Information Content of Analysts’ Stock Ratings and Earnings
Forecasts in the Presence of Management Earnings Forecasts*
Koji Ota†

Faculty of Commerce
Kansai University

ABSTRACTThis paper evaluates the informativeness of analysts’ ratings and earnings forecast
information contained in analyst reports beyond what is publicly provided by management
earnings forecasts. Using only analyst reports that have been released practicallysimultaneously with management forecasts, I find that both analysts’ ratings and earnings
forecasts have incremental information content conditional on management forecasts.
Further analysis also reveals that analysts’ earnings forecasts are significantly more accurate
than concurrently-announced management earnings forecasts. Overall, the findings in thispaper present strong evidence that analysts offer informational value to the market beyond
information that is provided by company management.
JEL Classification: G14; D82; G24; M41
Key Words: Analysts’ Roles in Financial Markets; Analysts’ Stock Ratings; Analysts’
Earnings Forecasts; Management Earnings Forecasts

1. Introduction
Analyst reports provided by equity analysts are commonly used in the major stock markets of
the world. Although the history of the so-called Western-style analyst reports in Japan is relatively
short, they have been available since 1993 when Nomura Research Institute began publishing them.
Numerous other stock brokerage firms soon followed suit. As of September 2007, 36 brokerage firms
own equity research divisions or research institutes in Japan, with over 800 equity analysts belonging* I appreciate financial supports from JSPS KAKENHI (Grant Number 23530391), the Kansai University Research Grants (Grantin-Aid for Encouragement of Scientists in 2011), and Ishii Memorial Securities Research Promotion Foundation (Grant-in-Aid
for Research 22-04).
† Corresponding Author. Address Kansai University, 3-3-35 Yamatecho, Suita, Osaka 5648680, JAPAN.
Telephone +81(6)6368-1348 E-mail koji_ota@nifty.ne.jp
Received April 26, 2012; accepted July 4, 2012
Copyright©2012 Research Institute for Economics & Business Administration – Kobe University.

88

The Japanese Accounting Review, 2 (2012), 87-116

to these firms (Analyst Directory 2007; Nikkei Research Inc.). Most global investment banks,
including Goldman Sachs, JP Morgan Chase, and Credit Suisse are among these firms.
Analyst reports are the final products of a process involving the collection, evaluation, anddissemination of information related to firms’ future prospects, and those published in Japanare comparable to those in other countries.1 However, there is one unique feature of the analyst
reports published in Japan that is not found in other countries: they include information on current
management earnings forecasts along with analysts’ earnings forecasts. This is because listed firms
in Japan are required to provide forecasts of next year’s earnings under the disclosure guidelines
prescribed by Japanese stock exchanges; therefore, management forecast information is publicly
available. This practice dates to 1974, when the Tokyo Stock Exchange sent a letter to listed firms
requesting the disclosure of forecasts of key accounting information. After some changes in thedisclosure guidelines, Japanese corporate managers are now required to provide annual forecasts ofnext year’s sales, operating profit, recurring profit, net income, and dividends at quarterly and annual
earnings announcements. In addition to periodic disclosure at earnings announcements, Japanesemanagers are also required to announce revised forecasts immediately when there is a significant
change in previously published forecasts (e.g., ±10% of a sales forecast, ±30% of an earnings forecast,any change in a dividends forecast). This unique feature of analyst reports published in Japan
motivated me to undertake this study.
Security analyst reports have been the subject of extensive empirical study over the past
three decades. Many of these studies have investigated the stock market’s reaction to the releaseof information contained in analyst reports using a short event window; these are often called
information content studies. However, most information content studies on analyst reports assume
that the reports provide new information to the market and are not concurrent with other public
releases. If this assumption does not hold, there is a possibility that analysts are merely publicizingor reiterating information that is already public. This possibility is not well considered in the current
literature (Asquith, Mikhail, and Au, 2005).
This paper investigates the informativeness of analysts’ ratings and earnings forecasts conditionalon the information contained in management earnings forecasts. I use 2,277 analyst reports
publicized for the two-year period from July 2005 through June 2007 about 399 firms. I first
replicate previous studies and find that both analysts’ ratings and earnings forecast revisions provide
independent information to the stock market. Then, I examine the timing of the publication ofanalyst reports relative to the announcement of management earnings forecasts. Investigating thetiming of the release of analyst reports helps understand the value that analysts bring to the market.
First, analysts’ value may lie in their ability to interpret publicly available information through theirexpertise. Second, analysts’ service may lie in their ability to collect and process a wide variety of
information through independent research (Park and Pincus, 2000; Ivković and Jegadeesh, 2004).
While analysts’ roles as collectors of new information are well documented in the literature, theirroles as interpreters of public information have not been thoroughly explored. Corporate insiders’views of future earnings are probably one of the most important sources of public information. Thus,
investigating the informativeness of information contained in analyst reports after controlling for the1

Foreign securities firms operating in Japan appear to have a standard format for the report, and therefore their analyst reports arevery similar to those used in other countries. On the other hand, analyst reports publicized by domestic securities firms are slightly
different from those issued by foreign securities firms. Domestic brokerage firms usually employ a five-category system for ratingstocks (strong buy, buy, hold, sell, and strong sell) and do not issue target prices, while foreign brokerage firms have a three-categorysystem for rating stocks (buy, hold, and sell) and publicize target prices.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

89

impact of management forecasts will help shed light on whether analysts possess valuable expertise tointerpret public information.
After examining the timing of the publication of analyst reports, I find that over half of theanalyst reports are issued on the day after the announcement of management earnings forecasts.
These analyst reports are released almost simultaneously with management forecasts because theannouncement of management forecasts usually takes place after the market closes, and analyst
reports are disseminated to clients before the market opens the next day. Thereafter, I investigate
the informativeness of analysts’ ratings and earnings forecasts on the subsample of concurrently
released analyst reports, and find that both analysts’ ratings and earnings forecasts have incremental
information content beyond information provided by management earnings forecasts. Controlling
for the impact of actual earnings surprises does not alter the results. These findings suggest thatthe stock market considers the information contained in analyst reports more credible than that inmanagement forecasts.
In order to gain insights into the market’s reliance on analysts’ forecasts, I examine the ex post
forecast accuracy of management earnings forecasts and concurrently released analysts’ earnings
forecasts. The results reveal that analysts’ forecasts are significantly more accurate than management
forecasts. This may offer a clue to why the market relies more on analysts’ outlooks than corporate
managers’ views of future earnings. Security analysts appear to possess expertise to process public
information and supply the market with new analysis and assessment on a company’s future
performance.
The remainder of this paper is organized in the following manner. The next section summarizes
prior research. Section 3 describes the data and the sample. Section 4 discusses the empirical results.
Section 5 provides the results of a battery of sensitivity tests. The last section presents the conclusion.

2. Prior Research
Early research on analyst reports investigates the information content of analysts’ stock ratings andearnings forecasts separately. For example, Elton, Gruber, and Grossman (1986) examine the market
reaction to analysts’ rating changes using monthly returns, and find that upgrades to strong buy earn anaverage abnormal return of 1.91%, while downgrades to strong sell produce an average abnormal returnof –0.38% in the month of rating changes. Womack (1996) examines not only stock price reactions but
also volume reactions to stock rating changes to and from the most extreme buy and sell categories.
He finds that upgrades to the buy rating and downgrades to the sell rating earn size-adjusted returnsof 3.0% and –4.7%, respectively, and generate 190% and 300% of normal volume, respectively, in thethree-day event period surrounding the release of rating changes. Other studies investigating changes inanalysts’ ratings also find positive associations between abnormal returns and the direction of the ratingchange (Barber, Lehavy, McNichols, and Trueman, 2001; Ivković and Jegadeesh, 2004).
The information content of analysts’ earnings forecasts has also been extensively investigated.
Abdel-khalik and Ajinkya (1982) examine the impact of 288 earnings forecast revisions by Merrill
Lynch analysts on stock prices for the 1977–78 period and find significant abnormal returns during
the publication week of the revisions. Studies using larger sample sizes also document evidence
consistent with analysts’ earnings forecast revisions having information content (Lys and Sohn, 1990;
Stickel, 1991).
More recent research has simultaneously examined the information content of analysts’ ratings,

90

The Japanese Accounting Review, 2 (2012), 87-116

earnings forecasts, and other elements in the reports. For example, Francis and Soffer (1997) examine
the relative informativeness of analysts’ ratings and earnings forecast revisions and find that analysts’
rating changes and earnings forecast revisions both affect stock prices. Stickel (1995) also investigates
the information content of analysts’ ratings and earnings forecast revisions using almost 17,000
observations and obtain results consistent with the Francis and Soffer study. He also finds that themagnitude of the recommendation revision, reputation of the analyst, size of the brokerage house,and size of the sample firm all influence stock prices. Brav and Lehavy (2003) extend Francis andSoffer’s work by incorporating target prices as a source of information. Their evidence indicatesthat target price revisions are informative and provide significant incremental information over andabove that contained in analysts’ stock ratings and earnings forecast revisions. Moreover, Asquith etal. (2005) consider the written content of a report in conjunction with the three summary measures:
analysts’ ratings, earnings forecasts, and target prices. After a thorough investigation of 1,126 analyst
reports, they find that the strength of analysts’ arguments provide significant information to themarket beyond the three summary variables.
Thus, the information content of analyst reports has been widely investigated. However, theseinformation content studies assume that the publication of analyst reports does not occur withother public releases. If this is not the case, there is a possibility that the observed market reaction
is triggered by the information in public releases and not by that in analyst reports. In order to deal
with this concurrent event problem, many studies rerun the analysis in the robustness tests on thesubsample of observations that occur independently (Francis and Soffer, 1997; Brav and Lehavy,
2003; Asquith et al., 2005). In this paper, instead of circumventing the problem of simultaneous
public announcements by using a subsample, I directly control for the impact of public releases andexamine the incremental informativeness of analyst reports.
My work also relates to a stream of research that investigates the value that analysts bringto the market. Ivković and Jegadeesh (2004) document that the information content of analysts’
rating changes and earnings forecast revisions is the weakest during the week after earnings
announcement and increases through the final week before the next earnings announcement. On thebasis of this finding, they conjecture that the value of analysts’ stock ratings and earnings forecasts
stems more from their independent collection of information than from their interpretation of
public information. However, they exclude from their analysis the analysts’ rating changes andearnings forecast revisions that occur around the earnings announcement date, which accounts forapproximately 25% of the entire sample, citing difficulty in disentangling the price reaction. Asquith
et al. (2005) also find that 53% of the 1,126 analyst reports examined are published around the datesof other public releases, particularly earnings announcements. They analyze these reports and obtainstatistically weaker but significant results supporting the informativeness of analyst reports. On thebasis of the results obtained, Asquith et al. surmise that analysts have a role in interpreting otherpublic releases. However, since they did not directly control for the impact of other simultaneous
announcements, they could not clearly determine whether the market reaction was triggered byinformation from analyst reports or from concurrent public releases. Thus, they suggest that futurestudies on analyst reports should address the question of whether these analyst reports with otherinformation releases contain new or merely interpretive information. In this study, I focus on
the analyst reports that are contemporaneous with one of the most important releases of public
information to the market—earnings guidance provided by management—in order to investigate
whether analysts add value to the market beyond what is publicly disseminated by corporate
managers.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

91

3. Sample and Data
In this study, I examined 2,405 analyst reports provided by one of the three leading brokeragefirms in Japan for the two-year period from July 2005 through June 2007. Other data necessary to
carry out the empirical tests in the following sections are collected from NEEDS-FinancialQUEST,
a commercial database provided by Nikkei Media Marketing Inc., and Kaisha Shikihou ( Japan
Company Handbook), a quarterly publication on listed firms in Japan issued by Toyo Keizai Inc.
The analyst reports cover 442 firms and are written by 52 sell-side analysts. Of the 2,405 analyst
reports, 127 are initiations of coverage and one is a stoppage of coverage, and these are omitted fromthe sample. This leads to a final sample of 2,277 analyst reports for 399 firms written by 51 sell-side
analysts. The number of reports per analyst varies from 1 to 167, with a mean (median) of 48.9 (26.0)
reports.2 The number of reports per sample firm also varies from 1 to 18, with a mean (median) of 5.7(6.0) reports.
In order to identify the characteristics of firms that analysts follow, the stock trading markets
on which the sample firms are listed are presented in Panel A of Table 1. The far-right column ofPanel A shows the breakdown of all listed firms as of June 2007 for referrence purposes. Of the 399
sample firms, 349 (87.5%) are listed on the first section of the Tokyo Stock Exchange (TSE), while
only 1,734 (44.0%) of all listed firms are traded on the first section of the TSE. The first section of
the TSE comprises blue-chip companies in Japan and is by far Japan’s largest stock trading market,
accounting for over 90% of the nation’s stock trading (93.3% of the trading volume and 92.5% of the
trading value in 2007). Thus, the high concentration of sample firms on the first section of the TSEclearly indicates the tendency for analysts to cover large firms.

