior levels of Strong Buy and Sell. The logit regression is: Downgradej,t = β0 + β1

Approach52j,t−1 + δ′Xj + Industry FE + Year FE + εj,t. The dependent variable is the downgrade dummy. The regressions include

all the control variables in baseline equation (1) and year and Fama-French 48 industry fixed effects, but those coefficients are

not reported. All variables are defined in Appendix A. Numbers in parentheses are z-statistics based on two-way clustered

standard errors by firm and year. ***, **, and * denote the significance levels of 1%, 5%, and 10%, respectively.

Panel A: Categories of recommendation downgrades near 52-week high

Frequency

Buy downgraded to Hold 1,570

Strong Buy downgraded to Hold 1,170

Strong buy downgraded to Buy 567

Hold downgraded to Underperform 423

Hold downgraded to Sell 163

Buy downgraded to Underperform 57

Strong Buy downgraded to Sell 32

Strong Buy downgraded to Underperform 17

Underperform downgraded to Sell 25

Buy downgraded to Sell 17

Panel B: Subsamples by prior recommendation levels

Prior recommendation level Total excluding

prior levels of

Strong Buy and Sell

Strong Buy Buy Hold Underperform

(1) (2) (3) (4) (5)

Approach52 0.176** 0.302*** 0.287*** 0.489 0.309***

(2.05) (4.58) (2.76) (1.42) (4.67)

Controls Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes

Observations 33,049 46,365 58,552 9,541 114,458

Pseudo R2 0.043 0.060 0.072 0.087 0.025

44

Table 8

The Effect of Target Price and Other Stock Price Highs on Recommendation Downgrade

This table shows the horse-race regression between the 52-week high and target price (Panel A) and other stock price highs

(Panel B). The logit regression is: Downgradej,t = β0 + β1 Approach52j,t−1 + β2 Approach target price (or other highs) dummyj,t−1

+ δ′Xj + Industry FE + Year FE + εj,t. The dependent variable is the downgrade dummy. Approach target price (5% or 10%) is a

dummy that equals 1 if stock price at day t−1 is within 5% (or 10%) below the target price issued by the same analyst who

revises the recommendation. Approach historical high, Approach 65-week high, and Approach 39-week high are dummies that

equal to 1 if stock price at day t−1 is within 5% below the historical high, 65-week high, or 39-week high, respectively. All

regressions include year and Fama-French 48 industry fixed effects. Control variables are added where specified. All variables

are defined in Appendix A. Numbers in parentheses are z-statistics based on two-way clustered standard errors by firm and year.

***, **, and * denote the significance levels of 1%, 5%, and 10%, respectively.

Panel A: Horse race between the 52-week high and target price

(1) (2) (3) (4)

Approach52 0.194*** 0.197*** 0.347*** 0.345***

(3.32) (3.31) (5.21) (5.12)

Approach target price (5%) 0.005

0.027

(0.11)

(0.54)

Approach target price (10%)

-0.015

0.034

(-0.44)

(0.80)

Controls No No Yes Yes



Observations 125,590 125,590 91,266 91,266

Pseudo R2 0.007 0.007 0.019 0.019

Panel B: Horse race between the 52-week high and other highs

(1) (2) (3)

Approach52 0.256*** 0.213*** 0.247***

(4.16) (3.71) (4.08)

Approach historical high 0.076

(1.12)

Approach 65-week high

0.108

(1.23)


0.042

(1.10)

Controls Yes Yes Yes

Year fixed effect Yes Yes Yes

Industry fixed effect Yes Yes Yes

Observations 152,422 152,393 152,422

Pseudo R2 0.019 0.019 0.019

45

Table 9

Robustness Tests: Different Methods

This table reports robustness tests by different methods including probit model, linear probability model, OLS, ordered logit

model and logit model. Panel A is based on our main sample and Panel B uses a subsample excluding reiterations (i.e.,

recommendation change=0). The dependent variable is downgrade dummy for probit model, linear probability model and logit

model. For OLS and ordered logit model, the dependent variable is recommendation change. The regressions include all the

control variables, constant term, and firm, analyst, year and Fama-French 48 industry fixed effects where indicated, but those

coefficients are not reported. All variables are defined in Appendix A. Numbers in parentheses are z-statistics or t-statistics based

on two-way clustered standard errors by firm and year ***, **, and * denote the significance levels of 1%, 5%, and 10%,

respectively.

