Analyst Characteristics Research Articles

Behavioral bias in number processing: Evidence from analysts’ expectations

Research in neuropsychology shows that individuals process small and large numbers differently. Small numbers are processed on a linear scale, while large numbers are processed on a logarithmic scale. In this paper, we show that financial analysts process small prices and large prices differently. When they are optimistic (pessimistic), analysts issue more optimistic (pessimistic) target prices for small price stocks than for large price stocks. Our results are robust when controlling for the usual risk factors such as size, book-to-market, momentum, profitability and investments. They are also robust when we control for firm and analyst characteristics, or for other biases such as the 52-week high bias, the preference for lottery-type stocks and positive skewness, and the analyst tendency to round numbers. Finally, we show that analysts become more optimistic after stock splits. Overall, our results suggest that a deeply-rooted behavioral bias in number processing drives analysts’ return expectations.

Bold Stock Recommendations: Informative or Worthless?

We select a small set of recommendations that lie in the upper and lower tail of the empirical distribution of divergences between a recommendation, and the consensus over the window (-30, -1)-days prior to that recommendation. We classify these extremely divergent recommendations as bold, and then subdivide them into informative bold recommendations that lead other analysts (leading-bold) and those that are ignored by other analysts (contra-bold) based on the consensus change in the thirty days after the announcement. We focus on the information conveyed to the market by these bold, leading-bold, and contra-bold recommendations through their effects on Cumulative Abnormal Returns (CAR). We find that bold recommendations are not anticipated by market participants (CAR’s are negative before a bold buy and positive before a bold sell). The next finding is that the market responds strongly to both leading and contra bold recommendations over the (0, +4) window and that these reactions are stronger than that to non-bold recommendations. In contrast, over the longer (0, +30)-day window, leading-bold recommendations earn additional returns whereas contra-bold ones reverse significantly due to lack of confirmation. The overall pattern is one of rational market reaction both in the short and long windows. We support the rationality of the market reaction by showing that the percentage of leading-bold recommendations exceeds that of contra-bold recommendations, and that these two types of recommendations cannot be separated using observable analyst characteristics such as experience or brokerage size.

Analyst Characteristics and the Level of Critical Perception of Goodwill Accounting

AbstractWhile inputs used in analysts’ valuation models have been documented (Brown et al. 2015), it has not been studied how analysts’ personal characteristics are associated with their level of critical perception regarding agency conflict manifested in published financial statements. This is important because analysts are, to some extent, substitutes for industry‐specialised auditors in monitoring financial reporting (e.g., Sun and Liu 2011). To address this shortcoming, we investigate analysts’ level of critical perception concerning current goodwill accounting standards, which provide significant reporting latitude. We use data from a survey of Nordic financial analysts and examine this data with statistical methods, including factor analysis and structural equation modelling. We find two latent constructs, one which reflects a critical attitude to the current goodwill accounting standards and one which reflects an uncritical attitude to the current standards. Our structural equation model suggests that having a background in auditing, as well as general experience and exposure to different industries, is associated with a higher degree of critical perception of current standards. In contrast, having a basic (low) level of formal business (accounting) education is associated with a more uncritical perception than having a high level of education.

The Walk-Down to Beatable Forecasts: International Evidence

We use the framework developed in Richardson et al. (2004) to identify country, firm and analyst characteristics that we expect to be associated with the prevalence of the analyst walk-down forecast pattern. Based on a large sample of 50,649 analysts covering 33,645 firms from 46 countries during 1992-2014, we find that the walk-down pattern positively correlates with country characteristics related to insider trading restrictions and equity sales. It also positively relates to the stock market reward for beating analyst forecasts, firm-level characteristics underlying management concerns with share prices after earnings announcements, and analysts’ incentives to cooperate with management. The effects of these factors on the walk-down pattern are more pronounced in countries with better media-coverage institutions. Overall, these findings suggest that capital market incentives affecting the communication between managers and analysts and the resulting analyst forecast bias involves various forces including a country’s institutional infrastructure, and firm and analyst characteristics.