Panel A: Stock Exchange Market

Table 1: Sample Firm Characteristics

Stock Exchanges in JapanTokyo Stock Exchange 1st section

Tokyo Stock Exchange 2nd section

Osaka Securities Exchange 1st section

Osaka Securities Exchange 2nd section
Nagoya Stock Exchange 1st section

Nagoya Stock Exchange 2nd section
Fukuoka Stock Exchange

Sapporo Securities Exchange

N

Sample firms

%

87.5%

1,734

44.0%

0

0.0%

35

0.9%

10

2.5%

3

0

0

Hercules of Osaka Securities Exchange

2

5

399

211

0.0%

105

0.0%

23

0.0%

0

483

0.8%

0.0%

0

30

Total

N

349

JASDAQ

Mothers of Tokyo Stock Exchange%

All listed firms

8

40

12.2%

5.4%

0.2%

2.7%

1.0%

0.6%

7.5%

955

24.2%

0.5%

158

4.0%

1.3%

100.0%

191

3,943

4.8%

100.0%(Continued )2

When analyst reports are written by multiple analysts, they are counted multiple times.

The Japanese Accounting Review, 2 (2012), 87-116

92(-Continued )Panel B: Industry
33 sectors designated by Securities Identification
Code Committee (SICC)
Fishery, Agriculture & Forestry
MiningN

Sample firms
02Construction

20

Textiles & Apparels

1

Foods

0

Pulp & Paper

4

Chemicals38

Oil & Coal Products

0

Pharmaceuticals

18

Rubber Products

4

Glass & Ceramic Products

11

Nonferrous Metals

8

Iron & Steel

Metal Products

78Land Transportation

9

3

0

0

0

219

0.0%

14

4.5%

1.0%

2.8%

1.8%

2.0%

52

21

73

57

43

7.0%

5.3%

252

107

53

4.3%

117

2.3%

66

1.3%

0.8%

0.0%

0.0%

25

18

6

44

393

0

0.0%

0

Other Financing Business

10

Services

35

Total

9.5%

12.3%

0

Real Estate

28

49

Banks

Insurance

1.0%

364

18

Securities & Commodity Futures

81

153

0.0%

Wholesale Trade
Retail Trade

0.3%

0.0%

2.0%

5

Information & Communication

216

8

17

Warehousing & Harbor Transportation Services

5.0%

313

Other Products

Air Transportation

7

14.3%

21

Marine Transportation

11

57

Transportation Equipment

Electric Power & Gas

0.5%

100

28

Precision Instruments

0.0%

N

2.0%

Machinery

Electric Appliances%

All listed firms

18

399

4.5%

0.0%

0.0%

2.5%

0.3%

0.2%

5.5%

3.9%

2.1%

0.7%

5.6%

1.3%

0.4%

0.5%

1.9%

1.4%

1.1%

2.5%

6.4%

7.9%

2.7%

1.3%

3.0%

0.6%

1.7%

0.5%

0.2%

1.1%

9.2%

393

10.0%

98

2.5%

40

11

58

4.5%

135

100.0%

3,943

8.8%

%

375

10.0%

1.0%

0.3%

1.5%

3.4%

9.5%

100.0%

Note: This table presents a summary of sample firm characteristics. 399 firms that are covered by analyst reports
in this study are referred to as sample firms in the table. The breakdown of all listed firms, 3,943 firms as ofJune 2007, by stock exchange market and industry is shown for reference purposes in the far-right column.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

93

Table 2: Matrix of Changes in Analysts’ Stock Ratings
Old Rating

Strong Buy

Buy

Hold

Strong Buy
198

c

15

a

2a

Sell

Strong Sell

Total
%

0a
0

215

9.4%

a

New Rating

Buy

Hold

21

b

753

c

Sell

14

b

82

b

Strong Sell

0

b

2

b

61a

1,002c

27b

0

0

0

1a
a

836

36.7%

22a
a

1,120

49.2%

77c
a

106

4.7%

0

b

0

b

0b
0b
0

Total

233
852

1,092
100
0

c

0

0

2,277

100%

Note: This table shows the matrix of changes in analysts’ stock ratings. There are 101 upgrades (a), 146
downgrades (b), and 1,980 reiterations (c).

The industry classification of the sample firms is also shown in Panel B along with the
breakdown of all listed firms. Sample firms in Pharmaceuticals, Electric Appliances, andTransportation Equipment have ratios almost twice as high as those of all listed firms. As an
export-oriented country, many household-name companies in Japan, such as Toyota, Honda, Sony,
Panasonic, and Takeda Pharmaceutical, belong to these industries. In addition, 131 of the 399 sample
firms are used to calculate Japan’s benchmark Nikkei 225 stock index. Taken together, the sample
firms that analysts follow tend to be large, well-known blue-chip companies in Japan.
The analyst reports in this study use a five-category system to rate stocks (strong buy, buy, hold,sell, and strong sell). Table 2 presents the matrix of 2,277 changes in analysts’ ratings. The bottom
row of the table shows that 9.4% of current analysts’ ratings are strong buy, 36.7% are buy, 49.2% are
hold, 4.7% are sell, and none of the analysts’ ratings is strong sell. This finding is consistent with manyother studies. Analysts appear to be reluctant to issue sell or strong sell ratings. Further, Table 2 also
shows that there are 101 upgrades, 146 downgrades, and 1,980 reiterations. It appears that analysts
are also hesitant to change analysts’ ratings. In the following tests, I combine hold and sell ratings
into one category and convert the five-category system into a three-category system (strong buy, buy,
and hold/sell).3
In addition to stock ratings, analysts also provide forecasts of key accounting figures for one
or more future years. The key accounting figures include sales, operating profit, recurring profit, net
income, EPS, CFPS, and dividends per share. In the following analysis, I use only sales, operating
profit, recurring profit, and net income.
Table 3 summarizes the frequency of sales and earnings forecast revisions as well as forecasthorizons. Forecast horizons vary from one-year ahead to three-years ahead. The one-year-ahead forecast
is defined as the earliest year for which actual earnings had not been announced by the sample firm. Of
the 2,277 analyst reports examined, 2,053 reports provide one-year-ahead forecasts, 1,977 reports provide
two-year-ahead forecasts, and only 596 reports supply three-year-ahead forecasts. I do not calculate sales
and earnings forecast revisions from two successive in time reports because occasionally analysts do notwrite reports on the same firm for months. Therefore, when previous sales and earnings forecasts are not
3This treatment has been commonly used in other studies as well. For example, Francis and Soffer (1997) treat strong buy as buy
and strong sell as sell, and Stickel (1995), using only changes in analysts’ ratings, also regards hold as sell.

The Japanese Accounting Review, 2 (2012), 87-116

94

Table 3: Analysts’ Sales and Earnings Forecasts
Forecasted
Item

Revisions
UpwardSales

Unchanged

Downward

Total

UpwardOperating
ProfitUnchanged

Downward

Total

UpwardRecurring
ProfitUnchanged

Downward

Total

UpwardNet Income

Unchanged

Downward

Total

One-year-ahead

Two-year-ahead

Three-year-ahead

995

937

249

N

622

436

%

48.5%

30.3%

21.2%

N

630

410

%

47.4%

596

100.0%

224

37.6%

1,977

100.0%

662

32.2%

638

32.3%

652

31.8%

727

612

36.8%

31.0%

2,053

100.0%

1,977

100.0%

627

30.5%

598

30.2%

793

633

2,053
830

587

636

2,053

38.6%

30.8%

100.0%
40.4%

28.6%

31.0%

100.0%

773

606

1,977
790

597

590

1,977

41.8%

250

100.0%
36.0%

%

31.9%

20.7%

2,053
739

N

39.1%

30.7%

100.0%
40.0%

30.2%

29.8%

100.0%

97

229

143

41.9%

16.3%

38.4%

24.0%

596

100.0%

217

36.4%

238

141

596

246

216

134

596

39.9%

23.7%

100.0%
41.3%

36.2%

22.5%

100.0%

Note: This table summarizes the frequency of sales and earnings forecast revisions and of the forecasthorizons. Of the 2,277 analyst reports reviewed, 2,053 reports provide one-year-ahead sales and earnings
forecasts, 1,977 reports provide two-year-ahead sales and earnings forecasts, and 596 reports provide threeyear-ahead sales and earnings forecasts.

reported in a single analyst report, the report is treated as missing sales and earnings forecasts.
With regard to the 2,053 one-year-ahead sales forecasts, 995 (48.5%) are upward revisions, 622(30.3%) are unchanged from previous forecasts, and 436 (21.2%) are downward revisions. In sales
forecasts, the number of upward revisions is more than twice the number of downward revisions.
The forecast revisions of three types of earnings forecasts, operating profit, recurring profit, and net
income, are more evenly distributed. For example, of the 2,053 one-year-ahead operating profit
forecasts, 739 (36.0%) are upward revisions, 662 (32.2%) are unchanged from previous forecasts, and652 (31.8%) are downward revisions. The distribution of two- and three-year-ahead forecast revisionsof earnings forecasts are similar to that of one-year-ahead forecast revisions. It appears that analysts
simultaneously change the three types of earnings forecasts in the same direction.
One noticeable finding that is evident from Table 3 is that there are fewer reiterations in sales
and earnings forecasts than stock ratings. Approximately 30% of sales and earnings forecasts areunchanged from previous forecasts, while over 80% of stock ratings are reiterations. This suggeststhat it takes more for analysts to change stock ratings than to revise sales and earnings forecasts.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts95