Panel A: Main sample

Dependent variable: Downgrade Dependent variable: Recommendation change

Probit Linear Prob. Linear Prob. OLS OLS Ordered Logit

(1) (2) (3) (4) (5) (6)

Approach52 0.175*** 0.068*** 0.072*** -0.197*** -0.209*** -0.269***

(4.75) (5.08) (5.17) (-4.86) (-5.04) (-4.69)

Controls Yes Yes Yes Yes Yes Yes

Firm fixed effect No Yes Yes Yes Yes No

Analyst fixed effect No No Yes No Yes No

Year fixed effect Yes Yes Yes Yes Yes Yes

Industry fixed effect Yes No No No No Yes

Observations 152,422 152,422 152,422 152,422 152,422 152,422

Pseudo R2 /Adjusted R2 0.019 0.022 0.031 0.016 0.005 0.007

Panel B: Subsample excluding reiterations (i.e., recommendation change=0)

Dependent variable: Downgrade Dependent variable: Recommendation chg.

Logit Probit

Linear

Prob.

Linear

Prob. OLS OLS Ordered Logit

(1) (2) (3) (4) (5) (6) (7)

Approach52 0.355*** 0.220*** 0.087*** 0.094*** -0.249*** -0.268*** -0.299***

(4.78) (4.76) (4.88) (4.94) (-4.85) (-5.00) (-4.60)

Controls Yes Yes Yes Yes Yes Yes Yes

Firm fixed effect No No Yes Yes Yes Yes No

Analyst fixed effect No No No Yes No Yes No

Year fixed effect Yes Yes Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes No No No No Yes

Observations 121,158 121,158 121,158 121,158 121,158 121,158 121,158

Pseudo R2 /Adjusted R2 0.026 0.026 0.027 0.016 0.020 0.006 0.010

46

Figure 1. Daily Stock Price of eBay Inc. and Analyst Recommendation Changes

Fig. 1. This figure shows the daily stock price of eBay Inc. from Mar 1, 2004 to Sep 30, 2004 and analyst

recommendation changes. The 52-week high price is $ 92.81 on Jun 28, 2004. Analyst R. Becker (non-star analyst)

issued a Hold recommendation on Jan 23, 2004 and a Strong Buy recommendation on Aug 11, 2004. After stock

price ($90.07) approached the 52-week high on Sep 10, 2004, the analyst downgraded the recommendation to Hold

on Sep 13, 2004.

65

70

75

80

85

90

95

1-Mar-2004 1-Apr-2004 1-May-2004 1-Jun-2004 1-Jul-2004 1-Aug-2004 1-Sep-2004

Stock price 52-week high

Strong Buy

Hold


47

Figure 2. The log odds ratio of being downgraded with nearness to the 52-week high

Fig. 2. This figure shows the log odds ratio of being downgraded with nearness to the 52-week high. Approaching 52-week high

dummy equals 1 if the stock price at trading day t−1 is within x below the 52-week high (i.e. (1− x) × 52-week high < price at

day t−1< 52-week high, where x takes values from 3% to 50%). The plotted coefficients (log odds ratio) of approaching 52-week

high dummy are from estimation of equation (1) with all the controls.

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0% 10% 20% 30% 40% 50%

Significant at 1% level Significant at 5% level Insignificant

Lo

g o

dd

s ra

tio

of

bei

ng d

ow

ngra

ded

Insignificant

Significant

% below the 52-week high

48

Appendix A. Variable Definitions

Variable Definition

Downgrade a dummy equals 1 for recommendation downgrades and 0 for reiterations and upgrades.

