Market implied future earnings and analysts’ forecasts

This study provides evidence on market implied future earnings based on the residual income valuation (RIV) framework and compares these earnings with analyst earnings forecasts for accuracy (absolute forecast error) and bias (signed forecast error). Prior research shows that current stock price reflects future earnings and that analyst forecasts are biased. Thus, how price-based imputed forecasts compare with analyst forecasts is interesting. Using different cost of capital estimates, we use the price-earnings relation and impute firms’ future annual earnings from three residual income (RI) models for up to 5 years. Relative to I/B/E/S analyst forecasts, imputed forecasts from the RI models are less or no more biased when cost of capital is low (equal to a risk-free rate or slightly higher). Analysts slightly outperform these RI models in terms of accuracy for immediate future (1 or 2) years in the forecast horizon but the opposite is true for more distant future years when cost of capital is low. A regression analysis shows that, in explaining future earnings changes, analyst forecasts relative to imputed forecasts do not impound a significant amount of earnings information embedded in current price. In additional tests, we impute future long-term earnings growth rates and find that they are more accurate and less biased than I/B/E/S analyst long-term earnings growth forecasts. Together, the results suggest that the RIV framework can be used to impute a firm’s future earnings that are high in accuracy and low in bias, especially for distant future years.     

Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism?

A rational analysis of analyst behavior predicts that analysts immediately and without bias incorporate information into their forecasts. Several studies document analysts' tendency to systematically underreact to information. Underreaction is inconsistent with rationality. Other studies indicate that analysts systematically overreact to new information or that they are systematically optimistic. This study discriminates between these three hypotheses by examining the interaction between the nature of information and the type of reaction by analysts. The evidence indicates that analysts underreact to negative information, but overreact to positive information. These results are consistent with systematic optimism in response to information.     

The Association between Trading Recommendations and Broker-Analysts' Earnings Forecasts

This study examines analyst forecast errors within the context of stock recommendations. We predict positive forecast error (i.e., optimism) for buy recommendations and negative forecast error (i.e., pessimism) for sell recommendations. We offer two explanations for this prediction: (1) the unconscious tendency to process information in a manner that supports one's goal, which we refer to as the "objectivity illusion" hypothesis, and (2) the economic incentive to boost trade, which we refer to as the "trade boosting" hypothesis. The pattern of analyst forecast bias we predict (i.e., optimism for buys and pessimism for sells) is opposite in direction to that predicted by the management relations hypothesis—a commonly cited hypothesis for analyst forecast bias.
We find broker-analyst earnings forecast errors are significantly optimistic for buy recommendations and significantly pessimistic for sell recommendations, consistent with the objectivity illusion and trade boosting hypotheses. Our study indicates that the pattern of results reported in prior research (i.e., increasingly optimistic earnings forecasts as the stock recommendation becomes less favorable) is likely driven by a correlated omitted variable, actual earnings. Results of an analysis to distinguish between trade boosting and objectivity illusion appear more consistent with the objectivity illusion.     

Experience, information asymmetry, and rational forecast bias

This study examines whether it is ever rational for analysts to post biased estimates and how information asymmetry and analyst experience factor into the decision. Using a construct where analysts wish to minimize their forecasting error, we model forecasted earnings when analysts combine private information with consensus estimates to determine the optimal forecast bias, i.e., the deviation from the consensus. We show that the analyst??s rational bias increases with information asymmetry, but is concavely related with experience. Novice analysts post estimates similar to the consensus but as they become more experienced and develop private information channels, their estimates become biased and deviated from the consensus. Highly seasoned analysts, who have superior analytical skills and valuable relationships, need not post biased forecasts.     