4. Empirical Results
4.1 Information Content of Analysts’ Stock Ratings
First, I investigate the share price reaction to the release of analysts’ stock rating information. The
market reaction to an analyst report published on day 0 is measured as the compounded abnormal
return over days 0 to +1, CAR(0,+1). This allows for a possible delay in the market’s reaction to therelease of new information contained in analyst reports.4 Daily abnormal returns are calculated using
the conventional market model (Campbell, Lo, and McKinlay, 1996; MacKinlay, 1997). The market
model parameters are generated from a 100-day estimation period from 130 trading days through 31
trading days before the release of the analyst report.
The market reactions to analysts’ rating changes, analysts’ rating levels, and both analysts’ ratingchanges and levels are investigated using the following regressions.
CAR(0,+1) = a 1UPGRADE + a 2REITERATE + a 3DNGRADE + e ,		 (1a)CAR(0,+1) = a 1STRBUY + a 2BUY + a 3HOLDESELL + e , and		 (1b)CAR(0,+1) = a 1UPSTRBUY + a 2UPBUY + a 3RESTRBUY + a 4REBUY +(1c)
a 5REHOLDSELL +a 6DNBUY + a 7DNHOLDSELL + e ,		
where UPGRADE is an indicator variable taking the value of one if analysts’ ratings are upgradedand zero otherwise, REITERATE is an indicator variable taking the value of one if analysts’ rating
are unchanged and zero otherwise, DNGRADE is an indicator variable taking the value of one if
analysts’ ratings are downgraded and zero otherwise, STRBUY is an indicator variable taking thevalue of one if analysts’ ratings are strong buy and zero otherwise, BUY is an indicator variable taking
the value of one if analysts’ ratings are buy and zero otherwise, HOLDSELL is an indicator variabletaking the value of one if analysts’ ratings are hold or sell and zero otherwise, UPSTRBUY is anindicator variable taking the value of one if analysts’ ratings are upgraded to strong buy and zerootherwise, UPBUY is an indicator variable taking the value of one if analysts’ ratings are upgradedto buy and zero otherwise, RESTRBUY is an indicator variable taking the value of one if analysts’
ratings are unchanged from strong buy and zero otherwise, REBUY is an indicator variable taking thevalue of one if analysts’ ratings are unchanged from buy and zero otherwise, REHOLDSELL is anindicator variable taking the value of one if analysts’ ratings are unchanged from hold or sell and zerootherwise, DNBUY is an indicator variable taking the value of one if analysts’ ratings are downgradedto buy and zero otherwise, and DNHOLDSELL is an indicator variable taking the value of one if
analysts’ ratings are downgraded to hold or sell and zero otherwise.
The estimated results of Eqs. (1a), (1b), and (1c) are presented in Panel A of Table 4. The resultsof Eq. (1a) indicate that the ordering of market reactions to stock rating changes is consistent withexpectations (UPGRADE > REITERATE > DOWNGRADE). The estimated coefficients show that
upgrades, reiterations, and downgrades are associated with average market responses of 4.54%, 0.36%,
and −4.18%, respectively, and are all statistically significant at the 1% level. The results of estimating
Eq. (1b) also exhibit the expected ordering of coefficients (STRBUY > BUY > HOLDSELL). Strong
4

The empirical results obtained in the following subsections are qualitatively similar when the abnormal return on day 0, AR(0), andthe compounded abnormal return over days –1 to +1, CAR(–1,+1), are used.

The Japanese Accounting Review, 2 (2012), 87-116

96

Table 4: Information Content of Analysts’ Stock Ratings
Panel A: Information Content of Rating Levels and Changes(1a)

UPGRADEREITERATEDNGRADESTRBUY

Coefficient
0.04540.0036−0.0418

BUY

t-statistic
11.40**(1b)

Coefficientt-statistic

0.0192

5.11**

UPSTRBUY−0.0040−2.92**

REBUY

5.51**

0.01544.04**

8.99**

0.0052REHOLDESELL

0.0002

DNBUY

N

0.06350.0435

RESTRBUY

adj.R2t-statistic

4.98**UPBUYDNHOLDSELL

Coefficient

3.71**−8.179**0.0075HOLDSELL(1c)−0.0206
0.10592,053−0.0457

0.02730.1106

2,053

3.40**
0.16−1.83−8.13**

2,053

Panel B: F tests for the Incremental Information Content of Rating Changes and Levels
Informativeness of rating changes conditional on rating levels: (1b) against (1c)H0 : UPSTRBUY = RESTRBUY, UPBUY = REBUY = DNBUY, and REHOLDSELL = DNHOLDSELL

F-statistic

48.97**

F-statistic

3.66**

Informativeness of rating levels conditional on rating changes: (1a) against (1c)H0 : UPSTRBUY = UPBUY, RESTRBUY = REBUY = REHOLDSELL, and DNBUY = DNHOLDSELL
Note: This table presents the results of estimating the following OLS regressions.
CAR(0,+1) = a 1UPGRADE + a 2REITERATE + a 3DNGRADE + e ,(1a)
CAR(0,+1) = a 1STRBUY + a 2BUY + a 3HOLDESELL + e , and(1b)
CAR(0,+1) = a 1UPSTRBUY + a 2UPBUY + a 3RESTRBUY + a 4REBUY + a 5REHOLDSELL+ a 6DNBUY + a 7DNHOLDSELL + e ,(1c)where CAR(0,+1) is the cumulative abnormal stock return (raw return minus the market model predicted return) overa two day window comprising the day of the analyst report release and the following trading day, UPGRADE is anindicator variable taking the value of one if analysts’ ratings are upgraded and zero otherwise, REITERATE is anindicator variable taking the value of one if analysts’ ratings are unchanged and zero otherwise, DNGRADE is anindicator variable taking the value of one if analysts’ ratings are downgraded and zero otherwise, STRBUY is an indicatorvariable taking the value of one if analysts’ ratings are strong buy and zero otherwise, BUY is an indicator variable taking
the value of one if analysts’ ratings are buy and zero otherwise, HOLDSELL is an indicator variable taking the value of
one if analysts’ ratings are hold or sell and zero otherwise, UPSTRBUY is an indicator variable taking the value of one
if analysts’ ratings are upgraded to strong buy and zero otherwise, UPBUY is an indicator variable taking the value of
one if analysts’ ratings are upgraded to buy and zero otherwise, RESTRBUY is an indicator variable taking the value of
one if analysts’ ratings are unchanged from strong buy and zero otherwise, REBUY is an indicator variable taking thevalue of one if analysts’ ratings are unchanged from buy and zero otherwise, REHOLDSELL is an indicator variabletaking the value of one if analysts’ ratings are unchanged from hold or sell and zero otherwise, DNBUY is an indicatorvariable taking the value of one if analysts’ ratings are downgraded to buy and zero otherwise, and DNHOLDSELL
is an indicator variable taking the value of one if analysts’ ratings are downgraded to hold or sell and zero otherwise.
t-statistics are calculated using White (1980) heteroskedasticity-consistent standard errors. ** and * represent two-tailed
significance at the 0.01 and 0.05 levels.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

97

buy and buy ratings are associated with average price increases of 1.92% and 0.75%, respectively,
while hold/sell ratings are associated with an average price decline of 0.40%. The estimatedcoefficients are also all statistically significant at the 1% level.
The results of Eqs. (1a) and (1b) indicate that analysts’ stock rating changes and stock ratinglevels are both individually informative to the market. Eq. (1c) investigates the effects of ratingchanges and rating levels on market prices conditional on one another. The results of Eq. (1c)display an interesting ordering of estimated coefficients (UPSTRBUY > UPBUY > RESTRBUY >REBUY > REHOLDSELL > DNBUY > DNHOLDSELL). This ordering suggests that the directionof rating changes (upgrade, reiteration, and downgrade) is a more decisive factor in explaining themarket reaction than the level of analysts’ ratings (strong buy, buy, and hold/sell). However, when the
direction of rating changes is the same, the rating level appears to determine the magnitude of themarket reaction. For example, when newly released analysts’ ratings are unchanged from the previous
ratings (RESTRBUY, REBUY, and REHOLDSELL), reiterations of strong buy, buy, and hold/sell
ratings are associated with average market responses of 1.54%, 0.52%, and 0.02%, respectively. Thisis true with rating upgrades as well as rating downgrades. Upgrades to strong buy trigger a higher
average price rise than upgrades to buy, 6.35% versus 4.35%, while downgrades to hold/sell evoke afurther average price decline than downgrades to buy, −4.57% versus −2.06%.
The F-tests reported in Panel B of Table 4 investigate whether the changes in and levels ofanalysts’ ratings explain stock returns conditional on one another by comparing the explanatory
power of Eq. (1c) with that of Eqs. (1b) and (1a). First, if the rating change is an important factor
in explaining share price reactions beyond the rating level, then the null hypothesis of UPSTRBUY= RESTRBUY, UPBUY = REBUY = DNBUY, and REHOLDSELL = DNHOLDSELL would be
rejected. The F-test comparing the explanatory power of Eq. (1b) with that of Eq. (1c) rejects thenull hypothesis at the 1% level (F-statistic = 48.97), which indicates the incremental informativenessof rating changes conditional on rating levels. Next, if the rating level adds explanatory power beyond
that conveyed by the rating change, then the null hypothesis of UPSTRBUY = UPBUY, RESTRBUY= REBUY = REHOLDSELL, and DNBUY = DNHOLDSELL would be rejected. The F-test
comparing the explanatory power of Eq. (1a) with that of Eq. (1c) rejects the null hypothesis at the
1% level (F-statistic = 3.66), thereby suggesting the incremental informativeness of rating levels
conditional on rating changes. Overall, the results of the joint tests in Panel B indicate that both
rating changes and rating levels have incremental information content over one another.
4.2 Information Content of Analysts’ Sales and Earnings Forecasts
Next, I investigate the share price reaction to the release of analysts’ sales and earnings forecast
information. The sales and earnings forecast revisions are defined as

Forecast Revison =Current analyst forecast − Previous analyst forecast
MVE at the beginning of the fiscal year,

where both current and previous analysts’ forecasts are one-year-ahead sales and earnings forecasts
reported in a single analyst report.
Using the forecast revisions defined above, the following regressions are estimated.
CAR(0,+1) = b 0 + b 1ΔSALES + e ,

(2a)

98

The Japanese Accounting Review, 2 (2012), 87-116

CAR(0,+1) = b 0 + b 1ΔOPEPROFIT + e ,(2b)

CAR(0,+1) = b 0 + b 1ΔRECURPROFIT + e ,(2c)

CAR(0,+1) = b 0 + b 1ΔNETINCOME + e , and(2d)CAR(0,+1) = b 0 + b 1ΔSALES + b 2ΔOPEPROFIT + b 3ΔRECURPROFIT +b 4ΔNETINCOME + e ,(2e)where ΔSALES is a revision of analysts’ one-year-ahead sales forecasts deflated by the MVE at the
beginning of the fiscal year, ΔOPEPROFIT is a revision of analysts’ one-year-ahead operating profit
forecasts deflated by the MVE at the beginning of the fiscal year, ΔRECURPROFIT is a revision ofanalysts’ one-year-ahead recurring profit forecasts deflated by the MVE at the beginning of the fiscal
year, and ΔNETINCOME is a revision of analysts’ one-year-ahead net income forecasts deflated by
the MVE at the beginning of the fiscal year.
The results of estimating Eqs. (2a) through (2e) are presented in Table 5. The estimatedcoefficients of Eqs. (2a) through (2d) are all statistically significantly positive at the 5% level orhigher, which suggests that sales and the three types of earnings forecasts are all informative to thestock market on a stand-alone basis. However, in terms of the explanatory power of these regressions,
ΔRECURPROFIT has the highest adj.R2 value, 0.0807, followed by 0.0760 for ΔOPEPROFIT,
0.0282 for ΔNETINCOME, and 0.0058 for ΔSALES. It appears that analysts’ recurring profit
forecasts are more informative to the market than sales and other earnings forecasts. When all four
variables (ΔSALES, ΔOPEPROFIT, ΔRECURPROFIT, and ΔNETINCOME) are included in the
regression model (2e), only the estimated coefficient of ΔRECURPROFIT remains statisticallysignificant, 0.7330 (t-statistic = 2.99), while those of other variables become insignificant.
The results of both univariate and multivariate tests indicate that among analysts’ sales and thethree types of earnings forecasts, recurring profit forecasts provide the most valuable information to
the market. On the basis of this finding, I use analysts’ recurring profit forecasts as analysts’ earnings
forecasts in the following analysis.
4.3 Information Content of Analysts’ Stock Ratings and Earnings Forecasts
Thus far, the analysis has investigated the information content of analysts’ stock ratings andearnings forecast revisions separately. In this section, I examine the market reaction to analysts’
rating changes, rating levels, and earnings forecast revisions, each of which is conditional on theothers. I augment Eq. (1c) by adding revisions of analysts’ one-year-ahead recurring profit forecasts(ΔRECURPROFIT), which I use here as a proxy for analysts’ earnings forecast revisions (ΔAEF ).
CAR(0,+1) = g 1UPSTRBUY + g 2UPBUY + g 3RESTRBUY + g 4REBUY + g 5REHOLDSELL +g 6DNBUY + g 7DNHOLDSELL + g 8ΔAEF + e ,(3)where UPSTRBUY is an indicator variable taking the value of one if analysts’ ratings are upgradedto strong buy and zero otherwise, UPBUY is an indicator variable taking the value of one if analysts’
ratings are upgraded to buy and zero otherwise, RESTRBUY is an indicator variable taking the valueof one if analysts’ ratings are unchanged from strong buy and zero otherwise, REBUY is an indicatorvariable taking the value of one if analysts’ ratings are unchanged from buy and zero otherwise,Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