Determinants of analysts’ revenue forecast accuracy

This study investigates financial analysts’ revenue forecasts and identifies determinants of the forecasts’ accuracy. We find that revenue forecast accuracy is determined by forecast and analyst characteristics similar to those of earnings forecast accuracy—namely, forecast horizon, days elapsed since the last forecast, analysts’ forecasting experience, forecast frequency, forecast portfolio, reputation, earnings forecast issuance, forecast boldness, and analysts’ prior performance in forecasting revenues and earnings. We develop a model that predicts the usefulness of revenue forecasts. Thereby, our study helps to ex ante identify more accurate revenue forecasts. Furthermore, we find that analysts concern themselves with their revenue forecasting performance. Analysts with poor revenue forecasting performance are more likely to stop forecasting revenues than analysts with better performance. Their decision is reasonable because revenue forecast accuracy affects analysts’ career prospects in terms of being promoted or terminated. Our study helps investors and academic researchers to understand determinants of revenue forecasts. This understanding is also beneficial for evaluating earnings forecasts because revenue forecasts reveal whether changes in earnings forecasts are due to anticipated changes in revenues or expenses.

The impact of executive inside debt on sell-side financial analyst forecast characteristics

Debt-like compensation, referred to as inside debt, is prevalent in US firms and affects firm operating, investing and financial reporting activities. The amount of inside debt can be used to extract information that benefits analyst forecasting activities. This study finds that forecast accuracy increases, while forecast dispersion and revision volatility decrease with the magnitude of inside debt. Further analysis shows that inside debt is associated with increased propensity of firms to provide voluntary disclosures and the documented benefits on analyst characteristics accrue only to firms that offer close to optimal level of inside debt (Jensen and Meckling in J Financ Econ 3:305–360, 1976; Edmans and Liu in Rev Finance 15:75–102, 2011). Our research is the first to link debt-like compensation to financial analyst behavior and contributes to the understanding of the implications of inside debt to outside market participants.

Predicting Sell-Side Analysts' Relative Earnings Forecast Accuracy When It Matters Most

We introduce a novel framework to predict the relative accuracy of sell-side analysts’ annual earnings forecasts out-of-sample. Prior studies only evaluate forecasts shortly before the corresponding earnings release. In contrast, our study is the first to provide long-term predictions which are of particular value for both investors and academics. Overall, we show that analysts classified as superior outperform their inferior counterparts by 8.4 percent, on average. The prediction performance is even more pronounced for longer-term forecasts and for firms with high dispersion of analysts’ forecasts, that is, when the identification of superior forecasts matters most. Moreover, we challenge the conclusion of existing literature that characteristics reflecting an analyst’s skill set are not helpful to obtain better predictions. In particular, when evaluating forecasts which draw on similar information sets, we find that a model based on analyst characteristics outperforms a model focusing simply on the forecast horizon, for example.

The Effect of Accounting Reporting Complexity on Financial Analysts

We examine the relation between accounting reporting complexity and analysts’ performance and whether analysts’ qualifications, experience, and expertise in specific financial domains help them more effectively process complex information. We document an inverse relation between complexity and analysts’ performance. Further, we show that analysts’ firm-specific experience, industry focus, and CFA certification alleviate some of the adverse effects of complexity, whereas analysts’ general experience does not appear to do so. Using an XBRL-based approach, we also develop new measures of analysts’ expertise and find that expertise in the areas of fair value, derivatives and pension are more valuable than other analyst characteristics in attenuating the negative effects of complexity arising from transactions and events in these areas. Overall, this study underscores the importance of analyst characteristics and the need to simplify the complex disclosures in the notes to the financial statements.

Quantifying the Effect of Information and Ability Spillovers on Analyst Earnings Forecast Accuracy

Prior research suggests a large information and ability spillover effect among sell-side analysts. We use an iterative model that relates mean peer group ability along with the analyst’s own ability and firm-quarter fixed effects to the analyst’s EPS forecast accuracy and find surprising evidence of no spillovers. We perform three tests to understand this result. First, we find that this result is robust to controlling for time-varying analyst characteristics. Second, this result relates to the inclusion of firm-quarter fixed effects which control for the changes in firms’ information environment across quarters and analysts’ self-selection to provide forecasts for certain quarters. When we include firm or firm-year fixed effects in lieu of firm-quarter fixed effects, the effect of spillovers on analysts’ EPS forecast accuracy is large and significantly positive. Lastly, this result is unique to EPS spillover. We find significant and positive spillovers for other analyst performance measures such as revenue forecast accuracy, forecast adjustment speed, and the number of non-EPS forecast type production. This study, thus, extends the existing literature by presenting a new way to estimate the spillovers and providing new insights on how analysts interact and learn.

