Managerial Ability, Analyst Recommendations, and Price
Informativeness
Abstract
This paper examines whether managerial ability convey information about thefirm’s future earnings to the capital markets. Using the future earnings response coefficient (FERC) methodology, we find that the current stock returns of firms withhigh-ability CEOs reflect more future earnings than does the stock returns of low-ability
firms. Our results are robust to controlling for potential omitted variables, loss versus
profit firms, and serial correlation of error terms.

We further find that managerial abil-

ity has a positive impact on analyst recommendations for the firm. In addition, analyst
recommendations at least partially mediate the effects of managerial ability on FERC.
That is, analyst recommendations represent a mechanism through which managerial ability affects the extent to which stock returns reflects future earnings. Overall, this study
reveals the impact of managerial ability on analyst-based outcomes and current pricing
weight on expected earnings and calls attention to the construct of managerial ability as a
key intangible asset for the investor community.

Keywords: Managerial Ability; Future Earnings Response Coefficient (FERC); Analyst
Recommendations; Mediating Effect11. Introduction
Managerial ability is clearly an important ingredient in the success of a business.
Previous research has focused on how managerial ability “matter” for corporate policies
and outcomes. For example, In Milbourn’s (2003) study, he focuses on the managerial
ability and measures it in terms of the number of press articles that cited the CEO. He
find that compensation contracts given to CEOs with a high ability (i.e., those with more
media-counts) exhibited greater equity instruments.

Chemmanur and Paeglis (2005)

report that better quality management can affect the firm’s IPO characteristics. Somestudies have examined whether the reputational motivations of CEOs affect their decision-making behaviors such as investment and reporting decisions (Hirshleifer 1993;
Sridar 1994). Very few studies, however, have empirically examined whether managerial
ability sustains firm good performance and whether the expectation of superior performance of highly ability CEOs, in fact, is delivered.
My focus on managerial ability is motivated by three considerations. First, managerial ability is one of the most important intangible assets that a firm has (Gaines-Ross,
2003). Second, it captures the dimension of managerial human capital (Francis, Huang,
Rajgopal and Zang 2008). Lastly, according to Burson-Marsteller’s survey in 1999, almost half of a firm’s reputation is based upon the image of its management team.
Thus this manager’s characteristic potentially can have a palpable impact on corporate
economic benefits. In fact, one of the reasons for the small number of empirical studies related to managerial ability is the difficulty of measuring the abilities of managers.
To overcome this concern, I measure managerial ability using four proxies which aremost often employed by previous studies: Press Citation, CEO awards from businessjournals, manager-specific efficiency derived from data envelope analysis (DEA), and industry-adjusted firm performance (Milbourn 2003; Francis et al. 2004; Malmendier andTate 2005).
Empirical investigation of the economic benefits of managerial ability is importantbecause top executives with highly developed management skills often receive high pay.
The ability perspective advocates there exists economic benefits of managerial ability.
Most of previous studies have focused on the impact of managerial ability on the future
operating performance and yielded mixed evidence (Nanda et al. 1996; Malmendier andTate 2005). In this paper, I think that the important question is persistence of profits.
2

In fact, Gaines-Ross (2003) find that analysts recommend a company’s stock based on
CEO reputation because a CEO with a well-established reputation, believed to have highability, will sustain good performance or turn around poor performance. A board of
directors will give a highly regarded CEO more opportunities to make up for his or her
mistakes, because they believe that the CEO will reverse current poor performance in thenear term (Gaines-Ross 2003).

Therefore, I expect that higher ability managers are able

to sustain stable performance over the long run (i.e., higher earnings persistence).
It is almost taken as an article of faith that high-ability managers help to firms’ information environment, thereby mitigating problems arising from information asymmetry between the firm and the investors. Baik et al. (2011) report that managerial ability is positively related to the likelihood of voluntary management earnings forecasts.
This finding is consistent with highly reputed CEOs having incentives to keep the market
informed about their firms’ economic prospects.

Moreover, managers with high abili-ties may have higher ability to obtain more precise information on investment opportunities, make better investment choices and achieve successful project outcomes with greater
likelihood (Chemmanur, Paeglis, and Simonyan 2009).

Successful corporate invest-

ments enhance firm future profits. Given that managerial ability provides useful information about future profit streams, it helps investors to better predict future earnings.
Thus, more information about future earnings should be reflected in current stock prices.
Using future earnings response coefficient (FERC) as proxy for price informativeness, we
expect that it correlates positively with the level of managerial ability.
This research contributes to the literature in several ways. First, this study contributes to the understanding of the economic benefits of managerial ability. A large
body of literature emphasizes the importance of managerial ability (Fama 1980; Gibbons
and Murphy 1992) and develops predictions about managerial behaviors (Hirshleifer
1993; Sridar 1994).

However, scarce literature empirically investigates whether the abil-

ity of a CEO provides economic benefits to the firm. Second, although the value impact of managerial ability is recognized (Agarwal, Taffler, and Brown 2007), the effects
of managerial ability on the ability of stock returns to reflect future earnings, however,
have been little studied. Our analysis provides fresh evidence about the information
content of managerial ability: the market understands and can better anticipate firms’
future earnings based on forward-looking information contained in managerial ability.
Third, given the scant evidence regarding the determinants of cross-sectional variation in
price informativeness with respect to future earnings, the inclusion of managerial ability3

as an explanatory variable may improve our understanding of financial market behavior.
The rest of the paper is organized as follows. Section 2 provides a review of therelevant literature and develops the hypotheses. In section 3, we describe our models,variable measurements, sample selection and data sources.

2. Literature Review and Hypotheses
2.1 Importance of Managerial Ability
Managerial ability is viewed as a vital element in the capital market.

Managers with

better ability can identify changes in their firm’s underlying economics. They convey
the intrinsic value of their firms more credibly to outsiders, thus reducing the information asymmetry facing in the equity market.