Disclosure of internal control material weaknesses and optimism in analyst earnings forecasts

This paper investigates how analyst forecast optimism is associated with disclosures of internal control material weaknesses (ICMWs) and their remediation under Section 404 of the Sarbanes–Oxley Act (SOX). Drawing on agency theory, I hypothesize that analysts are likely to issue earnings forecasts that are more optimistic for firms with ICMW disclosures than for those without ICMW disclosures. Using a sample of 20,875 firm-year observations with 10-K (10-Q) reports from 2004 to 2018, I find a positive association between ICMW disclosures and analyst forecast optimism. This positive association is partially driven by investors’ inability to unravel analyst forecast bias and analysts’ intentions to curry favor with management for private information. In addition, analysts are found to issue less optimistic forecasts for firms with ICMW remediation disclosures compared with those without ICMW remediation disclosures. A series of propensity score matching and regression analyses are conducted to test the robustness of my inferences. Overall, the paper suggests that analysts have incentives to take the opportunity of firms disclosing ICMWs to bias their forecasts upward for self-interest. The findings have the potential to assist regulators in guiding analyst behavior and educating investors to unravel positive bias in analyst forecasts.     

The Two Faces of Analyst Coverage

We find that positive excess (strong) analyst coverage is associated with overvaluation and low future returns. This finding is consistent with the view that excessive analyst coverage, driven by investment banking incentives and analyst self-interests, raises investor optimism causing share prices to trade above fundamental value. However, weak analyst coverage causes stocks to trade below fundamental values. This finding indicates that investors tend to believe that these firms are more likely to be plagued by information asymmetries and agency problems. The results remain robust after controlling for the possible endogenous nature of analyst coverage and analysts' self-selection bias.     

A Simultaneous Equations Analysis of Analysts' Forecast Bias, Analyst Following, and Institutional Ownership

In this paper we use a simultaneous equations model to examine the relationship between analysts' forecasts, analyst following, and institutions' investment decisions. Estimates of our three equation model using US data indicate that higher institutional demand leads to greater optimism among analysts and lower analyst following. At the same time, institutional demand increases with increasing optimism in analysts' forecasts but decreases with analyst following. We also investigate firm characteristics as determinants of analysts' and institutions' decisions. Empirical estimates of the effects of these characteristics indicate that agency‐driven behavioral considerations are significant.     

Why do analysts issue forecast revisions inconsistent with prior stock returns? Determinants and consequences

We examine the informativeness of analyst forecast revisions that are directionally inconsistent with prior stock price movements (sign‐inconsistent revisions). Sign‐inconsistent revisions represent approximately one‐half of the forecast revisions from 1995 through 2010. Our tests indicate that sign‐inconsistent revisions are less informative than are sign‐consistent revisions. Sign‐inconsistent revisions are less likely to be closer to actual earnings realizations and they generate smaller stock price reactions. We also find evidence that sign‐inconsistent revisions are associated with analysts' economic incentives to generate trading volume and their behavioural limitations related to information uncertainty. These results suggest that sign‐inconsistent revisions do not necessarily benefit investors.     

Information asymmetry,the cost of debt,and credit events: Evidence from quasi-random analyst disappearances

We hypothesize that greater information asymmetry causes greater losses to debtholders. To test this, we identify exogenous increases in information asymmetry using the loss of an analyst that results from broker closures and broker mergers. We find that the loss of an analyst causes the cost of debt to increase by 25 basis points for treatment firms compared to control firms, and the rate of credit events (e.g., defaults) is roughly 100–150% higher. These results are driven by firms that are more sensitive to changes in information (e.g., less analyst coverage). The evidence is broadly consistent with both financing and monitoring channels, although only a financing channel explains the impact of the loss of an analyst on firms' cost of debt.     

