


































http://journals.sfu.ca/abr  ADVANCES IN BUSINESS RESEARCH 
2014, Volume 5, pages 1-15 

 
 

1 

 

Does Management Forecast Guidance Impair the 
Usefulness of Analyst Forecasts in Firm Valuation?  

Yao Tian 
San Jose State University 

Analyst earnings forecasts are an important input to the Residual Income Valuation model; 
however, these forecasts are increasingly subject to management guidance. This study 
examines the impact of management forecast guidance on the usefulness of analyst earnings 
forecasts in firm valuation. It finds that valuation models estimated using guided forecasts 

have less ability to explain stock price and predict future returns through value-to-price ratios 
than valuation models estimated using non-guided forecasts. These results provide evidence 
that management forecast guidance reduces the usefulness of analyst forecasts in firm 
valuation and impairs the performance of forecast-based valuation models to predict firm 
value. 

Keywords: firm valuation; analyst forecast; expectations management 

Introduction 
 
Ohlson (1995) provides a conceptual framework for relating accounting earnings to firm value. 

Analyst earnings forecasts are important inputs to the empirical implementation of Ohlson’s (1995) 
residual income valuation model (RIM). In particular, Dechow et al. (1999) and Frankel and Lee (1998) 
show that intrinsic value metrics (V) estimated using analyst forecasts are better than other value 
metrics at explaining contemporaneous stock price (P) and predicting future returns through intrinsic 
value-to-price (V/P) ratios. This establishes the usefulness of analyst forecasts in firm valuation.  

While analyst earnings forecasts have gained popularity among researchers and investors, 
analyst forecasts are increasingly subject to management guidance. According to the surveys by NIRI, 
around 79% of firms provide some form of earnings guidance. The ability of valuation models to 
estimate firm value is affected by the quality of model inputs. Gaio and Raposo (2014) report that 
earnings quality affects firm valuation. However, existing valuation studies have not considered the 
impact of forecast quality on the performance of forecast-based valuation models. Kasznik and 
McNichols (2002) call for future studies to investigate the consequences of management manipulation 
on firm valuation. In response, this paper examines the impact of management forecast guidance on 
the usefulness of analyst forecasts in firm valuation. 

Whether management forecast guidance improves or impairs the usefulness of analyst 
forecasts in firm valuation depends on the purpose of management forecast guidance. Most studies in 
the expectations management literature, notably Bartov et al. (2002) and Richardson et al. (2004), take 
the view that management issues earnings guidance for the purpose of dampening analyst earnings 
expectations to produce beatable forecasts upon earnings release. This view is supported by 
subsequent studies. For instance, Graham et al. (2005) document in a survey study that CFOs admit 
that they would guide analyst forecasts down to artificially low levels in order to meet or beat analyst 
earnings expectations (hereafter, MBE); Burgstahler and Eames (2006) and Koh et al. (2008) 
document that firms manage analyst forecasts downward to just meet analyst expectations; and Kross 
et al. (2011) find that firms that consistently MBE are more likely to issue bad-news management 

http://journals.sfu.ca/abr


USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

2 

 

forecasts to guide down analysts' expectations and produce beatable forecasts. Similar findings are 
reported in the UK context (e.g., Brown and Higgins, 2005; Athanasakou et al., 2009).   

However, not all forecast guidance is issued for the purpose of manipulating analysts' earnings 
expectations. Several studies in the voluntary disclosure literature document that sometimes firms 
issue earnings guidance to truthfully communicate inside information and abate analyst over-
optimism. In particular, Kasznik and Lev (1995), Coller and Yohn (1997), and Hutton, Miller, and 
Skinner (2003) document that firms issue earnings guidance to reduce information asymmetry between 
the firm and the market. Lansford, Lev and Tucker (2013) document that firms’ voluntary earnings 
guidance assists analysts and investors in predicting future operating performance. Such earnings 
guidance reduces information asymmetry and improves forecast accuracy (Lang and Lundholm 1996).  

Whether management forecast guidance improves or impairs the usefulness of analyst 
forecasts in firm valuation depends on the relative mix of these two types of guidance. That is, if firms 
issue earnings guidance mainly for the purpose of walking down analyst expectations to produce 
beatable forecasts at earnings release, then forecast guidance will reduce the ability of analyst forecasts 
to convey information about firms’ future performance and therefore impair the usefulness of analyst 
forecasts in firm valuation. This hypothesis is formally stated as follows: 

 
H: Management forecast guidance impairs the usefulness of analyst earnings forecasts in forecast-based 

valuation models. 
 