In addition, managerial ability is also afactor in determining a firm’s financial and investment policies. The firms which havetalent manger access the equity market more easily, in turn to affect the firms’ financial
policies. Managers have force on the corporate decisions, such as investing and financing decision, executive compensation, and earnings quality. Managers with high reputations have incentives to protect their reputations by enhancing their firms’ performances.
Several studies have examined the impact of CEO characteristic on corporate decision.

Chemmanura and Paeglisb (2005) suggest that IPOs firms with higher manage-

ment quality will be characterized by lower underpricing, greater institutional interest,
more reputable underwriters, and smaller underwriting expenses.

Management reputa-tion can reduce agency problems, managers intend to obtain funds and earn rents, andfirms must establish a reputation for generating profits and repaying these profits to investors. (Fama 1980, Narayanan 1985, Holmstrom and Ricart-Costa 1986, Holmstrom
1999) Chemmanur et al. (2009) indicate that better and more reputable managers may
be able to select better projects, characterized by a larger NPV for any given scale.
Taken together, these studies suggest that talent managers are able to cut down financing
costs, improve corporate governance and select the better investment project.
A substantial body of research is dedicated to understanding the determinants ofmanagerial ability and information environment.

Trueman (1986) theorizes that man-agers voluntarily issue earnings forecasts to signal their ability.

Based on this theory,

Baik et al. (2010) find that firms with highly reputed CEOs are associated with lower
opacity, suggesting that CEOs act to protect their reputations by increasing the flow of
information to the market. They also views from an investor perspective and find that4

firm value is increasing in CEO reputation, suggesting that investors perceive that highly
reputed CEOs improve the information environment.

Baik et al. (2011) show that apersonal characteristic of the CEO is associated with the forecast accuracy increases in
CEO ability. The better managers may be able to recognize the firm’s quality, thus declining the extent of information asymmetry between firm insiders and outsiders. Thismeans that managerial ability as a signal to help the investor can predict or determine
future profit
However, the extent literature focuses on the impact of managerial ability on firm
performance.

Jensen and Fuller (2002) assert that CEO reputation is a major determi-

nant of the long-term success and survival of a firm. Chemmanura and Paeglisb (2005)
find that firms with better management quality have stronger its long-term post-IPO operating performance and greater long-term stock returns.

Agarwal et al. (2007) also

consider that good management enhances firm value: well managed firms have higher
profitability, are able to sustain superior operating performance for longer, and are rewarded by higher market valuations.

Switzer and Bourdon (2010) show several proxies

for management quality significantly affect operating performance of IPOs in Canada.
Above these papers only discuss the relation between managerial ability and current
firm’s performance.

In this paper, we extend the line of research about the information

content of Managerial ability: whether better managerial ability help stock prices to reflect more future earnings given they convey forward-looking information.
2.2 Research on Future Earnings Response Coefficients (FERC)
Prior accounting literature has documented that stock prices reflect the expectationsof investors about future earnings, because the information can be aggregated with various public signals then reflected in the change in current stock price. Thus, the change
in current stock price captures the change in investors’ expectation for future earnings.
As the investors are better able to understand future earnings, more information about
future earnings will be reflected in current period stock returns.

We follow the ap-

proach of Collins et al. (1994) and measure the extent to which current stock returns reflects future earnings by the future earnings response coefficient (FERC). Extent literature has used this approach to verify that FERCs can be enhanced by the better information environment.
For example, Lundholm and Myers (2002) find that increased disclosure activity“brings the forward” into current stock returns. Gelb and Zarowin (2002) consider that5the informativeness of firm disclosures influence FERCs. Ayers and Freeman (2003),
Piotroski ,Roulstone (2004) and Choi and Jung (2008) show that the informativeness ofstock returns for future earnings, measured as the FERC, increases with the number of
information intermediaries such as equity analysts and institutional investors. Choi et al.
(2011) find that the association between current returns and future earnings is increasing
the frequency and precision of management earnings per share forecasts.
In addition, researchers have begun to find more compelling evidence to verify the
association between corporate accounting policies and the future earnings response coefficient. Ettredge et al. (2005) find the firm which adopts the disclosure rule SFAS
No.131 on segment reporting contains more information about its future earnings andthen reflected in higher FERC. Tucker and Zarowin (2006) find that managers use thediscretionary accruals to smooth income and garbles future accounting earnings information.

Oswald and Zarowin (2007) show that firms capitalize R&D has higher FERC

than expensing. Orpurt and Zang (2009) find that the direct method is valuable to investors with the higher FERCs. Hanlon et al. (2007) consider that firms initiate paying
improve the strength of relation between current returns and future earnings. Given
that managerial ability is a forward-looking indicator of future profits, this study add toextent literature by investigating how managerial ability affect FERC.
2.3 Empirical Hypothesis
Since management ability has a critical impact on firm performance, this factor is an
important consideration in terms of investors’ firm valuation. Because managers may
have reputations for being skilled operators and cost cutters, the ability of a firm’s management can have a certifying effect on future profit streams. With regard to firm development and performance, those firms with high ability management are also better
equipped to be more innovative (Staw and Epstein, 2000) and have higher internal governance strength (Karuna 2010).

Furthermore, highly ability managers have strong in-centives to promote transparency by actively disclosing information about their firms’
economic prospects because the market values such information, thus reducing the information asymmetry that investors face when they predict future profits.

Given thatmanagerial ability serves a forward-looking indicator of future profit streams, this metric
may help investors to better evaluate firm future earnings, which in turn reflected in current stock returns. As a result, the association between current stock returns and future
earnings, FERC, should be higher for firms enjoying higher managerial ability.
6

I for-

malize the second hypothesis as:
H1: The extent to which future earnings is reflected in current stock prices increases
with managerial ability.

The ability perspective found in the agency literature posits that the future firm
performance of more-skilled managers is likely to exceed that of less-skilled managers(Gibbons and Murphy, 1992).

