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.     

Financial Analysts' Forecast Accuracy and Dispersion: High-Tech versus Low-Tech Stocks

This study focuses on systematic differences in security analysts' forecast accuracy and dispersion between high- and low-tech firms. In line with the recent development in theoretical models and empirical findings, it posits that security analysts' unsigned forecast error and forecast dispersion are expected to differ between high-tech and low-tech firms. The results of this study provide evidence of lower unsigned error and dispersion for high-tech firms vis-à-vis low-tech firms. The higher forecast accuracy and forecast convergence for high-tech firms relative to low-tech firms in financial analysts' forecasts of earnings can be attributed to the information effect prevailing over the noise effect. Given the lack of empirical studies that compare analysts' forecast accuracy and dispersion between high-tech and low-tech sectors, the results of this paper provide a fresh basis for assessing how market participants vary in their treatment of New Economy stocks and factors that affect such decisions. In the light of the fact that the 1990s is a period characterized by the start of the Information Revolution through the Internet, the results of this study shed light on the usefulness of examining factors that differentiate between high-tech firms (New Economy stocks) and low-tech firms (Old Economy stocks) in financial analysts' forecasting earnings.     

The Implications of Dispersion in Analysts' Earnings Forecasts for Future ROE and Future Returns

Dispersion in analysts' forecasts is empirically evaluated by associating dispersion with a firm's future accounting rate of return-on-equity (ROE) and future returns. Forecast dispersion is significantly and negatively associated with future ROE, consistent with the notion that firm disclosures and analysts' information acquisition efforts increase as firm prospects improve. Forecast dispersion is negatively associated with future returns. This appears due to the implications of dispersion for future ROE, and suggests that the market does not immediately assimilate the information contained in forecast dispersion. Dispersion also conveys information about firm-specific risk not captured by beta and firm size.     

The Value Relevance of Management Forecasts and Their Impact on Analysts' Forecasts: Empirical Evidence From Japan

A major financial disclosure feature in Japan is that stock exchanges require firms to provide next year's earnings forecasts. This study investigates the value relevance of Japanese management earnings forecasts and their impact on analysts' earnings forecasts. First, the value relevance of management forecasts is investigated using a valuation framework provided by Ohlson (2001 ), in which firm value is expressed as a function of book value, current earnings and next year's expected earnings. The analysis yields that of the three accounting variables examined, management forecasts have the highest correlation and incremental explanatory power with stock price.
Next, the impact of management forecasts on analysts' forecasts is examined. The results show that more than 90% of changes in analysts' forecasts are explained by management forecasts alone. Further analysis reveals that the heavy dependence of financial analysts on management forecasts in formulating their own forecasts may partially be attributed to the relatively high accuracy of management forecasts. At the same time, financial analysts also somewhat modify management forecasts when certain financial factors indicate that the credibility of management forecasts is in doubt.
Overall, this study presents empirical evidence that Japanese management forecasts provide useful information for the market and have a significant influence on analysts' forecasts.     

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.     

The Relevance of Analysts' Earnings Forecasts in Japan

Abstract:   This study examines the role of financial analysts in equity valuation in Japan by comparing the relevance of financial analysts' earnings forecasts, over financial statement information, to investors' decisions. We find that the value‐relevance of a set of accounting variables is very modest, but the incremental contribution of analysts' forecasts is very significant. This is in line with the expectation that the skill and expertise of analysts are more valuable in markets with poor financial disclosure, such as Japan. We also find that the importance of the financial statements increases over time while the importance of the analysts' forecasts does not change. We also provide evidence of the effect of Japanese corporate groupings, keiretsu, on the informativeness of accounting signals and earnings forecasts. The results show that the contribution of accounting variables to valuation is lower for keiretsu firms, which supports the exclusionary hypothesis that companies which are a part of keiretsu, disclose less information than do non‐keiretsu companies. The analysts' forecasts are equally important for investors in both types of firms.     

Financial Analysts' Earnings Forecast Dispersion and Intraday Stock Price Variability Around Quarterly Earnings Announcements

This study investigates the relationship between the dispersion of analysts' earnings forecasts and stock price variability around quarterly earnings announcements. Consistent with theoretical predictions, the empirical analysis shows that stock price variability at the time of earnings announcements is positively related to the degree of analysts' predisclosure earnings forecast dispersion. Additionally, firms with high levels of forecast dispersion exhibit significant increases in price variability for longer periods prior to and following earnings announcements than do firms with low levels of forecast dispersion. These results suggest that there is information about the earnings announcement that becomes available to at least a subset of investors prior to the earnings release and that market participants take different amounts of time to process the information conveyed by the earnings announcement.     

Japanese Corporate Groupings (Keiretsu) and the Characteristics of Analysts' Forecasts

This study examines differences in the characteristics of analysts' forecasts of earnings for keiretsu and non-keiretsu firms in Japan. Japanese industrial organization is characterized by enterprise groupings, keiretsu, composed of firms in different industries, but interrelated through cross-holdings of ownership. The strong interrelations of the Japanese keiretsu have been shown to increase the monitoring of managerial performance. An alternative view of keiretsu stewardship suggests the exclusionary environment within a keiretsu creates an information monopoly, resulting in greater information asymmetry between inside and outside constituents. These two views provide opposing predictions on how keiretsu groupings potentially affect the characteristics of earnings forecasts made by analysts: increased monitoring will improve the forecast characteristics of earnings, while the opposite is true under an information monopoly.Our results suggest that forecast accuracy (dispersion) is higher (lower) for keiretsu firms than non-keiretsu firms, supporting a monitoring role by keiretsu. The results also show that keiretsu firms' forecast characteristics are related to the strength of the keiretsu relationship, providing further evidence that it is indeed the keiretsu relationship that increases the monitoring of management, which ultimately improves the accuracy and dispersion of analysts' forecast.     