However, if firms issue earnings guidance mainly for the purpose of communicating inside 

information and correcting analyst optimism, then such guidance will improve the usefulness of 
analyst forecasts in firm valuation. The alternative hypothesis is stated as follows: 

 
H(a): Management forecast guidance improves the usefulness of analyst earnings forecasts in forecast-

based valuation models. 
 

Research Design 
 

This section describes the research design including estimating value metrics, the forecast 
guidance measure, the intrinsic value measure, and the empirical analyses. 

 
Estimating intrinsic value metrics (V) 

Following Dechow et al. (1999), this study uses Ohlson’s (1995) RIM as the valuation 
framework to estimate V. Specifically, at the end of each forecast year t, V is constructed for each firm 
using earnings in year t-1, book value at the beginning of year t, and the consensus forecast for year t’s 
earnings measured at the end of year t (prior to the earnings announcement). Better V metrics have 
greater ability to explain contemporary stock price and predict future returns through V/P ratios 
(Frankel and Lee, 1998; Lee et al., 1999; Dechow et al., 1999; Sohn, 2012). In particular, Lee et al. 
(1999) argue that although accounting regulators are more concerned with the value relevance of 
accounting-based measures and portfolio managers are more interested in V’s predictive power, both 
stock tracking ability and return prediction power are desirable properties of the V metrics; they find 
that better V performs better in both dimensions. Therefore, this study employs both analyses—it 
investigates the impact of forecast guidance on the usefulness of analyst forecasts by comparing the 
performance of V estimated using guided versus non-guided forecasts to explain stock price and 
predict future returns through V/P ratios. 

 



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3 
 

Forecast guidance measure  
Firms can use different channels to guide analyst expectations. In particular, they can issue 

management forecasts or host conference calls, or guide analyst expectations via private conversations 
with analysts in informal settings. This study constructs a measure of management forecast guidance 
that captures management guidance through different channels. Prior research has developed two main 
approaches to capture both the explicit and implicit forecast guidance. In the first approach, developed 
by Bartov et al. (2002) and later adopted by Brown and Pinello (2007), Bartov and Cohen (2009), etc., 
forecast guidance is suspected when analyst forecasts are optimistic at the beginning of a period and 
pessimistic at the end of the period. This approach identifies both management’s motive to guide (in 
the form of initial optimism) and the result of management forecast guidance (in the form of a 
downward forecast revision).  However, analysts may voluntarily revise down their forecasts to 
incorporate firm-level, industry-level or even market-level bad news occurring during the year - such 
downward revision is not driven by management forecast guidance. This approach does not distinguish 
news-driven revision from guidance-driven revisions. 

In the second approach, developed by Matsumoto (2002) and subsequently adopted by Brown 
and Higgins (2005), Koh et al. (2008), etc., forecast manipulators are defined to be the firms whose 
last consensus forecasts are lower than the expected forecasts.  Matsumoto (2002) constructs the 
expected forecast by modeling the seasonal changes in earnings as a function of prior seasonal changes 
in earnings and the cumulative returns during the year. The strength of this approach is that it takes 
into account the effect of economy-wide and/or firm-specific bad news on analyst forecasts. However, 
this approach does not consider whether there are guidance-driven downward revisions in analyst 
forecasts throughout the year.  

The above discussion suggests that while each approach has its strengths, neither provides a 
desirable measure of forecast guidance in the context of this study. Therefore, this study builds on 
these two approaches to develop a measure that attempts to capture the notion of management 
forecast guidance as defined in this study.  Figure 1 illustrates the timeline for the forecast-related 
measures. Forecast year t starts 360 days prior to the announcement of year t’s earnings and ends one 
day prior to the earnings announcement. A forecast year consists of 12 forecast months, each month 
represents a 30-day period. To avoid the staleness problem1 often associated with the consensus 
forecasts published by the Institutional Brokers’ Estimate System (IBES), the last forecast issued by 
each analyst within each time interval is used to compute the consensus forecast for that time interval. 
In particular, this study defines the consensus forecast at the beginning of the year for firm i in year t 

(
early

itAF ) as the median of the latest forecast issued by each analyst in the first three months of year t, 

and the “late consensus forecast” for firm i in year t (
late

itAF ) as the median of the latest forecast issued 

by each analyst in the last three months of year t. This study then uses the early and late consensus 
forecasts to identify expectations manipulators in the following two steps. 
Step 1: Establish the motive to guide 

Following Bartov et al. (2002), this study identifies a firm’s motive to guide analyst 
expectations by the initial optimism in analyst forecasts at the beginning of the year. Specifically, 

previous year’s reported earnings ( 1itx ) is used as a proxy for the manager’s earnings expectation at 

the beginning of the year; this study then compares the consensus forecast at the beginning of the year 

(
early

itAF ) with the previous year’s earnings and classify firm-years with 1 it

early

it xAF  as possible 

forecast guiders. 

