An examination of factors affecting Chinese financial analysts information comprehension, analyzing ability, and job quality

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1 Rev Quant Finan Acc (2008) 30: DOI /s ORIGINAL PAPER An examination of factors affecting Chinese financial analysts information comprehension, analyzing ability, and job quality Yiming Hu Æ Thomas W. Lin Æ Siqi Li Published online: 2 October 2007 Ó 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. Hu Department of Accounting and Finance, Antai School of Management, Shanghai Jiao Tong University, 535 Fahua Zhen Road, Shanghai , China cafr0066@tom.com T. W. Lin (&) S. Li Leventhal School of Accounting, University of Southern California, 3660 Trousdale Parkway, Los Angeles, CA , USA wtlin@marshall.usc.edu S. Li siqili@marshall.usc.edu

2 398 Y. Hu et al. 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 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 multidimensional 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/relationship 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 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

3 Factors affecting information comprehension, analyzing ability, and job quality 399 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 experience), 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 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 recommendations, 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 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.

4 400 Y. Hu et al. (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 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 Analysts communication with managers Analysts communication with managers is an essential source of task-relevant information, 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 strategies on analysts earnings forecasts. They find that analysts future earnings forecasts are higher for firms with conservative preannouncements than firms with accurate preannouncements. Firms with optimistic preannouncements have the lowest forecasts. Using both cross-sectional and within-firm analyses, Bowen et al. (2002) find that earningsrelated 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 relatively 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 Regulation 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 Firms characteristics and business operations Prior research has documented various impacts of firms characteristics on analysts performance. Barth et al. (2001) and Barron et al. (2002) report the association between firms

5 Factors affecting information comprehension, analyzing ability, and job quality 401 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 performance. 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 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 FIFObased 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 estimates. 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 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 significant 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 Barron et al. (1999) indicate that highly rated

6 402 Y. Hu et al. 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 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 controlling 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 background 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

7 Factors affecting information comprehension, analyzing ability, and job quality 403 analysts information sources are associated with their information comprehension, analyzing 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 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 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 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.

8 404 Y. Hu et al. 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 performing 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 decisionmaking researchers use experimental, empirical data or experts opinions to assess analysts 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., whether 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 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 information disseminated by the media, intermediaries, local governments, stock exchanges, and regulators; and (4) informal information gathering through private conversations or hearsays.

9 Factors affecting information comprehension, analyzing ability, and job quality Research design We adopt a multivariate regression model to test our hypotheses: Y abj ¼ b 0 þ b 1 X 1j þ b 2 X 2j þ b 3 X 3j þ b 4 X 4j þ b 5 X 5j þ b 6 X 6j þ b 7 X 7j þ ɛ j 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, Y 11 = a = 1, b = 2, Y 12 = a = 2, b = 1, Y 21 = a = 2, b = 2, Y 22 = a = 2, b = 3, Y 23 = a = 3,b = 1, Y 31 = a = 3, b = 2, Y 32 = a = 3, b = 3, Y 33 = Independent variables X 1 = X 2 = Information Comprehension Metric 1: Attention to Accounting Policies and Estimates measured as the average score of the degree of attention paid to accounting policies and estimates information in the questionnaire items; Information Comprehension Metric 2: Attention to Financial Statements and Footnotes measured as the average score of the degree of attention paid to financial statements and footnotes in the questionnaire items; Analyzing Ability Metric 1: Usage of Analyzing Tools measured as the average score of three items in the questionnaire: financial ratios commonly cited in textbooks, financial ratios introduced by academic publications and financial ratios self-designed for specific purposes. The purpose of this index is to identify financial ratios used by financial analysts in analyzing historical and industrial comparisons; Analyzing Ability Metric 2: Usage of Stock Valuation Models measured as the average score of five items in the questionnaire: Discounted Cash Flow Model, Dividend Discount Model, Residual Income Model, Economic Value-Added Model and Capital Assets Pricing Model, which are used in financial analysis and valuation; Analyzing Ability Metric 3: Usage of Statistics Models measured as the average score of three items in the questionnaire: arguments with numbers and cases, simple statistical methods and sophisticated statistical modeling, which are employed by listed companies; Job Quality Metric 1: Work Impact measured as the average score of six items in the questionnaire: self-evaluation of own influence on ordinary investors investment behaviors, influence on correction of inappropriate operations by listed companies, investment decision reference by own brokerage firm, investment decision reference by other institutional investors, influence on the image of a particular stock and influence on the current price of a particular stock. These items measure analysts selfevaluation on the impact or influence of his/her reports or comments; Job Quality Metric 2: Report Publication Probability measured as the score of probability of analysts reports to be published in nation-wide newspapers or magazines in the questionnaire; Job Quality Metric 3: Report Accuracy measured as the score of accuracy of the analysts reports in the questionnaire. This item measures analysts self-evaluation of the quality of their reports. Work Experience, measured as the score of years of work experience as a financial analyst in the questionnaire; Educational Background, measured as the score of whether the analyst holds a master s degree in the questionnaire. It is a dummy variable, which equals 1 with the answer of Yes and 0 with No ;

