证券分析师胜任能力模型与模糊综合评价

关于证券分析师工作能力的探讨,一直是国内外学者的研究热点。本文首次构建了由四大维度20个指标组成的证券分析师胜任能力模型,并运用问卷调查以及专家咨询等调研手段,对证券分析师胜任能力进行了模糊综合评价,结果表明：当前我国证券分析师总体上还不具备胜任能力,尤其是在＂研究报告客观真实＂、＂预测准确可靠＂以及＂创新意识及独立见解＂方面表现最差,综合素质还有待进一步提高。     

Audit quality and properties of analysts’ information environment

We consider how audit quality impacts sell‐side analysts’ information environment. Using the method outlined by Barron et al., we examine whether higher audit quality is associated with differences in the weight analysts place on common information relative to private information, as well as the extent to which audit quality separately impacts the precision of analysts’ private and common information. Our results show that, in instances where analysts revise their earnings forecasts for year t+1 shortly after the release of year t earnings, higher audit quality results in analysts placing more weight on public information. The precision of private (as well as public) information is improved. These results extend our understanding of how audit quality impacts on attributes of analysts’ forecasts and provides support for the argument that audit quality has important capital market implications.     

Do banks care about analysts' forecasts when designing loan contracts?

We investigate whether banks rely on the information content in equity analysts’ annual earnings forecasts when assessing the risk of potential borrowers. While a long literature finds that analysts provide useful information to market participants, it is not clear that banks, which have access to privileged information, would benefit from publicly available analysts’ forecasts. If, however, banks do rely on this information, then more precise private information in earnings forecasts may inform banks. We focus our analysis on the requirement of collateral because it is a direct measure of default risk, whereas other loan terms such as interest spread and debt covenants can also protect against other risks, such as asset misappropriation. The direct link between collateral and default risk allows us to examine whether information from analysts is relevant to banks when designing loan contracts. Consistent with our predictions, we find that higher precision of the private information in analysts’ earnings forecasts is associated with a lower likelihood of requiring collateral, and this effect is larger when a borrower does not have a prior relationship with the lender or their accounting or credit quality is low. We also find that this association disappears after the implementation of Regulation FD, consistent with this regulation reducing analysts’ access to private information.     

Asset price prediction using seasonal decomposition

We propose a new forecasting procedure for asset prices using seasonal decomposition methods (SD hereafter), e.g., SABL and X-11. Such SD's are based on moving average methods, and they are thus easy to use and are capable of computing the seasonal pattern that changes over time. A SD typically decomposes a series intoT (trend),S (seasonal component), andR (residual or sometimes referred to as the irregular component). We use an ARIMA model onR to obtain its forecast. TheS component is forecasted by an extrapolation taking into account its changing pattern within the sample period. We propose to set up some scenarios on theT component by examining its possibly nonlinear and nonstationary behavior, and in the paper we suggest one possible way for this. Suppose that the forecasting horizon is relatively short compared toT's several cycles just before the end of the sample. Then we may safely extrapolateT linearly into the forecasting period. LinearizingT in such a case, makes sense. As to the slope of the linear line, we suggest the average rate of change of the most recent upward phase of a cycle to be used if we needed an optimistic scenario. Obviously, that of the downward phase may be used for constructing a pessimistic scenario, and that of one entire cycle is suitable for ‘average’ scenario. Once the forecasted values of the three components are obtained, we may put them back to make predictions on the original series based upon various different scenarios. In addition to proposing a new prediction method, we looked into the following issues, among others, in the paper: (1) on what sort of asset prices would our forecasting method work well? (2) Any significant differences if we used X-11 instead of SABL?     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

Analysts’ earnings forecasts: coexistence and dynamics of overconfidence and strategic incentives

This paper formulates a two-stage model to capture the decision process of financial analysts when issuing earnings forecasts. Our model extends the model of Chen and Jiang [(2005). Analysts’ weighting of private and public information. Review of Financial Studies, 19 (1), 319–355], by allowing for a distortion of forecasts independent of whether an analyst has private information. Using quarterly earnings forecasts, we provide empirical evidence on the coexistence of overconfidence and strategic incentives. Financial analysts overweight their private information and at the same time strategically inflate their forecast.     

Book Review

This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication.     

Ratings Changes,Ratings Levels,and the Predictive Value of Analysts’ Recommendations

We show that abnormal returns to analysts’ recommendations stem from both the ratings levels assigned and the changes in those ratings. Conditional on the ratings change, buy and strong buy recommendations have greater returns than do holds, sells, and strong sells. Conditional on the ratings level, upgrades earn the highest returns and downgrades the lowest. We also find that both ratings levels and changes predict future unexpected earnings and the associated market reaction. Our results imply that 1) investment returns may be enhanced by conditioning on both recommendation levels and changes; 2) the predictive power of analysts’ recommendations reflects, at least partially, analysts’ ability to generate valuable private information; and 3) some inconsistency exists between analysts’ ratings and the formal ratings definitions issued by securities firms.     

