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.     

Political patronage and analysts’ forecast precision

We test whether the channel by which the government plays the role of political patron to selected firms influences analysts’ forecast precision in Malaysia. Correcting for analysts’ self-selection bias, we find a negative relation between analysts’ forecast errors and the social dimension of political patronage, as proxied by government-controlled institutional ownership. The reverse is found for the economic dimension of political patronage, as proxied by the percentage shareholding of government-linked corporations. We find no evidence that the personal dimension of political patronage influences analysts’ forecast precision.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

Analysts’ forecast revisions and firms’ research and development expenses

This study examines whether reported values for firms’ research and development (R&D) affect analysts’ annual earnings forecast revisions following quarterly earnings announcements. Because R&D introduces uncertainty into earnings forecasts, analysts may benefit from additional information searches in an effort to increase forecast accuracy. Also, accounting standards mandate an immediate expensing of R&D, in essence projecting a zero value for the R&D. To the extent that R&D will produce future payoffs, the expense treatment reduces the informativeness of reported earnings for forecasting future earnings. Thus, the marginal benefit of analysts’ efforts to produce more information may increase with the magnitude of the R&D component of earnings announcements and trigger additional forecast revisions. Alternatively, if the cost of information searches exceeds the benefit, analysts’ forecast revisions may decrease. Our results show a positive relation between R&D expenses and analysts’ forecast revision activity. We also find a positive and significant association between the level of R&D expenses and the magnitude of analysts’ forecast revisions following quarterly announcements. These results point to a greater amount of analyst scrutiny when reported earnings are accompanied by high levels of R&D expenses.

CEO stock options and analysts’ forecast accuracy and bias

This paper investigates the relationship between CEO stock options and analysts’ earnings forecast accuracy and bias. A higher level of stock options may induce managers to undertake riskier projects, to change and/or reallocate their effort, and to possibly engage in gaming (such as opportunistic earnings and disclosure management). These managerial behaviors result in an increase in the complexity of forecasting and hence, less accurate analysts’ forecasts. Analysts’ optimistic forecast bias may also increase as the level of stock options pay increases. Because forecast complexity increases with stock options pay, analysts, needing greater access to management’s information to produce accurate forecasts, have incentives to increase the optimistic bias in their forecasts. Alternatively, a higher level of stock options pay may lead to improved disclosure because it better aligns managers’ and shareholders’ interests. The improved disclosure, in turn, may result in more accurate and less biased analysts’ forecasts. Our empirical evidence indicates that analysts’ earnings forecast accuracy decreases and forecast optimism increases as the level of CEO stock options increases. This evidence suggests that the incentive alignment effects of stock options are more than offset by the investment, effort allocation and gaming incentives induced by stock options grants to CEOs.     

Does education affect how well students forecast the market?

This study examines the results of a student stock market forecasting project used in our basic and advanced investments classes. Students fail to outperform a random walk model over the entire period, but do perform well in some subperiods. Students receiving an above average grade in the basic investments class provide more accurate forecasts than all other groups of students. Further, poorer performing students tend to be more pessimistic in their expectations of the market. The results suggest that education improves the forecasting ability of students.     

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.     

Accounting flexibility and managers’ forecast behavior prior to seasoned equity offerings

This study examines the effect of accounting flexibility on managers’ forecasting behavior prior to seasoned equity offerings (SEOs). Although SEO firms have a strong incentive to convey optimistic information to boost the pre-SEO stock price, they also face enhanced litigation risk arising from SEO-related regulations. Thus, I hypothesize that managers will release positive news through their forecasts (relative to the prevailing analyst consensus) prior to an SEO only if they have the accounting flexibility to manage subsequent reported earnings to meet or exceed their forecasts. I find that managers with greater accounting flexibility are more likely to issue a forecast prior to the SEO and that their forecasts are more likely to convey positive news and are more specific. Furthermore, I find no effect of accounting flexibility for non-SEO control firms or for non-SEO periods. My results suggest that when managers experience a tension between the incentive for voluntary disclosure and high litigation risk, accounting flexibility is an important factor that determines their forecasting behavior.     

Can VPIN forecast geopolitical events? Evidence from the 2014 Crimean Crisis

We study the recent Crimean Crisis and the sequence of outcomes that led to the intervention by the Russian Army, which directly affected equity prices in Russia, to investigate how informed traders may have used their advantage to trade prior to the moment markets fell. We compute the Volume-synchronized Probability of Informed Trading (VPIN) for the Russian RTS equity index and for individual stocks, documenting that levels of informed trading increased considerably between one and three trading days before market prices reflected the invasion. We also investigate the predictive power of the cumulative distribution of VPIN on future stock prices, showing a statistically significant (negative) relation during the period of elevated tensions between Russia and Ukraine. Last, we investigate the levels of VPIN measured for global depositary receipts of Russian firms, documenting a similar increasing pattern prior to the invasion date but generally subsequent to the spikes obtained from the corresponding securities locally traded in Russia. Overall, our results provide additional support for the use of VPIN as a tool for monitoring the likelihood of undesirable geopolitical events.     