10 406 Y. Hu et al. continued X 3 = X 4 = X 5 = X 6 = X 7 = Work Environment, measured as the business scale of the brokerage firms. The data, drawn from the information disclosed by the Shanghai Stock Exchange, is the ratio of the firm s transaction amounts to the total transaction amounts of the top #1 firm; Analysts collect their information through publicly disclosed information, measured as the score of frequency of using annual reports, semi-annual reports and quarterly reports of the listed companies in the questionnaire; Analysts collect their information through company surveys, measured as the average score of frequency that analysts use three survey methods onthe-spot visit to the listed companies, news conferences of the listed companies and telephone surveys to the listed companies ; Analysts collect their information through indirect information sources, measured as the average score of frequency that analysts use five indirect information sources media coverage of the operation of the listed company, department concerned in the listed company or other securities firms, accounting firms, lawyer s offices and investment consulting firms, stock exchange or supervisory authorities and academic publications ; Analysts collect their information through informal information channels, measured as the score of frequency that analysts use private conversations or 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, Y 11 (Attention to Accounting Policies and Estimates) and Y 12 (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 (Y 21 Usage of Analyzing Tools) and Metric 3(Y 23 Usage of Statistical Models) are close to each other, and have higher mean values and lower standard deviations compared with Metric 2 (Y 22 Usage of Valuation Models). Among the three indices of analysts job quality, Job Quality Metric 3 (Y 33 ), measured as the score of accuracy of the analysts reports, has the highest mean value of 3.82, followed by Job Quality Metric 2 (Y 32 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 (Y 31 Work Impact), measured as analysts self-evaluation on the impact or influence of his/her reports or comments, with the mean score of Moreover, Job Quality Metric 2 (Y 32 ) has the greatest standard deviation among the three indices (standard deviation = 1.10). Table 1 also provides the descriptive statistics for the independent variables. Specifically, 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

11 Factors affecting information comprehension, analyzing ability, and job quality 407 Table 1 Descriptive statistics N Minimum Maximum Mean Std. deviation Y 11 (Information Comprehension Metric 1) Y12 (Information Comprehension Metric 2) Y21 (Analyzing Ability) Y 22 (Analyzing Ability) Y 23 (Analyzing Ability) Y31 (Job Quality) Y32 (Job Quality) Y 33 (Job Quality) X 1 (Work Experience) X2 (Education Background) X3 (Work Environment) X 4 (Information Disclosure) X 5 (Company Survey) X6 (Indirect Information) X7 (Informal Information) Valid N (listwise) 145 Table 1 provides descriptive statistics of key variables used in the regression analyses. Variable Definitions Y11 = Information Comprehension Metric 1: Attention to Accounting Policies and Estimates measured as the average score of the degree of attention paid to accounting policies and estimates information in the questionnaire items; Y12 = Information Comprehension Metric 2: Attention to Financial Statements and Footnotes measured as the average score of the degree of attention paid to financial statements and footnotes in the questionnaire items; Y 21 = Analyzing Ability Metric 1: Usage of Analyzing Tools measured as the average score of three items in the questionnaire: financial ratios commonly cited in textbooks, financial ratios introduced by academic publications and financial ratios self-designed for specific purposes. The purpose of this index is to identify financial ratios used by financial analysts in analyzing historical and industrial comparisons;