Table 5: Information Content of Analysts’ Sales and Earnings Forecasts(2a)Intercept
ΔSALES

0.0019(1.72)

0.0614(2.22)*ΔOPEPROFIT(2b)

0.0038(4.01)**

0.9104(3.98)**ΔRECURPROFIT(2c)

0.0037(3.86)**

N

0.00582,053

0.07602,053

0.08072,053(2e)0.0037(3.54)**
0.0050(0.22)
0.2336(0.79)

0.9042(4.27)**ΔNETINCOME
adj.R2(2d)0.0031(3.13)**

99

0.7330(2.99)**
0.5090(2.64)**−0.0671(−0.63)

2,053

2,053

0.02820.0804

Note: This table presents the results of estimating the following OLS regressions.
CAR(0,+1) = b 0 + b 1ΔSALES + e ,(2a)CAR(0,+1) = b 0 + b 1ΔOPEPROFIT + e ,(2b)
CAR(0,+1) = b 0 + b 1ΔRECURPROFIT + e ,(2c)
CAR(0,+1) = b 0 + b 1ΔNETINCOME + e , and(2d)CAR(0,+1) = b 0 + b 1ΔSALES + b 2ΔOPEPROFIT + b 3ΔRECURPROFIT + b 4ΔNETINCOME + e ,(2e)where CAR(0,+1) is the cumulative abnormal stock return (raw return minus the market model predicted
return) over a two day window comprising the day of the analyst report release and the following trading
day, ΔSALES is a revision of analysts’ one-year-ahead sales forecasts deflated by the MVE at the beginningof the fiscal year, ΔOPEPROFIT is a revision of analysts’ one-year-ahead operating profit forecasts deflatedby the MVE at the beginning of the fiscal year, ΔRECURPROFIT is a revision of analysts’ one-year-ahead
recurring profit forecasts deflated by the MVE at the beginning of the fiscal year, and ΔNETINCOME isa revision of analysts’ one-year-ahead net income forecasts deflated by the MVE at the beginning of the
fiscal year. t-statistics are provided in parentheses below the coefficient estimates and are calculated using
White (1980) heteroskedasticity-consistent standard errors. ** and * represent two-tailed significance at the0.01 and 0.05 levels.

REHOLDSELL is an indicator variable taking the value of one if analysts’ ratings are unchanged
from hold or sell and zero otherwise, DNBUY is an indicator variable taking the value of one if
analysts’ ratings are downgraded to buy and zero otherwise, DNHOLDSELL is an indicator variabletaking the value of one if analysts’ ratings are downgraded to hold or sell and zero otherwise, andΔAEF is a revision of analysts’ one-year-ahead recurring profit forecasts deflated by the MVE at the
beginning of the fiscal year.
The estimated results of Eq. (3) are presented in the third column of Panel A of Table 6. The
first and second columns of Panel A show the estimated results of Eqs. (1c) and (2c) for reference
purposes. The results of Eq. (3) do not materially differ from the combined results of Eqs. (1c)and (2c). The ordering of the estimated coefficients for analysts’ rating variables remains the same,
UPSTRBUY > UPBUY > RESTRBUY > REBUY > REHOLDSELL > DNBUY > DNHOLDSELL,
which indicates the primary and secondary roles that rating changes and rating levels play inexplaining stock price reactions. The estimated coefficient of ΔAEF is also statistically significantly
positive, 0.6762 (t-statistic = 3.66), thereby suggesting the informativeness of analysts’ earnings
forecast revisions after controlling for the information in analysts’ ratings.

The Japanese Accounting Review, 2 (2012), 87-116

100

Table 6: Information Content of Analysts’ Stock Ratings and Earnings Forecasts
Panel A: Information Content of Rating Changes, Rating Levels, and Analysts’ Earnings Forecasts(1c)

UPSTRBUYUPBUYRESTRBUYREBUYREHOLDESELL
DNBUY

DNHOLDSELL
ΔAEF
adj.R

2

N

Coefficientt-statistic

0.0435

8.99**

0.06350.01540.00520.0002−0.0206−0.0457
0.11062,053

5.51**(2c)

Coefficientt-statistic

4.04**

Coefficientt-statistic

0.04088.49**

0.0601

5.13**

0.0146

3.40**

3.88**

0.00480.16

0.0013−1.83−8.13**(3)0.90420.0807

4.27**

2,053

3.22**
1.00−0.0153−1.33

0.6762

3.66**−0.0344−6.55**

0.15192,053

Panel B: F tests for the Incremental Information Content of Rating Changes and Levels
Informativeness of rating changes conditional on rating levels and ΔAEF

H0 : UPSTRBUY = RESTRBUY, UPBUY = REBUY = DNBUY, and REHOLDSELL = DNHOLDSELL

F-statistic

33.82**

F-statistic

2.44*

Informativeness of rating levels conditional on rating changes and ΔAEF
H0 : UPSTRBUY = UPBUY, RESTRBUY = REBUY = REHOLDSELL, and DNBUY = DNHOLDSELL
Note: This table presents the results of estimating the following OLS regressions.
CAR(0,+1) = a 1UPSTRBUY + a 2UPBUY + a 3RESTRBUY + a 4REBUY + a 5REHOLDSELL +a 6DNBUY + a 7DNHOLDSELL + e ,(1c)
CAR(0,+1) = b 0 + b 1ΔAEF + e , and(2c)
CAR(0,+1) = g 1UPSTRBUY + g 2UPBUY + g 3RESTRBUY + g 4REBUY + g 5REHOLDSELL + g 6DNBUY +g 7DNHOLDSELL + g 8ΔAEF + e ,(3)where CAR(0,+1) is the cumulative abnormal stock return (raw return minus the market model predicted
return) over a two day window comprising the day of the analyst report release and the following trading
day, UPSTRBUY is an indicator variable taking the value of one if analysts’ ratings are upgraded tostrong buy and zero otherwise, UPBUY is an indicator variable taking the value of one if analysts’ ratings
are upgraded to buy and zero otherwise, RESTRBUY is an indicator variable taking the value of one if
analysts’ ratings are unchanged from strong buy and zero otherwise, REBUY is an indicator variable taking
the value of one if analysts’ ratings are unchanged from buy and zero otherwise, REHOLDSELL is anindicator variable taking the value of one if analysts’ ratings are unchanged from hold or sell and zerootherwise, DNBUY is an indicator variable taking the value of one if analysts’ ratings are downgraded tobuy and zero otherwise, DNHOLDSELL is an indicator variable taking the value of one if analysts’ ratings
are downgraded to hold or sell and zero otherwise, and ΔAEF is a revision of analysts’ one-year-ahead
recurring profit forecasts deflated by the MVE at the beginning of the fiscal year. t-statistics are calculatedusing White (1980) heteroskedasticity-consistent standard errors. ** and * represent two-tailed significanceat the 0.01 and 0.05 levels.

In order to further examine the incremental information content of rating changes and ratinglevels, two F-tests are conducted and the results are reported in Panel B of Table 6. The first F-test
investigates the incremental informativeness of rating changes conditional on rating levels andanalysts’ earnings forecast revisions, and the second F-test examines the incremental informativenessof rating levels conditional on rating changes and analysts’ earnings forecast revisions. The twoOta: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

101

F-statistics in Panel B, 33.82 and 2.44, both reject the null hypotheses of no incremental information
content of rating changes and rating levels at the 5% level or higher. Overall, these results indicatethat neither analysts’ stock ratings nor analysts’ earnings forecast revisions subsume the informationof the other.
4.4 Timing and Frequency of Analyst Report Publications
Most analyst reports published in Japan include information on management sales and earnings
forecasts along with analysts’ forecasts. In fact, of the 2,053 analyst reports examined, 2,037 (99.2%)
contained the figures of the most recently publicized management forecasts. This is because Japanesefirms are required by the stock exchange disclosure guidelines to provide one-year-ahead sales andearnings forecasts at quarterly and annual earnings announcements. In addition, when a significant
change in previously published forecasts arises (±10% of sales forecasts, ±30% of earnings forecasts),
corporate managers in Japan are required to announce revised forecasts immediately. A survey
conducted in 2006 reveales that 3,790 of the 3,831 listed firms (98.9%) provided management
forecasts in that year. Thus, management forecasts are publicly available in Japan and existing evidence
documents that they provide valuable information to financial analysts and investors (Conroy, Harris,and Park, 1998; Conroy, Eades, and Harris, 2000; Ota, 2010).
This section examines the timing and frequency of analyst report publications relative tothe announcement dates of management earnings forecasts. I compute the number of trading
days between the analyst report publication date and the previously and subsequently announced
management forecast date. Figure 1(A) presents the frequency of analyst reports at various points intime relative to the following announcement of management forecasts. It is clear from the figure thatfew reports are published before the releases of management forecasts. In fact, only 43 of the 2,037
analyst reports are published within 10 trading days (two weeks) before the release of management
forecasts. The figure also reveals that the issuance of analyst reports is concentrated in the interval
between 50 and 69 trading days before the release of the following management forecasts. The
average (median) number of trading days between the analyst report publication date and thesubsequent management forecast announcement date is 55.9 (58.0).
Figure 1(B) presents the frequency of analyst reports at various points in time relative tothe most recently announced management forecasts. It is evident from the figure that over halfof the analyst reports (1,062 of 2,037 or 52.1% of all reports) are published on the day after the
management forecast announcement. A high frequency of analyst report publications is also observed
over the next several days. The average (median) number of trading days between the analyst reportpublication date and the most recent management forecast announcement date is 10.7 (1.0). The
finding that over half of the analyst report publications are driven by the public announcement ofmanagement forecasts is not exactly unexpected, as a majority of management forecasts are released
at quarterly and annual earnings announcements and earlier studies document that analysts’ ratingsand earnings forecast revisions are concentrated on the earnings announcement day and following
several days (Stickel, 1989; Bowen, Davis, and Matsumoto, 2002; Ivković and Jegadeesh, 2004). Thisis also consistent with the finding shown in Figure 1(A) that a large fraction of analyst reports are
published 50 to 69 trading days (10 to 14 weeks) before the following announcement of management
forecasts because the length of the period corresponds approximately to the length of a fiscal quarter.
One aspect of the release of management forecasts and analyst reports that requires special
attention is the time of day when they are published. Stock exchanges in Japan require listedcompanies to make timely disclosures of material corporate information such as financial results andThe Japanese Accounting Review, 2 (2012), 87-116

102

Figure 1(A): Frequency of Analyst Reports Prior to The Issuance of Management Forecasts

603

700

626

600

Number of analyst reports

500
400
300
202
118

178
97

75

71

100
4

1

8

2

6

3

4

5

-7

-6

-5

-4

-3

-2

-1

-10-19-20-29-30-39-40-49-50-59-60-69-70-79-80-89-90-99Under -100

6

-8

11

-9

17

200

0

Trading days until the following management forecast announcement
Note: This figure presents the frequency of analyst reports at various points in time relative to the following management
forecast announcement date. The total number of analyst reports with management forecast information is 2,037.

