an examination of factors affecting chinese financial ......an examination of factors affecting...
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ORI GIN AL PA PER
An examination of factors affecting Chinese financialanalysts’ information comprehension, analyzing ability,and job quality
Yiming Hu Æ Thomas W. Lin Æ Siqi Li
Published online: 2 October 2007� Springer Science+Business Media, LLC 2007
Abstract This paper examines various factors affecting Chinese financial analysts’
information comprehension, analyzing ability and job quality. We hypothesized that
financial analysts with better educational background, more experience, superior resources
provided by large brokerage firms and more information sources have better information
comprehension, stronger analyzing ability, and higher job quality. Using a survey method
to collect data, we found that information sources have a significant impact on analysts’
information comprehension, analyzing ability and job quality. Specifically, analysts tend to
exhibit greater information comprehension and better job quality when they conduct more
company-level surveys. Additionally, when they have more access to firms’ indirect
information, analysts tend to have a stronger analyzing ability and better job quality. We
also found that analysts’ educational background has a positive impact on their analyzing
ability while analysts’ work experience improves their job quality. This study’s results
indicate that financial analysts are still a fledgling profession in current China, and the
capabilities of Chinese financial analysts need to be improved through additional training
and continued education. Moreover, our study is important in highlighting the urgency of
fostering the development of the financial analysis profession in China.
Keywords Financial analysts � Information comprehension � Analyzing ability �Job quality � Chinese
Y. HuDepartment of Accounting and Finance, Antai School of Management, Shanghai Jiao Tong University,535 Fahua Zhen Road, Shanghai 200052, Chinae-mail: [email protected]
T. W. Lin (&) � S. LiLeventhal School of Accounting, University of Southern California, 3660 Trousdale Parkway,Los Angeles, CA 90089-0441, USAe-mail: [email protected]
S. Lie-mail: [email protected]
123
Rev Quant Finan Acc (2008) 30:397–417DOI 10.1007/s11156-007-0058-3
JEL Classifications G24 � G29 � M41
As an integral part of the capital market, financial analysts provide earnings forecasts,
securities investment recommendations, and other information relevant to various market
participants. The quality of analysts’ work affect investors’ decision-making process and
the efficiency of information flow in the market. The extent to which analysts’ earnings
forecasts serve as an accurate surrogate for the earnings expectations, for instance, is of
interest to various capital market participants (e.g., Brown et al. 1987; Wiedman 1996).
Financial analysts’ performance is also important to the analysts themselves. Anecdotal
evidence suggests that an analyst’ bonus, besides his/her base salary, is mainly determined
by the investment activities generated from his/her security recommendations (e.g.,
Morgenson 1997, p. 165). Mikhail et al. (1999) indicate that an analyst is more likely to
turn over if his/her forecast accuracy is lower than his/her peers.
China established the Shanghai Stock Exchange in December 1990 and the Shenzhen
Stock Exchange in July 1991. It has approximately a total of 1,400 firms listed in these two
stock exchanges. Therefore, the Chinese financial analyst profession is still in the early
development stage. The objective of this study is to examine factors affecting Chinese
financial analysts’ information comprehension, analyzing ability and job quality.
The remainder of this paper is organized as follows. Section 1 reviews the relevant
literature. Section 2 discusses the hypothesis development. Section 3 presents data, a
questionnaire, and measures. Section 4 describes the research design. Section 5 presents
the empirical results. Section 6 contains concluding remarks.
1 Literature review
Prior research has offered considerable evidence regarding the determinants of financial
analysts’ performance. Pope (2003) argues that analysts’ forecast quality, as a multi-
dimensional concept, consists of three main components: accuracy, bias, and efficiency.
Factors such as characteristics of information uncertainty, quality of inputs to the forecast
task, analysts’ skills and incentives are all attributable to the outcome of analysts’ work.
Specifically, there are two categories of the literature related to this study: (1) innate sources
of analysts’ performance differences, such as analysts’ ability and skills, learning-by-doing,
resources, information sources and other behavior, and (2) environmental sources such as
firms’ disclosure policy and practice, accounting choices and methods, alignment/rela-
tionship between analysts and firms, and firms’ prior earnings characteristics.
1.1 Innate sources
Despite some evidence of insignificant differences in forecast accuracy across brokerage
firms and individual analysts (O’Brien 1985, 1987, 1990), a number of studies have
identified systematic variation and associated innate determinants in analysts’ behavior and
performance.
1.1.1 Analysts’ ability, skills and experience
Prior research provides mixed results regarding the effects of analysts’ experience on their
forecast performance. Mikhail et al. (1997, 2003) define firm-specific experience as the
398 Y. Hu et al.
123
number of prior quarters for which the analyst has issued an earnings forecast for the firm.
Their studies find a statistically significant decline in the absolute value of quarterly
forecast errors and in analysts’ under-reaction to prior earning information as analysts’
experience increases. Moreover, they document a greater earnings response coefficient on
forecast errors from more experienced analysts, indicating the market recognizes the
improved forecast accuracy. Using the I/B/E/S Detail History database, Clement (1999)
argues that a combination of analysts’ ability and skill (as measured by analysts’ experi-
ence), resources (as measured by brokerage size), and task complexity (as measured by the
number of firms and industries followed by the analyst) contribute to the systematic
differences in analysts’ forecast accuracy. In contrast, Jacob et al. (1999) generate different
results by using a larger sample and cross-sectional analyses. They investigate the effects
of analysts’ aptitude (or native ability), learning-by-doing (or experience), and brokerage
house characteristics on analysts’ forecast accuracy. Their evidence shows that after
controlling for analysts’ ability in forecasting the earnings of a specific company, analysts’
experience no longer has effects on forecasting performance.
1.1.2 Analysts’ behavior
Research has documented significant persistence of analysts’ behavior, either individually
or collectively, as they perform the tasks of their profession. In a study on financial
analysts’ information search behavior, Biggs (1984) provides knowledge about the
financial statement information utilized by analysts involved in the assessment of corporate
earnings power. He finds that analysts adopt either a historical or predictive strategy when
they perform the assessment task. He also shows a considerable similarity among the
analysts regarding the amount and type of information they attend to. Butler and Lang
(1991) find analysts are either persistently optimistic or persistently pessimistic relative to
consensus earnings forecasts. This persistent behavior is significantly related to analysts’
forecast accuracy over their sample period in which analysts’ forecasts consistently
overestimate actual earnings. Eames et al. (2002) argue that after controlling for earnings,
analysts’ forecasts are optimistic for buy recommendations and pessimistic for sell rec-
ommendations, which is inconsistent with prior research findings that analysts’ forecasts
are optimistic on average, and increasingly optimistic as the stock recommendation
becomes less favorable. Mest and Plummer (2003) further indicate that the extent of
analyst issuing optimistically biased forecasts depends on the importance of these forecasts
to management. They show that the optimistic bias is greater for earnings forecasts than for
sales forecasts as the latter is attached less importance to management.
Analysts’ performance and behavior also have an impact among analysts themselves.
Welch (2000) examines the properties of herding among securities analysts. He finds that
the buy or sell recommendations of analysts have a significant positive impact on the
recommendations of the next two analysts.