Recommendation change analyst recommendation change, computed as the current rating minus the prior active

rating by the same analyst. By construction, recommendation change ranges between −4

and 4. Each recommendation change is categorized as downgrade (<0), reiteration (=0), or

upgrade (>0). (Analyst rating ranges from 1 to 5, specifically, 5 for Strong Buy, 4 for Buy, 3

for Hold, 2 for Underperform, and 1 for Sell.)

Runupt−5, t−1 cumulative return from day t−1 to t−5 before recommendation revision date t.

Runupt−21, t−6 cumulative return from day t−6 to t−21.

Runupm−6, m−2 cumulative return from month −2 to −6 (day t−22 to t−126).

Runupm−12, m−7 cumulative return from month −7 to −12 (day t−127 to t−252).

Size the natural logarithm of market value at day t−22.

B/M book-to-market ratio, book value of equity divided by market value of equity.

Approach52 approaching 52-week high dummy, which equals 1 if stock price at day t−1 is within 5%

below the 52-week high (i.e. (1−0.05) × 52-week high < price at day t−1 <52-week high).

Price/52-week high stock price at day t−1 divided by 52-week high.

Square of price/52-week high square of price/52-week high.

Sales growth rolling sum of sales for preceding four quarters / rolling sum of sales for second preceding

set of four quarters as in Jegadeesh et al. (2004).

Accruals total accruals to total assets, calculated as the change in non-cash current assets less the

change in current liabilities excluding the change in debt included in current liabilities and

the change in income taxes payable, minus depreciation and amortization expense, and

scaled by average total assets.

Capital expenditure capital expenditures to total assets, calculated as rolling sum of four quarters of capital

expenditure scaled by average total assets.

Earnings to price rolling sum of earnings per share before extraordinary items for preceding four quarters,

deflated by price at the end of the quarter.

Turnover average daily turnover in previous three months.

Idiosyncratic volatility the standard deviation of the residuals of the Fama-French 3-factor model estimated using

the daily stock returns in past three months.

Institutional ownership a firm’s shares held by institutions from Thomson Reuters scaled by shares outstanding

from CRSP.

Analyst coverage the number of I/B/E/S analysts who provide one-year earnings forecasts in prior three

months (divided by 100 in regressions).

Analyst dispersion the standard deviation across earnings forecasts in prior three months.

SUE standardized unexpected earnings, unexpected earnings divided by the standard deviation of

unexpected earnings over eight preceding quarters, unexpected earnings are calculated as

earnings at quarter t minus earnings at quarter t−4.

Earnings forecast revisions analyst earnings forecast revisions to price, rolling sum of preceding six months’ revisions

to price ratios, using mean consensus analyst FY1 forecast from I/B/E/S.

Long-term growth forecast most recent mean consensus long-term growth forecast from I/B/E/S (divided by 100 in

regressions).

Analyst experience the natural logarithm of number of years that the analyst exists in I/B/E/S.

Star a dummy variable that equals 1 if the analyst is an all-star (top3) in year t−1 based on the

October issue of Institutional Investor magazine.

TopExp a dummy for experienced analyst working in top brokerage firm, which equals 1 if the

analyst worked in a top brokerage firm with a highest Carter-Manaster rank of 9 and the

49

analyst’s experience was above cross-sectional median in year t−1.

Ivy a dummy that equals 1 if the analyst has ever attended an Ivy League school.

IvyMBA a dummy that equals 1 if the analyst has ever attended an Ivy League school and also has a

MBA degree.

Discretionary accrual absolute value of discretionary accrual calculated based on modified Jones (1991) model.

PIN probability of informed trading, calculated based on Venter and DeJong (2006) model, data

is obtained from Stephen Brown’s website,

http://www.rhsmith.umd.edu/faculty/sbrown/pinsdata.html.

Forecast accuracy absolute earnings forecast error that is calculated as actual earnings per share (EPS) minus

the median forecasted EPS, scaled by stock price measured at the end of the fiscal quarter

to which EPS relates.