A Temporal Analysis of Earnings Surprises: Profits versus Losses

I show that median earnings surprise has shifted rightward from small negative (miss analyst estimates by a small amount) to zero (meet analyst estimates exactly) to small positive (beat analyst estimates by a small amount) during the 16 years, 1984 to 1999. I show that a rightward temporal shift in median surprise from negative to positive describes earnings, but neither profits nor losses. Median profit surprise shifts within the positive quadrant, from zero to one cent per share. Median loss surprise shifts within the negative quadrant from extreme negative (about -33 cents per share) to zero. I show that the median surprise for profits exceeds that for losses in every year. I document significant positive temporal trends in both meet and beat analyst estimates for both profits and losses, but I find a greater frequency of profits that either meet or beat analyst estimates in every year. I find a significant positive temporal trend in positive profits that are "a little bit of good news," and a significant negative temporal trend in managers who report losses that are an "extreme amount of bad news." My results are robust to the four internal validity threats I consider—namely temporal changes in: (1) analyst forecast accuracy, (2) the mix of earnings of one sign preceded by earnings of another sign four quarters ago, (3) the timeliness of the most recent analyst forecast, and (4) the I/B/E/S definition of actual earnings. I find that managers of growth firms are relatively more likely than managers of value firms to report good news profits. I show that when they do report positive profit surprises, managers of growth firms are more likely to report "a little bit of good news" in every year.     

Improving the Precision of Analysts' Earnings Forecasts by Adjusting for Predictable Bias

This research demonstrates that publicly-available information can be used to develop estimates of analysts' optimistic bias in earnings forecasts. These bias estimates can be used to produce more accurate forecasts, resulting in significant reductions of both cross-sectional mean forecast error and error variance. When bias estimates are based on past observations of forecast error alone, however, reductions in mean forecast error are smaller, and forecast precision is unimproved. Further tests provide evidence of a significant association between returns and the bias predictable from contemporaneously-available information, suggesting that predictable bias is only partially discounted by market participants. This study has significant implications for researchers and investors. The pricing of predictable bias in analysts' forecasts may add error toinferences which are based on the association between returns and analyst forecast errors, and knowledge of the market's partial discounting of predictable bias may help investors to make more efficient resource allocations.     

Behavioural Bias and Conflicts of Interest in Analyst Stock Recommendations

Abstract:  This paper tests whether sell-side analysts are prone to behavioural errors when making stock recommendations as well as the impact of investment banking relationships on their judgments. In particular, we analyse their report narratives for evidence of cognitive bias. We find first that new buy recommendations on average have no investment value whereas new sell recommendations do, and take time to be assimilated by the market. We also show that new buy recommendations are distinguished from new sells both by the level of analyst optimism and representativeness bias as well as with increased conflicts of interest. Successful new buy recommendations are characterised by lower prior returns, value stock status, smaller firms and weaker investment banking relationships. On the other hand, successful new sells do not differ from their unsuccessful counterparts in terms of these measures. As such, we provide evidence that analysts are prone both to behavioural bias as well as potential conflicts of interest in their new buy stock recommendation decisions. We also show that these two explanations of analyst behaviour are to a great extent independent of each other. Consequently, the recent attempts by regulators to address potential conflicts of interest in analyst behaviour may have only limited impact.     

Bias in estimates of discrimination and default in mortgage lending: The effects of simultaneity and self-selection

The common practices of estimating single-equation models of mortgage rejection to test for discrimination in mortgage markets or single-equation ex ante mortgage default equations to validate underwriting criteria produce biased and inconsistent parameter estimates. This is due to problems of simultaneous equations bias which arise because, in a world of imperfect information, mortgage terms are not exogenous to the rejection or default decision. In addition, mortgage default estimates are also subject to selection bias. Monte Carlo experiments are used to study the nature and extent of likely bias in single-equation estimation results. We find that rejection equation estimates indicate discrimination when none exists and that estimated coefficients of mortgage terms, such as the loan-to-value ratio, are also subject to significant bias in both rejection and default equations.     

On the Structure of Analyst Research Portfolios and Forecast Accuracy

This study provides insights into the forces and constraints that shape analyst research coverage along country and sector dimensions and the impact of the structure of an analyst's portfolio on forecast accuracy. We find that analyst specialization by country and sector is sensitive to the extent to which firms  within  a country or sector and firms  across  country-sectors are exposed to common economic forces, the potential for revenue generation, and broker culture. Our tests indicate that existing research on the relation between analyst portfolio structure and forecast accuracy may suffer from an endogeneity bias. We use our analysis of analyst specialization to develop controls for this bias. Once we employ these controls, we find that country diversification is associated with superior forecast accuracy. However, the relation between sector diversification and forecast accuracy is context-specific. Specifically, sector diversification enhances forecast accuracy in an international context, while it detracts from forecast accuracy in a domestic U.S. context.     