1.1.3 Analysts’ resources and incentives
Recent research has examined the impact of brokerage firms’ attributes, such as size
(resources) and underwriting relationships (incentives), on analysts’ performance. Jacob
et al. (1999) find that broker-related factors, such as outgoing broker-analyst turnover as
well as size and industry specification, are associated with forecast accuracy. Dechow et al.
Factors affecting information comprehension, analyzing ability, and job quality 399
123
(2000), Dugar and Nathan (1995), and Lin and McNichols (1998) find that analysts tend to
issue more optimistic forecasts for firms which are also underwriting clients of the broker.
They do not find, however, a significant effect of underwriting relationships on forecast
accuracy.
1.1.4 Analysts’ information attributes
Prior research has demonstrated the importance of information attributes, such as the
format of financial statements, the complexity of financial information, and the specific
accounting information items, when investigating analysts’ use of the information. In an
experimental study, Hirst and Hopkins (1998) show that clear reporting of comprehensive
income and its components are effective in facilitating the detection of firms’ earnings
management activities by the analysts, and thus in affecting analysts’ valuation judgments.
McEwen and Hunton (1999) find that specific accounting information items used during
financial statement analysis differ among more or less accurate analysts. Specifically, more
accurate analysts emphasize income indicators over longer time-horizons and tend to use
summary indicators, while less accurate analysts emphasize balance sheet items and
footnotes. Plumlee (2003) shows that analysts impound less complex information more
fully than they do more complex information.
1.1.5 Analysts’ communication with managers
Analysts’ communication with managers is an essential source of task-relevant informa-
tion, and thus to a large extent, influence analysts’ behavior and performance. Francis et al.
(1997) report a significant increase in analysts’ following, but not in dispersion, accuracy,
or bias in analysts’ forecasts subsequent to the corporate presentations to the New York
Society of Securities Analysts (NYSSA). In contrast, Tan et al. (2002) document in an
experimental study a significant effect of management earnings preannouncement strate-
gies on analysts’ earnings forecasts. They find that analysts’ future earnings forecasts are
higher for firms with conservative preannouncements than firms with accurate prean-
nouncements. Firms with optimistic preannouncements have the lowest forecasts. Using
both cross-sectional and within-firm analyses, Bowen et al. (2002) find that earnings-
related conference calls increase the total information available about a firm, which helps
to improve analysts’ forecast accuracy. This effect is more salient for analysts with rela-
tively weak prior forecasting performance. Sedor (2002) shows that the forms in which
managers communicate information, i.e., scenario vs. list, affect analysts’ unintentional
forecast optimism, especially for loss firms. In addition, Irani (2004) suggests that Regu-
lation FD, which eliminates selective communications between analysts and managers,
improves the relevance of conference calls to analysts’ forecast accuracy and consensus.
1.2 Environmental sources
1.2.1 Firms’ characteristics and business operations
Prior research has documented various impacts of firms’ characteristics on analysts’ per-
formance. Barth et al. (2001) and Barron et al. (2002) report the association between firms’
400 Y. Hu et al.
123
intangible assets and various properties of analysts’ forecasts. They find that firms with a
greater level of intangible assets have higher analyst coverage and a lower degree of
consensus among analysts’ forecasts.
Further, there are associations between firms’ business operations and analysts’ per-
formance. Haw et al. (1994) document a significant but temporary decrease in forecast
accuracy after business mergers. Duru and Reeb (2002) argue that firm-level international
operations increase the complexity of the forecasting task and are related to less predictable
earnings, which leads to less accurate but more optimistic earnings forecasts. Lang et al.
(2003) find that cross-listing on US exchanges for non-US firms increases information
transparency and thus improve analyst coverage and forecast accuracy. Using a sample of
Japanese firms, Douthett et al. (2004) report that analyst forecast accuracy (dispersion) is
higher (lower) for keiretsu firms than non-keiretsu firms, highlighting the effect of
industrial organizations on characteristics of analyst forecasts.
1.2.2 Firms’ accounting choices and methods
Other research has examined the impact of firms’ accounting choices on analysts’ forecast
performance. Hand (1995) suggests that analysts sophistically update their forecasts in
response to fiscal 1974 LIFO adoptions. Using a larger database from multiple analyst
sources, he concludes that analysts’ forecast errors for explicitly LIFO-based and FIFO-
based forecasts are not biased as previously documented (i.e., Biddle and Ricks 1988).
Hopkins et al. (2000) provide evidence that the accounting method and timing for a
business combination affect analysts’ stock-price judgments. In an experiment involving
113 buy-side equity analysts, they find that analysts’ price judgments are lower for firms
with purchase accounting and later amortized goodwill than firms with either pooling
accounting or purchase accounting with immediate write-off of the acquisition premium.
Moreover, the timing of the business combination also affects analysts’ stock price esti-
mates. Abarbanell and Lehavy (2003) find that firm reporting choices play an important
role in determining analysts’ forecast errors by examining two statistically influential
asymmetries in the tail and the middle of error distributions. Finally, Ho et al. (2007)
provide evidence of a greater amount of analyst scrutiny, i.e., more forecast revision
activities, when reported earnings are accompanied by high levels of R&D expenses.
1.2.3 Firms’ disclosure policies and practices
Mixed results have been provided on the associations between firms’ disclosure practices
and analysts’ forecast performance. Adrem (1999) and Eng and Teo (2000) find no sig-
nificant relationship between firm disclosure strategy and forecast accuracy in Sweden and
Singapore, respectively. On the other hand, using Financial Analysts Federation’s (FAF’s)
ratings of firms’ disclosures as a proxy for disclosure quality, Lang and Lundholm (1996)
suggest that US firms with more informative disclosure policies tend to have a greater
analyst following, more accurate analysts’ earnings forecasts, less dispersion among
individual analyst forecasts and less volatility in forecast revisions, after controlling for
size, earnings surprise and other environmental attributes. Using a time-series approach,
Healy et al. (1999) find that expanded voluntary disclosure increases analyst following.
Baldwin (1984) documents that both the mean and variance of forecast error decrease after
the SEC required segment reporting in 1970. Barron et al. (1999) indicate that highly rated
Factors affecting information comprehension, analyzing ability, and job quality 401
123
Management Discussion and Analyses (MD&As), holding other disclosures constant, are
associated with less error and less dispersion in analysts’ earnings forecast. Lacina and
Karim (2004) suggest that analysts react less negatively to the disclosure of improved
management earnings expectations than to management forecasts of bad earnings. Basu
et al. (1998) and Khanna et al. (2000) document a significant and positive impact of
country-level disclosure metrics on forecast accuracy across borders. Hope (2003), using a
cross-country, firm-level approach, provides international evidence that the CIFAR index
of the level of disclosure in annual reports is positively associated with analysts’ forecast
accuracy.
1.2.4 Firms’ prior earnings characteristics
Firms’ prior earnings characteristics also affect analysts’ forecasts. Kross et al. (1990)
investigate the degree to which the superiority of analysts’ earnings forecasts over a
univariate time-series model is associated with certain firm-specific characteristics. Their
findings indicate that earnings variability and the level of the Wall Street Journal coverage
are positively related to analysts’ advantage in forecasting a firm’s earnings, after con-
trolling for firm size, analysts’ timing advantage, firms’ business lines, and industry
membership. In an experimental study, Sedor (2002) documents that analysts make more
optimistic forecasts for a prior-loss than a prior-profit firm.