12 408 Y. Hu et al. Table 1 continued Y 22 = Analyzing Ability Metric 2: Usage of Stock Valuation Models measured as the average score of five items in the questionnaire: Discounted Cash Flow Model, Dividend Discount Model, Residual Income Model, Economic Value-Added Model and Capital Assets Pricing Model, which are used in financial analysis and valuation; Y 23 = Analyzing Ability Metric 3: Usage of Statistics Models measured as the average score of three items in the questionnaire: arguments with numbers and cases, simple statistical methods and sophisticated statistical modeling, which are employed by listed companies; Y31 = Job Quality Metric 1: Work Impact measured as the average score of six items in the questionnaire: self-evaluation of own influence on ordinary investors investment behaviors, influence on correction of inappropriate operations by listed companies, investment decision reference by own brokerage firm, investment decision reference by other institutional investors, influence on the image of a particular stock and influence on the current price of a particular stock. These items measure analysts self-evaluation on the impact or influence of his/her reports or comments; Y32 = Job Quality Metric 2: Report Publication Probability measured as the score of probability of analysts reports to be published in nation-wide newspapers or magazines in the questionnaire; Y 33 = Job Quality Metric 3: Report Accuracy measured as the score of accuracy of the analysts reports in the questionnaire. This item measures analysts self-evaluation of the quality of their reports; X1 = Work Experience, measured as the score of years of work experience as a financial analyst in the questionnaire; X 2 = Educational Background, measured as the score of whether the analyst hold a master s degree in the questionnaire. It is a dummy variable, which equals 1 with the answer of Yes and 0 with No ; X3 = Work Environment, measured as the business scale of the brokerage firms. The data, drawn from the information disclosed by the Shanghai Stock Exchange, is the ratio of the firm s transaction amounts to the total transaction 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-annual reports and quarterly reports of the listed companies in the questionnaire; X 5 = Analysts collect their information through company surveys, measured as the average score of frequency that analysts use three survey methods on-the-spot visit to the listed companies, news conferences of the listed companies and telephone surveys to the listed companies ; X 6 = Analysts collect their information through indirect information sources, measured as the average score of frequency that analysts use five indirect information sources media coverage of the operation of the listed company, department concerned in the listed company or other securities firms, accounting firms, lawyer s offices and investment consulting firms, stock exchange or supervisory authorities and academic publications ; X 7 = Analysts collect their information through informal information channels, measured as the score of frequency that analysts use private conversations or hearsay.

13 Factors affecting information comprehension, analyzing ability, and job quality 409 mean values, financial analysts in China tend to rely mostly on publicly disclosed information (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 information 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 information 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, Y 11 (Attention to Accounting Policies and Estimates) and Y 12 (Attention to Financial Statements and Footnotes), are positively associated with a two-tailed p value of For the Analyzing Ability variable, the three metrics, Y 21 (Usage of Analyzing Tools), Y 22 (Usage of Stock Valuation Models) and Y 23 (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, Y 31,Y 32 and Y 33. 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 insignificant associations of Y 12 ( the degree of attention paid to financial statements ), Y 23 (the average of three items in the questionnaire of arguments with figures and cases, simple statistical methods and sophisticated statistical models ), and Y 32 ( accuracy of the analysts reports ) with other metrics. Table 2 Pearson correlations among the dependent variables with two-sided p-value in italics Y 11 Y 12 Y 21 Y 22 Y 23 Y 31 Y 32 Y Y Y Y Y Y Y Table 2 provides the Pearson correlation coefficients among the dependent variables used in the regression analyses. For variable definitions, please see Table 1

