Analyst Behavior Research Articles

Do Corporate Disclosures Constrain Strategic Analyst Behavior?

We show that U.S. analysts alter their behavior in response to a randomly assigned shock that exogenously varies the timeliness and cost of accessing companies’ mandatory disclosures in the cross-section of investors: analysts drop coverage of stocks with high disclosure quality, issue less optimistic and more accurate forecasts that are less bold and less informative, and collectively reduce forecast dispersion. Our investigation of possible channels favors the explanation that analysts reduce a strategic component of their behavior: the changes are more pronounced among analysts with stronger incentives to strategically skew their forecasts, such as affiliated analysts and those catering to retail investors. We conclude that mandatory disclosure can be a substitute for information production by analysts, whose behavior is constrained by investors’ ability to verify their forecasts using corporate filings.

Model Dependent Realism and the Rule-Governed Behavior of Behavior Analysts: Applications to Derived Relational Responding.

A fundamental assumption within radical behaviorism is that all human behavior, including the rule-governed behavior of scientists, can be understood within a functional account. I propose that models of human behavior can be best described as a set of rules that are selected by behavior analysts to solve applied challenges, rather than descriptions of nature as it "truly exists." Model dependent realism (MDR) developed within the field of physics may provide useful criteria that could allow behavior analysts to more accurately track the relative probability of success of a given model within applied contexts. As a case example, I examine dispersive models of derived relational responding in terms of the criteria outlined within MDR, and I describe a preliminary level-scaling account of derived relational responding that encompasses several models in pursuit of a unified account. The account is context dependent and adopts a pragmatic truth criterion, consistent with assumptions within functional contextualism and radical behaviorism as an overarching rule governing the behavior of our applied subfield.

Financial restatements and sell-side analysts' stock recommendations: evidence from Malaysia

PurposeThe purpose of this paper is to examine the influence of financial restatements on the sell-side analysts' stock recommendations.Design/methodology/approachThe sample of this study is based on a dataset from a panel of 246 Malaysian public listed companies for the period 2008 to 2013 (651 company-year observations). This study employs feasible generalized least squares regression.FindingsThis study finds a negative and significant relationship between restated companies and sell-side analysts' stock recommendations, which means that sell-side analysts issue less favorable stock recommendations for restated companies.Practical implicationsThe findings based on observations from an emerging economy complement the results of the US studies that analysts revise their earnings forecasts or recommendations downwards or drop coverage following financial restatements. The results of this study should be useful to capital market participants in understanding how analysts perceive and evaluate restated companies.Originality/valueThis paper expands the literature on financial restatements consequences in an emerging market which is largely unstudied. Prior research on analyst behavior towards restatements has focused on the consequences of restatements in terms of analyst following and forecast accuracy and dispersion. This study examines if and how the restatements affect the analysts' final output as reflected in the recommendation opinion, an area that has so far received little attention.

Sustainability of Analyst Recommendations in Multiple Lead Underwriter IPOs

Recently, a new organizational form of syndicate—multiple lead underwriter (MLU) initial public offerings (IPOs)—has emerged in IPOs. In addition to the increased deal complexity, lead underwriters in MLU IPOs face a new competition between them after underwriter selection, which is not present in single lead underwriter (SLU) IPOs. It is therefore questionable whether recommendations by analysts from lead underwriters of the MLU IPOs are as sustainable as those of the SLU IPOs. We examine IPO recommendations to capture how this new syndicate structure affects analyst behavior in terms of analyst optimism and investment value. In contrast to the popular conflict of interest perspective, our findings point to the notion that the new syndicate structure suppresses bias in recommendations and that reputation upholding incentive dominates pressure from competition. MLU-affiliated analysts are not more optimistic and provide more informative research coverage whose informativeness, however, fades away shortly after the recommendation releases. Our findings overall indicate the existence of sustainability in the MLU IPO recommendations.