2 Hypothesis development
According to the survey conducted by Hu et al. (2003a), work experience of financial
analysts in China varies from less than one year to about several years, with an average
experience of less than four years. They also find that most Chinese analysts possess
master’s degrees. Since prior studies suggest that analysts with greater experience may
produce higher-quality forecasts because their general skills and knowledge improve with
time (e.g., Mikhail et al. 1997, 2003; Clement 1999), and analysts with more educational
training tend to gain a better understanding of firms’ reporting practices, we expect that
there are positive relationships between financial analysts’ experience, educational back-
ground and their job performance including the degree of information comprehension,
analyzing ability and job quality.
It is argued that financial analysts in large brokerage firms have access to better data and
administrative support as well as private information about the firms which they follow
(Clement 1999; Stickel 1992). Moreover, as Welch (2000) indicates, analysts’ performance
and behavior tend to have a strong impact on those of other analysts. Therefore, we expect
that analysts employed by large brokers have superior resources and environment in
performing their research, and thus have better information comprehension, stronger
analyzing ability and higher job quality.
An analyst’s information sources are also likely to affect his/her information processing
process, skills and performance. McEwen and Hunton (1999) find that specific accounting
information items used during financial statement analysis differ among more or less
accurate analysts. Further, analysts’ communication with managers, such as corporate
presentations, earnings preannouncements, and conference calls, are an essential source of
task-relevant information, and hence to a large extent, influence analysts’ behavior and
performance (Francis et al. 1997; Tan et al. 2002; Bowen et al. 2002). Thus, we expect
402 Y. Hu et al.
123
analysts’ information sources are associated with their information comprehension, ana-
lyzing ability and job quality.
All together, we have the following three hypotheses stated in alternative forms:
H1: Financial analysts with better educational background, more experience, superior
resources provided by large brokerage firms and more information sources have better
information comprehension.
H2: Financial analysts with better educational background, more experience, superior
resources provided by large brokerage firms and more information sources have stronger
analyzing ability.
H3: Financial analysts with better educational background, more experience, superior
resources provided by large brokerage firms and more information sources have higher job
quality.
3 Data, questionnaire, and measures
3.1 Sample, questionnaire, and data
Data related to Chinese financial analysts were collected through a large-scale survey on
Chinese domestic brokerage firms in 2002–2003. The questionnaire includes five parts: (1)
questions on analysts’ professional background, such as education and work experience;
(2) analysts’ research content and output; (3) analysts’ information sources; (4) analysts’
information use, such as their information depth and comprehension, especially analysts’
understanding of financial statement information and quality; and (5) methods and tools
which analysts use to perform their tasks. Respondents are brokerage firms in China.
Except for questions on analysts’ background information, we adopt a 1–5 scale for all
survey questions. For example, the scale for the question on analysts’ usage of a specific
research method is 1 (don’t use it at all), 2 (rarely use it), 3 (use it occasionally), 4 (use it
frequently), and 5 (use it all the time).
To more comprehensively reflect the situation of the Chinese financial analyst profession,
we determine the number of surveys sent out according to each brokerage firm’s trading
volume. Specifically, based on the trading volume in the Shanghai Stock Exchange in August
2002, we sent out 30 copies of the questionnaire to every brokerage firm with trading volumes
of more than RMB 1 million, 20 to those between RMB 500 thousands to RMB 1 million, 5 to
those between RMB 100 thousands to RMB 500 thousands, and 3 to those less than RMB 100
thousands. One U.S. dollar was equivalent to 8.27 RMB in 2003. A total of 1,012 survey
questionnaires were sent out with 184 valid responses. The usable response rate is 18.2%.
3.2 Variable measurement
3.2.1 Dependent variables
We use three dependent variables: information comprehension, analyzing ability, and job
quality. The dependent variable ‘‘information comprehension’’ is measured by two items:
‘‘the degree of attention paid to accounting policies and accounting estimates information’’
and ‘‘the degree of attention paid to financial statements and footnotes’’.
Factors affecting information comprehension, analyzing ability, and job quality 403
123
Previous research has used many indicators to measure analysts’ ability and skills. In
this study, we focus on research methods, tools and models which analysts use in per-
forming their tasks. Specifically, we adopt three measures for the second dependent
variable ‘‘analyzing ability’’: (1) the usage of analyzing tools, including financial ratios in
textbooks, academic and professional journals, or ratios designed by analysts themselves.
The effective application of financial ratios is associated with analysts’ educational
background, experience and work environment. Analysts with strong educational training,
more experience and superior work environment tend to have more comprehensive
information search and research abilities; (2) the usage of theoretical models, especially
stock valuation models including discounted cash flow model, discounted dividend model,
residual income model, EVA model and CAPM. Analysts’ educational background,
experience and work environment are positively related to the application of these models;
and (3) the analyzing methods analysts adopt, including arguments with numbers and
cases, simple statistical methods, and sophisticated statistical models. A good financial
analyst needs to master necessary statistical skills in order to process information related to
specific companies, industries and the market.
Analysts’ job quality, the third dependent variable, is frequently measured by analysts’
forecast accuracy and related metrics such as forecast consensus, dispersion and bias (e.g.,
Clement 1999; Barron et al. 1999; Barron et al. 2002; Pope 2003). Judgment and decision-
making researchers use experimental, empirical data or experts’ opinions to assess ana-
lysts’ performance quality. Without such data available regarding Chinese analysts, we use
three measures of job quality in this study: (1) analysts’ evaluation of their own impact,
such as ‘‘influence on ordinary investors’ decisions and behaviors’’ and ‘‘influence on
correcting listing firms’ improper practices;’’ (2) frequency of media exposure, i.e., whe-
ther analysts’ reports are adopted by national media, including national radio and TV
stations on business channels, and national economic, financial and accounting papers and
magazines. This measure reflects the degree to which analysts’ reports are of high quality
and strongly impact market participants. However, since sell side analysts target their
services specifically to the brokerage firms or firm clients, this measure may not be
effective in capturing their job performance; and (3) analysts’ report accuracy.
3.2.2 Independent variables
We measure analysts’ educational background based on their academic degrees, such as
bachelors, masters and doctorate degrees. Since most analysts in China have a relevant
master’s degree, a dummy variable is used with its value taking 1 if master’s, 0
otherwise. Analysts’ work experience is measured as the years for which they work as
analysts. Analysts’ work environment is measured based on the size of the brokerage
firms in which they work. This metric is collected from the ratio of individual broker
trading volume over the largest broker trading volume in 2002, disclosed by the
Shanghai Stock Exchange.
Chinese analysts usually collect their information through the following four channels:
(1) publicly disclosed information, i.e., firms’ annual, semi-annual and quarterly financial
statements; (2) investigations and surveys on firms, including visiting firms, attending
corporate conference calls and interviewing firms via phone calls; (3) indirect infor-
mation disseminated by the media, intermediaries, local governments, stock exchanges,
and regulators; and (4) informal information gathering through private conversations or
hearsays.