The firm information environment and capital structure: international evidence

ABSTRACTThis article investigates the effect of the firm information environment, characterized by the analyst characteristics, on firms’ capital structure choices and whether this effect varies according to country-level institutional environments. Using a comprehensive international data set that covers 19 939 firms across 41 countries between 2000 and 2010, we document two key findings. First, firms with lower analyst coverage, higher forecast dispersion and higher forecast errors have higher leverage. Second, the effect of the firm information environment on corporate leverage is attenuated in countries with stronger governance mechanism and better information transparency. This result suggests that a firm’s information environment is an important factor influencing its capital structure decision and that country-level institutional environments matter to this effect.

The Effects of Disclosure and Analyst Regulations on the Relevance of Analyst Characteristics for Explaining Analyst Forecast Accuracy

AbstractWe posit and find an effect of disclosure and analyst reporting regulations implemented from 2000 through 2003 (including Regulation Fair Disclosure, the Sarbanes‐Oxley Act and the Global Settlement Act) on the importance of analyst and forecast characteristics for analyst forecast accuracy. Following the enactment of these regulations, more experienced analysts and All‐Star analysts do not maintain their superior forecast accuracy, and analysts employed by large brokerage houses perform worse than other analysts. In addition, we find a decrease in the importance of analyst effort, the number of industries and firms followed, days elapsed since the last forecast, and forecast horizon. While the importance of bold upward forecast revisions does not change, bold downward revisions lose their relevance for forecast accuracy after 2003. Finally, we find an increase in the importance of prior forecast accuracy. We find that the importance of these characteristics varies with the precision of publicly available information. Specifically, the decrease in the importance of most analyst and forecast characteristics and the increase in the importance of prior forecast accuracy are greater when the precision of publicly available information is low. Overall, our results suggest that the positive effects of experience, effort, brokerage house size and All‐Star status on forecast accuracy in the pre‐regulation period were because of the information advantages that these analysts enjoyed (rather than their ability to generate private information). In contrast, our results suggest that prior forecast accuracy is related to analysts’ ability to generate private information.

Do U.S. Analysts Improve the Local Information Environment of Cross-Listed Stocks? Evidence from Recommendation Revisions

We investigate the role of U.S. analysts in facilitating home market information transmission for firms from 40 countries cross-listed in the U.S.. Recommendation revisions by U.S. analysts lead to significantly higher (lower) abnormal returns (volumes) in the home market compared to those by local analysts. This U.S.-location premium to information production cannot be explained by a bonding or certification role of U.S. analysts or differences in broker or analyst characteristics. Our results suggest that U.S. analysts facilitate U.S. investors’ access to foreign firms’ home markets and improve the information environment particularly in countries where the local analyst advantage is smaller.

Changes in the I/B/E/S Database and Their Effect on the Observed Properties of Analyst Forecasts

I/B/E/S is a common source of analyst forecast data, and the consistency of the data is important for both practice and academic research. We document substantial changes in the historical contents of I/B/E/S over time. We compare two versions of the I/B/E/S EPS detail file: one available in 2009 and the other available in 2015. Examining a common sample period (1993 through 2007), we find that 12 percent of the observations in the earlier version of I/B/E/S are omitted from the later version, whereas 6 percent are added to the later version as new observations that did not exist in the earlier version, and 9 percent of the observations in both versions report different forecasts of earnings. These discrepancies are systematically associated with various firm and analyst characteristics. We find that earnings forecasts in the later version are significantly more accurate, less biased, and bolder than the forecasts in the earlier version. We also find substantial differences in the firms identified as meeting or just beating analysts’ expectations of earnings. Finally, these discrepancies are less pronounced when comparing separate versions of the EPS summary file, but are more pronounced when examining individual analyst quarterly forecasts and annual cash flow forecasts.

Effect of Domain Knowledge on Elicitation Effectiveness: An Internally Replicated Controlled Experiment

Context. Requirements elicitation is a highly communicative activity in which human interactions play a critical role. A number of analyst characteristics or skills may influence elicitation process effectiveness. Aim . Study the influence of analyst problem domain knowledge on elicitation effectiveness. Method . We executed a controlled experiment with post-graduate students. The experimental task was to elicit requirements using open interview and consolidate the elicited information immediately afterwards. We used four different problem domains about which students had different levels of knowledge. Two tasks were used in the experiment, whereas the other two were used in an internal replication of the experiment; that is, we repeated the experiment with the same subjects but with different domains. Results. Analyst problem domain knowledge has a small but statistically significant effect on the effectiveness of the requirements elicitation activity. The interviewee has a big positive and significant influence, as does general training in requirements activities and interview experience. Conclusion. During early contacts with the customer, a key factor is the interviewee; however, training in tasks related to requirements elicitation and knowledge of the problem domain helps requirements analysts to be more effective.