The effect of affiliated analysts on stock recommendations: Evidence from share pledges in China

We use the share pledge context in China to examine how affiliated analysts whose securities companies are pledgees of share pledge firms issue stock recommendations on these listed firms. We find that their recommendations are more optimistic than those of non-affiliated analysts, and they are more likely to issue Buy and Add recommendations, suggesting that they issue optimistic rating reports for share pledge firms due to their conflicts of interest. We also find a dynamic adjustment in the stock recommendation behavior of these analysts, and their probability after issuing optimistic stock recommendations is significantly reduced before and after the years that the affiliation relationship between them and share pledge firms both began and ended. These affiliated analysts continue to issue optimistic stock recommendations after visiting the share pledge firms if they work in the same location as the firms, or if they are star analysts among New Fortune’s “top five analysts,” and when the information transparency of the share pledge firms is higher. In addition, the optimistic stock recommendation behavior of affiliated analysts is more significant in our sample of firms with high share pledge ratios and downward stock price pressure. The earnings forecast quality of affiliated analysts is also found to be lower, and they are less inclined to downgrade stock recommendations for these share pledge firms. Buy recommendations issued by both non-affiliated and affiliated analysts can bring cumulative excess returns in the short event window, but those issued by affiliated analysts are significantly negative in the long-term event window, and significantly lower than those issued by non-affiliated analysts. Overall, our study shows that affiliated analysts issue optimistic rating reports on share pledge firms due to conflicts of interest, which leads to decision-making bias in investors and thus decreases the stock price crash risk of the firms. Our findings further reveal the economic consequences of share pledging and extend our understanding of the behavior of analysts in a conflict of interest situation from the share pledge perspective.

How do Banking Analysts behave around Unanticipated News? Evidence from Operational Risk Event Announcements

We study earnings per share (EPS) forecast revision and accuracy of banking analysts around operational risk event announcements in U.S. banks. We find that first announcements of operational risk events are more informative than their settlement announcements. Optimistic banking analysts revise their forecasts downward more aggressively around operational risk disclosures, thereby improving forecast accuracy. Career concerns of banking analysts cause an upward bias in forecast revision and deterioration in forecast accuracy only if the potential employer is a systemically important bank (SIB). We find consistent evidence linking competition among banking analysts with optimistic and inaccurate forecasts, which is consistent with analysts seeking to use inflated forecasts to curry favour and attract businesses to their brokerage house around the time of operational risk disclosures. Global settlement has no favourable impact on analyst forecast accuracy around operational risk event announcements. We find evidence supporting a materiality threshold of $10 million for the informativeness of operational risk event announcements in SIBs. Overall, our results shed light on optimism bias in banking analyst behaviour upon the arrival of unanticipated news.

Social Networks Analysis in Accounting and Finance

Social network analysis (SNA) shifts the focus from individuals and organizations in isolation to their environment and the connections around them, making it a useful tool to investigate the underlying mechanisms of corporate decisions. In this article we systematically summarize SNA research in accounting and finance and offer a roadmap that may help this emerging literature to move forward. We identify 56 studies published in top accounting and finance journals between 2007 and 2020 employing SNA. These studies examine a wide variety of topics, including corporate governance, financial reporting and audit quality, investor decisions, lender decisions, analyst behavior, management compensation, and tax strategies. We provide an overview of the most relevant paradigms of network theory and classify the 56 studies accordingly. We also provide an outline of the key concepts and tools of SNA, including an empirical illustration. Finally, we critically discuss major empirical findings, opportunities, and challenges for future research. We believe SNA will continue to offer an interesting avenue to conduct high-impact and cross-disciplinary research in accounting and finance.