404 Y. Hu et al.
123
4 Research design
We adopt a multivariate regression model to test our hypotheses:
Yabj ¼ b0 þ b1X1j þ b2 X2j þ b3X3j þ b4 X4j þ b5 X5j þ b6X6j þ b7 X7j þ εj
Where: a,b = 1,2,3 j = 1,2,…,n, and ej are random errors.
The variables are defined as below:
Dependent variables
a = 1, b = 1, Y11= Information Comprehension Metric 1: Attention to Accounting Policies andEstimates—measured as the average score of ‘‘the degree of attention paidto accounting policies and estimates information’’ in the questionnaireitems;
a = 1, b = 2, Y12= Information Comprehension Metric 2: Attention to Financial Statements andFootnotes—measured as the average score of ‘‘the degree of attention paidto financial statements and footnotes’’ in the questionnaire items;
a = 2, b = 1, Y21= Analyzing Ability Metric 1: Usage of Analyzing Tools—measured as theaverage score of three items in the questionnaire: ‘‘financial ratioscommonly cited in textbooks’’, ‘‘financial ratios introduced by academicpublications’’ and ‘‘financial ratios self-designed for specific purposes’’. Thepurpose of this index is to identify financial ratios used by financial analystsin analyzing historical and industrial comparisons;
a = 2, b = 2, Y22= Analyzing Ability Metric 2: Usage of Stock Valuation Models—measured asthe average score of five items in the questionnaire: ‘‘Discounted Cash FlowModel’’, ‘‘Dividend Discount Model’’, ‘‘Residual Income Model’’,‘‘Economic Value-Added Model’’ and ‘‘Capital Assets Pricing Model’’,which are used in financial analysis and valuation;
a = 2, b = 3, Y23= Analyzing Ability Metric 3: Usage of Statistics Models—measured as theaverage score of three items in the questionnaire: ‘‘arguments with numbersand cases’’, ‘‘simple statistical methods’’ and ‘‘sophisticated statisticalmodeling’’, which are employed by listed companies;
a = 3,b = 1, Y31= Job Quality Metric 1: Work Impact—measured as the average score of sixitems in the questionnaire: ‘‘self-evaluation of own influence on ordinaryinvestors’ investment behaviors’’, ‘‘influence on correction of inappropriateoperations by listed companies’’, ‘‘investment decision reference by ownbrokerage firm’’, ‘‘investment decision reference by other institutionalinvestors’’, ‘‘influence on the image of a particular stock’’ and ‘‘influence onthe current price of a particular stock’’. These items measure analysts’ self-evaluation on the impact or influence of his/her reports or comments;
a = 3, b = 2, Y32= Job Quality Metric 2: Report Publication Probability—measured as the scoreof ‘‘probability of analysts’ reports to be published in nation-widenewspapers or magazines’’ in the questionnaire;
a = 3, b = 3, Y33= Job Quality Metric 3: Report Accuracy—measured as the score of ‘‘accuracyof the analysts’ reports’’ in the questionnaire. This item measures analysts’self-evaluation of the quality of their reports.
Independent variables
X1= Work Experience, measured as the score of ‘‘years of work experience as afinancial analyst’’ in the questionnaire;
X2= Educational Background, measured as the score of ‘‘whether the analyst holdsa master’s degree’’ in the questionnaire. It is a dummy variable, whichequals ‘‘1’’ with the answer of ‘‘Yes’’ and ‘‘0’’ with ‘‘No’’;
Factors affecting information comprehension, analyzing ability, and job quality 405
123
continued
X3= Work Environment, measured as the business scale of the brokerage firms. Thedata, drawn from the information disclosed by the Shanghai StockExchange, is the ratio of the firm’s transaction amounts to the totaltransaction amounts of the top #1 firm;
X4= Analysts collect their information through publicly disclosed information,measured as the score of ‘‘frequency of using annual reports, semi-annualreports and quarterly reports of the listed companies’’ in the questionnaire;
X5= Analysts collect their information through company surveys, measured as theaverage score of frequency that analysts use three survey methods—‘‘on-the-spot visit to the listed companies’’, ‘‘news conferences of the listedcompanies’’ and ‘‘telephone surveys to the listed companies’’;
X6= Analysts collect their information through indirect information sources,measured as the average score of frequency that analysts use five indirectinformation sources—‘‘media coverage of the operation of the listedcompany’’, ‘‘department concerned in the listed company or other securitiesfirms’’, ‘‘accounting firms, lawyer’s offices and investment consultingfirms’’, ‘‘stock exchange or supervisory authorities’’ and ‘‘academicpublications’’;
X7= Analysts collect their information through informal information channels,measured as the score of frequency that analysts use ‘‘private conversationsor hearsay’’.
5 Empirical results
5.1 Descriptive statistics
Table 1 reports the descriptive statistics of the key dependent variables. The two measures
of the Information Comprehension Metric, Y11 (Attention to Accounting Policies and
Estimates) and Y12 (Attention to Financial Statements and Footnotes) have similar mean
and standard deviation scores (4.06 and 0.68, 4.05 and 0.66, respectively). As for analysts’
ability and skills, Analyzing Ability Metric 1 (Y21—Usage of Analyzing Tools) and Metric
3 (Y23—Usage of Statistical Models) are close to each other, and have higher mean values
and lower standard deviations compared with Metric 2 (Y22—Usage of Valuation Models).
Among the three indices of analysts’ job quality, Job Quality Metric 3 (Y33), measured as
the score of ‘‘accuracy of the analysts’ reports’’, has the highest mean value of 3.82,
followed by Job Quality Metric 2 (Y32—Report Publication Probability), measured as the
score of ‘‘probability of analysts’ reports to be published in nation-wide newspapers or
magazines’’, with the mean score of 3.26, and Job Quality Metric 1 (Y31—Work Impact),
measured as analysts’ self-evaluation on the impact or influence of his/her reports or
comments, with the mean score of 2.77. Moreover, Job Quality Metric 2 (Y32) has the
greatest standard deviation among the three indices (standard deviation = 1.10).