Prior literatures have demonstrated hat high ability

management associated with higher profits next year than lower ranked management(Wade et al. 2006; Nanda et al. 1996). This reasoning suggests that Managerial ability
can view as an indicator of more promising future firm profits.

In addition, accounting

literatures suggest that financial analysts play an important role in providing investment
advice for individual and institutional investors. Extant research shows that analyst releases stock recommendations to investors based on the prospects of firms’ future performance. The better the prospects of firms’ future performance, the greater is thelikelihood that analyst will issue more favorable recommendations. Therefore, the better managerial ability results in better prospects of firms’ future performance, positivechanges in managerial ability should lead analysts to recommend that investors hold or
buy a firm’s stock.

I formalize the second hypothesis as follow:

H2: Analyst recommendations are positively associated with managerial ability.

Thus far, we have offered hypotheses on the impact of managerial ability on analyst
recommendations. Managerial ability affects analyst recommendations, which in turnaffect the FERC, it is reasonable to expect a “chained” relationship: from managerial
ability to the intermediate outcome of analyst recommendations and then to investors'
pricing weight on future earnings for equity valuation. It implies analysts are information intermediaries between firms and investors, their recommendations likely act as
an informational channel through which the firm with better managerial ability and reachinvestors. The more the firm enjoys favorable recommendations with highly ability
CEOs, the most likely the future earnings information impounded in managerial ability
is to pass through analyst recommendations and result in contributing to the FERC.
Given that analyst recommendations may convey the impact of managerial ability on the
FERC to investors. We suggest analyst recommendations s mechanisms for market re7

actions to the intangible asset of managerial ability. We establish the third testable hypothesis is:
H3: Analyst recommendations at least partially mediate the associations between managerial ability and the FERC.

3. Methodology
3.1 Measures of Managerial Ability
It is difficult to measure the management quality directly and empirically. Consequently, I employ four proxies to estimate the value of managerial ability. The firstmeasure we follow the approach of Milbourn (2003), Rajgopal et al. (2006), and Francis
et al. (2008) who use CEO press citations. A CEO who is perceived to be an expert ismore likely to be interviewed and cited in the newspaper and magazine. Thus, the press
citation measure of managerial ability likely reflects the market’s assessment of a CEO’s
perceived ability (Milbourn 2003). Similar to research employing press citations as aproxy for managerial ability, we hand-collect CEOs’ press citations by searching news
articles from all publications in the Factiva database. We calculate the number of news
articles that mention the name of a CEO and the CEOs’ company. We assume that a
CEO develops his/her reputation over a number of years, so we sum the number of citations over the prior five years to measure ability.
For the second measure of managerial ability, we count the number of manager
awards from business journals as following Malmendier and Tate (2009). Various business journals pick “CEOs of the Year” or “Best Managers” annual.

We collected man-ager award data from highly respected magazines such as Business Week, Financial World,
Forbes, Industry Week, Chief Executive, and Electronic Business. The winners ofmanager awards from these business magazines are considered as a talent manager. We
set the dummy variable, equal to 1 if the manager was given any award for the last five
years or 0 otherwise.
We adopt the third measure is data envelope analysis (DEA). Demerjian et al. (2012)use data envelope analysis (DEA) to derive a measure of CEO-specific talent.

To re-

gress the firm-level measure on market share, size, the number of firm segments andforeign operations, and firm fixed effects. The residual from this regression is themeasure of managerial ability. This measure of managerial efficiency can be considered
as managerial ability based on company performance. In validity checks of their meas8

ure, Demerjian et al. (2012) find that it is positively related to returns and CEO pay, andthat the persistence of earnings growth and sales growth is increasing in their measure ofmanagerial ability. Prior studies have linked earnings growth and sales growth withmanagerial ability (e.g.Fee and Hadlock 2003), giving us added comfort that the DEA
measure is capturing some dimension of managerial ability.
The last proxy for managerial ability is the industry-adjusted ROA (IndAdjROA)
while the CEO has been at the helm of the firm (Milbourn, 2003). Under this industry-adjusted measure, we posit that a positive value of the industry-adjusted measure
represents higher managerial ability. This approach provides a more objective estimation, in that the market would not devalue a managerial ability due to poor operating
performance if most firms in the same industry also had such problems.

Following theapproach in Milbourn (2003), IndAdjROA is calculated by using income before extraordinary items scaled by average total assets for each firm and subtracting from it the average ROA for firms with the same two-digit SIC code for each firm-year. Then, we deleted observations if there were less than 10 firms within a two-digit SIC code for a givenyear.
3.2 Primary Models and Empirical Predictions
The ability of stock prices to reflect future earnings can be measured by the FERC.
This paper fellows the approach of Collins et al. (1994), Lundholm and Myers (2002),
and Tucker and Zarowin (2006). To reduce the measurement error problem in using
realized earnings for expected earnings CKSS include future returns. Based on the priorstudies, we recognize lag stock returns to measure value creation.
Rt  b0  b1 X t 1  b2 X t  b3 X t 3 b 4 Rt 3   t

In regression (1), Rt is current annual returns for Year t.

(1)

Xt-1 and Xt are the earn-ings per share (EPS) for Year t-1 and t, respectively, and Xt3 represents the realized future
earnings aggregate over three years with annual compounding.

All the EPS variables

are the basic EPS excluding extraordinary items, adjusted for stock splits and stock dividends, and according to Christie (1987), deflated by the stock price at the beginning ofYear t.

Rt3 is common stock return for three year period starting from three months

after t fiscal year-end with annual compounding. In regression (1), the coefficient b3 is
FERC, and is expected to be positive. The coefficient on past earnings (b1) is predicted9to be negative, the ERC (b2) is predicted to be positive, and coefficient on future returns(b4) is predicted to be negative.
The purpose of this paper is to examine whether better managerial ability help
stock prices to reflect more future earnings given they convey forward-looking information.

