Investor and (Value Line) Analyst Underreaction to Information about Future Earnings: The Corrective Role of Non-Earnings-Surprise Information

Prior research suggests that financial analysts' earnings forecasts and stock prices underreact to earnings news. This paper provides evidence that analysts and investors correct this underreaction in response to the next earnings announcement and to other (non-earnings-surprise) information available between earnings announcements. Our evidence also suggests that analysts and investors underreact to information reflected in analysts' earnings forecast revisions and that non-earnings-surprise information helps correct this underreaction as well. Controlling for corrective non-earnings-surprise information significantly increases estimates of the degree to which analysts' forecasting behavior can explain drifts in returns following both earnings announcements and analysts' earnings forecast revisions.     

Investors' Use of Historical Forecast Bias to Adjust Current Expectations

We explore the extent to which investor response to earnings information differs in the presence of historical bias in earnings forecasts. Overall, the results are consistent with the notion that investors take historical forecast bias into account when interpreting information in earnings announcements and that the market's reaction to forecast errors is larger (less negative) when forecasts are historically more optimistic and suggests that the functional form commonly used in the earnings response literature does not appropriately capture the effect of real unexpected earnings information (i.e., investors' expectation errors as opposed to analysts' forecast errors) on stock returns.     

A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?

I provide evidence that investors overweight analyst forecasts by demonstrating that prices do not fully reflect predictable components of analyst errors, which conflicts with conclusions in prior research. I highlight estimation bias in traditional approaches and develop a new approach that reduces this bias. I estimate characteristic forecasts that map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts analyst forecast errors and revisions. I find abnormal returns to strategies that sort firms by predicted forecast errors, consistent with investors overweighting analyst forecasts and predictable biases in analyst forecasts influencing the information content of prices.     

证券分析师VS统计模型:证券分析师盈余预测的相对准确性及其决定因素

本文使用2005年35家券商对我国上市公司做出的每股盈余预测数据,考察了证券分析师盈余预测相对于统计模型的相对准确性及其决定因素。我们发现,我国证券分析师做出的盈余预测,同以年度历史数据为基础的统计模型得出的盈余预测相比,预测误差较小,证券分析师盈余预测具有一定的优势;但同某些以季度历史数据为基础的统计模型得出的盈余预测相比,预测误差较大,证券分析师盈余预测不具有优势。我们同时考察了决定证券分析师盈余预测相对准确性的决定因素。我们发现,公司每股盈余的波动性越大,公司上市越晚,跟踪公司的分析师越多,证券分析师的优势就越大。我们的研究对证券分析师以及投资者都有一定的启示作用。     

An Empirical Analysis of Qualitative Management Earnings Forecasts

Despite efforts by the Securities and Exchange Commission (SEC) to encourage corporate disclosure of quantitative management earnings projections, only a small fraction of firms voluntarily do so. Instead of quantitative estimates, a large number of firms choose to disclose qualitative (verbal) assessments of their earnings prospects. This paper is a study of the information characteristics and the usefulness of this alternative form of forecast disclosure to investors. The study examines a sample of qualitative forecast statements from the 1979–1985 period and finds associations between these forecasts and percentage changes in realized earnings per share, the direction of financial analysts' forecast revisions following the disclosure of these forecasts, and abnormal stock returns on the date of their disclosure. These associations are, however, shown to be more significant for negative (bad news) than for positive (good news) forecasts.     

Intangible Assets, Information Complexity, and Analysts' Earnings Forecasts

Abstract:   We examine the relation between analysts' earnings forecasts and firms' intangible assets, including technology‐based intangibles, brand names, and recognized intangibles. We predict that high information complexity of intangible assets increases the difficulty for analysts to assimilate information and increases analysts' forecast error of intangibles‐intensive firms. We find a positive association between analysts' forecast error and the firm's intangible intensity that deviates from the industry norm. We also find that analysts' forecast errors are greater for firms with diverse and innovative technologies. In contrast, analysts' forecast errors are smaller for biotech/pharmaceutical and medical equipment firms that are subject to intangibles‐related regulation.     

