Gilles Hilary and Charles Hsu
Analyst forecast consistency
Journal of Finance | Volume 68, Issue 1 (Feb 2013), 271–297
Two wrongs make a right: when consistency trumps accuracy in forecasting.

It turns out that being wrong may be the right career move for financial analysts who want to move stock prices and markets while also moving up the professional ranks.

This curious fact emerged in our recent research of analyst forecasting. Our study found that forecast consistency, rather than accuracy, provided more useful information to a large investor segment—“savvy” investors, as distinct from their less sophisticated, “retail” counterparts. As a result, these analysts could move stock prices and influence the stock market more strongly than analysts who provided more accurate, but inconsistent, forecasts.

Specifically, we found that analysts who strategically introduced a downward bias in their forecasts (“lowballed”), enjoyed higher market credibility by demonstrating a lower standard deviation of forecast error. These analysts often curried favor with management, because managers frequently could outperform lowball forecasts. In return, these managers granted analysts greater access to company information. Consequently, these analysts enjoyed better career advancement and professional recognition than their peers who were inconsistent, if approximately more accurate, in their own forecasts.

In other words, to err is human, but to err with reliable frequency may make you an “All Star” analyst.

For example, consider two forecasts. Analyst A delivers forecasts that are consistently three cents below realized earnings, while Analyst B provides forecasts that are two cents above realized earnings half the time and two cents below realized earnings the other half of the time. Investors should prefer Analyst A's forecasts. Why? Despite their stated lower accuracy compared with the forecasts of Analyst B, Analyst A's forecasts prove more useful as they are a predictable transformation of realized earnings.

In fact, when examining 12 years of data, we found that this “consistency effect” on informativeness is some two to four times greater than the effect of accuracy. (Earnings and analyst forecast data are from the 1994–2006 I/B/E/S Detail History files, specifically quarterly forecasts from analysts with eight or more quarters of experience.)

By shifting the focus of forecast informativeness from “accuracy” to “consistency,” our research shows that the volatility of earnings forecast errors can prove more important than their magnitude. This fact has implications for investors, analysts and regulators alike.

For example, importantly, we found that the consistency affect worked in the presence of institutional investors, who functioned as our proxy for sophisticated investors capable of discerning systemic bias and extracting useful information from the biases. Less sophisticated investors, by contrast, were less likely to decipher the bias and so tended to prefer forecast accuracy, even when that accuracy was inconsistently delivered. Indeed, when investors failed to recognize systemic bias, they penalized analysts for issuing (consistently) inaccurate forecasts.

These results also have implications for analysts' careers. Consistent with our expectations, more consistent analysts are less likely to be demoted to less prestigious brokerage houses and are more likely to become All Stars. We also found that analysts who lowball are more consistent but less accurate. These effects are particularly strong for analysts covering firms with more institutional investors.

Our results also offer useful insights for evaluating legislature such as the Global Settlement of 2003 (that requires research analyst's historical ratings to be disclosed) or the Regulation Fair Disclosure (Reg FD) of 2000 (that requires all public companies to disclose relevant information to all investors simultaneously). Overall, regulation has curtailed selective disclosure, at least to some extent, and in turn decreased analyst lowballing activity, which has resulted in less consistent forecasts. In other words, removing systematic bias, as regulators have done, levels the playing field but reduces the efficiency of price formation.

Empirical design and main results

To test our basic intuition, we first needed a measure of forecast informativeness. Beta is the coefficient obtained by regressing a three-day abnormal stock returns around the forecast revision date on forecast revisions over all quarters for which analyst i covered firm j. We then regressed this measure of forecast informativeness (Beta) on our measures of consistency (Cons) and accuracy (Accu), controlling for different relevant variables. We measured our variables for each firm-analyst over the entire sample period. Cons is a rank measure based on the standard deviation of the forecast errors over all quarters for which analyst i covered firm j, while Accu is a rank measure based on the absolute value of analyst forecast error. Cons and Accu are only moderately correlated (approximately 0.30). Specifically, we estimate the following cross-sectional model for each analyst i and firm j: Betai,j = α0 + α1Consi,j + α2Accui,j + αk⋅ Xki,j + ei,j .

1: The association of informativeness (Beta) with consistency and accuracy
Dependent Variable: Informativeness
Coefficient (StdErr)
Cons(istency) –19.69*** (3.70)
Accu(racy) 8.71*** (0.78)
Others : See description
N = 38,096, R2 = 1.93%
Standard errors are in parentheses. One, two, and three stars mark statistical significance at the 10%, 5%, and 1% level, respectively.
The dependent variable, beta, measures the informativeness of analysts. It is the coefficient obtained by regressing a three-day abnormal stock returns around the forecast revision date on forecast revisions over all quarters for which analyst i covered firm j. An analyst who does not change/update with stock-market changes has a low beta. Cons(istency) is a rank measuring the variability of forecast errors. Accu(racy) is a rank measuring the difference between actual and forecast earnings. The regression includes other variables: Intercept (significant at 1%), Horizon (at 5%), Boldness (at 5%), Brokersize, Experience, Breadth, and Cover.

We report the results of this analysis in Table 1. As predicted the coefficient associated with Cons is more significant than the coefficient associated with Accu. To examine the effect of investor sophistication, we split our overall sample into two subsamples based on the percentage of institutional investor ownership and we reestimate Model (1) separately for each subsample. Untabulated results indicate that the effect of consistency is more significant in the subsample in which sophisticated investors are more present.

To investigate the effect of consistency and accuracy on analyst's career, we estimate the following models: Demoi,t = γ0 + γ1Consi,t + γ2Accui,t + γk⋅ Xki,t , AllStari,t = δ0 + δ1Consi,t + δ2Accui,t + δk⋅ Xki,t , where Demo is an indicator variable that equals one if analyst i is demoted in the following year (zero otherwise) and AllStar is an indicator variable that equals one if analyst i is on Institutional Investor magazine's All Star list (zero otherwise). Results of this analysis reported in Table 2 indicate that analysts exhibiting a high forecast consistency are less likely to be demoted and more likely to be nominated as an All-Star analyst.

2: The effect of consistency and accuracy on analyst demotions and promotions
Dependent
Variable Demotion AllStar
Cons(istency) –0.30*** 0.60***
(0.07) (0.12)
Accu(racy) –0.03 0.54***
(0.10) (0.13)
Others : See description
N 15,561 11,985
Pseudo R2 7.57% 23.18%
Standard errors are in parentheses. One, two, and three stars mark statistical significance at the 10%, 5%, and 1% level, respectively.
The left column predicts next-year demotions (transfer to a smaller broker). The right column predicts next-year nomination to the All-Star list. Cons(istency) is a rank measuring the variability of forecast errors. Accu(racy) is a rank measuring the difference between actual and forecast earnings. The regression includes other variables: Intercept (significant at 1%), Horizon (at 5%), Boldness (at 5%), Brokersize, Experience, Breadth, and Cover.

09-elgreco
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