Finding diamonds in the rough: Analysts’ selective following of loss‐reporting firms

Investors face greater difficulty valuing loss‐reporting than profit‐reporting firms: losses may be due to very different reasons (e.g., poor operating performance or investments in intangibles, and financial accounting information is of more limited use for valuing loss‐making firms than profit‐making firms. Because of increased uncertainty about loss firms’ future financial and business viability, we hypothesize that financial analysts will be more selective when choosing to follow loss firms than profit firms, with the result that “abnormal” analyst following will be more informative to investors regarding the future performance of loss firms than profit firms. Consistent with this prediction, we find that abnormal analyst coverage is useful for predicting firms’ future prospects, and is more strongly associated with future performance (stock returns and ROA) for loss firms than for profit firms. The market, however, does not seem to use this useful information when pricing loss firms: for loss firms a portfolio investment strategy based upon abnormal analyst following can generate positive excess returns over 1‐ to 3‐year holding periods. These results are stronger for persistent‐loss firms than for occasional‐loss firms. We conclude that abnormal analyst following contains useful information about firms’ future prospects, and even more so for loss firms than for profit firms.     

Inside the “Black Box” of Sell‐Side Financial Analysts

Our objective is to penetrate the “black box” of sell‐side financial analysts by providing new insights into the inputs analysts use and the incentives they face. We survey 365 analysts and conduct 18 follow‐up interviews covering a wide range of topics, including the inputs to analysts’ earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All‐Star status, and the factors they consider indicative of high‐quality earnings. One important finding is that private communication with management is a more useful input to analysts’ earnings forecasts and stock recommendations than their own primary research, recent earnings performance, and recent 10‐K and 10‐Q reports. Another notable finding is that issuing earnings forecasts and stock recommendations that are well below the consensus often leads to an increase in analysts’ credibility with their investing clients. We conduct cross‐sectional analyses that highlight the impact of analyst and brokerage characteristics on analysts’ inputs and incentives. Our findings are relevant to investors, managers, analysts, and academic researchers.     

Cash flow disaggregation and the prediction of future earnings

We examine the incremental information content of the components of cash flows from operations (CFO). Specifically the research question examined in this paper is whether models incorporating components of CFO to predict future earnings provide lower prediction errors than models incorporating simply net CFO. We use Australian data in this setting as all companies were required to provide information using the direct method during the sample period. We find that the cash flow components model is superior to an aggregate cash flow model in terms of explanatory power and predictive ability for future earnings; and that disclosure of non‐core (core) cash flows components is (not) useful in both respects. Our results are of relevance to investors and analysts in estimating earnings forecasts, managers of firms in regulators’ domains where choice is provided with respect to the disclosure of CFO and also to regulators’ deliberations on disclosure requirements and recommendations.     

公平信息披露与分析师预测精度——来自中国上市公司的经验证据

证券分析师是证券市场重要的信息加工者和传播者,他们的信息行为对中小投资者和市场效率有重要影响。本文基于2003~2009年分析师的年度盈利预测数据,运用面板计量模型实证检验了公平信息披露规则的实施对分析师预测精度的影响。研究结果表明:分析师预测精度在规则实施后显著下降了;而且,随着规则实施时间的推移,分析师预测精度进一步下降;另外,分析师对信息披露水平较差的上市公司的预测精度下降幅度更大。     

A Comparison of Between‐Country Measures of Legal Setting and Enforcement of Accounting Standards

Academics and practitioners agree that the enforcement of accounting standards has an important role in promoting high quality financial reporting and favourable capital market outcomes. We test three new enforcement proxies from Brown, Preiato and Tarca (2014) that focus specifically on auditing and accounting enforcement. We examine firms’ information environments, represented by the error in analysts’ consensus forecasts and the extent of disagreement among analysts, as indicated by forecast dispersion. For financial years ending from 2003 to 2009, we construct a sample of 357,034 firm–month observations on the errors and dispersion of analysts’ earnings forecasts for 10,769 firms domiciled in those 39 countries. We find that higher scores for all three proxies are associated with lower error and less disagreement in forecasts. In addition, we find that the indices have significant explanatory power when previously used enforcement proxies (such as Kaufmann et al.'s 2010 rule of law measure) are included in the regression models, pointing to the importance of specific measures of accounting enforcement. We conclude that accounting enforcement may be more important in securing favourable economic outcomes than has been previously realised, because researchers commonly have used noisier, more general legal proxies for enforcement that understate its marginal effects.     

Asymmetric trading by insiders – comparing abnormal returns and earnings prediction in Spain and Australia

This paper examines whether the ‘external governance’ imposed by comparative financial accounting standards reduces the trading advantage of insiders. We do this by directly comparing insider trading returns and insider’s ability to predict future earnings from accruals in Spain and Australia. Results show higher excess returns and greater prediction of future earnings from conditioned insider trading in Australia that is then utilized by financial analysts to lower forecast errors – particularly in contrarian‐based accruals trading. Possible explanations include: (i) a high asymmetric quality for market‐based accruals, (ii) information transfer from informed insiders to uninformed insiders and financial analysts and (iii) a more timely dissemination of financial information in Spain through different ownership and governance structures.     