Social Learning and Analyst Behavior

This study examines whether sell-side equity analysts engage in “social learning” where their earnings forecasts are influenced by the forecasts and outcomes of other analysts associated with other firms (i.e., the “peers”) in their respective portfolios. We find that analyst optimism is negatively correlated with the recent forecast errors among peers on other firms in the analyst portfolio. An analyst is also more likely to issue “bold” forecasts when similar forecasts were recently issued for other portfolio firms. Analysts learn more from their peers with similar personal characteristics. Overall, social learning is beneficial to analysts and improves their forecast accuracy.

Corporate organizational structure, tax havens and analyst forecast properties

ABSTRACTThis study investigates how corporate organizational complexity affects analyst forecast behavior and the information environment of the firm. Using a unique sample of publicly traded Taiwan companies which are required to disclose information on all of their affiliates, we capture the feature of organizational complexity by measuring the number of investment layers connecting the parent firm and the lowest-tiered subsidiary. We argue that long chains of investment layers, which are associated with higher information asymmetry and agency costs, are likely to reduce the quality of financial reporting and complicate analysts’ forecast tasks. We find that, after controlling for other determinants of forecasting behavior, analysts experience greater difficulty in predicting the earnings of firms with more investment layers than in predicting the earnings of firms with fewer layers. In addition, we find that a long chain of investment layers influences analyst forecast accuracy and dispersion more if firms also have a larger number of investees in tax havens and if firms have a higher deviation between controlling rights and cash flow rights. Overall, we document that organizational complexity exacerbates the agency costs between investors and managers, which in turn complicates analyst forecast tasks.

How analyst recommendations respond to corporate uncertainty caused by investment behavior

PurposeCorporate investment behavior increases the uncertainty of a company’s operation and performance. The purpose of this paper is to investigate how analyst recommendations respond to corporate uncertainty caused by investment behavior and what motivates analysts to react as they do.Design/methodology/approachThe authors test two motivation hypotheses: the hypothesis that analysts are currying favor with management to obtain private information and the hypothesis that analysts have conflicts of interest due to connections. Using Chinese analyst-level data from 2007 to 2015, the authors find that overall investment levels, R&D investment and M&A events are significantly positively correlated with analyst recommendations, suggesting that analysts tend to react optimistically to corporate investment behavior.FindingsAnalysts are only optimistic about companies with low information transparency, suggesting that analysts may be trying to curry favor with management to gain access to private information. The authors find that analysts with stronger recommendations have more private information and analysts with more private information publish more accurate earnings forecasts, which supports the hypothesis that analysts curry favor with management through optimistic recommendations to obtain more private information. This is consistent with the logic that the difficulty of earnings forecasting increases under uncertain conditions, increasing the demand for private information. The authors then group the analysts according to their underwriting connections, securities company’s proprietary connections and fund connections, and find that the positive correlation between corporate investment behavior and analyst recommendations exists only in the unconnected groups. This is evidence against the hypothesis that analysts have conflicts of interest due to their connections.Originality/valueFirst, the authors link the optimism of analysts with the uncertainty of analysts’ information inputs to partially unpack the black box of analysts’ analyses. Second, the authors test the two hypotheses mentioned. There is a lack of comparative studies on the influence of different motivations on the behavior of analysts.

Can analysts predict breaks in earnings strings?

PurposeThis study aims to investigate the behavior of sell-side analysts covering firms that are about to experience breaks in strings of consecutive quarterly earnings increases.Design/methodology/approachThe authors estimate the likelihood of analysts predicting a break by using logit regressions for nearly half a million earnings per share forecasts issued by individual analysts from 1992 to 2017.FindingsThe authors find that analysts can predict breaks in earnings strings by issuing less favorable earnings estimates ahead of a break announcement. The probability of detecting a break is higher for longer and more severe breaks, for more skillful analysts and for firms with richer information environments. The authors find that analysts’ warnings are heeded by investors and result in less severe reactions to break announcements.Originality/valueBreaks in strings of earnings increases are situations in which information asymmetry exists and could be mitigated by information intermediaries such as sell-side analysts. Therefore, it is important to examine whether analysts have any informational advantages or disadvantages over insiders and institutional investors in the quarters prior to breaks in strings and whether they communicate such information to the market in a timely and accurate manner, thus reducing information asymmetry by “leveling the field” across the investment community.