Thus, we expand the above regression by adding the managerial ability, denot-

ed as ABILITYt and we interact ABILITYt with earnings variables. The empirical model is as follows:Rt  b0  b1 X t 1  b2 X t  b3 X t 3  b4 Rt 3  b5 ABILITYt  b6 ABILITYt  X t 1
 b7 ABILITYt  X t  b8 ABILITYt  X t 3  b9 ABILITYt  Rt 3   t(2)

In regression (2), we are interest in b8 if the managerial ability is to convey information about future earnings, then the coefficient on ABILITYt  X t 3 should be positive.
If the managerial ability contains less forward-looking information, then the coefficienton ABILITY t  X t 3 should be negative. We have no prediction for the coefficients on
ABILITYt , ABILITYt  X t 1 , ABILITYt  X t and ABILITYt  Rt 3 .

In order to prevent some other factors may make the stock price impound more information about future earnings, we control for firm size (SIZE), growth opportunities(BM), future earnings variability (EARNSTD), and analyst following (NANAL). Firm
size is measured as the market value of common equity at the beginning of Year t.
Growth opportunities are measured by the book-to-market ratio at the beginning of Yeart. For future earnings variability, we measure by the standard deviation of EPS fromYear t+1 to Year t+3, and then deflated by the stock price at the beginning of Year t.
Analyst following is measured as the average number of analysts’ forecasts in themonthly consensus, gather from I/B/E/S during Year t.
Our H2 is to ascertain whether security analysts are influenced by managerial ability.
If analysts recognize that managerial ability could indicate better earnings prospectus, weshould observe a positive association between consensus recommendations and managerial
ability. We test for such an effect using multivariate estimations in which the dependent
variable equals the level of consensus recommendation, and the explanatory variables of interest is managerial ability proxies (ABILITY). We include several factors that are known
to influence recommendations (e.g. Jegadeesh et al., 2004). These factors include the market-adjusted returns over the past twelve months (RETP) to capture momentum effects, average daily turnover (TURN), sales growth (SG), earnings / price (EP), and book / market
10(BM) to capture contrarian effects. We additionally include firms size (SIZE), analyst forecast revisions (FREV), standardized unexpected earnings (SUE), total accruals (TA), andcapital expenditures (CAPEX). Specifically, the model is specified as follows:RECt  b0  b1 ABILITYt  b2 RETPt  b3TURN t  b4 SIZEt  b5 FREVt
 b6 SUEt  b7 SGt  b8TAt  b9 CAPEt  b10 BM t  b11EPt   t(3)

where analyst recommendations (REC) are the expert advice from financial analysts toinvestors before the actual earnings announcements at the end of the year.

The meas-ure of analyst recommendation is a Likert scale which ranges from “1”=sell-recommendation for the stocks to “5”= strong buy-recommendation for the stocks(Bryan and Tiras 2007; Womack 1996).
Table 1.

All of independent variables are defined in the

We focus on the coefficient b1. We predict b1 to be positive indicating thatmanagerial ability improves analyst recommendations.
To test the mediating role of analyst recommendations in managerial ability’s possible impact on the FERC, we use the approach of Baron and Kenny (1986). We extend
regression (2) by including Analyst recommendations (REC) and its interactions withindependent variables as follows:Rt  b0  b1 X t 1  b2 X t  b3 X t 3  b4 Rt 3  b5 ABILITYt  b6 ABILITYt  X t 1
 b7 ABILITYt  X t  b8 ABILITYt  X t 3
 b9 ABILITYt  Rt 3  b10 RECt  b11 RECt * X t 3   t(4)

If the impact of managerial ability on the FERC is decreased after the inclusion ofanalyst activities, that suggests evidence for the partial mediation role of analyst-based
processes.
3.3 Data and Sample Selection
We gather data from CRSP and Compustat databases. The data period is start from1990 to 2007. The sample ends in 2007 because we require three years subsequentearnings and returns in formation.

We exclude regulated utilities (SIC 40-49) and finan-cial institutions (SIC 60-69) from the initial sample. To minimize the effect of outliners,
we follow Tucker and Zarowin (2006) and delete observations that are in the top or bottom 0.5 percent of the distributions of past, current, and future three years’ earnings,
and of current and future three years’ returns.
11

4. Empirical Results
4.1 Variables and Descriptive Statistics
Panel A of Table 2 reports descriptive statistics for the variables used in the FERC
regressions. Current annual returns (Rt) have a mean (median) of 11.6 percent (7.2
percent), and earnings per share deflated by the beginning svtock price have the mean
(median) of 4.3 percent (5.0 percent). We use four proxies to measure the managerial
ability. The mean number of press citations is 2.5 citations and about 10% of CEOs
received an award in the past 5 years. Note that, the ability variable, as measured by the
total number of media citations, is highly skewed. We adopt two ways to fix this in later
analysis: first, we take the logarithm of the citation plus 1; second, we define the dummy
variables for CEOs of low and high ability. Both measures give economically similar
results in our tests. So we only report the results using the logarithm of (citiation+1).
Managerial ability measured by DEA score has a mean and median close to zero, by construction, as this is a residual from firm efficiency model.

The standard deviations of

future earnings (EARNSTD) have a mean (median) of 3.8 percent (1.8 percent).
Unreported correlation analyses show that current stock returns, Rt, are positively
correlated with current and future earnings (Xt and Xt3), as expected. Returns are negatively correlated with past earnings, Xt-1, in line with the mean-reverting nature of earnings. As expected, the future returns variable, Rt3 are positively correlated with future
earnings, Xt3. However, one concern is the statistically significant negative correlation
between current returns, Rt, and future returns, Rt3 (Pearson correlation=-0.182 andSpearman correlation=-0.093).