Factors associated with price reactions and analysts’ forecast revisions around SEC filings

We investigate the extent to which rapid accessibility of financial reports filed electronically through the Securities and Exchange Commission’s EDGAR system has affected the ability of investors and security analysts to use accounting data in pricing decisions and forecasting. Consistent with prior research, we find evidence confirming that stock price reactions to SEC filings are significant in the EDGAR period but not the pre-EDGAR period. We also find significant revisions in analysts’ one-quarter-ahead earnings forecasts around SEC filings dates in both the pre-EDGAR and EDGAR periods. The price and forecast revision evidence indicates that financial analysts have used SEC filings all along. However, it is the advent of EDGAR that has allowed individual investors to also use 10-K and 10-Q filings. Cross-sectional analyses indicate that in the EDGAR period, trading volume around the preceding earnings announcements may influence individual investors to react to SEC filings. In contrast, variables such as the earnings surprise and the level of total accruals attract the attention of financial analysts. Interestingly, analysts appear to have been less likely in the pre-EDGAR period to bear the cost of searching out each SEC filing to identify those with large total accruals, which are known only after examining the SEC filing itself.     

The impact of accruals and lines of business on analysts’ earnings forecast superiority

In this paper, we examine the linkage between analyst advantage (AA) (compared to the seasonal random walk model) in the prediction of quarterly earnings-per-share (EPS) and a broad set of economic determinants. Specifically, we employ a pooled cross-sectional time-series regression model where AA is linked to a set of firm-specific economic determinants that have been employed in extant work (e.g., Brown et al. in J Account Res 22:49?C67, 1987; Kross et al. in Account Rev 65:461?C476, 1990). We refine this set of independent variables by including a new variable (RATIODEV) based upon Sloan (Account Rev 71(3):289?C315, 1996) who documents that differential levels of accruals impact future earnings performance. This variable is particularly salient in explaining AA since analysts may be in a position to identify the permanent component of accruals via fundamental financial analysis. Additionally, we refine the measurement of lines of business??consistent with the reporting requirements of SFAS No. 131 relative to extant work that operationalized proxies for this variable based upon SFAS No. 14. Parameters for these aforementioned variables are significantly positively related to AA, consistent with theory.     

Two currencies,one model? Evidence from the Wall Street Journal forecast poll

We use the foreign exchange forecasts of the Wall Street Journal (WSJ) poll to analyze the expectation formation process of forecasters for the exchange rates of the euro and the yen vis-à-vis the U.S. dollar for the period 1999–2005. We also compare the expectation formation process with the actual exchange rate process. We find that most forecasters have contrarian exchange rate expectations, but our results also indicate significant heterogeneity between forecasters. While the actual exchange rate process of the yen/dollar exchange rate shows negative autocorrelation, the dollar/euro exchange rate exhibits positive autocorrelation.     

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.     

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.     

Improvement in clinical trial disclosures and analysts’ forecast accuracy: evidence from the pharmaceutical industry

This paper examines whether financial analysts use the information contained in clinical trial disclosures to improve their forecast accuracy for pharmaceutical companies. Findings indicate that the improved clinical trial disclosures due to a quasi-regulation issued by the International Committee of Medical Journal Editors (ICMJE) significantly reduce analysts’ long-term forecast error. In addition, a propensity-score matching analysis provides additional strong evidence that issuance of the 2005 ICMJE’s regulation is accompanied by an average 45 % decrease in long-term forecast error, and a more than 50 % decrease in long-term forecast dispersion. This study contributes to the accounting literature regarding nonfinancial disclosures by providing the first insights into financial analysts’ use of clinical trial disclosures in their forecasts of future earnings. In addition, because the major event examined in this study is a quasi-regulation issued by the ICMJE, we provide additional insights on the effectiveness of industry-initiated regulations (or quasi-regulations) on nonfinancial disclosure practice.     

Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets

Prior international accounting studies have examined mainly the supply side of analyst research. Specific trading rights and financial reporting systems in China provide a unique opportunity to test both demand and supply factors of analyst information. For shares traded only by less-informed foreign investors, the increased information demand may create incentives for analysts to exert greater efforts than for shares traded by local investors. This study provides evidence that analyst effort (proxied by analyst coverage) and expertise (proxied by consensus cross-analyst forecast variability) affect forecast accuracy more significantly for shares traded by foreign investors than for shares traded by local investors. Additionally, I find that the relation between analyst characteristics and relative forecast accuracy is stronger for shares traded by the foreign investors. The findings are consistent with analysts responding to demands for information by less-informed investors.