14 410 Y. Hu et al. 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 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 (Y 11, measured as the value of the degree of attention paid to accounting policies and estimates information ), only X 5 (analysts collect their information through company surveys) has significant impact with the coefficient of 0.20 and t-statistics of 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 comprehension when they conduct more company-level surveys and investigations (coefficient of 0.27 and t-statistics of 4.33). In addition, X 6 (analysts collect their information through indirect information sources) is weakly associated with analysts information comprehension (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 comprehension. 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 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 information 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 information 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 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

15 Factors affecting information comprehension, analyzing ability, and job quality 411 Table 3 Analysis of factors influencing information comprehension Constant x1 x2 X3 x4 x5 x6 x7 work experience educational background work environment info disclosure company survey indirect info sources informal sources Panel A: Dependent Variable is Y 11 (Information Comprehension Metric 1) Coeff Two-sided p-value 6.103*** *** N 153 Adj. R Panel B: Dependent Variable is Y12 (Information Comprehension Metric 2) Coeff Two-sided p-value 6.298*** *** 1.831* N 153 Adj. R ***0.01 sig. level; **0.05 sig. level; *0.1 sig. level Table 3 reports the OLS regression results on the information comprehension model. In Panel A the dependent variable is Y 11, where Y 12 is the dependent variable in Panel B. For variable definitions, please see Table 1

16 412 Y. Hu et al. Table 4 Analysis of factors influencing analyzing ability Constant x 1 x 2 x 3 x 4 x 5 x 6 x 7 work experience educational background work environment info disclosure company survey indirect info sources informal sources Panel A: Dependent Variable is Y 21 (Analyzing Ability Metric 1) Coeff Two-sided p-value 5.8*** *** ** *** 2.547** N 151 Adj. R Panel B: Dependent Variable is Y 22 (Analyzing Ability Metric 2) Coeff Two-sided p-value 3.512*** ** *** N 151 Adj. R Panel C: Dependent Variable is Y23 (Analyzing Ability Metric 3) Coeff Two-sided p-value 6.754*** ** N 151 Adj. R ***0.01 sig. level; **0.05 sig. level; *0.1 sig. level Table 4 reports the OLS regression results on the analyzing ability model. The dependent variables are Y 21,Y 22, and Y 23 for Panels A, B, and C, respectively. For variable definitions, please see Table 1

17 Factors affecting information comprehension, analyzing ability, and job quality 413 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 information channels also impact analysts analyzing ability 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 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%), indicating 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 questionnaire, 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 and t-statistics of In addition, X5 (analysts collect their information through company

18 414 Y. Hu et al. Table 5 Analysis of factors influencing job quality Constant x 1 X 2 x 3 x 4 x 5 x 6 x 7 work experience educational background work environment info disclosure company survey indirect info sources informal sources Panel A: Dependent Variable is Y 31 (Analyzing Quality Metric 1) Coeff Two-sided p-value 4.156*** 3.02*** ** 1.703*** 2.131** N 145 Adj. R Panel B: Dependent Variable is Y 32 (Analyzing Quality Metric 2) Coeff Two-sided p-value 1.704** * N 145 Adj. R Panel C: Dependent Variable is Y33 (Analyzing Quality Metric 3) Coeff Two-sided p-value 9.482*** 3.38*** ** ** N 145 Adj. R ***0.01 sig. level; **0.05 sig. level; *0.1 sig. level Table 5 reports the OLS regression results on the analyzing quality model. The dependent variables are Y 31,Y 32, and Y 33 for Panels A, B, and C, respectively. For variable definitions, please see Table 1