                                                           
1 Abarbanell and Bernard (1991) note that the staleness problem arises because the IBES consensus forecast includes 
forecasts that are made in prior months and are not yet updated.   



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

4 

 

 
Figure 1. 
Timeline – forecast year, forecast month, and consensus analyst forecast construction 
 
 
 
Step 2: Identify the evidence of management forecast guidance  

Prior studies use downward revisions in analyst forecasts as evidence for forecast guidance. 
However, downward revisions can also be caused by unexpected bad news that arises throughout the 
forecast year. In particular, analysts may revise their earnings forecasts downward to incorporate any 
unexpected firm-specific or economy-wide bad news that occurs during the year. This study uses the 
following regression model to isolate management guidance-induced forecast revisions from forecast 
revisions driven by economic or firm-specific news occurring during the year. 

             ititit CRETbbRev  10                                              (1) 

In this model, 



Rev it  is the change in analyst consensus forecasts from the beginning to the end of the 

year (i.e., ), and 



CRETit  is the cumulative return during the year. The 

accumulation period starts from the first day of the forecast year and ends 20 days before the current 
year’s earnings announcement.  The cumulative return term (



CRETit ) captures the impact of economy-

wide and/or firm-specific news on analyst forecast revisions. This regression is estimated for each 2-

digit SIC-code industry group in each year and the residuals (i.e., the estimate of it ) from this 

regression are used as the measure for the unexpected forecast revision ( itUnExpRev ). This study 

classifies firm-years with unexpected downward revisions ( itUnExpRev < 0) as possible forecast 

guiders. Combining these two steps, forecast guiders are defined as the firm-years that are classified 
as possible guiders in both Step 1 and Step 2. 

This study creates a matched non-guider sample based on industry, year, and forecast error to 
minimize sample differences in these aspects. Prior studies, such as Frankel and Lee (1998) and Easton 
and Sommers (2007), suggest that errors in analyst earnings forecasts reduce the accuracy of forecast-
based estimates. Matching on forecast errors ensures that the observed difference in the accuracy of 
V between the guider and non-guider samples is not driven by differences in forecast errors. To 
construct the non-guider sample, for each guider, all firms that are in the same industry and year are 
selected from the group of firms that are not in the guider sample. Among these selected observations, 
the one with the closest forecast error to the guider is defined as the matched non-guider.  
 
 

early

it

late

itit AFAFRev 

forecast year t    

360 days 

 

AF 2 , a 
AF1,b 

AF early  = 

AF3,a 

Median { AF3,a ,AF2,b ,AF1,c} 

AF1,a 
AF1,c 

AFlate  

mth 10       mth 11         mth 12 

30 days 30 days 

 mth 1         mth 2            mth 3 



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5 
 

Intrinsic value measure 
Ohlson (1995) develops the RIM to express firm value as a linear function of accounting book 

value, abnormal earnings and the other information about future abnormal earnings: 

t

a

ttt vxb =V 21                                                    (2) 

In this model, tb  is the accounting book value of shareholders’ equity at time t, tx  is the earnings for 

the period from t-1 to t, and 
a

tx  is abnormal earnings ( 1 tt

a

t rbxx ). tv  is the other information 

variable, defined as the difference between the expectation of future abnormal earnings based on all 
information and the expectation of future abnormal earnings based only on the current abnormal 

earnings ( tv = ][ 1

a

tt xE   -
a

tx ).  The parameters 1 and 2  are computed as )1/(1 tt r    and 

)1(2 r )1)(1/( tt rr   , where t  and t  are the first-order autoregressive coefficients for 

abnormal earnings and other information, estimated using the following pooled time-series cross-
sectional regressions:

 
1,11   tt

a

t

a

t uvxx                                                 (3)  

 
   1,21   ttt uvv                                                   (4)  

In these regressions, 1,1 tu  and 1,2 tu
 
are the mean zero disturbance terms.