Table 1 also provides the descriptive statistics for the independent variables. Specifi-
cally, the mean number of years that respondents worked as financial analysts is 3.62,
indicating a lack of work experience for sample analysts in China. On average about 71%
of financial analysts in our sample have master’s degrees. The mean ratio of a firm’s
transaction amounts to the total transaction amounts of the top #1 firm is 0.39, suggesting
that Chinese brokerage firms are mostly in medium or small sizes. Based on the ranking of
406 Y. Hu et al.
123
Tab
le1
Des
crip
tive
stat
isti
cs
NM
inim
um
Max
imu
mM
ean
Std
.d
evia
tio
n
Y11
(Info
rmat
ion
Com
pre
hen
sio
nM
etri
c1
)1
53
25
4.0
60
.68
Y12
(Info
rmat
ion
Com
pre
hen
sio
nM
etri
c2
)1
53
1.9
25
4.0
50
.66
Y21
(Anal
yzi
ng
Ab
ilit
y)
15
11
.33
53
.46
0.5
6
Y22
(Anal
yzi
ng
Ab
ilit
y)
15
11
52
.84
0.7
7
Y23
(Anal
yzi
ng
Ab
ilit
y)
15
11
53
.59
0.6
3
Y31
(Jo
bQ
ual
ity)
14
51
.55
2.7
70
.62
Y32
(Jo
bQ
ual
ity)
14
51
53
.26
1.1
0
Y33
(Jo
bQ
ual
ity)
14
53
53
.82
0.5
4
X1
(Wo
rkE
xp
erie
nce
)1
53
01
03
.62
2.2
8
X2
(Ed
uca
tio
nB
ack
gro
un
d)
15
30
10
.71
0.4
5
X3
(Wo
rkE
nv
iro
nm
ent)
15
30
10
.39
0.3
6
X4
(Info
rmat
ion
Dis
closu
re)
153
25
4.4
00.7
5
X5
(Co
mpan
yS
urv
ey)
15
31
52
.91
0.9
5
X6
(In
dir
ect
Info
rmat
ion
)1
53
1.1
75
3.0
10
.69
X7
(In
form
alIn
form
atio
n)
15
31
52
.51
0.9
9
Val
idN
(lis
twis
e)1
45
––
––
Tab
le1
pro
vid
esdes
crip
tive
stat
isti
csof
key
var
iable
suse
din
the
regre
ssio
nan
alyse
s.
Var
iable
Defi
nit
ions
Y11
=In
form
atio
nC
om
pre
hen
sion
Met
ric
1:
Att
enti
on
toA
ccounti
ng
Poli
cies
and
Est
imat
es—
mea
sure
das
the
aver
age
score
of
‘‘th
edeg
ree
of
atte
nti
on
pai
dto
acco
un
tin
gpoli
cies
and
esti
mat
esin
form
atio
n’’
inth
eques
tionnai
reit
ems;
Y12
=In
form
atio
nC
om
pre
hen
sion
Met
ric
2:
Att
enti
on
toF
inan
cial
Sta
tem
ents
and
Footn
ote
s—m
easu
red
asth
eav
erag
esc
ore
of
‘‘th
edeg
ree
of
atte
nti
on
pai
dto
fin
anci
alst
atem
ents
and
foo
tno
tes’
’in
the
qu
esti
on
nai
reit
ems;
Y21
=A
nal
yzi
ng
Ab
ilit
yM
etri
c1
:U
sag
eo
fA
nal
yzi
ng
To
ols
—m
easu
red
asth
eav
erag
esc
ore
of
thre
eit
ems
inth
eq
ues
tio
nn
aire
:‘‘
fin
anci
alra
tio
sco
mm
on
lyci
ted
inte
xtb
oo
ks’
’,‘‘
fin
anci
alra
tio
sin
tro
duce
db
yac
adem
icp
ub
lica
tio
ns’
’an
d‘‘
fin
anci
alra
tio
sse
lf-d
esig
ned
for
spec
ific
pu
rpose
s’’.
Th
ep
urp
ose
of
this
ind
exis
toid
enti
fyfi
nan
cial
rati
os
use
db
yfi
nan
cial
anal
yst
sin
anal
yzi
ng
his
tori
cal
and
indu
stri
alco
mp
aris
on
s;
Factors affecting information comprehension, analyzing ability, and job quality 407
123
Ta
ble
1co
nti
nued
Y22
=A
nal
yzi
ng
Ab
ilit
yM
etri
c2
:U
sag
eo
fS
tock
Val
uat
ion
Mo
del
s—m
easu
red
asth
eav
erag
esc
ore
of
fiv
eit
ems
inth
eq
ues
tion
nai
re:
‘‘D
isco
un
ted
Cas
hF
low
Mo
del
’’,
‘‘D
ivid
end
Dis
count
Model
’’,
‘‘R
esid
ual
Inco
me
Model
’’,
‘‘E
conom
icV
alue-
Added
Model
’’an
d‘‘
Cap
ital
Ass
ets
Pri
cing
Model
’’,
whic
har
euse
din
finan
cial
anal
ysi
san
dv
alu
atio
n;
Y23
=A
nal
yzi
ng
Ab
ilit
yM
etri
c3
:U
sag
eo
fS
tati
stic
sM
odel
s—m
easu
red
asth
eav
erag
esc
ore
of
thre
eit
ems
inth
eq
ues
tio
nn
aire
:‘‘
arg
um
ents
wit
hn
um
ber
san
dca
ses’
’,‘‘
sim
ple
stat
isti
cal
met
hods’
’an
d‘‘
sophis
tica
ted
stat
isti
cal
model
ing’’
,w
hic
har
eem
plo
yed
by
list
edco
mpan
ies;
Y31
=Jo
bQ
ual
ity
Met
ric
1:
Wo
rkIm
pac
t—m
easu
red
asth
eav
erag
esc
ore
of
six
item
sin
the
qu
esti
on
nai
re:
‘‘se
lf-e
val
uat
ion
of
ow
nin
flu
ence
on
ord
inar
yin
ves
tors
’in
ves
tmen
tb
ehav
iors
’’,
‘‘in
flu
ence
on
corr
ecti
on
of
inap
pro
pri
ate
op
erat
ion
sb
yli
sted
com
pan
ies’
’,‘‘
inv
estm
ent
dec
isio
nre
fere
nce
by
ow
nb
roker
age
firm
’’,
‘‘in
ves
tmen
td
ecis
ion
refe
ren
ceb
yo
ther
inst
itu
tio
nal
inv
esto
rs’’
,‘‘
infl
uen
ceo
nth
eim
age
of
ap
arti
cula
rst
ock
’’an
d‘‘
infl
uen
ceo
nth
ecu
rren
tp
rice
of
ap
arti
cula
rst
ock
’’.