The Iterative Processing Framework: A New Paradigm for Automatic Event Building

Abstract In a traditional data‐processing pipeline, waveforms are acquired, a detector makes the signal detections (i.e., arrival times, slownesses, and azimuths) and passes them to an associator. The associator then links the detections to the fitting‐event hypotheses to generate an event bulletin. Most of the time, this traditional pipeline requires substantial human‐analyst involvement to improve the quality of the resulting event bulletin. For the year 2017, for example, International Data Center (IDC) analysts rejected about 40% of the events in the automatic bulletin and manually built 30% of the legitimate events. We propose an iterative processing framework (IPF) that includes a new data‐processing module that incorporates automatic analyst behaviors (auto analyst [AA]) into the event‐building pipeline. In the proposed framework, through an iterative process, the AA takes over many of the tasks traditionally performed by human analysts. These tasks can be grouped into two major processes: (1) evaluating small events with a low number of location‐defining arrival phases to improve their formation; and (2) scanning for and exploiting unassociated arrivals to form potential events missed by previous association runs. To test the proposed framework, we processed a two‐week period (15–28 May 2010) of the signal‐detections dataset from the IDC. Comparison with an expert analyst‐reviewed bulletin for the same time period suggests that IPF performs better than the traditional pipelines (IDC and baseline pipelines). Most of the additional events built by the AA are low‐magnitude events that were missed by these traditional pipelines. The AA also adds additional signal detections to existing events, which saves analyst time, even if the event locations are not significantly affected.

A Closer Look at Credit Rating Processes: Uncovering the Impact of Analyst Rotation

We investigate the effect of credit analyst changes on long-term ratings of S&P 500 issuers between 2002 and 2015. We find that analyst changes are associated with a higher probability of rating changes during first two quarters following such a change. In contrast to the argumentation of rating agencies, our results show that rating processes are prone to the influence of individual analyst behavior. Analyst changes are also associated with lower credit ratings. However, this lowering effect is limited in terms of economic magnitude. Our results empirically inform policies requiring mandatory credit analyst rotation.

PERILAKU TAKING PROFIT INVESTOR JANGKA PENDEK DALAM INVESTASI SAHAM

The research objective was to determine the behavior of securities analysts in the Indonesian capital market based on financial perspective. Behavior is identified by testing the fundamental aspects of financial statements such as accounting profit and dividend policy on stock returns. If the fundamental aspects are influential in considering stock selection, investors tend to be rational. However, if the fundamental aspects are not influential, fundamental analysis tends to be ignored. The research population is the BEI company that entered the 100 compass index from 2013-2017. The sampling technique uses purposive sampling with predetermined criteria. The data analysis technique uses panel data regression which includes: descriptive statistics, regression model estimation, regression model selection, F test, coefficient of determination and to prove the hypothesis used t test. The results of the study prove that accounting profit has an effect on stock returns, while dividend policy have no effect on stock returns. Research findings: 1. In stock investments, investors tend to be taking profit in the form of capital gains because they are considered faster in providing profits; 2. Investors prefer technical analysis rather than fundamental analysis because they are considered faster in providing information.Keywords: Accounting Profit, Dividend Policy and Stock Return

有限关注度下科技关联的定价作用 (The Role of Technological Links in Asset Pricing Under Limited Attention Assumption)