Figure 1(B): Frequency of Analyst Reports Subsequent to The Issuance of Management
Forecasts
1,200

1,062

Number of analyst reports

1,000
800
600
400
185

7

8

9

10

42

33

18

24

21

Over 50

6

68

46-50

5

62

41-45

18

36-40

24

31-35

25

26-30

27

21-25

29

16-20

31

165

82

11-15

56

4

2

1

0

65

3

200

Trading days after the previous management forecast announcement
Note: This figure presents the frequency of analyst reports at various points in time relative to the most recent
management forecast announcement date. The total number of analyst reports with management forecast information is2,037.

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

103

forecasts through the Timely Disclosure Network (TDnet). Once the information is disclosed on
TDnet for public inspection, trading in shares based on the information is exempt from the insider
trading regulation. The Company Announcement Disclosure Service, which is a part of TDnet, keeps
records of such disclosed information, and the database shows that the vast majority of company
announcements are made public after the market closes at 3 p.m. and particularly on Fridays. Thisis presumably done to give the market sufficient time to digest newly released information and to
avoid sudden fluctuations in stock prices. As a result, there is a one-day discrepancy between the
management forecast announcement date and the date when the market actually incorporates theinformation. On the other hand, analyst reports are usually prepared and disseminated to clients inthe early morning before the market opens at 9 a.m. Thus, the 1,062 analyst reports that are publishedthe day after the announcement of management forecasts are released practically simultaneously withmanagement forecasts.
4.5 Information Content of Analysts’ Forecasts in the Presence of Concurrently Announced Management
Forecasts
In the following sections, I use the subsample of analyst reports published the day after theannouncement of management forecasts, which are the 1,062 analyst reports in Figure 1(B). The
simultaneously announced subsample provides an ideal opportunity, with less concern for otherevents taking place, to investigate the incremental information content of analysts’ ratings andearnings forecasts conditional on the publicly available management forecast information. There arethree cases where concurrent announcements occur, and different treatments must be applied in eachcase.
Case 1: Simultaneous releases of analyst reports and management forecasts at quarterly earnings
announcements.
This is probably the most contaminated case of the three. Since only annual earnings forecasts
are provided by both analysts and management, the surprise element in actual quarterly earnings
cannot be controlled for. However, it can be argued that if there is a surprise in actual quarterly
earnings, it may be reflected in the company’s contemporaneously released annual earnings forecasts.
Figure 2(A) illustrates the decomposition of surprise elements at quarterly earnings announcements.
I divide the revision of analysts’ earnings forecasts, ΔAEF, into two components, ΔMEF_PAEF
and ΔAEF_MEF. The former is the difference between current management earnings forecasts(MEF) and previous analysts’ earnings forecasts (PAEF), and the latter is the difference between
current analysts’ earnings forecasts (AEF) and current management forecasts (MEF). As with ΔAEF,
both variables are deflated by the MVE at the beginning of the fiscal year. The former component
represents the surprise element brought by the release of new management earnings forecasts,
while the latter captures the surprise element conveyed by the publication of new analysts’ earnings
forecasts. If analysts’ earnings forecasts have informational value beyond concurrently released
management earnings forecasts, ΔAEF_MEF will add some explanatory power in explaining shareprice reactions. I use the following regression model to investigate the impact.
CAR(0,+1) = d 1UPSTRBUY + d 2UPBUY + d 3RESTRBUY + d 4REBUY + d 5REHOLDSELL +d 6DNBUY + d 7DNHOLDSELL + d 8ΔMEF_PAEF + d 9ΔAEF_MEF + e ,(4)The Japanese Accounting Review, 2 (2012), 87-116

104

Figure 2(A): Decomposition of Surprise Elements in Cases 1 and 3
Day K

Day K+1

3 p.m.

9 a.m.

MarketClosing

MarketOpening

MEF
Announced

PAEF
Published∆ MEF_PAEF

AEF
Published∆ AEF_MEF

Note: This figure illustrates the decomposition of surprise elements at quarterly earnings announcements (Case 1) andat announcements of revisions of management forecasts (Case 3). ΔMEF_PAEF = MEF − PAEF and ΔAEF_MEF =AEF – MEF, where MEF is management recurring profit forecasts, AEF is analysts’ recurring profit forecasts, and PAEF
is analysts’ previous recurring profit forecasts. All forecasts are one-year-ahead annual forecasts and are deflated by the
MVE at the beginning of the fiscal year.

Figure 2(B): Decomposition of Surprise Elements in Case 2
Day K

Day K+1

3 p.m.

9 a.m.

MarketClosing

MarketOpening

Et & MEFt+1
Announced

PAEFt & PAEF t+1
Published∆ MEF_PAEF t+1

AEFt+1
Published∆ AEF_MEF t+1∆ EARNSURP t

Note: This figure illustrates the decomposition of surprise elements at annual earnings announcements (Case 2). ΔMEF_
PAEFt+1 = MEFt+1 − PAEFt+1, ΔAEF_MEFt+1 = AEFt+1 – MEFt+1, and ΔEARNSURPt = Et – PAEFt , where MEFt+1 ismanagement recurring profit forecasts for year t + 1, PAEFt+1 is analysts’ previous recurring profit forecasts for year t + 1,
AEFt+1 is analysts’ recurring profit forecasts for year t + 1, Et is actual recurring profit for year t , and PAEFt is analysts’
previous recurring profit forecasts for year t. All forecasts are one-year-ahead annual forecasts except for PAEFt+1,
which are two-year-ahead annual forecasts at the time of the publication. All variables are deflated by the MVE at the
beginning of the fiscal year.

where UPSTRBUY is an indicator variable taking the value of one if analysts’ ratings are upgradedto strong buy and zero otherwise, UPBUY is an indicator variable taking the value of one if analysts’
ratings are upgraded to buy and zero otherwise, RESTRBUY is an indicator variable taking the valueOta: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

105

of one if analysts’ ratings are unchanged from strong buy and zero otherwise, REBUY is an indicatorvariable taking the value of one if analysts’ ratings are unchanged from buy and zero otherwise,
REHOLDSELL is an indicator variable taking the value of one if analysts’ ratings are unchanged
from hold or sell and zero otherwise, DNBUY is an indicator variable taking the value of one if
analysts’ ratings are downgraded to buy and zero otherwise, DNHOLDSELL is an indicator variabletaking the value of one if analysts’ ratings are downgraded to hold or sell and zero otherwise, ΔMEF_
PAEF is management recurring profit forecasts minus analysts’ previous recurring profit forecasts(MEF − PAEF ) deflated by the MVE at the beginning of the fiscal year, and ΔAEF_MEF is analysts’recurring profit forecasts minus management recurring profit forecasts (AEF – MEF ) deflated by the
MVE at the beginning of the fiscal year. All earnings forecasts are one-year-ahead annual forecasts.
Case 2: Simultaneous releases of analyst reports and management forecasts at annual earnings
announcements.
This is the most complicated case of the three. In addition to the two types of earnings
forecasts for the upcoming year, MEF and AEF, actual earnings for the recently completed year
are also announced at the same time. Figure 2(B) depicts the surprise elements at annual earnings
announcements. As with Case 1, I use ΔMEF_PAEF and ΔAEF_MEF to capture the surprisecomponents brought by two current earnings forecasts. For the surprise element conveyed by actual
earnings, I define ΔEARNSURP as the difference between actual earnings and previous analysts’
earnings forecasts for the recently completed year. The following regression is used to assess these
impacts.
CAR(0,+1) = d 1UPSTRBUY + d 2UPBUY + d 3RESTRBUY + d 4REBUY + d 5REHOLDSELL +d 6DNBUY + d 7DNHOLDSELL + d 8ΔMEF_PAEF + d 9ΔAEF_MEF +d 10ΔEARNSURP + e ,(5)where ΔEARNSRUP is actual recurring profit for the recently completed year minus previous
analysts’ recurring profit forecasts for the recently completed year deflated by the MVE at the
beginning of the recently completed year. The definitions of other variables are the same as those in
Eq. (4).
Case 3: Simultaneous releases of analyst reports and management forecasts at announcements of revisionsof management earnings forecasts.
This is the ideal case of the three for inspecting the respective informativeness of analysts’ andmanagement earnings forecasts. Companies’ managers are required by the disclosure guidelines
prescribed by the stock exchanges to announce revised forecasts immediately when there is asignificant change in previously published forecasts (±10% of sales forecasts and ±30% of earnings
forecasts). Thus, the forecast revisions occur without prior notice and usually without otherconcurrent announcements. The regression model employed in Case 3 is the same as Case 1, which is
Eq. (4). Only the sample observations used for estimation differ between Case 3 and Case 1.
The results of the analysis using the subsample of 1,062 analyst reports issued simultaneouslywith management forecasts are presented in Table 7. The first and second columns in Panel A show
the results of estimating Eqs. (3) and (4) using all observations in the subsample. Eq. (3) ignores