As a result, future returns may influence our regression

results beyond their role as a measurement error proxy. Indeed, Orpurt and Zang (2009)
also show a significant correlation between these variables in their Panel D of Table 2.
Both Pearson and Spearman correlations show that four proxies of managerial ability,
ABILITYt is positively correlated with future earnings figures. Besides, multicollinearity
is not a serious concern as the variance inflation factor (VIF) is under 10 for all our regression specifications (Belsley et al. 1980).
Panel B of Table 2 presents descriptive statistics for the analyst recommendation model.
The mean (median) sample firm receives a three-tier consensus recommendation of 2.68
(2.66), which is between sell and hold.

The sample is tilted toward large firms as the aver-age company in our sample has market capitalization of $1.4 billion.
12

The average sample

firm has a book-to-market ratio of 0.449, sales growth of about 1.15% compared to the prior four quarters, capital expenditures equaling about 6.5% of assets, and total accruals
equaling about 4.9% of assets.
Unreported correlation analyses show that analyst recommendations have a correlationof 0.28 – 0.09 with the managerial ability measures. Analyst recommendations are also
positively correlated with past returns (0.10 – 0.14), standardized unexpected earnings (0.16),
and sales growth (0.19).

Recommendations are negatively correlated with the firm’s

book-to-market ratio (–0.22). All of these results are consistent with Jegadeesh et al. (2004).
Our managerial ability measures generally have low correlations with firm characteristics,
although CEO award is positively correlated with the natural logarithm of market capitalization (0.39) and the sales growth (0.27).

4.2 The Effect of Managerial Ability on the FERC (H1)
In this section, we use OLS regression analyses to test whether firms with higher
managerial ability have greater FERC than firms with lower ability.

Standard errors for

all regressions are corrected for heteroskedasticity and within-firm clusters. To compare with previous studies, in Panel A of Table 3 we first present the results of the baseline FERC model (Eq. (1)). In the first column, as previously predicted, both the coefficient on Xt3 (FERC) and the coefficient on Xt, are significantly positive (0.639 and 0.717,
respectively), and the coefficient on Xt-1 is significantly negative (-0.698). The coefficient on Rt,1-3 is also significantly negative (-0.086), which confirms the successful role ofthe instrumental variable. The adjusted R2 of the baseline model is 12.2 percent, which
is higher than that of the traditional ERC model (7.64 percent). The significant improvement in explanatory power is consistent with the findings of Collins et al. (1994).
Our primary objective is to measure the impact of managerial ability on the FERCs.
We estimate four separate models, one for each measure of managerial ability. The results of the regression analysis are shown in right four columns of Panel A. After including the interaction between Abilityt and the other variables in the benchmark FERC
model, the coefficients on Xt and Xt3 are significantly positive and the coefficients on Xt-1
and Rt3 are significantly negative.

The focus of this table is on the regression coefficientof ABILITYt *Xt3, corresponding to b8 in Eq. (2). The results show that all of four ability interaction variables exhibit positive and significant coefficients (b8 ranging from 0.008
to 1.048 at the 5% level or even lower); it suggests that the extent to which current returns reflect the future earnings increases with managerial ability.

The results strongly

support H1 and suggest that managerial ability conveys valuable information about fu13

ture earnings capacity that investors can use to place a greater current pricing weight on
future expected earnings.
We then perform analyses to control for potential correlated omitted variables.
Based on the work of Lundholm and Myers (2002) and Tucker and Zarowin (2006), firmsize, negative earnings, firm growth, earnings volatility, and analyst following have all
been shown to be significantly related to the FERC. Thus, we estimate the model similar to Eq. (2) above but with the addition of these explanatory variables, each separatelyinteracted with the future earnings variable as well as included simultaneously.
Panel B of Table 3 shows the estimation results for the models adding the full control variable.

After controlling for these factors, the coefficients on ABILITYt *Xt3 re-main statistically positive, strongly supporting our hypothesis that managerial ability provides valuable information and thus improves market’s ability to impound future earnings into current returns.

Besides, the coefficients on Xt3*SIZEt are positive, suggestingthat firm size is associated with an increase in the ability of stock prices to reflect future
earnings. In terms of BMt variable, growth may take time to be materialized and recognized in earnings while the return reflects expectation for future growth immediately.
Thus, growth firms have a higher FERC than mature firms.

The coefficients on

Xt3*LOSSt and Xt3*EARNSTDt are statistically negative, suggesting that the extent towhich stock returns reflect future earnings are smaller for firms reporting losses and forfirms with more volatile earnings stream. Consistent with the findings of Tucker andZarowin (2006), we find that the association between current returns and future earnings
is decreasing in the number of analyst following. Overall, the findings support thatmanagerial ability is associated with stock prices that reflect more information about future earnings, attesting to the important role of management quality in providing relevant
information about future earnings.
4.3 Managerial Ability and Analyst Recommendations (H2)
Panel A of Table 4 presents regression results for test of H2. The dependent variable equals the level of analysts’ consensus recommendation. The first through fourthcolumns provide the estimates from four models, one for each measure of managerial
ability.

Standard errors are clustered both along the year and firm dimensions. The

coefficient estimates of ABILITYt are positive and highly significant, indicating thathigher level of managerial ability is associated with more favorable consensus recommendations, after controlling for the known determinants of recommendations. The
14