Seeing is believing: analysts’ corporate site visits

This study examines the impact of corporate site visits on analysts’ forecast accuracy based on a sample of such visits to Chinese listed firms during 2009–2012. We find that analysts who conduct visits (“visiting analysts”) have a greater increase in forecast accuracy than other analysts. Consistent with the notion that site visits facilitate analysts’ information acquisition through observing firms’ operations, we find that the results are stronger for manufacturing firms, firms with more tangible assets, and firms with more concentrated business lines. Moreover, we find that the effect of a site visit is greater when the site visit is an analyst-only visit, when the current visit is preceded by fewer visits, and when visiting analysts are based far from the visited firms. Furthermore, we find that site visits partially mitigate nonlocal analysts’ information disadvantage. Collectively, these results indicate that site visits are an important information acquisition activity for analysts.     

When do stock analysts find bond rating changes informative?

Credit rating agencies (CRAs) have considerable privileged access to corporate management and are therefore a potentially important source of information to the equity market. We study how stock analysts incorporate bond ratings in their earnings forecasts. We develop an economic framework to explain why equity analysts might look to CRAs as an information source, especially after Regulation Fair Disclosure. Using this framework, we characterize the association between ratings changes and earnings forecast revisions surrounding these changes. We examine whether the extent to which equity analysts glean information from ratings changes is related to the extent and importance of information conveyed in the ratings change and analysts’ information uncertainty. We find that characteristics we examine are strongly related to stock analysts’ use of information in rating downgrades.     

An Analysis of Accounting Frauds and the Timing of Analyst Coverage Decisions and Recommendation Revisions: Evidence from the US

This paper provides a comprehensive exploration of the types of accounting fraud committed by firms over the period 1995–2009. Using detailed data from US SEC Accounting and Auditing Enforcement Releases (AAER), we examine the likelihood and timing of analyst coverage decisions and recommendation revisions related to fraud firms versus firms without accounting fraud. We find that analysts have a higher probability of taking the more severe action of dropping coverage rather than only revising down recommendations for firms with any type of accounting fraud and also for specific egregious types of accounting fraud. Through the use of competing hazards models, we also find that accounting frauds and their egregiousness are positively (negatively) associated with the timeliness of the analysts’ action to drop coverage (revise only). Overall, we find that analysts’ actions may be useful in determining the occurrence of accounting fraud prior to the public announcement of the fraud.     

Before an Analyst Becomes an Analyst: Does Industry Experience Matter?

Using hand‐collected biographical information on financial analysts from 1983 to 2011, we find that analysts making forecasts on firms in industries related to their preanalyst experience have better forecast accuracy, evoke stronger market reactions to earning revisions, and are more likely to be named Institutional Investor all‐stars. Plausibly exogenous losses of analysts with related industry experience have real financial market implications—changes in firms’ information asymmetry and price reactions are significantly larger than those of other analysts. Overall, industry expertise acquired from preanalyst work experience is valuable to analysts, consistent with the emphasis placed on their industry knowledge by institutional investors.     

Motivations behind users’ participation in the standard-setting process: Focus on financial analysts

The aim of this study is to investigate what motivates financial analysts to participate in the accounting standard-setting process. We focus on financial analysts because they are an important group of the financial statements users. The paper employs the meso-level approach used by Durocher et al. (2007) that integrates the macro domain’s focus on the standard setters with the micro domain’s focus on individuals and thus it links the characteristics of due process for standard setting with users’ attitudes. We develop a survey for the Chartered Financial Analysts Institute (CFA), which is one of the largest associations of investment professionals in the world, and collected data through computer-assisted Web interviews. We use a structural equation model with PLS to test our hypotheses. Our main findings confirm that a combination of micro and macro domains explains the frequency of financial analysts’ participation in the standard setting process. This investigation, thus, deepens our understanding of motivations behind analysts’ involvement in the accounting standard-setting process and delivers both theoretical contributions and practical insights.     

An Assessment of Empirical Model Performance When Financial Market Transactions are Observed at Different Data Frequencies: An Application to East Asian Exchange Rates

This paper compares the performance of alternative models of east Asian exchange rates at different data frequencies. Selected models employ different specifications of the conditional variance and the conditional error distribution. Conditional variance specifications include: homoscedasticity, GARCH, LGARCH, and EGARCH. Conditional error distribution specifications include normal and Student t. The best exchange rate model specification is clearly conditional on data frequency. Higher frequency (daily, weekly) data commonly exhibit characteristics that demand more sophisticated estimation methods than analysts commonly employ. These characteristics generally vanish at lower (monthly, quarterly) frequencies. Overall we find significant benefit from accommodating heteroscedasticity and leptokurtic properties of the conditional distribution as data frequency increases. Using a likelihood ratio test we compare the relative gain from addressing heteroscedasticity (through use of GARCH models) versus accommodation of leptokurtosis. This comparison suggests that the gains from correct specification of the conditional distribution dominate those obtained from addressing problems of heteroscedasticity.