106

The Japanese Accounting Review, 2 (2012), 87-116

the management earnings forecast information contained in analyst reports and uses revisions ofanalysts’ earnings forecasts, ΔAEF, while Eq. (4) includes the management forecast information andsplits ΔAEF into two components, ΔMEF_PAEF and ΔAEF_MEF. The estimation results of Eq. (3)are similar to those reported in Table 6. The estimated coefficient of ΔAEF is significantly positive,
1.3159, indicating the informativeness of analysts’ earnings forecasts. The results of Eq. (4) show thatthe estimated coefficients of ΔMEF_PAEF and ΔAEF_MEF are 1.3464 and 1.1256, respectively, andare both significant at the 1% level. These findings indicate that neither companies’ earnings forecasts
nor analysts’ earnings forecasts subsume the information of the other.
Panel B of Table 7 reports the results of two F-tests that investigate the incremental information
content of rating changes and rating levels using Eq. (4) with all observations in the subsample. The firstF-test examines the incremental information content of rating changes conditional on rating levels andmanagement and analysts’ earnings forecasts. The F-statistic obtained is 13.17, which is significant at the
1% level. This indicates the incremental informativeness of rating changes beyond information containedin rating levels and two types of earnings forecasts. The second F-test investigates the incremental
information content of rating levels conditional on rating changes and two types of earnings forecasts. It
rejects the null hypothesis of no information content of rating levels at the 10% level (F-statistic = 2.28).
These somewhat weaker results of the two F-tests, compared with those reported in Table 6, may be
attributed to the reduction in the sample size. The ordering of the estimated coefficients for analysts’ stock
rating variables remains the same, UPSTRBUY > UPBUY > RESTRBUY > REBUY > REHOLDSELL> DNBUY > DNHOLDSELL, thereby suggesting the primary and secondary roles of rating changes andrating levels in explaining stock price reactions.
The third, fourth, and fifth columns of Panel A of Table 7 present the estimated results forCases 1, 2, and 3, respectively. Of the 1,062 analyst reports that are issued contemporaneouslywith management forecasts, 694 are announced at quarterly earnings announcements (Case1), 217 at annual earnings announcements (Case 2), and 151 at announcements of revisions ofmanagement forecasts (Case 3). The estimation results of Eq. (4) in Case 1 are similar to those withall observations in the second column. The estimated coefficients of ΔMEF_PAEF and ΔAEF_MEF
are both significantly positive, 0.9671 and 1.0593, which indicates the incremental informativenessof analysts’ earnings forecasts conditional on management earnings forecasts, and vice versa. One
notable finding in Case 1 is that adj.R2 is 0.0977, which is much lower than the other two cases.
The low explanatory power in Case 1 may be attributed to the lack of control for the impact of theannouncement of actual quarterly earnings.
The estimation results of Eq. (5) in Case 2 reveal that while the estimated coefficients of
ΔMEF_PAEF and ΔAEF_MEF are both significantly positive, 1.4973 and 0.9039, the coefficientof ΔEARNSURP is positive, 0.1040, but not statically significant. These findings suggest that theannouncement of actual annual earnings does not convey valuable information to the market beyond
information contained in corporate managers’ next-year earnings forecasts and analysts’ forecasts.
This is not unpredicted considering the results reported in prior research. For example, Conroy etal. (1998) and Conroy et al. (2000) document that the market reaction to the surprise componentof management earnings forecasts is much more pronounced than to the unexpected component of
actual earnings at annual earnings announcements. Ota (2010) also finds that the value relevance ofactual earnings almost disappears once the impact of management earnings forecasts is accountedfor. It appears that the market is more concerned with analysts’ and corporate insiders’ views of future
earnings than the mere publication of historical numbers.
There are only 151 observations for estimating Eq. (4) in Case 3, and they contain neither

13.17**

2.28†

Note: This table presents the results of estimating the following OLS regressions using only the subsample of analyst reports that are publicized simultaneously with management
earnings forecasts (1,062 reports).
(3)
CAR(0,+1) = g 1UPSTRBUY + g 2UPBUY + g 3RESTRBUY + g 4REBUY + g 5REHOLDSELL + g 6DNBUY + g 7DNHOLDSELL + g 8ΔAEF + e ,CAR(0,+1) = δ 1UPSTRBUY + δ 2UPBUY + δ 3RESTRBUY + δ 4REBUY + δ 5REHOLDSELL + δ 6DNBUY + δ 7DNHOLDSELL + δ 8ΔMEF_PAEF + δ 9ΔAEF_MEF + e , and(4)
CAR(0,+1) = δ 1UPSTRBUY + δ 2UPBUY + δ 3RESTRBUY + δ 4REBUY + δ 5REHOLDSELL + δ 6DNBUY + δ 7DNHOLDSELL + δ 8ΔMEF_PAEF + δ 9ΔAEF_MEF + δ 10ΔEARNSURP + ε , (5)where ΔMEF_PAEF is management recurring profit forecasts (MEF) minus analysts’ previous recurring profit forecasts (PAEF) deflated by the MVE at the beginning of the fiscal
year, ΔAEF_MEF is analysts’ current recurring profit forecasts (AEF) minus management recurring profit forecasts (MEF) deflated by the MVE at the beginning of the fiscal year, andΔEARNSRUP is actual recurring profit for the recently completed year minus previous analysts’ recurring profit forecasts for the recently completed year deflated by the MVE at the
beginning of the recently completed year. Other variables are defined in Table 6. t-statistics are calculated using White (1980) heteroskedasticity-consistent standard errors. **, *, and †represent two-tailed significance at the 0.01, 0.05, and 0.10 levels.

F-statistic

H0 : UPSTRBUY = UPBUY, RESTRBUY = REBUY = REHOLDSELL, and DNBUY = DNHOLDSELL

Informativeness of rating levels conditional on rating changes, analysts’ earnings forecasts, and management earnings forecasts

F-statistic

H0 : UPSTRBUY = RESTRBUY, UPBUY = REBUY = DNBUY, and REHOLDSELL = DNHOLDSELL

Informativeness of rating changes conditional on rating levels, analysts’ earnings forecasts, and management earnings forecasts

Panel B: F tests for the Incremental Information Content of Rating Changes and Levels (All Observations)

Panel A: Information Content of Rating Changes, Rating Levels, Analysts’ Earnings Forecasts, and Management Earnings Forecasts
Ignoring management
Including management
Case 1:Case 2:
Case 3:earnings forecasts
earnings forecasts
At quarterly earnings
At annual earnings
At non-periodic forecast(All observations) (3)(All observations) (4)announcements (4)announcements (5)revisions (4)Coefficient t-statistic
Coefficient t-statistic
Coefficient t-statistic
Coefficient t-statistic Coefficient t-statistic
UPSTRBUY
0.0593
2.82**
0.06102.85**
0.05002.23*
0.091136.58**
n/a
n/a
UPBUY
0.03004.05**
0.03094.20**
0.03082.26*
0.02902.91**
0.03413.01**
RESTRBUY
0.01482.37*
0.0161
2.57**
0.02132.70**
0.00450.22*−0.0080−0.73
REBUY
0.00492.18*
0.00592.50*
0.00170.60
0.0166
2.99**
0.0109
1.78†
REHOLDESELL
0.00201.08
0.00221.20
0.00140.65
0.00491.05
0.00500.89
DNBUY−0.0167−1.12−0.0156−1.04−0.0117−0.75−0.0598−8.83**n/a
n/a
DNHOLDSELL−0.0465−4.36**−0.0451−4.23**−0.0481−3.37**−0.0109−0.94−0.0704−2.48*
ΔAEF
1.31596.65**
ΔMEF_PAEF
1.3464
6.58**
0.9671
3.50**
1.4973
5.41**
1.4538
3.73**
ΔAEF_MEF
1.1256
5.31**
1.0593
3.44**
0.90391.99*
1.1504
3.26**ΔEARNSURP
0.10400.25
0.20920.21030.09770.23430.4419adj.R2
N
1,062
1,062
694
217
151

Table 7: Information Content of Analysts’ Forecasts in The Presence of Concurrently Announced Management Earnings
Forecasts

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts
107

108

The Japanese Accounting Review, 2 (2012), 87-116

upgrade to strong buy nor downgrade to hold/sell. Thus, the two indicator variables in Eq. (4),
UPSTRBUY and DNHOLDSELL, cannot be estimated. As with Cases 1 and 2, the estimatedcoefficients of ΔMEF_PAEF and ΔAEF_MEF are both significantly positive, 1.4538 and 1.1504,
thereby indicating the incremental information content of management and analysts’ earnings
forecasts conditional on one another. Case 3 also exhibits the highest adj.R2 value, 0.4419. This may
be attributable to the fact that Case 3 provides the most ideal setting for testing the information
content of analysts’ and management forecasts because no other confounding events take place at
non-periodic revisions of management forecasts.
Overall, the results reported in Table 7 indicate that neither companies’ earnings forecasts nor
concurrently released analysts’ earnings forecasts subsume the information of the other. Analysts’
stock rating information also possesses incremental information content beyond information
provided by the two types of earnings forecasts. These findings suggest that analysts are not merely
conveying the information disclosed by managers, but rather are bringing value to the stock market
by providing their insights on the firm’s future performance. The incremental informativeness ofanalysts’ forecasts in the presence of management forecasts can also be construed as an indication of
the stock market relying more on analysts’ earnings forecasts than on management earnings forecasts.
This possibility is further explored in the next section.
4.6 Forecast Accuracy of Previous Analysts’ Forecasts, Management Forecasts, and Current Analysts’ Forecasts
In this section, I compare the forecast accuracy of previous analysts’ forecasts (PAF),
management forecasts (MF), and contemporaneously publicized analysts’ forecasts (AF) in order togain insights into the incremental informativeness of AF over MF. Forecast accuracy is measured as
the absolute value of the difference between actual and forecasted values deflated by the MVE at the
beginning of the fiscal year.

Forecast accuracy =Actual value − Forecasted value

MVE at the beginning of the fiscal year.

All forecasts are one-year-ahead annual forecasts, and the forecasted items that are investigatedare sales, operating profit, recurring profit, and net income. The number of observations for each
forecasted item differs slightly because managers occasionally disclose forecasts of only certain items.
Table 8 presents the descriptive statistics of the forecast accuracy of PAF, MF, and AF. The
average (median) forecast accuracy of PAF, MF, and AF for recurring profit is 0.0169 (0.0080),
0.0145 (0.0069), and 0.0133 (0.0055), respectively. AF has the smallest average (median) absoluteforecast errors followed by MF, and PAF has the largest average (median) absolute forecast errors.
Figure 3 depicts the median forecast accuracy of all forecasted items in bar-chart form. The orderingof the forecast accuracy among PAF, MF, and AF remains the same. AF provides the most accurate
forecast for all items, followed by MF and PAF.
Panel A of Table 9 presents the results of the paired forecast accuracy tests based on the numberof observations. The first, second, and third rows compare the forecast accuracy between PAF and
AF, PAF and MF, and MF and AF, respectively. For example, when the forecast accuracy of PAF and
MF is compared for recurring profit forecasts in the second row of the third column of Panel A, PAF
is more accurate than MF in 42.1% of the observations, PAF is as accurate as MF in 7.8% of theobservations, and MF is more accurate than PAF in 50.1% of the observations. The forecast accuracy

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

109

Table 8: Descriptive Statistics of The Forecast Accuracy of Previous Analysts’ Forecasts,
Management Forecasts, and Current Analysts’ Forecasts with Management
Forecasts and Current Analysts’ Forecasts Being Announced Simultaneously

Previous analysts’
forecasts (PAF)

Management
forecasts (MF)
Current analysts’
forecasts (AF)SALES

Average
0.0658S.D.
0.1000Min
0.0001

1Qrt Median 3Qrt
0.0108 0.0307 0.0849

Max
1.4304

RECURPROFIT

0.01690.02780.00000.00330.2793OPEPROFIT

NETINCOME
SALES
OPEPROFITRECURPROFIT
NETINCOME
SALES
OPEPROFITRECURPROFIT
NETINCOME

0.01500.01450.05410.01300.01450.01190.04780.01190.01330.01150.02330.02820.08980.02150.02490.02590.08210.02040.02410.02360.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.00320.00750.01830.00230.00800.00240.00240.00190.00640.00230.00230.00170.00580.02440.00640.00690.00490.01910.00550.00550.00440.0152
0.06380.01520.01640.0116
0.05580.01340.01380.01070.00800.01910.27090.3827
1.19500.25440.25460.4357
1.19500.2627
0.27110.2650Note: This table presents the descriptive statistics of the forecast accuracy of previous analysts’ forecasts, management
forecasts, and current analysts’ forecasts contained in analyst reports that are publicized simultaneously withmanagement forecasts. The forecast accuracy is measured as the absolute value of the difference between actual andforecasted values deflated by the MVE at the beginning of the fiscal year. All forecasts are one-year-ahead annual
forecasts and the forecasted items that are investigated are sales (SALES), operating profit (OPEPROFIT), recurring
profit (RECURPROFIT), and net income (NETINCOME).