Chinese Abstract: 本文探讨了当投资者具有有限关注度时，因科技价值信息的延迟扩散带来的投资机会。在有限关注度假设下，投资者倾向于忽略企业间的科技关联，导致科技价值在同类科技公司中的传递产生延迟，从而带来投资机会。然而，在我国股票收益率受非理性因素影响过多，不适合用作科技溢出效应的代理变量。本文首次提出，分析师一致盈余预测修正比股票收益率更适合用来衡量上市公司的科技价值。本文根据上市公司持有的专利计算了不同公司间的科技关联，并结合分析师一致盈余预测修正设计了“科技修正因子”。通过该因子构建的套利组合可以获得1.00%的月均收益。本文不仅验证了科技关联在我国股票市场的信息传递能力，而且深入探讨了投资者有限关注度在此过程中起到的作用。本文发现科技修正因子的投资价值一方面来源于其能够预测未来的分析师一致盈余预测修正，一方面来源于其能够预测公司未来的盈余变化。本文首次将分析师行为信息与上市公司间的科技关联结合起来运用，该思路不仅有助于增加投资者对科技关联的应用，而且有助于更加充分地挖掘分析师行为信息的价值。 English Abstract: This paper investigates the investment opportunities from the delayed information diffusion through technological links among firms. Under the assumption of limited attention, investors tend to neglect the technological linkages among firms, resulting in delayed information diffusion among technological peers, thus bringing investment opportunities. However, the stock return is affected by too many irrational factors in China, which makes it unsuitable to be treated as proxy for the spillover effect of technology development. This paper proposes for the first time that analyst consensus earnings forecast revision is more suitable than stock return to measure technology value of listed companies. Using analyst consensus earnings forecast revision, this paper proposes the TechRev factor. Then the roles of technological links and limited attention in asset pricing are investigated. Firstly, this paper defined the technological correlation between firms based on the patents belonging to the focal firm. Then the TechRev for the focal firm can be calculated by averaging the analyst consensus earnings forecast revision of its technological peers, using the technological correlations as the weights. The empirical results show that: (1) the TechRev factor has significant investment value and the long-short strategy based on it yields monthly return of 100 basis points; (2) both portfolio test and the Fama-Macbeth regression verify that the investment value of TechRev is not derived from the well-known anomaly factors, but based on the specific information it contains essentially; (3) the TechRev factor is more efficient among the firms with stronger patent intensity or less analyst reports coverage, which means that the limited attention mechanism plays an important role; (4) the predictability of TechRev factor for the future stock return of the focal firm is derived from its predictability for the analyst consensus forecast revisions and the unexpected earnings of the focal firm; (5) the TechRev anomaly is more attributed to mispricing instead of risk premium. Because of the huge difference between the stock markets of China and developed countries, many effective investment strategies in mature markets lose their effects in China. However, this does not mean that the operation mode of China's stock market does not conform to the basic economic or financial laws. At least, the limited attention paradigm embodies well in the markets of both China and the US. And the securities analysts play indispensable roles in the stock market, although the independence of China's securities analysts has been suspected for a long time. As the information medium between listed companies and investors, securities analysts can not only the transmit information among the market, but also provide deeper and more effective investment information for the majority of investors based on their professional investment knowledge. At present, the market still cannot make full use of the analyst behavior information. This paper opens a new perspective by combining the analyst behavior information with the technological linkages of the listed firms, which is not only conducive to providing investors with new investment strategies, but also conducive to improving the efficiency of the whole stock market in China.

Can Analysts Predict Breaks in Earnings Strings?

Purpose: This study investigates the behavior of sell-side analysts covering firms that are about to experience breaks in strings of consecutive quarterly earnings increases. Design/methodology/approach: We estimate the likelihood of analysts predicting a break using logit regressions for 1.8M EPS forecasts issued by individual analysts from 1992 to 2017. Findings: We find that analysts can predict breaks in earnings strings by issuing less favorable earnings estimates ahead of the break announcement. The probability of detecting a break is higher for longer and more severe breaks, for more skillful analysts, and for firms with richer information environment. We find that analysts’ warnings are heeded by investors and result in a less severe reaction to the break announcements. Originality/value: Breaks in strings of earnings increases are situations when information asymmetry exists and could be mitigated by information intermediaries such as sell-side analysts. Therefore, it is important to examine whether analysts have any informational advantages or disadvantages over insiders and institutional investors in the quarters prior to breaks in strings and whether they communicate it to the market in a timely and accurate manner, thus reducing information asymmetry by “leveling the field” across the investment community.