results strongly support H2 and suggest that analysts have a favorable view of managerial
ability, possibly because management quality contributes with the most productive andefficient way of enabled resources within the organization, thereby achieving long term
corporate value creation. This effect is significant in economic terms. When managerial ability is measured by DEA score, a one standard deviation increase in ABILITY increases the consensus recommendation by 0.02, which is similar to the impact ofwell-known determinants. For example, a one standard deviation increase in EP or SG
increases the consensus recommendation by 0.01 or 0.06, respectively. Given that analyst recommendations tend to concentrate above 2.5, these small incremental effects arepotentially large in economic importance.
Analysts issue more favorable recommendations to stocks with positive price momentum, higher sales growth, lower turnover, lower book/market ratios, and higher
earnings/price ratios. Firms with higher earnings surprises, more positive analyst earnings forecast revisions, greater total accruals, and more capital expenditures are associatedwith higher recommendations. These estimates are generally consistent with prior literature.
4.4 The Mediating Role of Managerial ability (H3)
H3 predict that analyst recommendations at least partially mediate the association
between managerial ability and the FERC. According to Baron and Kenny (1986), toestablish mediation, managerial ability must affect analyst recommendations, and analyst
recommendations must affect the FERC. As we found previously, managerial ability
affects analyst recommendations. In additional, the results of Table 3 suggest thatmanagerial ability has effect on the FERC. As we report in Table 5, because inclusionof analyst recommendations in the model reduces the strength of the effects of managerial ability on the FERC (from 0.07, p=0.094, to 0.563, p=0.057), only marginally significant), the results support a partial mediating role of analyst recommendations. In addition, the inclusion of mediating effects of recommendations significantly improves thefit of the FERC models, as Table 5 shows. Specifically, adding the direct and mediating
effects of recommendations leads to an incremental R-square ranging from 0.06 to 0.08
in various model specifications, thus explaining significantly more variance of firm stock
returns.
Finally, we conduct Sobel’s (1982) test for mediation to assess whether the indirect
mediation effects are statistically significant. The standard Sobel test model is z = ab /15

, where a andare coefficient and standard error, respective-

ly, for the impact of the independent variable on the mediator and b andare coeffi-

cient and standard error, respectively, for the impact of the mediator on the dependent
variable. We find that the Sobel test results are consistently significant (smallest z-value= 2.32, p<0.05) for all indirect mediation effects. Thus, managerial ability’s indirect effects through the mediating role of recommendations are mostly significant, supporting
the prediction of H3.

5. Conclusion
In this paper, we examine whether managerial ability are informative about thefirm’s future earnings. If information regarding future earnings is revealed by managerial ability, the stock price should reflect more of future earnings news for firms withhigher quality management. Our empirical analyses reveal that the current stock returnsof firms with higher managerial ability reflect more future earnings than does the returnsof lower-ability firms. Our results remain after controlling for potential omitted variables. We conclude that the market attaches value to managerial ability, because the ability enables investors better anticipate a firm’s future profitability. We further investigate
whether the stock recommendations of security analysts are influenced by firm managerial ability.

If security analysts identify managerial ability as a factor that contributes tofuture earnings streams, they will issue more favorable recommendations to stocks withquality management. We empirically find that managerial ability has a positive impact
on analyst recommendations for the firm. In addition, analyst recommendations atleast partially mediate the effects of managerial ability on FERC. That is, analyst recommendations represent a conduit through which managerial ability influences investors’
pricing weights of future earnings component in current stock returns. Given sophisticated analysts pay attention to management quality, market investors should have goodreason to listen and follow.

As such, the findings of this study also have practical im-

plications.

16

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20

TABLE 1
Variable Measurements
Panel A: FERC model
Variables
Rt
Xt-1
Xt
Xt3
Rt3
SIZEt
BMt
EARNSTDt
NANALtMeasurements
The buy-and-hold annual returns during the 12-month period starting
three months following the firm's t-1 fiscal year-end.
Earnings per share for Fiscal Year t-1, deflated by the stock price at the
beginning of Fiscal Year t.
Earnings per share for Fiscal Year t, deflated by the stock price at the
beginning of Fiscal Year t.
The sum of earnings per share for Fiscal Years t+1 through t+3, deflated by the stock price at the beginning of Fiscal Year t.
The buy-and-hold stock return for Fiscal Years t+1 through t+3 starting
three months following the firm's t fiscal year-end.
Market capitalization ($millions) at the beginning of Fiscal Year t. We
use log transformation in correlation and regression analyses.
Book value of equity divided by market value of common shares outstanding at the beginning of Fiscal Year t.
The standard deviation of earnings per share for Fiscal Years t+1 to
t+3, deflated by the stock price at the beginning of Fiscal Year t.
The number of analysts following in the latest month prior to earnings
announcement for Fiscal Year t. We use log transformation in correlation and regression analyses.

Panel B: Analyst recommendation model
Variables
RECt

SIZEt
RETPt
FREVt
SUEt

EPt

Measurements
The mean of analyst recommendations made or reviewed at least two days
but no more than a year prior to the calendar year’s end. If an analyst had
more than one recommendation in this window, the newest one is taken.
The recommendations are coded on a scale from 1 (strong sell) to 5
(strong buy). REC is a continuous variable (1, 5).
Natural logarithm of market capitalization at the end of each calendar
year.
Market adjusted buy-end-hold return over the month (-1,-12) prior to thecalendar year’s end. The market return is the value-weighted return fromCRSP.
The sum of price-scaled mean forecast revisions (taken from the IBES
summary statistics) using months (-1, -12) relative to the end of each calendar year.
SUE: Standardized unexpected earnings. (EPSq-EPSq-4)/ stdq, where EPS
is earnings per share excluding extraordinary items from the most recent
quarterly earnings announcement prior to the end of the calendar year, andthe denominator is the standard deviation of unexpected earnings(EPSt-EPSt-4) from quarters t=q-7 to t=q.
Sum of the past four quarters earnings per share divided by the stock priceat the end of the most recent fiscal year with an earnings announcement
preceding the current calendar year end.
21

BMt

TAt

CAPEXtSGt

TURNt

Book value of asset scaled by the sum of book value of assets, negative
one times long-term debt (including current part), and market value ofcommon stock at the end of the most recent fiscal year with an earnings
announcement preceding the current calendar year end.
(total accrualst-total accrualst-1)/(total assetst), where total accruals is defined as change in current assets minus change in cash, minus change incurrent liabilities, plus change in long-term debt, minus change in deferredtaxes, minus depreciation. Changes are from four quarters earlier. t refers
to the year of the most recent fiscal year with an earnings announcement
preceding the current calendar year end.
Sum of capital expenditures from the most recent four quarters scaled bytotal assets
Sales growth, defined as the sum of sales in quarters 0,-3 scaled by the sumof sales in quarters -4,-7, where quarter zero refers to the end of the mostrecent fiscal quarter with an earnings announcement preceding the current
calendar year end.
Percentile rank of the daily average turnover per month, averaged over the
past six months, percentiles calculated by exchange, where turnover is defined as share volume over shares outstanding.