Removing predictable analyst forecast errors to improve implied cost of equity estimates

Prior research documents a weak association between the implied cost of equity inferred from analyst forecasts and realized returns. It points to predictable errors in analyst forecasts as a possible cause. We show that removing predictable errors from analyst forecasts leads to a much stronger association between implied cost of equity estimates obtained from adjusted forecasts and realized returns after controlling for cash flow news and discount rate news. An estimate of implied risk premium based on the average of four commonly used methods after making adjustments for predictable errors exhibits strong correlations with future realized returns as well as the lowest measurement error. Overall, our results confirm the validity of implied cost of equity estimates as measures of expected returns. Future research using implied cost of equity should remove predictable errors from implied cost of capital estimates and then average across multiple metrics.     

The best of all possible worlds: unraveling target price optimism using analysts’ scenario-based valuations

We document that the relative placement of analysts’ target price within their subjective distribution of scenario-based valuations for the covered firm (i.e., tilt) is informative to investors. When analysts forecast price appreciation, tilt incrementally predicts ex post valuation errors and realized returns; the predictive value of tilt disappears when analysts forecast price declines. In additional analyses, we find that tilt appears to reflect an optimistic bias in target price forecasts as opposed to information about asymmetric state-contingent risk payoffs. Finally, we document that investors can use estimates of implied tilt based on observable firm characteristics to distinguish between investments with equally optimistic target price forecasts, yet lacking scenario-based information.     

Rationality and Analysts' Forecast Bias

This paper proposes and tests a quadratic-loss utility function for modeling corporate earnings forecasting, where financial analysts trade off bias to improve management access and forecast accuracy. Optimal forecasts with minimum expected error are optimistically biased and exhibit predictable cross-sectional variation related to analyst and company characteristics. Empirical evidence from individual analyst forecasts is consistent with the model's predictions. These results suggest that positive and predictable bias may be a rational property of optimal earnings forecasts. Prior studies using classical notions of unbiasedness may have prematurely dismissed analysts' forecasts as being irrational or inaccurate.     

Dispersion in Analyst Forecasts and the Profitability of Earnings Momentum Strategies

This paper shows that the dispersion in analysts' consensus forecasts contains incremental information to predict future stock returns. Consistent with prior research, stock prices in the German market underreact to news about future earnings and drift in the direction suggested by analysts' forecasts revisions. Even higher abnormal returns can be achieved by applying such an earnings momentum strategy to stocks with a low dispersion in analyst forecasts. These results support one of the recent behavioural models in which investors underweight new evidence and conservatively update their beliefs in the right direction, but by too little in magnitude with respect to more objective information.     

Analysts’ earnings forecast errors and cost of equity capital estimates

This study investigates the relation between analysts’ forecast errors and cost of equity capital estimates implied from analysts’ earnings forecasts and price. My analysis predicts and removes forecast errors from analysts’ earnings forecasts on an out-of-sample basis and then uses these adjusted analysts’ forecasts to reverse-engineer cost of equity capital estimates. While the correction for predictable analysts’ forecast errors meaningfully lowers each of three firm-level implied COEC estimates employed in this study and commonly used in the literature, I do not find that this correction improves their association with realized returns.     

High-Technology Intangibles and Analysts' Forecasts

This study examines the association between firms' intangible assets and properties of the information contained in analysts' earnings forecasts. We hypothesize that analysts will supplement firms' financial information by placing greater relative emphasis on their own private (or idiosyncratic) information when deriving their earnings forecasts for firms with significant intangible assets. Our evidence is consistent with this hypothesis. We find that the consensus in analysts' forecasts, measured as the correlation in analysts' forecast errors, is negatively associated with a firm's level of intangible assets. This result is robust to controlling for analyst uncertainty about a firm's future earnings, which we also find to be higher for firms with high levels of internally generated (and expensed) intangibles. Given that analyst uncertainty increases and analyst consensus decreases with the level of a firm's intangible assets, we also expect and find that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst's forecast increases with a firm's intangible assets. Finally, additional analysis reveals that lower levels of analyst consensus are associated with high-technology manufacturing companies, and that this association is explained by the relatively high R&D expenditures made by these firms. Overall, our results are consistent with financial analysts augmenting the financial reporting systems of firms with higher levels of intangible assets (in terms of contributing to more accurate earnings expectations), particularly R&D-driven high-tech manufacturers.     