Age the natural logarithm of firm age, calculate as recommendation announcement year minus

the first year that the firm appears in CRSP plus one year.

Leverage the total debt (debt in current liabilities plus long-term debt) divided by total assets

1

Appendix B. Internet Appendix. This Internet Appendix reports the results of supplementary and robustness tests.

Appendix Table 1

Firm Performance after Recommendation Downgrade

This table shows firm performance in the next quarter or next four quarters after analyst recommendation downgrade. Columns

1-2 are for firm performance in the next quarter, and columns 3-4 are for the next four quarters. The estimated OLS regression is:

Tobin's q or ROAj,q+1 = β0 + β1 Downgradej,t + β2 Approach52j,t−1 × Downgradej,t + β3 Approach52j,t−1 + δ′Xj,q

+ Firm FE + Year FE + εj,q+1. The dependent variables are Tobin's q in column 1 and 3, and ROA in column 2 and 4. ROA is return on assets, defined as

operating income before depreciation divided by book value of total assets. Tobin's q is market value of assets over book value of

assets. Approach52 × Downgrade is the interaction term of approaching 52-week high dummy with downgrade dummy. Asset is

the natural logarithm of the total asset. Leverage is long-term debt plus debt in current liabilities divided by the total assets. Cash

is the natural logarithm of cash. Sales is the natural logarithm of sales. Capital Expenditure is the ratio of capital expenditure to

the total asset. ROA is multiplied by 100%. All regressions control for firm and year fixed effects. Numbers in parentheses are t-

statistics based on firm and year double clustered standard errors. ***, **, and * denote the significance levels of 1%, 5%, and

10%, respectively.

Tobin's q ROA Tobin's q ROA

Next one quarter Next four quarters

(1) (2) (3) (4)

Downgrade -0.136*** -0.255*** -0.100*** -0.192***

(-5.57) (-7.14) (-6.00) (-8.02)

Approach52 × Downgrade 0.124*** 0.139** 0.107*** 0.150***

(3.37) (2.10) (3.62) (3.71)

Approach52 0.131*** 0.300*** 0.090*** 0.218***

(6.03) (5.52) (5.64) (5.78)

Asset -0.981*** -2.245*** -0.958*** -2.042***

(-13.12) (-12.62) (-13.80) (-15.15)

Leverage -0.592*** -0.749 -0.560*** -1.001***

(-4.09) (-1.64) (-4.14) (-3.36)

Cash 0.117*** 0.056* 0.111*** 0.035

(7.00) (1.79) (8.08) (1.46)

Sales 0.399*** 2.420*** 0.416*** 2.304***

(8.45) (12.78) (8.34) (17.05)

Capital Expenditure 0.861*** -2.463* 0.742*** -2.076**

(3.80) (-1.65) (3.06) (-2.15)

Intercept 6.729*** 5.323*** 6.498*** 4.578***

(15.74) (5.58) (17.86) (7.85)

Firm fixed effect Yes Yes No No


Observations 177,513 167,137 688,512 649,233

Adjusted R2 0.624 0.592 0.639 0.643

2

Appendix Table 2

Different Definitions of Approaching 52-Week High

This table reports robustness tests by different definitions of approaching 52-week high based on logit regression. The estimated

logit regression is: Downgradei,j,t = β0 + β1 Approach dummyj,t−1 + δ′Xj,t−1 + Industry FE + Year FE + εi,j,t.

The dependent variable is a downgrade dummy. In column 1 to 3, Approach52 is a dummy that equals 1 if the stock price at

trading day t−3 is within 4%, 5% and 6% below the 52-week high, respectively. In column 4 to 6, Approach52 is a dummy that

equals 1 if the stock price on at least one day of the past three trading days (t−1, t−2 and t−3) is within 4%, 5% and 6% below the

52-week high, respectively. The regressions include all the control variables and year and Fama-French 48 industry fixed effects,

but those coefficients are not reported. All variables are defined in Appendix A. Numbers in parentheses are z-statistics based on

two-way clustered standard errors by firm and year. ***, **, and * denote the significance levels of 1%, 5%, and 10%,

respectively.