 

This study uses the consensus analyst earnings forecast at the end of the year (
late

tAF ) to estimate the 

expectation of future abnormal earnings: 

t

late

t

a

tt rbAFxE  ][ 1 .  
 
                                      (5) 

It then expresses the other information variable as  

a

ttt

late

tt xrbAFv  )( .                                   (6) 

The forecast-based intrinsic value metric V is constructed for each firm at the end of each forecast 
year as follows: 

   )(21

a

tt

late

t

a

ttt xrbAFxb =V   .                     (7) 

 
Empirical analyses 
 The empirical analysis include explanation of contemporaneous stock prices as well as 
prediction of future returns through V/P ratios. These are explained below. 

Explanation of contemporaneous stock prices. Prior studies (e.g., Frankel and Lee, 1998; Lee et al., 
1999) examine the cross-sectional correlations between V and P to assess the ability of V to explain 
stock price. These studies find that analyst forecast-based V has higher correlations with stock price 
than other value metrics. Following prior studies, this study also employs a correlation analysis to 
assess the ability of V to explain stock price. If forecast guidance impairs the ability of V to predict 
firm value as hypothesized, V estimated using guided forecasts would have lower correlations with 
stock price than those estimated using non-guided forecasts.  



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

6 

 

To carry out the correlations test, for the guider and non-guider samples separately, this study 
calculates the cross-sectional Spearman correlation coefficients between V and P at the end of each 
year. This study then compares these annual correlation statistics between the two samples, expecting 
the correlations to be lower for the guiders than the non-guiders. To ensure that the correlation 
difference between the guiders and non-guiders is not due to sample differences in aspects other than 
forecast guidance, this study examines the conditional correlations between V and P, controlling for 
firm size, earnings performance, book-to-market ratio, and analyst forecast error. Specifically, for the 
guider and non-guider samples separately, this study computes the conditional correlations for each 
sample in the following steps. First, it estimates the following regressions: 

ittititititi eFEcMBcEPScBVcP  ,4,3,2,1, /
                    (8) 

ittititititi vFEcMBcEPScBVcV  ,4,3,2,1, /                    (9) 

In these regressions, itBV is the accounting book value of shareholders’ equity per share at the 

beginning of year t. itEPS  is earnings per share for year t-1. itFE  is analysts’ consensus forecast for 

year t’s earnings measured at the end of year t prior to the earnings announcement; the residual itê  (

itv̂ ) represents the part of tiP ,  ( tiV , ) that cannot be explained by these control variables for firm size, 

earnings performance, B/M ratio, and forecast accuracy. The residuals itê  and itv̂  are then used to 

estimate the following regression: 

ititit uvbae  ˆˆ                                                   (10) 

In this model, b̂  captures the strength of the relation between V and P that is left unexplained after 
factoring out the effect of the control variables. This study then calculates the sample standard 

deviations of itê and itv̂  and denote these standard deviations as ê̂  and v̂̂
 
 respectively. Finally, the 

conditional correlation between V and P is calculated as 













e

v
ve b 

ˆ

ˆ
ˆˆ

ˆ

ˆˆˆ





                                                      
(11)

 
This study compares the conditional correlations for the guider and non-guider samples, expecting 
the conditional correlations for the guiders to be lower than the conditional correlations for the non-
guiders. 

Prediction of future returns through V/P ratios. Better V estimates have greater ability to predict 
future returns through V/P ratios. Frankel and Lee (1998) and Dechow et al. (1999) find the 12-month 
cumulative returns to the V/P ratio portfolio strategy of buying high V/P ratio (under-valued) stocks 
and selling low V/P ratio (over-valued) stocks to be positive and significant. The returns prediction 
analysis compares the V/P ratio portfolio returns for forecast guiders and non-guiders over the 12 
months following forecast guidance, expecting the portfolio returns to be greater for the non-guiders 
than for the guiders. 

Firms report earnings in different months. To align the sample in calendar time, this study 
uses firms that report earnings in February as the sample in the returns analysis. At the end of each 
forecast year, the V/P ratio for each firm-year observation is computed using stock price at the end 
of the month. For the guiders and non-guiders separately, this study ranks firms by V/P ratios and 
assign these firms to quintiles. It then calculates the cumulative return for each quintile portfolio over 
the subsequent 12 months. The hedge portfolio return is computed as the difference in returns 



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7 
 

between the top and bottom V/P quintiles. This study compares the hedge portfolio returns for the 
guider and non-guider samples, expecting a higher return for the non-guider sample than for the guider 
sample. 