22

TABLE 2
Sample Statistics
Panel A：Descriptive Statistics
VariableN
Mean
Rt
10,286
0.116
Xt-1
10,2860.041
Xt
10,2860.043
Xt3
10,2860.129
Rt3
10,286
0.295
DJHITSt
10,286
2.489
AWARDt10,286
0.096
MGTEFFt
7,044-0.040ADJROAt
10,2260.058
SIZEt
10,286
7.555
LOSSt
10,2860.104
BMt
10,2860.449
EARNSTDt
10,2860.038
NANALt10,286
2.403

Median
0.072
0.048
0.050
0.138
0.111
0.000
0.000
-0.033
0.039
7.422
0.000
0.399
0.018
2.398

Q1
-0.142
0.029
0.028
0.063
-0.2400.000
0.000-0.111
0.005
6.500
0.000
0.254
0.009
1.946

Q3
0.305
0.066
0.070
0.215
0.577
2.000
0.000
0.017
0.092
8.461
0.000
0.579
0.041
2.833

Panel B：Descriptive Statistics ─ Consensus Recommendation Level
RECt
10,286
2.678
2.667
0.567
2.333
RETPt
10,286
0.116
0.072
0.427
-0.142
TURNt
10,28651.70253.00028.42727.000SIZEt
10,286
7.555
7.422
1.446
6.499
FREVt
10,286
0.003
0.004
0.034
-0.002
SUEt
10,286-0.4250.278
28.783-0.799SGt
10,286
1.147
1.107
0.219
1.040
TAt
10,286-0.049-0.0460.080-0.081CAPEXt10,286
0.065
0.047
0.065
0.024
BMt
10,286
0.449
0.399
0.285
0.254
EPt
10,286
0.040
0.046
0.079
0.028

3.000
0.305
76.000
8.461
0.010
1.187
1.206
-0.015
0.083
0.579
0.064

23

Std. Dev.
0.427
0.072
0.069
0.197
0.886
9.919
0.295
0.128
0.091
1.446
0.305
0.285
0.059
0.585

TABLE 3
Regressions of FERC Models: Analyzing the Effect of Mangerial Ability
Panel A: Main Tests
BaselineModel

Variables
Intercept
Xt-1
Xt
Xt3
Rt3
ABILITYt

Not Included
Coefficientestimate
0.053***(0.000)-0.698***(0.000)0.717***(0.000)0.639***(0.000)-0.086***(0.000)ABILITYt * Xt-1
ABILITYt * Xt
ABILITYt * Xt3
ABILITYt * Rt3
Adjusted R2
Observations

0.122
10,286

Managerial ability (ABILITY) proxies =Press
Citation
Coefficientestimate
0.059***(0.000)-0.700***(0.000)0.691***(0.000)0.627***(0.000)-0.088***(0.000)-0.003***(0.000)0.007(0.498)0.004(0.564)0.008**(0.013)0.001**(0.016)0.124
10,286Award
Coefficientestimate
0.069***(0.000)-0.848***(0.000)0.756***(0.000)0.600***(0.000)-0.094***(0.000)-0.098***(0.000)0.096(0.780)-0.181(0.554)0.575***(0.000)0.038**(0.030)0.123
10,28624

DEA Score
Coefficientestimate
0.058***(0.000)-0.766***(0.000)0.833***(0.000)0.725***(0.000)-0.087***(0.000)0.006(0.900)-0.906(0.149)1.158*(0.078)0.534**(0.028)0.090**(0.033)
0.126
7,044

IndAdjROA
Coefficientestimate
0.055***(0.000)-0.859***(0.000)0.754***(0.000)0.614***(0.000)-0.097***(0.000)-0.048(0.335)-0.917***(0.006)1.903***(0.008)1.048***(0.000)0.183***(0.000)0.125
10,226

Panel B: Including Potentially Correlated Omitted Variables
Adding a single control variable
Managerial ability (ABILITY) proxies =Variables
Intercept
Xt-1
Xt
Xt3
Rt3
ABILITYt
ABILITYt *Xt-1
ABILITYt *Xt
ABILITYt *Xt3
ABILITYt * Rt3
SIZEt
SIZEt * Xt3
LOSSt
LOSSt * Xt3
BMt
BMt * Xt3
EARNSTDt
EARNSTDt * Xt3
NANALtNANALt * Xt3
Adjusted R2
Observations

Press Citation
Coefficientestimate
0.074**(0.020)-0.623***(0.000)0.434***(0.000)1.258***(0.000)-0.103***(0.000)-0.002**(0.015)0.009(0.354)0.007(0.330)0.010***(0.002)0.001*(0.075)-0.051***(0.000)0.002*(0.916)-0.048***(0.004)-0.199***(0.000)0.069***(0.000)-0.129**(0.019)0.700***(0.000)-0.969***(0.000)0.122***(0.000)-0.133***(0.006)0.165
10,286Award
Coefficientestimate
0.132***(0.000)-0.750***(0.000)0.565***(0.000)1.026***(0.000)-0.112***(0.000)-0.055**(0.022)0.064(0.849)-0.141(0.636)0.420***(0.002)0.047***(0.006)-0.061***(0.000)0.067***(0.001)-0.032*(0.064)-0.209***(0.000)0.081***(0.000)-0.162***(0.002)0.145***(0.000)-0.709***(0.000)0.128***(0.000)-0.234***(0.000)0.168
10,28625