名义价格幻觉——基于证券分析师目标价格预测的经验证据

本文利用证券分析师发布的股票目标价格预测,为名义价格幻觉提供了能够直接反映心理预期的经验证据。研究发现,证券分析师对低价股未来收益的心理预期显著高于高价股,该行为偏误在规模小、上市时间短、股票波动性大、财务透明度低和无形资产占比高等估值难度更大的股票中表现得更加明显。我们还利用股票送转,对证券分析师是否受到名义价格幻觉的影响做进一步验证,发现由送转引起的与基本面无关的名义价格下降显著提升了证券分析师对股票未来回报的心理预期。进一步研究表明,上述发现并不是因为证券分析师准确预见了低价股和高价股未来有不同的投资机会,也不是为了最大化其供职证券公司的利益而有意迎合投资者。     

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。     

Back to Basics: Forecasting the Revenues of Internet Firms

This paper examines the roles played by past revenues, web usage data, and analysts in forecasting the future revenues of internet firms during the years 1998 to 2000. For this time period our analysis shows that estimates of web traffic growth have significant incremental value in the prediction of revenues above time-series forecasts. Furthermore, analysts almost always underestimate the revenues of internet firms. Historical revenue growth has incremental predictive power over analysts' forecasts for portal and content/community firms, but not for our e-tailer sample. Moreover, the stocks of the portal and content/community firms with high historical revenue growth earn higher abnormal returns during our sample period than do those with low historical growth. Estimates of web usage growth generally do not have incremental value over analysts' forecasts for predicting the revenues of either set of firms. However, perfect foreknowledge of actual web usage growth would provide incremental predictive power. Collectively, our findings point to the potential value for forecasting purposes of both improving upon the web usage estimates and obtaining more timely reports of actual web traffic.     

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.     

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.     

Analysts' Incentives and Street Earnings

We examine whether analysts' incentives are associated with street earnings. Because prior research argues that analysts' incentives to promote stocks increase in the extent to which the stock exhibits glamour characteristics, we predict that analysts are more likely to make income-increasing adjustments in determining street earnings for glamour stocks than for value stocks. We find that analysts are more likely to exclude expense items from street earnings for glamour stocks than for value stocks and that excluded expense items help predict future earnings for glamour stocks but not for value stocks. Overall, our results suggest that analysts' self-interest influences street earnings and this self-interest leads to street earnings that are less useful in predicting future earnings for glamour stocks.     

Accrual usage to manage earnings toward financial analysts' forecasts

This study examines whether management uses discretionary accounting accruals to move earnings upward toward analysts' earnings forecasts when it appears that earnings before discretionary accruals will fall short of the forecast. An earnings shortfall relative to analysts' forecasts could lead management to fear lower compensation and an increase in the likelihood of job termination. The article finds that firms whose earnings before discretionary accruals are below analysts' forecasts use income-increasing discretionary accruals and do so to a greater extent than do firms whose earnings before discretionary accruals are above analysts' forecasts.     

The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility

Does cross-sectional dispersion in the returns of different stocks help forecast volatility of the S&P 500 index? This paper develops a model of stock returns where dispersion in returns across different stocks is modeled jointly with aggregate volatility. Although specifications that allow for feedback from cross-sectional dispersion to aggregate volatility have a better fit in sample, they prove not to be robust for purposes of out-of-sample forecasting. Using a full cross-section of stock returns jointly, however, I find that use of cross-sectional dispersion can help improve parameter estimates of a GARCH process for aggregate volatility to generate better forecasts both in sample and out of sample. Given this evidence, I conclude that cross-sectional information helps predict market volatility indirectly rather than directly entering in the data-generating process.     

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.     

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.     

Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts

We examine security analysts' career concerns by relating their earnings forecasts to job separations. Relatively accurate forecasters are more likely to experience favorable career outcomes like moving up to a high-status brokerage house. Controlling for accuracy, analysts who are optimistic relative to the consensus are more likely to experience favorable job separations. For analysts who cover stocks underwritten by their houses, job separations depend less on accuracy and more on optimism. Job separations were less sensitive to accuracy and more sensitive to optimism during the recent stock market mania. Brokerage houses apparently reward optimistic analysts who promote stocks.     

Analysts' Reactions to Earnings Preannouncement Strategies

Preannouncements of earnings tend to overstate negative or understate positive news, which decreases the chance of a negative surprise when actual earnings is announced. We conduct an experiment to investigate how experienced sell-side analysts' earnings forecasts are affected by preannouncements that either understate, accurately state, or overstate the magnitude of positive or negative total earnings news, holding total earnings news constant. We find that firms with negative (positive) total news receive the highest post-earnings announcement forecasts of future earnings when the earlier preannouncement overstates (understates) the magnitude of the news. These forecasts are consistent with the analysts' perceptions about the firms' future prospects, but not their perceptions of management. While analysts expect preannouncements to be lower than actual earnings, they do not adjust their forecasts for these beliefs. These insights into analysts' responses have implications both for managers and analysts.