One remaining concern is that the difference in the hedge portfolio returns for the guider and 
non-guider samples might be caused by their different risk profiles. To address this concern, this study 
uses the Fama and French (1993) three-factor model to control for risks and examine the risk-adjusted 
returns. To implement the test, for the guider and non-guider samples separately, firms are ranked by 
V/P ratios and partitioned into quintiles. This study then computes the average monthly return for 
each V/P quintile and calculates the monthly hedge portfolio return as the difference in returns 

between the top and bottom V/P quintiles (i.e., G

tHret = Q1G,

t

Q5G,

t RetRet  , and
 

N

tHret =
Q1N,

t

Q5N,

t RetRet  ). This study then regresses the monthly hedge portfolio returns on the three monthly 

Fama and French risk factors and the momentum factor as follows: 
G

ttttt

G

t

G

t UMDHMLSMBMktRFHret   4321            (12) 

N

ttttt

N

t

N

t UMDHMLSMBMktRFHret   4321             (13) 

In these regressions, G

t  and 
N

t  represent the risk-adjusted returns to the V/P ratio portfolio 

strategy for the guider and non-guider samples respectively. To examine the significance of the 
difference in the risk-adjusted returns between the two samples, this study takes the difference in the 
monthly hedge portfolio returns between the guider and non-guider samples (

G

t

N

t

GN

t HretHretHret  ) and regresses these monthly return differences on the monthly Fama and 

French (1993) risk factors and the momentum factor as follows: 

   GN

ttttt

GN

t

GN

t UMDHMLSMBMktRFHret    4321     (14) 

The estimated intercept ( NG

t

 ) summarizes the difference in returns to the V/P ratio portfolio 

strategy for the non-guider and guider samples after controlling for risk differences between the two 
samples.  
 

Empirical Results 
 

This section describes the data collection and sample statistics. Then, descriptive statistics are 
presented, follow by the empirical analyses. 

 
Data collection and sample statistics 

The sample consists of non-regulated and non-financial U.S. firms for the time period from 
1986 to 2007. Following Richardson et al. (2004) and Matsumoto (2002), firms in the regulated and 
financial industries are excluded because their accounting rules differ from the accounting rules for 
firms in other industries. Thus, firms in the regulated and financial industries may therefore have 
different motives with respect to forecast guidance. The final sample consists of 8,324 guiders and 
8,324 matched non-guiders2. 

                                                           
2 To construct the sample, this study first obtains the forecasted and actual earnings-per-share (EPS) data from IBES 
History U.S. Edition tape (Actual File) through the Wharton Research Data System (WRDS). This study then matches the 
resulting sample to the Center for Research in Security Prices (CRSP) database and Compustat database, resulting in an 
initial sample of 11,494 firms and 80,570 firm-years. Firms in the financial and regulated industries, and firms without 



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

8 

 

 
Descriptive statistics 

Table 1 reports the summary statistics on the variables used to construct the V metrics in each 

year of the sample period from 1986 to 2007. The control variables itBV , itEPS , itMB / , and itFE  

are as defined earlier. t  and t  are the first-order autoregressive coefficients for abnormal earnings 

and other information in year t. V is the intrinsic value metric estimated at the end of each year using 
BVPS, EPS, AF,  , and  . These annual statistics illustrate the stability of the key input variables 

over the sample period.  
 

Table 1.  
Summary statistics of model input variables 

        
Year Obs. BVPS ESP         AF Omega Gamma     V 

1986 456 12.05 0.45 0.49 0.65 0.49 13.03 
1987 624 11.74 0.59 0.68 0.64 0.39 12.52 
1988 592 10.66 0.73 0.81 0.65 0.46 11.12 
1989 624 9.63 0.60 0.65 0.65 0.46 9.65 
1990 720 10.22 0.54 0.61 0.65 0.49 10.35 
1991 476 9.45 0.30 0.40 0.63 0.50 9.71 
1992 632 9.89 0.46 0.51 0.60 0.53 10.62 
1993 746 8.89 0.45 0.48 0.61 0.49 9.16 
1994 900 8.49 0.52 0.55 0.63 0.51 8.72 
1995 968 8.50 0.57 0.60 0.63 0.46 8.49 
1996 1076 8.61 0.58 0.60 0.67 0.46 8.48 
1997 1030 8.33 0.52 0.53 0.68 0.44 8.56 
1998 970 8.12 0.62 0.64 0.72 0.43 8.59 
1999 804 8.55 0.65 0.65 0.73 0.39 8.56 
2000 944 9.29 0.79 0.80 0.74 0.35 9.10 
2001 668 8.47 0.47 0.48 0.75 0.32 8.39 
2002 662 8.23 0.51 0.52 0.74 0.26 8.78 
2003 720 8.83 0.46 0.46 0.71 0.17 8.39 
2004 910 10.81 0.96 0.94 0.72 0.15 10.48 
2005 1154 9.34 0.93 0.91 0.73 0.18 9.57 
2006 904 9.64 1.08 1.08 0.74 0.14 9.85 
2007 68 10.37 1.23 1.21 0.79 0.21 11.07 