Teaching requirements elicitation interviews: an empirical study of learning from mistakes

Interviews are the most widely used elicitation technique in requirements engineering (RE). However, conducting a requirements elicitation interview is challenging. The mistakes made in design or conduct of the interviews can create problems in the later stages of requirements analysis. Empirical evidence about effective pedagogical approaches for training novices on conducting requirements elicitation interviews is scarce. In this paper, we present a novel pedagogical approach for training student analysts in the art of elicitation interviews. Our study is conducted in two parts: first, we perform an observational study of interviews performed by novices, and we present a classification of the most common mistakes made; second, we utilize this list of mistakes and monitor the students’ progress in three set of interviews to discover the individual areas for improvement. We conducted an empirical study involving role-playing and authentic assessment in two semesters on two different cohorts of students. In the first semester, we had 110 students, teamed up in 28 groups, to conduct three interviews with stakeholders. We qualitatively analysed the data to identify and classify the mistakes made from their first interview only. In the second semester, we had 138 students in 34 groups and we monitored and analysed their progress in all three interviews by utilizing the list of mistakes from the first study. First, we identified 34 unique mistakes classified into seven high-level themes, namely question formulation, question omission, interview order, communication skills, analyst behaviour, customer interaction, teamwork and planning. In the second study, we discovered that the students struggled mostly in the areas of question formulation, question omission and interview order and did not manage to improve their skills throughout the three interviews. Our study presents a novel and repeatable pedagogical design, and our findings extend the body of knowledge aimed at RE education and training by providing an empirically grounded categorization of mistakes made by novices. We offer an analysis of the main pain points in which instructors should pay more attention during their design and training.

The impact of corporate tax avoidance on analyst coverage and forecasts

Corporate tax avoidance is likely to be associated with a high level of earnings management and with high financial opacity in the time-series. On this basis, we hypothesize that analyst coverage is negatively associated with corporate tax avoidance. Our results confirm this conjecture, and are robust to using a firm-fixed-effects model and a quasi-natural experiment to control for potential endogeneity. Additional analysis shows that analyst coverage is negatively related to tax risk, but there is no evidence that the informativeness of, or errors in, analyst forecasts are associated with tax avoidance. Overall, our study advances understanding of the implications of corporate tax avoidance for analyst behavior.

Investment Behaviour of Analysts: A Case Study of Pakistan Stock Exchange

Security prices in efficient markets reflect all relevant information. Past price formations and even fundamental analysis cannot guarantee abnormal returns consistently to any pre-identified strategy or market participant, be they novice or expert traders. There have been various studies conducted with the aim to test market efficiency in emerging markets. However, in this study, we have surveyed the professional investment community and have studied their stated actions in making investments. Our results indicate the prevalence of herding and overconfidence in professional analysts. We also found that analysts extrapolate the past into future forecasts. We also discovered an association between demographic characteristics and the choice of security valuation methods that the analysts use. In line with Chevalier and Ellison (1998), we found that younger analysts herd less than the older ones.

Do Financial Analysts Herd?

Financial analysts may have strategic incentives to herd or to anti-herd when issuing forecasts of firms' earnings. This paper develops and implements a new test to examine whether such incentives exist and to identify the form of strategic behavior. We use the equilibrium property of the finite-player forecasting game of Kim and Shim (2019) that forecast dispersion decreases as the number of forecasters increases if and only if there is strategic complementarity in their forecasts. Using the I/B/E/S database, we find strong evidence that supports strategic herding behavior of financial analysts. This finding is robust to different forecast horizons and sequential forecast release.