Th
ese
item
sm
easu
rean
alyst
s’se
lf-e
val
uat
ion
on
the
imp
act
or
infl
uen
ceo
fh
is/h
erre
po
rts
or
com
men
ts;
Y32
=Jo
bQ
ual
ity
Met
ric
2:
Rep
ort
Pu
bli
cati
on
Pro
bab
ilit
y—
mea
sure
das
the
sco
reo
f‘‘
pro
bab
ilit
yo
fan
alyst
s’re
po
rts
tob
ep
ub
lish
edin
nat
ion
-wid
en
ewsp
aper
sor
mag
azin
es’’
inth
eq
ues
tion
nai
re;
Y33
=Jo
bQ
ual
ity
Met
ric
3:
Rep
ort
Acc
ura
cy—
mea
sure
das
the
score
of
‘‘ac
cura
cyof
the
anal
yst
s’re
port
s’’
inth
eques
tionnai
re.T
his
item
mea
sure
san
alyst
s’se
lf-e
val
uat
ion
of
the
qual
ity
of
thei
rre
port
s;
X1
=W
ork
Ex
per
ien
ce,
mea
sure
das
the
sco
reo
f‘‘
yea
rso
fw
ork
exp
erie
nce
asa
fin
anci
alan
alyst
’’in
the
qu
esti
on
nai
re;
X2
=E
du
cati
on
alB
ack
gro
un
d,m
easu
red
asth
esc
ore
of
‘‘w
het
her
the
anal
yst
ho
lda
mas
ter’
sd
egre
e’’
inth
eq
ues
tio
nn
aire
.It
isa
du
mm
yv
aria
ble
,w
hic
heq
ual
s‘‘
1’’
wit
hth
ean
swer
of
‘‘Y
es’’
and
‘‘0
’’w
ith
‘‘N
o’’
;
X3
=W
ork
En
vir
on
men
t,m
easu
red
asth
eb
usi
nes
ssc
ale
of
the
bro
ker
age
firm
s.T
he
dat
a,d
raw
nfr
om
the
info
rmat
ion
dis
clo
sed
by
the
Sh
ang
hai
Sto
ckE
xch
ang
e,is
the
rati
oo
fth
efi
rm’s
tran
sact
ion
amou
nts
toth
eto
tal
tran
sact
ion
amou
nts
of
the
top
#1
firm
;
X4
=A
nal
yst
sco
llec
tth
eir
info
rmat
ion
thro
ug
hp
ub
licl
yd
iscl
ose
din
form
atio
n,
mea
sure
das
the
sco
reo
f‘‘
freq
uen
cyo
fu
sin
gan
nu
alre
po
rts,
sem
i-an
nual
repo
rts
and
qu
arte
rly
rep
ort
so
fth
eli
sted
com
pan
ies’
’in
the
qu
esti
on
nai
re;
X5
=A
nal
yst
sco
llec
tth
eir
info
rmat
ion
thro
ug
hco
mp
any
surv
eys,
mea
sure
das
the
aver
age
sco
reo
ffr
equ
ency
that
anal
yst
su
seth
ree
surv
eym
eth
ods—
‘‘on
-th
e-sp
ot
vis
itto
the
list
edco
mp
anie
s’’,
‘‘n
ews
con
fere
nce
so
fth
eli
sted
com
pan
ies’
’an
d‘‘
tele
ph
on
esu
rvey
sto
the
list
edco
mp
anie
s’’;
X6
=A
nal
yst
sco
llec
tth
eir
info
rmat
ion
thro
ug
hin
dir
ect
info
rmat
ion
sou
rces
,m
easu
red
asth
eav
erag
esc
ore
of
freq
uen
cyth
atan
alyst
su
sefi
ve
ind
irec
tin
form
atio
nso
urc
es—
‘‘m
edia
cov
erag
eo
fth
eo
per
atio
no
fth
eli
sted
com
pan
y’’
,‘‘
dep
artm
ent
con
cern
edin
the
list
edco
mp
any
or
oth
erse
curi
ties
firm
s’’,
‘‘ac
cou
nti
ng
firm
s,la
wy
er’s
offi
ces
and
inv
estm
ent
con
sult
ing
firm
s’’,
‘‘st
ock
exch
ang
eo
rsu
per
vis
ory
auth
ori
ties
’’an
d‘‘
acad
emic
pu
bli
cati
on
s’’;
X7
=A
nal
yst
sco
llec
tth
eir
info
rmat
ion
thro
ug
hin
form
alin
form
atio
nch
ann
els,
mea
sure
das
the
sco
reo
ffr
equen
cyth
atan
aly
sts
use
‘‘p
riv
ate
con
ver
sati
on
so
rh
ears
ay’’
.
408 Y. Hu et al.
123
mean values, financial analysts in China tend to rely mostly on publicly disclosed infor-
mation (mean value of 4.40), followed by indirect information sources (mean value of
3.01) and company surveys (mean value of 2.91), with the least usage of informal infor-
mation sources (mean value of 2.51).
5.2 Correlation matrix
We note that the three dependent variables, analyst information comprehension, analyzing
abilities and skills and job quality are not independent from each other. Greater infor-
mation comprehension indicates stronger analyzing ability, while higher analyzing ability
and skills usually suggest better job quality. Table 2 presents the Pearson correlation
matrix among the measures of the dependent variables.
First, there are significant correlations among individual measures under each dependent
variable. Specifically, for the Information Comprehension variable, the two metrics, Y11
(Attention to Accounting Policies and Estimates) and Y12 (Attention to Financial State-
ments and Footnotes), are positively associated with a two-tailed p value of 0.76. For the
Analyzing Ability variable, the three metrics, Y21 (Usage of Analyzing Tools), Y22 (Usage
of Stock Valuation Models) and Y23 (Usage of Statistical Models) are all significantly and
positively correlated with each other. Similar observations can be made for the three
metrics of the Job Quality variable, Y31, Y32 and Y33. These correlations suggest that the
metrics we adopt for each variable clearly represent the same construct.
Second, the metrics of Information Comprehension are significantly and positively
correlated with the metrics of Analyzing Ability and Job Quality, except for the insig-
nificant associations of Y12 (‘‘the degree of attention paid to financial statements’’), Y23
(the average of three items in the questionnaire of ‘‘arguments with figures and cases’’,
‘‘simple statistical methods’’ and ‘‘sophisticated statistical models’’), and Y32 (‘‘accuracy
of the analysts’ reports’’) with other metrics.
Table 2 Pearson correlations among the dependent variables with two-sided p-value in italics
Y11 Y12 Y21 Y22 Y23 Y31 Y32
Y12 0.76
0.00
Y21 0.34 0.28
0.00 0.00
Y22 0.21 0.10 0.43
0.01 0.18 0.00
Y23 0.20 0.12 0.42 0.38
0.01 0.13 0.00 0.00
Y31 0.29 0.23 0.47 0.38 0.29
0.00 0.00 0.00 0.00 0.00
Y32 0.21 0.15 0.24 0.19 0.06 0.42
0.01 0.06 0.00 0.02 0.42 0.00
Y33 0.34 0.29 0.34 0.25 0.21 0.40 0.26
0.00 0.00 0.00 0.00 0.01 0.00 0.00
Table 2 provides the Pearson correlation coefficients among the dependent variables used in the regressionanalyses. For variable definitions, please see Table 1
Factors affecting information comprehension, analyzing ability, and job quality 409
123
In summary, these correlations indicate that analysts’ abilities and skills increase with
their information comprehension. To yield high job quality, analysts need to master strong
analyzing skills as well as gain a more professional and deeper understanding of relevant
information.
5.3 Regression analysis results
5.3.1 Information comprehension
We use the multivariable regression model to test Hypothesis 1, i.e., the impact of analysts’
work experience, educational background, work environment and various information
sources on analysts’ information comprehension. Table 3 reports the regression results.
Panel A of Table 3 suggests that for the Information Comprehension Metric 1:
Accounting policies and Estimates (Y11, measured as the value of ‘‘the degree of attention
paid to accounting policies and estimates information’’), only X5 (analysts collect their
information through company surveys) has significant impact with the coefficient of 0.20
and t-statistics of 3.09. The overall regression is highly significant with an adjusted R
squared of 17.3%.
Panel B of Table 3 reports similar results. Analysts have better information compre-
hension when they conduct more company-level surveys and investigations (coefficient of
0.27 and t-statistics of 4.33). In addition, X6 (analysts collect their information through
indirect information sources) is weakly associated with analysts’ information compre-
hension (coefficient of 0.14 and t-statistics of 1.83). The overall regression is highly
significant with an adjusted R squared of 22.8%.