Figure 3: Forecast Accuracy of PAF, MF, and AF.
0.009
0.008

PAF

MF

AF

0.007

Forecast accuracy

0.006
0.005
0.004
0.003
0.002
0.001
0.000

Sale

Operating Profit

Recurrint Profit

Net Income

Median forecast accuracy of PAF, MF, and AF
Note: This figure depicts the median forecast accuracy of previous analysts’ forecasts (PAF), management forecasts (MF),and current analysts’ forecasts (AF) contained in analyst reports that are publicized simultaneously with management
forecasts. The forecast accuracy is measured as the absolute value of the difference between actual and forecasted
values deflated by the MVE at the beginning of the fiscal year. All forecasts are one-year-ahead annual forecasts
and the forecasted items that are investigated are sales (SALES), operating profit (OPEPROFIT), recurring profit(RECURPROFIT), and net income (NETINCOME). Note that the median sales forecast accuracy is scaled by one-fifth
to make it comparable to others.

OPEPROFIT
N
%
149
15.0%
384
38.6%
462
46.4%
995
100.0%
407
40.9%
82
8.2%
506
50.9%
995
100.0%
327
32.9%
133
13.4%
535
53.8%
995
100.0%

RECURPROFIT
N
%
189
17.8%
371
34.9%
502
47.3%
1,062
100.0%
447
42.1%
83
7.8%
532
50.1%
1,062
100.0%
336
31.6%
141
13.3%
585
55.1%
1,062
100.0%NETINCOME
N
%
201
18.9%
349
32.9%
511
48.2%
1,061
100.0%
409
38.5%
102
9.6%
550
51.8%
1,061
100.0%
357
33.6%
177
16.7%
527
49.7%
1,061
100.0%

Note: This table presents the results of paired forecast accuracy tests between previous analysts’ forecasts (PAF), management forecasts (MF), and current analysts’ forecasts (AF)contained in analyst reports that are publicized simultaneously with management forecasts. The forecast accuracy is measured as the absolute value of the difference between actualand forecasted values deflated by the MVE at the beginning of the fiscal year. All forecasts are one-year-ahead annual forecasts and the forecasted items investigated are sales (SALES),
operating profit (OPEPROFIT), recurring profit (RECURPROFIT), and net income (NETINCOME). The mean and median differences in forecast accuracy are statistically tested
using the paired t-test and Wilcoxon signed-rank test, and the respective test statistics are reported in Panel B. **, *, and † represent two-tailed significance at the 0.01, 0.05, and 0.10levels.

Panel B: Paired Statistical Tests of Forecast Accuracy between Previous Analysts’ Forecasts, Management Forecasts, and Current Analysts’ Forecasts
SALES
OPEPROFITRECURPROFIT
NETINCOME
PairedWilcoxon
PairedWilcoxon
PairedWilcoxon
PairedWilcoxonPairs
t-testtest
t-testtest
t-testtest
t-testtest
PAF vs. AF
12.18**
16.72**
10.47**
12.93**
10.15**
12.88**
8.80**
12.29**
PAF vs. MF
6.09**
7.21**
5.56**
5.26**
5.72**
5.04**
7.39**
7.26**
MF vs. AF
4.29**
8.39**
4.17**
6.28**
3.93**
7.03**
1.65†
4.19**

Panel A: Paired Forecast Accuracy Tests Based on the Number of Observations
SALES
Pairs
AccuracyN%
Previous analysts’
PAF more accurate
120
11.3%
forecasts (PAF)
PAF equals AF
368
34.6%
vs.
AF
more
accurate
575
54.1%
Current analysts’
Total
1,063
100.0%
forecasts (AF)Previous analysts’
PAF more accurate
397
37.3%
forecasts (PAF)
PAF equals AF
92
8.7%
vs.
AF
more
accurate574
54.0%
Current analysts’
Total
1,063
100.0%
forecasts (AF)Previous analysts’
PAF more accurate
310
29.2%
forecasts (PAF)
PAF equals AF
166
15.6%
vs.
AF
more
accurate
587
55.2%
Current analysts’
Total
1,063
100.0%
forecasts (AF)

Table 9: Forecast Accuracy Comparisons Between Previous Analysts’ Forecasts, Management Forecasts, and Current Analysts’
Forecasts with Management Forecasts and Current Analysts’ Forecasts Being Announced Simultaneouuly

110
The Japanese Accounting Review, 2 (2012), 87-116

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

111

test between MF and AF for recurring profit forecasts in the third row of the third column of Panel
A reveals that MF is more accurate than AF in 31.6% of the observations, MF is as accurate as AF
in 13.3% of the observations, and AF is more accurate than MF in 55.1% of the observations. The
results of other forecasted items are similar to those of recurring profit forecasts.
The mean and median differences in forecast accuracy between PAF and AF, PAF and MF,
and MF and AF are statistically tested using the paired t-test and Wilcoxon signed-rank test, andthe results are reported in Panel B of Table 9. The results of both statistical tests indicate that AF
is significantly more accurate than PAF, MF is significantly more accurate than PAF, and AF issignificantly more accurate than MF for all forecasted items at the 1% level except for the meandifference between MF and AF for net income, which is statistically significant only at the 10% level.
Overall, the results presented in Table 9 reveal that management forecasts are more accurate
than previous analysts’ forecasts but less accurate than concurrently released analysts’ forecasts. The
superiority of current analysts’ forecasts over management forecasts in terms of forecast accuracy may
explain the stock market’s greater reliance on current analysts’ forecasts over management forecasts.

5. Additional Analysis
5.1 The Impact of Ex Post Forecast Accuracy on the Information Content of Analysts’ Earnings Forecasts
The findings in the previous sections show that current analysts’ earnings forecasts are
informative to the market and significantly more accurate than concurrently announced management
earnings forecasts. This raises the question of whether the stock market reacts to current analysts’
earnings forecasts unconditionally, even when analysts’ forecasts are ex post less accurate thanmanagement forecasts.
In order to answer this question, I use two subsamples of analyst reports in which current
analysts’ earnings forecasts are (i) ex post more accurate than management earnings forecasts and (ii)ex post less accurate than management earnings forecasts. The results of re-estimating Eq. (4) with thetwo subsamples are reported in Table 10.
The first column of Table 10 shows the estimated results of Eq. (4) using all observations.
The second and third columns of Table 10 present the estimated results for subsamples (i) and (ii),respectively. The estimated coefficients of ΔMEF_PAEF are both significantly positive: 1.1320 for the
subsample (i) and 0.9890 for the subsample (ii). However, while the estimated coefficient of ΔAEF_
MEF for subsample (i) is significantly positive, 1.1263 (t-statistic = 4.93), the estimated coefficientfor subsample (ii) is not statistically significant, 0.4773 (t-statistic = 1.00). These findings indicatethat the market responds to analysts’ earnings forecasts only when they provide better forecasts thancorporate managers. It appears that the stock market is able to discern the relative ex post forecast
accuracy of analysts’ earnings forecasts and reacts only to those forecasts that are ex post more accurate
than management forecasts.
5.2 The Impact of Other Firm-Specific Factors
Prior literature has identified a number of factors associated with the market reaction to therelease of analysts’ stock ratings and earnings forecasts. First, firm size and the number of analysts
following a firm are often used as proxies for the amount of pre-disclosure information available
for a firm (Atiase, 1985; Stickel, 1995; Asquith et al., 2005). Since less information is likely to beavailable to investors about smaller or less widely-followed firms, the market reaction to the release

The Japanese Accounting Review, 2 (2012), 87-116

112

Table 10: The Impact of Ex Post Forecast Accuracy on The Information Content ofAnalysts’ and Management Earnings Forecasts
All observations
UPSTRBUYUPBUYRESTRBUYREBUYREHOLDESELL

DNBUY(i) AEF more accurate
than MEF

Coefficientt-statistic

Coefficientt-statistic

Coefficient

0.03094.20**

0.03234.01**

0.04640.06100.01610.00590.0022−0.0156

2.85**

0.04742.01*

0.01861.96

1.20

0.00431.76

0.0008

0.23

2.50*
−1.04

0.00790.0146

2.70**–0.04424.32**

0.9890−4.02**

ΔAEF_MEF

1.1256

5.31**

1.1263

4.93**

N

1,062

6.58**

1.13200.1762585

0.0031

1.57−0.06080.2103

3.17**

1.68−4.23**

adj.R249.71*

0.0146−0.0451
1.34640.0942t-statistic

2.57**DNHOLDSELL

ΔMEF_PAEF(ii) AEF less accurate
than MEF

0.71
–1.87

−0.0483−2.21*

0.47731.00

0.1416

2.77**

336

Note: This table examines the impact of the relative ex post forecast accuracy between current analysts’ earnings forecasts(AEF) and management earnings forecasts (MEF) on the incremental information content of these earnings forecasts.
The following regression model is estimated using only the subsample of analyst reports that are publicized concurrently
with management forecasts.
CAR(0,+1) = δ 1UPSTRBUY + δ 2UPBUY + δ 3RESTRBUY + δ 4REBUY + δ 5REHOLDSELL + δ 6DNBUY +δ 7DNHOLDSELL + δ 8ΔMEF_PAEF + δ 9ΔAEF_MEF + e ,(4)where CAR(0,+1) is the cumulative abnormal stock return (raw return minus the market model predicted return) overa two day window comprising the day of the analyst report release and the following trading day, UPSTRBUY is anindicator variable taking the value of one if analysts’ ratings are upgraded to strong buy and zero otherwise, UPBUY is
an indicator variable taking the value of one if analysts’ ratings are upgraded to buy and zero otherwise, RESTRBUY
is an indicator variable taking the value of one if analysts’ ratings are unchanged from strong buy and zero otherwise,
REBUY is an indicator variable taking the value of one if analysts’ ratings are unchanged from buy and zero otherwise,
REHOLDSELL is an indicator variable taking the value of one if analysts’ ratings are unchanged from hold or sell andzero otherwise, DNBUY is an indicator variable taking the value of one if analysts’ ratings are downgraded to buy andzero otherwise, DNHOLDSELL is an indicator variable taking the value of one if analysts’ ratings are downgraded tohold or sell and zero otherwise, ΔMEF_PAEF is management recurring profit forecasts (MEF) minus analysts’ previous
recurring profit forecasts (PAEF) deflated by the MVE at the beginning of the fiscal year, and ΔAEF_MEF is analysts’
current recurring profit forecasts (AEF) minus management recurring profit forecasts (MEF) deflated by the MVE atthe beginning of the fiscal year. All earnings forecasts are one-year-ahead annual forecasts. t-statistics are calculatedusing White (1980) heteroskedasticity-consistent standard errors. ** and * represent two-tailed significance at the 0.01
and 0.05 levels.

of analysts’ ratings and earnings forecasts is expected to be greater in magnitude for these firms thanfor larger or more widely-followed firms. Second, Michaely and Womack (1999) document that themean excess returns around buy recommendations are lower when the recommendations are madeby underwriters rather than by non-underwriters, thereby suggesting that the market discounts thevalue of underwriter buy recommendations compared to those of non-underwriters. Third, analysts
with better reputations are expected to have more influence than others on the stock market. Stickel(1995) finds that rating upgrades (downgrades) from first-team All-American analysts trigger anincremental price increase (decrease) of 1.18% (–0.79%) beyond that associated with non-AllAmericans. Fourth, Skinner and Sloan (2002) document that growth firms with high market-tobook ratios exhibit an asymmetrically large negative price response to negative surprises of actualOta: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