Analysts' Rationality and Forecast Bias: Evidence from Sales Forecasts

When optimistic forecasts can improve access to management, rational analysts have incentives to issue optimistically-biased forecasts (Lim, 2001). This paper proposes that the extent of this optimistic forecast bias will depend on the forecast's importance to management. If management attaches less importance to a forecasted measure, analysts should decrease their forecast bias because the expected benefits of issuing optimistic forecasts are less. We examine analysts' earnings and sales forecasts, and predict that analysts' optimistic bias will be greater for earnings than for sales. Results are consistent with our predictions and contribute to the evidence that analysts' forecast bias is rational and intentional.     

Active Portfolio Management,Implied Expected Returns,and Analyst Optimism

This paper investigates whether implied expected returns based on the approach of CLAUS/THOMAS (2001) can be implemented in active portfolio management. This approach uses analysts' forecasts to derive return expectations by equating the present value of expected cash-flows to the current market price. It is found that active investment strategies which maximize implied expected returns significantly outperform a passive index investment. A significant part of this outperformance can be explained by the difference between the implied expected return and the return expectation justified by the CAPM. The empirical results suggest that a substantial part of this difference can be attributed to an optimism bias in analysts' forecasts.     

Alternative adjustments to analysts' earnings forecasts: relative and complementary performance

This study examines the relative and complementary performance of alternative earnings forecast adjustments using a common set of consensus analysts’ earnings forecasts. We document that a simple adjustment to analysts’ earnings forecasts, based solely on cross-sectional relationships between actual and forecasted earnings in the prior year, performs as well as more complicated adjustment methods, i.e., composite forecasts and persistence adjusted forecasts. A forecast adjustment that is based on prior year earnings and returns, however, provides significant incremental reductions in forecast error and dominates all of the other adjustment methods.     

EARNINGS EXPECTATIONS, FIRM SIZE, AND THE INFORMATIVENESS OF STOCK PRICES

The notion that prices impound a wide array of information, including market expectations, has led to earnings forecast models conditioned on prices. Yet, presumably, analysts' forecasts capture both public information and certain private information not previously impounded in prices. Accordingly, price-based models are seemingly an inefficient, and less effective, source of expecta-tions. This article investigates this hypothesis using financial analysts', price-based, and naive forecasts. Results indicate that analysts' forecasts (1) are at least as accurate as price-based and naive models, and (2) yield better expectations for market tests relating returns and earnings. These inferences are robust across different information environments. The evidence suggests that analysts either possess private information or are more effective information processors, or both.     

A Comparative Analysis of Earnings Forecasts in Europe

This study examines the accuracy and bias of financial analysts' EPS forecasts in nine European countries during 1987 to 1995. There are significant differences between the countries which may be due to the differences in earnings behaviour, accounting practices, and the influence of securities markets. An optimistic bias is endemic in European forecasts, consistent with research from the US. Investors who incorporate earnings forecasts in their stock selection procedures may be able to improve returns by explicitly adjusting their models for observed regularities in earnings forecast errors. However, we have shown that these regularities differ in incidence and magnitude across the countries studied, and further research is needed to effectively model these differences.     

剩余收益模型与股票未来回报

本文采用Hou et al.(2012)公司基本面盈余预测模型并结合剩余收益模型对上市公司的内在价值进行估计,并分析内在价值与市价比率(V/P)与股票未来回报之间的关系。我们发现基于V/P分组的投资组合,在未来一至三年规模调整的持有超额回报套利分别达到15.2%、37.9%和55.9%;在控制了市账比等因素以后,V/P对股票未来回报仍然具有显著的预测作用。本文的研究克服了以往文献中运用证券分析师盈余预测进行剩余收益模型估值的内在局限,并提供了我国资本市场背景下切实可行的基于剩余收益模型估值的投资组合策略。     

The Inefficiency of the Mean Analyst Forecast as a Summary Forecast of Earnings

We show analytically that mean analyst forecasts inefficiently aggregate information by assigning too much weight to analysts' common information relative to their private information when used as a summary forecast measure of forthcoming earnings. A more precise summary forecast of earnings than the current mean forecast is the current mean forecast plus a positive multiple of the change in the mean forecast.     

Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings

We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings changes. Although analysts underreact to these signals on average, we find that analysts who are most sensitive to signal informativeness achieve superior forecast accuracy relative to their peers and have a greater influence on the market. The results suggest that the ability to extract information from the actions of others serves as one source of analyst expertise.