The findings in Table 3 only partially support our Hypothesis 1 that financial analysts
with more information from company-level surveys have better information comprehen-
sion. We do not find evidence that financial analysts with better educational background,
more experience, or superior resources provided by large brokerage firms have better
information comprehension.
5.3.2 Analyzing ability
Table 4 reports the results for testing Hypothesis 2, which predicts that the impact of
analysts’ work experience, educational background, work environment and various infor-
mation sources on analysts’ analyzing ability will have a significant positive correlation.
Panel A of Table 4 shows that X2 (educational background) and X6 (analysts collect
their information through indirect information sources) are significantly and positively
related to the Analyzing Ability Metric 1: Usage of Analyzing Tools (coefficients of 0.28
and 0.22, with t-statistics of 2.70 and 3.38, respectively), indicating that analysts tend to
have better analyzing skills and abilities when they have received more education and have
more indirect information about the client firm. Moreover, X4 (analysts collect their
information through publicly disclosed information) and X7 (analysts collect their infor-
mation through informal information channels) are weakly associated with analysts’ ability,
with t-statistics of 2.16 and 2.55, respectively. We note that there is a weak but negative
association between analysts’ work environment and their analyzing ability (coefficient of
–0.002 and t-statistics of –1.68). This could be explained by our sample selection—the
brokerage firms in our sample are relatively medium or small in size. We find no evidence of
410 Y. Hu et al.
123
Tab
le3
An
aly
sis
of
fact
ors
infl
uen
cin
gin
form
atio
nco
mp
reh
ensi
on
Con
stan
tx
1x
2X
3x
4x
5x
6x
7
wo
rkex
per
ience
edu
cati
on
alb
ack
gro
un
dw
ork
env
iro
nm
ent
info
dis
clo
sure
com
pan
ysu
rvey
indir
ect
info
sou
rces
info
rmal
sou
rces
Pan
elA
:D
epen
den
tV
aria
ble
isY
11
(Info
rmat
ion
Com
pre
hen
sion
Met
ric
1)
Coef
f.2
.378
0.0
06
0.1
26
0.1
39
0.1
11
0.2
04
0.1
20
.028
Tw
o-s
ided
p-v
alu
e6
.103
**
*0
.25
91
.003
0.9
51
.503
3.0
89*
**
1.4
940
.459
N1
53
Ad
j.R
20
.17
Pan
elB
:D
epen
den
tV
aria
ble
isY
12
(Info
rmat
ion
Com
pre
hen
sio
nM
etri
c2
)
Coef
f.2
.278
0.0
28
0.1
49
0.1
16
0.1
13
0.2
65
0.1
36
-0.0
64
Tw
o-s
ided
p-v
alu
e6
.298
**
*1
.25
20
.271
0.8
58
1.6
364
.33*
**
1.8
31*
-1.1
38
N1
53
Ad
j.R
20
.23
**
*0.0
1si
g.
lev
el;
**
0.0
5si
g.
lev
el;
*0
.1si
g.
lev
el
Tab
le3
rep
ort
sth
eO
LS
reg
ress
ion
resu
lts
on
the
info
rmat
ion
com
pre
hen
sio
nm
od
el.In
Pan
elA
the
dep
end
ent
var
iab
leis
Y11,w
her
eY
12
isth
ed
epen
den
tv
aria
ble
inP
anel
B.
Fo
rv
aria
ble
defi
nit
ion
s,p
leas
ese
eT
able
1
Factors affecting information comprehension, analyzing ability, and job quality 411
123
Ta
ble
4A
nal
ysi
so
ffa
cto
rsin
flu
enci
ng
anal
yzi
ng
abil
ity
Co
nst
ant
x1
x2
x3
x4
x5
x6
x7
wo
rkex
per
ien
ceed
uca
tional
bac
kg
roun
dw
ork
env
iron
men
tin
fodis
closu
reco
mp
any
surv
eyin
dir
ect
info
sou
rces
info
rmal
sou
rces
Pan
elA
:D
epen
den
tV
aria
ble
isY
21
(Anal
yzi
ng
Abil
ity
Met
ric
1)
Co
eff.
1.8
67
0.0
01
0.2
79
-0.0
02
0.1
31
-0.0
31
0.2
24
0.1
25
Tw
o-s
ided
p-v
alu
e5
.8*
**
0.0
622
.69
8*
**
-1.6
812
.155
**
-0.5
83
.378
**
*2
.547
**
N1
51
Ad
j.R
20
.20
Pan
elB
:D
epen
den
tV
aria
ble
isY
22
(An
aly
zin
gA
bil
ity
Met
ric
2)
Co
eff.
1.6
68
0.0
10
.35
50
.001
–0
.03
20
.008
0.2
91
0.0
39
Tw
o-s
ided
p-v
alu
e3
.512
**
*0
.343
2.3
25*
*0
.316
–0
.351
0.0
992
.982
**
*0
.545
N1
51
Ad
j.R
20
.08
Pan
elC
:D
epen
den
tV
aria
ble
isY
23
(An
aly
zin
gA
bil
ity
Met
ric
3)
Co
eff.
2.6
68
0.0
26
0.2
90
.001
0.0
07
-0.0
27
0.1
69
0.0
49
Tw
o-s
ided
p-v
alu
e6
.754
**
*1
.058
2.2
84*
*0
.645
0.1
03-0
.402
0.8
14-1
.138
N1
51
Ad
j.R
20
.04
**
*0
.01
sig.
lev
el;
**
0.0
5si
g.
lev
el;
*0
.1si
g.
lev
el
Tab
le4
report
sth
eO
LS
regre
ssio
nre
sult
son
the
anal
yzi
ng
abil
ity
model
.T
he
dep
enden
tvar
iable
sar
eY
21,
Y22,
and
Y23
for
Pan
els
A,
B,
and
C,
resp
ecti
vel
y.
Fo
rv
aria
ble
defi
nit
ion
s,p
leas
ese
eT
able
1
412 Y. Hu et al.
123
multi-collinearity based on the variable inflation factors. In addition, Panel A shows the
highest adjusted R squared among the three metrics (adj. R squared equals 20.3%), which
implies that the explanatory variables in Panel A best capture the cross-sectional variation in
analysts’ ability and skills measured in Metric 1: Usage of Analyzing Tools.
Panel B of Table 4 investigates the impact of various factors on Analyzing Ability
Metric 2 (Usage of Stock Valuation Models). The results show that only X6 (analysts
collect their information through indirect information sources) and X2 (educational
background) have significant explanatory power for analysts’ ability and skills, with
coefficients of 0.29 and 0.36 and t-statistics of 2.98 and 2.33, respectively.
Panel C of Table 4 regarding Analyzing Ability Metric 3 (Usage of Statistical Models)
have similar results as in Panel B. Specifically, only X6 (analysts collect their information
through indirect information sources) and X2 (educational background) have significant
explanatory power for analysts’ abilities and skills, with coefficients of 0.17 and 0.29 and
t-statistics of 2.08 and 2.28, respectively.