Do analysts cater to investor beliefs via target prices

This study examines analyst target price bias within the framework of catering theory. Given that analyst catering is more probable when the clients of the analyst forecasts are less sophisticated investors who are less likely detect it, we focus on a unique stock market where individual investors rather than institutions are the predominant group and account for 75% of the total security trading. Our results show that analysts do cater to investors via overshooting actual end-of-forecast-period prices even after controlling market sentiment index, analyst and company characteristics. Furthermore, results show that foreign analysts produced more biased target prices compared to domestic peers.

Bloodstain pattern classification: Accuracy, effect of contextual information and the role of analyst characteristics

It is becoming increasingly apparent that contextual information can exert a considerable influence on decisions about forensic evidence. Here, we explored accuracy and contextual influence in bloodstain pattern classification, and how these variables might relate to analyst characteristics. Thirty-nine bloodstain pattern analysts with varying degrees of experience each completed measures of compliance, decision-making style, and need for closure. Analysts then examined a bloodstain pattern without any additional contextual information, and allocated votes to listed pattern types according to favoured and less favoured classifications. Next, if they believed it would assist with their classification, analysts could request items of contextual information – from commonly encountered sources of information in bloodstain pattern analysis – and update their vote allocation. We calculated a shift score for each item of contextual information based on vote reallocation. Almost all forms of contextual information influenced decision-making, with medical findings leading to the highest shift scores. Although there was a small positive association between shift scores and the degree to which analysts displayed an intuitive decision-making style, shift scores did not vary meaningfully as a function of experience or the other characteristics measured. Almost all of the erroneous classifications were made by novice analysts.

Do Analyst Forecasts Vary Too Much?

ABSTRACTIn this paper we examine the time-series and cross-sectional volatility in analyst forecasts. We derive a bound on the degree of variation in forecasts, analogous to the variance bound literature in finance, and document the frequency and circumstances surrounding violations of this bound. We find that the time-series of individual forecasts are excessively volatile approximately 17 percent of the time, affecting up to 50 percent of the aggregate market value of stocks. We also find that the market-wide frequency of excessively volatile forecasts in a year is positively correlated with aggregate stock market volatility and market sentiment, and is negatively correlated with future aggregate stock returns. We find that the cross-section of analyst forecasts are excessively volatile approximately 8 percent of the time, and observe that excessively volatile forecasts are more common for larger firms. As a precursor to identifying the underlying causes and consequences of excessively volatile forecasts, we describe the time period characteristics, analyst characteristics, and firm characteristics that are associated with these events.

Understanding Rankings of Financial Analysts

The prediction of the most accurate analysts is typically modeled in terms of individual analyst characteristics. This approach has the disadvantage that these data are hard to collect and often unreliable. We follow a different approach in which we characterize the general behavior of rankings of analysts based upon state variables rather than individual analyst characteristics or past accuracy. We use a common learning algorithm, naive Bayes, that we adapted to address the problem of ranking the analysts. Our results show that it is possible to model the relation between the selected variables and the rankings. We show that the uncertainty about future stock performance influences the rankings of the analysts while the macroeconomic variables have the most contribution to the changes in rankings.

Herding in analysts’ recommendations: The role of media

Abstract This study investigates the impact of media on analysts’ herding behavior when making stock recommendations. We find three main results. First, we find that analysts herd less when stocks are covered more in the media. Second, when the firm has negative media sentiment, analysts tend to herd more. Third, higher disagreement in the media is associated with a higher tendency to herd among analysts. These findings are robust to the confounding effect of news flows on returns as well as to alternative explanations. In addition, we find that the effect of media on the herding behavior is conditional on analyst characteristics.

Do Analysts Forecasts Vary Too Much?

In this paper we examine the time-series and cross-sectional volatility in analyst forecasts. We derive a bound on the degree of variation in forecasts, analogous to the variance bound literature in finance, and document the frequency and circumstances surrounding violations of this bound. We find that the time-series of individual forecasts are excessively volatile approximately 17 percent of the time, affecting up to 50 percent of the aggregate market value of stocks. We also find that the market-wide frequency of excessively volatile forecasts in a year is positively correlated with aggregate stock market volatility and market sentiment, and is negatively correlated with future aggregate stock returns. We find that the cross-section of analyst forecasts are excessively volatile approximately eight percent of the time, and observe that excessively volatile forecasts are more common for larger firms. As a precursor to identifying the underlying causes and consequences of excessively volatile forecasts, we describe the time period characteristics, analyst characteristics, and firm characteristics that are associated with these events.