Investor attention and information diffusion from analyst coverage

This study examines the impact of investor attention and analyst coverage on the diffusion of information. Using trading turnover as a proxy for investor attention, the results show that attention is crucial to the information diffusion from financial analysts. The effect of analyst coverage on improving stock synchronicity is greater when investors are more attentive. Firms with less analyst coverage rely more heavily on investor attention to assimilate information. The lead–lag effect in high and low analyst-following firms is driven by the relative more attention given to firms that have high analyst coverage.     

How Do Underwriters Select Peers When Valuing IPOs?

Valuing initial public offerings (IPOs) using multiples allows underwriters discretion when selecting comparable firms. We find that they systematically exclude candidate comparable firms that make a given IPO appear overvalued. On average, comparable firms published in official prospectuses have 13%‐38% higher valuation multiples than those obtained from matching algorithms or selected by sell‐side analysts, including the same underwriter's analyst after the IPO. Even if IPOs are priced at a discount as compared to peers selected by the underwriters, they are still at a premium with regard to alternatively selected peers. Greater bias in the underwriter's selection of peers leads to poorer long run performance.     

Management bias across multiple accounting estimates

Review of Accounting Studies - We examine whether managers appear to aggregate bias in multiple subjective accrual estimates to meet or just beat analyst expectations. We also consider whether the...     

The value relevance of timely information: The case of comparable store sales growth

Investors and analysts have called for more timely disclosure of corporate information. Responding to these demands, some retail firms issue comparable store sales (CSS) on a monthly or a quarterly basis in addition to an annual basis. This study examines whether a timely disclosure of CSS provides value-relevant information to market participants by examining investors' and financial analysts' responses at the time of CSS disclosures (short-horizon) and over the month or the quarter (long-horizon). We find that both monthly and quarterly CSS are associated with contemporaneous market returns and analyst forecast revisions. More importantly, we find that quarterly CSS news becomes less important to investors when firms provide more timely CSS information, indicating that monthly CSS reports may preempt the information content of quarterly CSS. Additional tests show that investors and analysts rely less on CSS if CSS news and earnings (sales) news are inconsistent.     

On the Determinants of Optimism in Financial Analyst Earnings Forecasts: The Effect of the Market's Ability to Adjust for the Bias

This paper examines whether the documented bias in analyst earnings forecasts is intentional by examining whether it is related to the market's ability to adjust for this bias. For intentional bias to exist it is not enough for analysts to face incentives but rather, analysts should also be willing to respond to these incentives. As the market's ability to adjust for the bias increases, its market effects decrease while analyst reputation costs increase reducing analyst willingness to bias their forecasts. The paper utilizes a firm‐specific design that allows for both the bias component of the forecast error and the market's ability to adjust for the bias to be computed at the firm level. Results suggest that even though forecast error is positive in the latter part of the period under review reflecting overall analyst pessimism, the bias embedded in the forecasts is optimistic throughout the period. More importantly, I find that analyst forecast bias is decreasing in the market's ability to adjust for it. This result provides further evidence that analysts knowingly bias their forecasts and provides support for the existence of reporting bias, in particular. Thus, the evidence provides justification for recent regulatory efforts to increase the objectivity of analyst research reports.     

Analyst talent,information, and insider trading

We examine 1984–2018 data and show that the talent or ability of sell-side financial analysts affects a covered firm's information environment—more so than the simple number of analysts covering a firm. We find that while analysts in general produce market and industry-level information, high-ability analysts contribute more firm-specific information. Firms covered by high-ability analysts experience significantly less insider trading prior to positive earnings news. Results only reside in opportunistic (not routine) trades. When an analyst initiates (terminates) coverage we find decreased (increased) subsequent insider trading. Both changes are primarily driven by analyst talent. Analyst ability also negatively relates to insider trading profitability.