In summary, the results in Table 4 generally support our Hypothesis 2, which predicts
that better educational experience and more indirect information available to analysts will
improve analysts’ analyzing abilities and skills. We find weak evidence that analysts
collect their information through publicly disclosed information and that informal infor-
mation channels also impact analysts’ analyzing ability.
5.3.3 Job quality
Table 5 presents the results regarding Hypothesis 3, which predicts that financial analysts
with better educational background, more experience, superior resources provided by large
brokerage firms and more information sources have higher job quality.
Panel A of Table 5 shows that X1 (work experience) and X6 (analysts collect their
information through indirect information sources) are significantly and positively related to
Job Quality Metric 1: Work Impact (coefficients of 0.07 and 0.13, with t-statistics of 3.02
and 1.70, respectively), indicating that analysts tend to have better job quality performance
when they have more work experience and have more indirect information about the client
firm. Moreover, X5 (analysts collect their information through company surveys) and X7
(analysts collect their information through informal information channels) are weakly
associated with analysts’ job quality, with t-statistics of 2.24 and 2.13, respectively. We
note that there is a weak but negative association between analysts’ work environment and
job quality (coefficient of –0.001 and t-statistics of –0.68). This could be explained by our
sample selection—the same pattern happened in Table 4 Panel A. Also, Panel A has the
highest adjusted R squared among all three panels (adj. R squared equals 16.6%), indi-
cating that the explanatory variables in Panel A best capture the cross-sectional variation in
analysts’ job quality measured in Metric 1.
Panel B of Table 5 indicates that only X7 (analysts collect their information through
informal information channels) and X1 (work experience) have weak impact on Job
Quality Metric 2 (Report Publication Probability), measured as the value of ‘‘probability of
analysts’ reports to be published in nation-wide newspapers or magazines’’ in the ques-
tionnaire, with t-statistics of 1.82 and 1.94, respectively.
Panel C of Table 5 shows that X1 (work experience) is significantly and positively
correlated with Job Quality Metric 3 (Report Accuracy), measured as the value of
‘‘accuracy of the analysts’ reports’’ in the questionnaire, with the coefficient of 0.068 and
t-statistics of 3.38. In addition, X5 (analysts collect their information through company
Factors affecting information comprehension, analyzing ability, and job quality 413
123
Tab
le5
An
aly
sis
of
fact
ors
infl
uen
cin
gjo
bq
ual
ity
Con
stan
tx
1X
2x
3x
4x
5x
6x
7
wo
rkex
per
ience
edu
cati
on
alb
ack
gro
un
dw
ork
env
iro
nm
ent
info
dis
clo
sure
com
pan
ysu
rvey
ind
irec
tin
foso
urc
esin
form
also
urc
es
Pan
elA
:D
epen
den
tV
aria
ble
isY
31
(Anal
yzi
ng
Qu
alit
yM
etri
c1
)
Coef
f.1
.496
0.0
68
0.1
5-0
.00
1–
0.0
29
0.1
37
0.1
29
0.1
2
Tw
o-s
ided
p-v
alu
e4
.156
**
*3
.02*
**
1.2
58-0
.67
8–0
.428
2.2
44*
*1
.703
**
*2
.131
**
N1
45
Ad
j.R
20
.17
Pan
elB
:D
epen
den
tV
aria
ble
isY
32
(Anal
yzi
ng
Qual
ity
Met
ric
2)
Coef
f.1
.185
0.0
85
0.2
20
.00
10
.177
-0.0
62
0.1
57
0.1
98
Tw
o-s
ided
p-v
alu
e1
.704
**
1.9
39
0.9
540
.41
21
.412
-0.5
211
.074
1.8
23*
N1
45
Ad
j.R
20
.04
Pan
elC
:D
epen
den
tV
aria
ble
isY
33
(Anal
yzi
ng
Qual
ity
Met
ric
3)
Coef
f.3
.042
0.0
68
0.1
45
-0.0
03
0.0
54
0.1
4-0
.00
8–
0.0
27
Tw
o-s
ided
p-v
alu
e9
.482
**
*3
.38*
**
1.3
59-2
.61
9*
*0
.881
20
56
8*
*-0
.113
–0
.544
N1
45
Ad
j.R
20
.13
**
*0.0
1si
g.
lev
el;
**
0.0
5si
g.
lev
el;
*0
.1si
g.
lev
el
Tab
le5
report
sth
eO
LS
regre
ssio
nre
sult
son
the
anal
yzi
ng
qual
ity
model
.T
he
dep
enden
tvar
iable
sar
eY
31,
Y32,
and
Y33
for
Pan
els
A,
B,
and
C,
resp
ecti
vel
y.
For
var
iable
defi
nit
ion
s,p
leas
ese
eT
able
1
414 Y. Hu et al.
123
surveys) and X3 (work environment) both have weak correlations with the accuracy of
analysts’ reports, with t-statistics of 2.57 and –2.62, respectively. The negative relationship
between work environment and the accuracy of analysts’ work is possibly due to the
sample selection problem described in previous section.
In general, Table 5 supports our Hypothesis 3 that analysts’ job quality is determined by
their work experience, information collected from company surveys and other indirect
sources. We do not find evidence that analysts’ educational background improves their job
quality.
6 Conclusions
Based on data collected through a questionnaire on Chinese domestic brokerage firms in
2002–2003, we examine the impact of analysts’ educational background, work experience,
work environment and information collection channels on analysts’ use of information,
abilities and skills of processing information and job quality. We found that information
sources have significant impact on analysts’ information comprehension, analyzing abilities
and job quality. Specifically, analysts tend to exhibit greater information comprehension and
better job quality when they conduct more company-level surveys. Also when they have more
access to firms’ indirect information, analysts tend to have a stronger analyzing ability and
better job quality. We also found that analysts’ educational background have a positive impact
on their analyzing abilities while analysts’ work experience improves their job quality.
Combined with prior studies (e.g., Hu et al. 2003a, b), our results indicate that financial
analysts are still a fledgling profession in current China, and the capabilities of Chinese
financial analysts need to be improved through increased training and continued education.
Our study is important in highlighting the urgency of fostering the development of this
profession in China. Specifically, this paper has several implications. First, training of
financial analysts should be focused on the ability to collect information, especially
through conducting field work and company surveys and/or through a variety of different
methods. Through this way financial analysts can deepen their understanding of the col-
lected information and sharpen their ability to extract more information from the collected
information. This helps assure the quality of the financial analysis. Second, there must be
on-going financial training for Chinese financial analysts. In light of our findings, the job
quality of financial analysts is heavily influenced by their work experience, although the
educational credentials of the financial analysts also have some bearings on their job
quality. Furthermore, considering the curriculum in related programs in Chinese educa-
tional institutions, the courses in corporate financial analysis are only rudimentary and
preliminary, and cannot satisfy the needs of fully training professional financial analysts.
Therefore, it is essential to encourage and develop continue education programs, especially
those sponsored by brokerage firms and/or associations.
Acknowledgements We thank participants of the 14th Annual Conference on Pacific Basin Finance,Economics and Accounting and the 2006 American Accounting Association Annual Meeting for helpfulsuggestions. We also thank Pengcheng Li for his excellent research assistance.
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