DEAScore
Coefficientestimate-0.010(0.787)-0.703***(0.000)0.551***(0.000)1.060***(0.000)-0.102***(0.000)0.036(0.434)-1.194*(0.051)0.746(0.244)
0.632***(0.009)0.084**(0.041)-0.053***(0.000)0.058**(0.047)-0.067***(0.001)-0.128*(0.061)0.133***(0.000)-0.087(0.241)0.758***(0.000)-1.102***(0.000)0.151***(0.000)-0.164**(0.012)
0.178
7,044

IndAdjROA
Coefficientestimate
0.070**(0.026)-0.839***(0.000)0.571***(0.000)0.804***(0.000)-0.113***(0.000)0.194***(0.000)-0.686**(0.036)2.179***(0.002)0.766**(0.010)0.197***(0.000)-0.059***(0.000)0.078***(0.000)-0.043**(0.013)-0.113**(0.036)0.129***(0.000)-0.140**(0.016)0.707***(0.000)-0.830***(0.000)0.130***(0.000)-0.185***(0.000)0.170
10,226

TABLE 4
Regressions of Consensus Recommendation Level (“REC”)
Panel A: Main Tests
Managerial ability (ABILITY) proxies =Press CitationAward
DEA Score

Variables
Intercept
ABILITYt
RETPt

TURNt
SIZEt
FREVt
SUEt
SGt
TAt
CAPEXtBPt
EPt
Adjusted R2
Observations

Coefficientestimate
2.889***(0.000)0.002***(0.001)0.233***(0.000)-0.001***(0.000)-0.044***(0.000)1.423***(0.000)0.000*(0.079)0.248***(0.000)0.181**(0.015)0.425***(0.000)-0.359***(0.000)0.143*(0.087)0.086
10,286

Coefficientestimate
2.864***(0.000)0.080***(0.000)0.226***(0.000)-0.001***(0.000)-0.042***(0.000)1.303***(0.000)0.000*(0.078)0.252***(0.000)0.189**(0.011)0.374***(0.000)-0.361***(0.000)0.160**(0.035)0.087
10,28626

Coefficientestimate
2.846***(0.000)0.145***(0.005)0.225***(0.000)-0.002***(0.000)-0.040***(0.000)1.600***(0.000)0.000(0.339)0.287***(0.000)0.211**(0.013)0.271**(0.014)-0.303***(0.000)0.174*(0.064)
0.083
7,044

IndAdjROA
Coefficientestimate
2.831***(0.000)0.128*(0.052)0.232***(0.000)-0.001***(0.000)-0.036***(0.000)1.272***(0.000)0.000*(0.089)0.249***(0.000)0.237***(0.003)0.412***(0.000)-0.333***(0.000)0.074(0.364)0.084
10,226

TABLE 5
Regressions of FERC Models
Panel A: Main Tests

Variables
Intercept
Xt-1
Xt
Xt3
Rt3
ABILITYt
ABILITYt * Xt-1
ABILITYt * Xt
ABILITYt * Xt3
ABILITYt * Rt3
RECt
RECt * Xt3
SIZEt
SIZEt * Xt3
LOSSt
LOSSt * Xt3
BMt
BMt * Xt3
EARNSTDt

Managerial ability (ABILITY) proxies =Press CitationAward
DEA Score
IndAdjROA
Coefficient
Coefficient
Coefficient
Coefficientestimate
estimate
estimate
estimate
***
***
***
-0.227-0.217-0.377-0.276***(0.000)(0.000)(0.000)(0.000)*********-0.744-0.740-0.772-0.854***(0.000)(0.000)(0.000)(0.000)
***
***
***
0.349
0.530
0.557
0.549***(0.000)(0.000)(0.000)(0.000)1.032***
1.001***
1.055***
0.757***(0.000)(0.000)(0.000)(0.000)-0.104***-0.107***-0.095***-0.106***(0.000)(0.000)(0.000)(0.000)*****-0.002-0.0460.039
0.201***(0.004)(0.043)(0.387)(0.000)0.008
0.144-0.748-0.701**(0.317)(0.649)(0.209)(0.030)0.010*-0.0350.487
1.994***(0.162)(0.903)(0.434)(0.003)0.007*
0.249**
0.470**
0.563*(0.094)(0.048)(0.046)(0.057)
***
***
**
0.001
0.049
0.095
0.148***(0.009)(0.003)(0.018)(0.002)
***
***
***
0.088
0.094
0.097
0.094***(0.000)(0.000)(0.000)(0.000)0.128***
0.096***
0.136***
0.097***(0.001)(0.006)(0.004)(0.007)-0.044***-0.045***-0.036***-0.045***(0.000)(0.000)(0.000)(0.000)0.010
0.005-0.0200.043**(0.652)(0.806)(0.488)(0.035)****-0.036-0.021-0.047-0.020(0.035)(0.202)(0.019)(0.248)*******-0.253-0.276-0.115-0.161***(0.000)(0.000)(0.078)(0.002)
***
***
***
0.099
0.114
0.159
0.153***(0.000)(0.000)(0.000)(0.000)****-0.112-0.130-0.025-0.102*(0.050)(0.017)(0.688)(0.070)
***
***
***
0.630
0.600
0.703
0.650***
27

EARNSTDt * Xt3
NANALtNANALt * Xt3

Adjusted R2
Observations(0.000)-0.987***(0.000)0.125***(0.000)-0.198***(0.000)0.186
10,286(0.000)-0.571***(0.000)0.113***(0.000)-0.144***(0.002)
0.195
10,28628(0.000)-0.765***(0.000)0.140***(0.000)-0.114*(0.068)
0.210
7,044

(0.000)-0.715***(0.000)0.122***(0.000)-0.180***(0.000)0.188
10,226