113

earnings. Asquith et al. (2005) also report similar results for rating downgrade announcements.
Thus, on the basis of the findings in prior studies, I investigate the impact of five firm-specificfactors—firm size, analyst following, analyst reputation, underwriting relationship, and firm
growth—on market reaction to the simultaneous releases of management earnings forecasts andanalysts’ rating and earnings forecasts. One problem associated with this test is that the sample must
be divided into good and bad news because the signs of estimated coefficients of certain factors are
predicted to vary depending on the content of the news. For example, while the sign of the estimatedcoefficient of firm size is expected to be negative for good news, the opposite sign is predicted forbad news. Concurrent announcements of management and analysts’ forecasts make it more difficult
to distinguish whether the announcements contain good or bad news overall. Therefore, I divide
the sample by the sign of CAR, which means that if an announcement is associated with a positive(negative) CAR, it is considered to be good (bad) news.
I estimate a version of Eq. (4) modified to include proxies for firm size, analyst following, analyst
reputation, underwriting relationship, and firm growth.
CAR(0,+1) = d 1UPSTRBUY + d 2UPBUY + d 3RESTRBUY + d 4REBUY + d 5REHOLDSELL +d 6DNBUY + d 7DNHOLDSELL + d 8ΔMEF_PAEF + d 9ΔAEF_MEF + d 10SIZE + (6)d 11NUMANAL + d 12STAR + d 13UNDWRITE + d 14BMR + e ,where SIZE is the log of market value of equity, NUMANAL is the number of analysts following thefirm as reported by IFIS Consensus Data, STAR is an indicator variable taking the value of one if
the analyst is chosen as the top analyst at least once in the Nikkei Veritas analyst rankings during the
2005–2007 period and zero otherwise, UNDWRITE is an indicator variable taking the value of one
if the brokerage house is the lead managing underwriter of the firm and zero otherwise, and BMR isthe firm’s book-to-market ratio. The definitions of other variables are the same as those in Eq. (4).
The first and second columns of Table 11 present the results of estimating Eq. (6) on the goodnews and bad news subsamples, respectively. The results indicate that although the signs of theestimated coefficients of these firm-specific variables are generally consistent with the expected
signs, their statistical significance is weak. The only significant variables are STAR for the good news
subsample and NUMANAL and BMR for the bad news subsample. These findings suggest thatanalysts with better reputations have a more positive impact on the market in the case of good news,
and that the market reaction is less negative for more widely-followed firms and more negative forhigher growth firms in the case of bad news.
With regard to analyst report variables, the noteworthy results from Table 7 still hold. The
estimated coefficients of ΔMEF_PAEF and ΔAEF_MEF are positive and significant for bothsubsamples; this indicates that the information content of management and analysts’ earnings
forecasts remains intact after controlling for the impact of other firm-specific factors.

6. Conclusion
The literature on financial analysts asserts that there are two main sources of value that analysts bringto the market (Park and Pincus, 2000; Ivković and Jegadeesh, 2004). First, analysts’ value may lie in theirskills to interpret public signals through their expertise. Second, analysts’ service may lie in their ability
to acquire private signals through their independent research. While analysts’ roles as acquirers of new

The Japanese Accounting Review, 2 (2012), 87-116

114

Table 11: The Impact of Other Firm-Specific Factors on The Information Content ofAnalysts’ and Management Earnings Forecasts
Good News (Positive CAR)

UPSTRBUY

Expected Sign Coefficient
+

UPBUY

+

REBUY

?

RESTRBUYREHOLDESELL

0.06380.0384t-statistic
2.89**

0.0249

1.68*–

ΔMEF_PAEF+

0.74533.34**–

0.0004

0.32

ΔAEF_MEFSIZE

NUMANALSTAR

UNDWRITE
BMR

adj.R2

N

–
+

n/a

0.99603.60**––0.0005–1.30–

0.00841.64+?

0.00210.0025

0.0961557?

–0.0559–3.17**?

–0.0587–3.63**

?

?

DNBUY

DNHOLDSELL

n/a

1.78*

2.61**

2.70**
0.50

t-statistic++

0.0426n/aExpected Sign Coefficient

2.36**?

0.0281

Bad News (Negative CAR)

–
–

+

+

+

+

–

–

+

n/a

–0.0522–0.0729–0.09680.95070.62010.00060.0007–3.24**–3.40**–4.95**

3.74**

2.57**
0.46

1.66*–0.0038–0.70

0.0225

3.63**–0.0061
0.2685–1.34505

Note: This table examines the impact of other firm-specific factors on the incremental information content of analysts’
and management earnings forecasts. The following regression model is estimated using only the subsample of analyst
reports that are publicized concurrently with management forecasts.
CAR(0,+1) = δ 1UPSTRBUY + δ 2UPBUY + δ 3RESTRBUY + δ 4REBUY + δ 5REHOLDSELL + δ 6DNBUY +δ 7DNHOLDSELL + δ 8ΔMEF_PAEF + δ 9ΔAEF_MEF + δ 10SIZE + δ 11NUMANAL + δ 12STAR +δ 13UNDWRITE + δ 14BMR + ε ,where SIZE is the log of market value of equity, NUMANAL is the number of analysts following the firm as reported by
IFIS Consensus Data, STAR is an indicator variable taking the value of one if the analyst is chosen as the top analyst atleast once in the Nikkei Veritas analyst rankings during the 2005-2007 period and zero otherwise, UNDWRITE is anindicator variable taking the value of one if the brokerage house is the lead managing underwriter of the firm and zerootherwise, and BMR is the firm’s book-to-market ratio. Other variables are defined in Table 10. t-statistics are calculatedusing White (1980) heteroskedasticity-consistent standard errors. ** and * represent one-tailed significance at the 0.01
and 0.05 levels.

information through their own research are well documented in the literature, their roles as interpreters of
public information have not been thoroughly explored thus far (Asquith et al., 2005).
The primary goal of this paper is to investigate whether analysts possess valuable expertise forinterpreting public information. Toward this goal, I examine the incremental informativeness ofanalyst reports released contemporaneously with companies’ earnings forecasts, which is probably oneof the most important sources of public information. Using 2,277 analyst reports published during
the two-year period from July 2005 through June 2007 covering 399 firms, I first replicate priorresearch and find that both analysts’ ratings and earnings forecast revisions are informative to themarket. Next, I examine the timing of the publication of analyst reports relative to the announcementof companies’ earnings forecasts. I find that over half of the analyst reports (1,062 reports) are issued
on the day after the announcement of management earnings forecasts. Since the vast majority ofcompanies announce earnings forecasts after the market closes for the day and analysts disseminate

Ota: Information Content of Analysts’ Stock Ratings and Earnings Forecasts in the Presence ofManagement Earnings Forecasts

115

the reports for their clients before the market opens the next morning, these analyst reports arepractically released contemporaneously with management forecasts.
Thereafter, I investigate the information content of analysts’ ratings and earnings forecasts using
only the subsample of analyst reports that occur with management earnings forecasts. I find thatanalysts’ ratings and earnings forecasts have incremental information content beyond information
provided by management forecasts. Controlling for the impact of actual earnings at annual earnings
announcements does not materially change the results.
The results of the incremental informativeness of analysts’ forecasts in the presence ofmanagement forecasts indicate that the market relies more on analysts’ forecasts than on
management forecasts. When the ex post forecast accuracy of management earnings forecasts andconcurrently released analysts’ earnings forecasts is compared, the results show that analysts’ forecasts
are significantly more accurate than management forecasts. This finding may partially explain why
investors depend more on analysts’ outlooks than on corporate managers’ views of future earnings.
In summary, this paper presents strong evidence that the stock market reacts to forecast information
contained in an analyst report beyond forecast information that is publicly disseminated by companies’
managers. The stock market’s reaction may be justified by the ex post higher accuracy of analysts’ earnings
forecasts than simultaneously announced management earnings forecasts. These findings indicate that
security analysts are not merely reiterating publicly announced information, but are adding value to themarket by providing new analysis and assessment of companies’ future performance.

REFERENCESABDEL-KHALIK, R., AND B. AJINKYA. 1982. Returns to informational advantages: The case of analysts’ forecast revisions. The
Accounting Review 57 (October): 661-680.
ASQUITH, P., M. MIKHAIL, AND A. AU. 2005. Information content of equity analyst reports. Journal of Financial Economics 75(February): 245-282.
ATIASE, R. 1985. Predisclosure information, firm capitalization, and security price behavior around earnings announcements. Journalof Accounting Research 23 (Spring): 21-36.
BARBER, B., R. LEHAVY, M. MCNICHOLS, AND B. TRUEMAN. 2001. Can investors profit from the prophets? Security analyst
recommendations and stock returns. The Journal of Finance 56 (Spring): 531-563.
BOWEN, R., A. DAVIS, AND D. MATSUMOTO. 2002. Do conference calls affect analysts’ forecasts? The Accounting Review 77 (April):285-316.
BRAV, A., AND R. LEHAVY, 2003. An empirical analysis of analysts’ target prices: short-term informativeness and long-term
dynamics. The Journal of Finance 58 (October): 1933-1967.
CAMPBELL, J., A. LO, AND C. MACKINLAY. 1996. The Econometrics of Financial Markets. Princeton University Press, Princeton, NJ.
CONROY, R., K. EADES, AND R. HARRIS. 2000. A test of the relative pricing effects of dividends and earnings: Evidence fromsimultaneous announcements in Japan. The Journal of Finance 55 ( June): 1199-1227.
CONROY, R., R. HARRIS, AND Y. PARK. 1998. Fundamental information and share prices in Japan: Evidence from earnings surprises
and management predictions. International Journal of Forecasting 14 ( June): 227-244.
ELTON, E., M. GRUBER, AND S. GROSSMAN. 1986. Discrete expectational data and portfolio performance. The Journal of Finance 41( July): 699-713.
FRANCIS, J., AND L. SOFFER. 1997. The relative informativeness of analysts’ stock recommendations and earnings forecast revisions.
Journal of Accounting Research 35 (Autumn): 193-211.
IVKOVIĆ, Z., AND N. JEGADEESH. 2004. The timing and value of forecast and recommendation revisions. Journal of Financial
Economics 73 (September): 433-463.
LYS, T., AND S. SOHN. 1990. The association between revisions of financial analysts’ earnings forecasts and security-price changes.
Journal of Accounting and Economics 13 (December): 341-363.
MACKINLAY, C. 1997. Event studies in economics and finance. Journal of Economic Literature 35 (March): 13-39.
MICHAELY, R., AND K. WOMACK. 1999. Conflict of interest and the credibility of underwriter analyst recommendations. The
Review of Financial Studies 12: 653-686.
OTA, K. 2010. The value relevance of management forecasts and their impact on analysts’ forecasts: Empirical evidence from Japan.

116

The Japanese Accounting Review, 2 (2012), 87-116

Abacus 46 (March): 28-59.
PARK, C., AND M. PINCUS. 2000. Market reactions to changes in analyst consensus recommendations following quarterly earnings
announcements. Unpublished Working Paper, Hong Kong University of Science and Technology.
SKINNER, D., AND R. SLOAN. 2002. Earnings surprises, growth expectations, and stock returns or don’t let an earnings torpedo sink
your portfolio. Review of Accounting Studies 7 ( June): 289-312.
STICKEL, S. 1989. The timing of and incentives for annual earnings forecasts near interim earnings announcements. Journal of
Accounting and Economics 11 (Special Issue) ( July): 275-292.
STICKEL, S. 1991. Common stock returns surrounding earnings forecast revisions: More puzzling evidence. The Accounting Review
66 (April): 402-416.
STICKEL, S. 1995. The anatomy of the performance of buy and sell recommendations. Financial Analysts Journal 51 (SeptemberOctober): 25-39.
WHITE, H. 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica
48 (May): 817-838.
WOMACK, K. 1996. Do brokerage analysts’ recommendations have investment value? The Journal of Finance 51 (March): 137-167.