(1) (2) (3) (4) (5) (6)

Approach52 (4%) 0.334***

0.346***

(6.19)

(6.27)

Approach52 (5%)

0.294***

0.310***

(5.82)

(5.92)

Approach52 (6%)

0.260***

0.282***

(5.38)

(6.01)

Intercept -0.398*** -0.404*** -0.408*** -0.414*** -0.418*** -0.422***

(-5.26) (-5.36) (-5.44) (-5.55) (-5.58) (-5.62)

Controls Yes Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes Yes

Observations 152,334 152,334 152,334 152,453 152,453 152,453

Pseudo R2 0.019 0.019 0.019 0.019 0.019 0.019

3

Appendix Table 3

Alternative Measure of Approaching 52-Week High

This table shows the effect of 52-week high on recommendation downgrade using an alternative measure of approaching 52-

week high. The dependent variable is recommendation change for OLS regression (Panel A) and downgrade dummy for logit

regression (Panel B). Price/52-week high is the ratio of stock price at day t−1 divided by 52-week high. Square of price/52-week

high is the square of Price/52-week high. The definitions of other variables are given in Appendix A. Numbers in parentheses are

t-statistics or z-statistics based on two-way clustered standard errors by firm and year. ***, **, and * denote the significance

levels of 1%, 5%, and 10%, respectively.

Panel A: OLS regression, recommendation change as dependent variable

(1) (2) (3) (4)

Price/52-week high 0.850*** 0.863*** 0.899*** 0.768***

(3.69) (3.79) (3.63) (3.05)

Square of price/52-week high -0.728*** -0.751*** -0.792*** -0.702***

(-3.52) (-3.57) (-3.52) (-3.09)

Runupt−5, t−1 0.472** 0.447** 0.524** 0.490**

(2.21) (2.03) (2.54) (2.35)

Runupt−21, t−6 -0.184 -0.212* -0.166 -0.183

(-1.61) (-1.82) (-1.40) (-1.55)

Runupm−6, m−2 0.314*** 0.314*** 0.348*** 0.343***

(11.20) (10.88) (11.15) (10.44)

Runupm−12, m−7 0.052** 0.042* 0.077*** 0.085***

(2.53) (1.94) (2.75) (2.97)


-0.076*** -0.064*** -0.063***

(-4.79) (-3.40) (-2.85)

SUE

0.018*** 0.015*** 0.013***

(4.45) (3.55) (3.29)

Size

-0.062*** -0.061***

(-4.24) (-3.56)

B/M

-0.001 0.001

(-0.28) (0.32)

Earnings to price

-0.052*** -0.062***

(-2.69) (-3.00)

Turnover

-3.722*** -2.669***

(-4.76) (-3.02)

Accruals

-0.408*** -0.400***

(-5.93) (-5.16)

Capital expenditure

-0.100 -0.067

(-1.43) (-0.90)

Sales growth

-0.007 -0.007

(-1.00) (-0.91)


-0.182*** -0.175**

(-3.23) (-2.57)


-1.873***

(-2.68)


-0.093**

(-2.55)

Analyst coverage

-0.101

(-0.84)

Analyst dispersion

0.017*

(1.89)

Analyst experience 0.009

(1.27)

Intercept -0.203*** -0.199*** 0.360*** 0.464***

(-3.35) (-3.55) (2.88) (3.27)

Firm fixed effect Yes Yes Yes Yes


Observations 214,691 203,689 162,302 152,422

Adjusted R2 0.010 0.010 0.014 0.016

4

Appendix Table 3 (continued)

Panel B: logit regression, downgrade as dependent variable

(1) (2) (3) (4) Price/52-week high -1.487*** -1.442*** -1.221*** -0.914***

(-5.24) (-5.12) (-4.06) (-3.27)

Square of price/52-week high 0.977*** 0.992*** 1.000*** 0.820***

(3.75) (3.83) (3.41) (2.87)

Runupt−5, t−1 -0.955*** -0.913*** -1.147*** -1.111***

(-2.84) (-2.65) (-3.48) (-3.33)

Runupt−21, t−6 0.123 0.172 -0.009 0.007

(0.64) (0.86) (-0.05) (0.04)

Runupm−6, m−2 -0.484*** -0.479*** -0.532*** -0.530***

(-12.52) (-11.60) (-11.63) (-11.70)

Runupm−12, m−7 -0.089*** -0.064** -0.104*** -0.122***

(-2.90) (-2.11) (-2.81) (-3.38)


0.076*** 0.084*** 0.115***

(3.13) (3.16) (3.80)

SUE

-0.038*** -0.038*** -0.035***

(-5.71) (-4.97) (-4.94)

Size

-0.044*** -0.033***

(-6.67) (-3.22)

B/M

-0.005** -0.007**

(-2.01) (-2.16)

Earnings to price

0.094** 0.117**

(2.24) (2.43)

Turnover

4.901*** 3.691***

(5.70) (4.50)

Accruals

0.671*** 0.689***

(7.74) (7.83)

Capital expenditure

0.242*** 0.210**

(3.12) (2.46)

Sales growth

0.031*** 0.027**

(2.81) (2.12)


0.188** 0.085

(2.39) (1.01)


3.671***

(3.02)


0.029

(0.82)

Analyst coverage

0.010

(0.07)

Analyst dispersion

0.011**

(2.16)

Analyst experience -0.047*** (-2.67) Intercept 0.058 0.031 0.050 -0.144

(0.66) (0.31) (0.52) (-1.45)

Year fixed effect Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Observations 214,691 203,689 162,302 152,422 Pseudo R2 0.015 0.014 0.018 0.018

5

Appendix Table 4

Subsample tests: Predictability of Approaching 52-Week High on Recommendation Downgrade

This table shows the predictability of approaching 52-week high on recommendation downgrade using the subsample with

available analyst education data. The dependent variable is a downgrade dummy. Approach52 is a dummy which equals 1 if price

at day t−1 is within 5% below the 52-week high. The definitions of other variables are given in Appendix A. All regressions

control for year and Fama-French 48 industry fixed effects. Numbers in parentheses are z-statistics based on two-way clustered


(1) (2) (3) (4)

Approach52 0.299*** 0.303*** 0.300*** 0.306***

(4.38) (4.20) (3.10) (3.23)

Runupt−5, t−1 -1.124*** -1.002*** -0.997*** -0.955***

(-3.28) (-3.01) (-3.00) (-2.78)

Runupt−21, t−6 -0.006 0.067 -0.028 0.003

(-0.03) (0.30) (-0.12) (0.01)

Runupm−6, m−2 -0.645*** -0.617*** -0.567*** -0.559***

(-10.50) (-9.35) (-9.36) (-8.86)

Runupm−12, m−7 -0.118*** -0.079*** -0.114*** -0.143***

(-4.28) (-2.79) (-3.22) (-4.13)


0.054 0.063 0.090*

(1.34) (1.24) (1.88)

SUE

-0.049*** -0.044*** -0.043***

(-5.33) (-4.49) (-4.67)

Size

-0.060*** -0.047***

(-6.90) (-3.36)

B/M

-0.012 -0.012

(-1.29) (-1.20)

Earnings to price

0.068 0.080

(1.48) (1.29)

Turnover

6.417*** 6.287***

(6.91) (7.27)

Accruals

0.675*** 0.665***

(4.09) (4.14)

Capital expenditure

0.222* 0.179

(1.90) (1.38)

Sales growth

0.057*** 0.055***

(3.69) (2.97)


0.003** 0.002

(2.30) (1.52)


1.722

(1.17)


0.062

(1.58)

Analyst coverage

-0.003

(-1.33)

Analyst dispersion

0.005

(0.84)

Analyst experience -0.035

(-1.37)

Intercept -0.325*** -0.358*** -0.167* -0.326**

(-4.59) (-3.97) (-1.68) (-2.24)



Observations 55,938 53,405 42,821 40,194

Pseudo R2 0.016 0.016 0.021 0.021

6

Appendix Table 5

The Effect of 52-Week Low on Recommendation Revisions

This table shows the predictability of approaching 52-week low on recommendation revisions. The dependent variables are

downgrade dummy in column 1-2 using logit model and recommendation change in column 3-4 using OLS model.

Approach52low is a dummy which equals 1 if price at day t−1 is within 5% (or 10%) above the 52-week low. The definitions of

other variables are given in Appendix A. Numbers in parentheses are z-statistics or t-statistics based on two-way clustered


Dependent variable Downgrade dummy Recommendation change

(1) (2) (3) (4)

Approach52low (5%) 0.033 -0.010

(1.08)

(-0.42)

Approach52low (10%)

0.037

-0.016

(1.51)

(-0.98)

Runupt−5, t−1 -1.090*** -1.087*** 0.469** 0.467**

(-3.29) (-3.29) (2.22) (2.21)

Runupt−21, t−6 0.010 0.014 -0.193 -0.196

(0.05) (0.07) (-1.58) (-1.60)

Runupm−6, m−2 -0.524*** -0.520*** 0.334*** 0.333***

(-11.12) (-11.07) (12.17) (12.23)

Runupm−12, m−7 -0.123*** -0.122*** 0.084*** 0.084***

(-3.51) (-3.50) (3.02) (3.02)

Earnings forecast revisions 0.111*** 0.111*** -0.058*** -0.059***

(3.73) (3.74) (-2.71) (-2.71)

SUE -0.033*** -0.032*** 0.010*** 0.010***

(-5.91) (-5.90) (3.42) (3.40)

Size -0.028*** -0.029*** -0.063*** -0.063***

(-2.81) (-2.82) (-3.64) (-3.64)

B/M -0.006* -0.006* 0.001 0.001

(-1.91) (-1.90) (0.13) (0.13)

Earnings to price 0.096** 0.096** -0.049** -0.049**

(2.10) (2.10) (-2.55) (-2.55)

Turnover 3.244*** 3.253*** -2.143*** -2.146***

(4.85) (4.86) (-2.60) (-2.60)

Accruals 0.660*** 0.659*** -0.379*** -0.378***

(7.12) (7.08) (-4.85) (-4.84)

Capital expenditure 0.209** 0.209** -0.068 -0.067

(2.41) (2.42) (-0.88) (-0.88)

Sales growth 0.032** 0.032** -0.009 -0.009

(2.51) (2.52) (-1.27) (-1.28)

Long-term growth forecast 0.089 0.090 -0.178** -0.178**

(1.09) (1.10) (-2.55) (-2.55)

Idiosyncratic volatility 4.138*** 4.187*** -2.267*** -2.288***

(3.54) (3.57) (-3.06) (-3.09)

Institutional ownership 0.030 0.030 -0.093** -0.093**

(0.85) (0.85) (-2.57) (-2.57)

Analyst coverage 0.001 0.001 -0.108 -0.107

(0.01) (0.01) (-0.87) (-0.86)

Analyst dispersion 0.011** 0.011** 0.016* 0.016*

(2.10) (2.11) (1.75) (1.74)

Analyst experience -0.048*** -0.048*** 0.010 0.010

(-2.74) (-2.74) (1.34) (1.33)

Intercept -0.383*** -0.386*** 0.634*** 0.636***

(-5.51) (-5.54) (4.51) (4.52)

Firm fixed effect No No Yes Yes


Industry fixed effect Yes Yes No No

Observations 152,422 152,422 152,422 152,422

Pseudo R2 /Adjusted R2 0.018 0.018 0.056 0.056

The 52-week high stock price and analyst recommendation ... · The 52-week high stock price and...

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