All years 16,648 9.46 0.64 0.66 0.68 0.38 9.69 

 
Explanation of contemporaneous stock prices 

The correlation test examines the unconditional and conditional correlations between V and P at 
the end of each year. Table 2 reports the annual unconditional correlation coefficients for the guiders 
and non-guiders and the difference in these statistics between the two samples. The last row of this 
panel reports the time-series means of the annual correlation statistics. As shown in the panel, the 

                                                           
necessary data are removed, reducing the sample to 5,548 firms and 31,763 firm-year observations. Out of these 31,736 
firm years, 8,324 are classified as forecast guiders and these guiders are matched with 8,324 non-guiders.  

 



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correlation between stock price and intrinsic value metrics estimated using guided forecasts is 0.47 on 
average. It is lower than the correlation between stock price and intrinsic value metrics estimated using 
non-guided forecasts (0.58). This correlation difference of 0.11 is statistically significant at the 1% 
level (t=2.81).  

 
Table 2 
Annual cross-sectional Spearman correlations between V and P 

Year ),( PVCorr N

t  ),( PVCorrG

t  Difference 

1986 0.72 0.59 0.13 
1987 0.65 0.51 0.13 
1988 0.70 0.68 0.01 
1989 0.60 0.56 0.04 
1990 0.61 0.52 0.09 
1991 0.54 0.32 0.22 
1992 0.64 0.58 0.07 
1993 0.72 0.50 0.22 
1994 0.62 0.38 0.24 
1995 0.61 0.40 0.21 
1996 0.62 0.54 0.08 
1997 0.67 0.53 0.14 
1998 0.58 0.21 0.38 
1999 0.22 0.13 0.09 
2000 0.38 0.32 0.07 
2001 0.49 0.41 0.09 
2002 0.46 0.46 0.00 
2003 0.49 0.54 -0.05 
2004 0.63 0.60 0.03 
2005 0.61 0.60 0.01 
2006 0.66 0.58 0.08 
2007 0.50 0.43 0.07 

All Years 
 

0.58 
 

0.47 
 

0.11*** 
(t=2.81) 

 

Table 3 reports the conditional correlation statistics between V and P, controlling for the 

effects of firm size (book value of equity), earnings performance (ESP), book-to-market (B/M) ratio, 

and forecast errors, one factor at a time. The last row of the panel reports the conditional correlation 

between V and P after controlling for these factors together. All statistics reported are the time-series 

means of the annual statistics.  

Results in Table 3 show that the conditional correlations for the guider sample are significantly 

lower than the conditional correlations for the non-guider sample. This suggests that the difference in 

correlations between the two samples cannot be explained by their differences in firm size, earnings 

performance, B/M ratio, or forecast accuracy. 

 

 

Table 3  



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

10 

 

Conditional Correlations 

Conditional correlation (V, P) N G N-G t Value 

Control for firm size (BV) 0.50 0.39 0.11*** 2.48 
Control for earnings performance (EPS) 0.42 0.31 0.11*** 2.23 
Control for book-to-market (B/M) ratio 0.64 0.53 0.11*** 2.82 
Control for forecast accuracy (FE) 0.58 0.47 0.11*** 2.82 
Control for size, EPS, B/M, FE 0.44 0.35 0.09*** 2.06 

*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level. 

 

In summary, results from the correlation analyses provide consistent evidence that V estimated 

using guided forecasts have less ability to explain contemporaneous stock price than V estimated using 

non-guided forecasts. 

 
Prediction of future returns through V/P ratios 
 The returns prediction analysis examines the returns to the V/P ratio portfolio strategy for 

forecast guiders and non-guiders. Table 4 reports, for the guiders and non-guiders separately, the 12-

month cumulative return to each V/P quintile portfolio and the hedge portfolio. In February of each 

year, for the guiders and non-guiders separately, observations are ranked by V/P ratios and partitioned 

into quintiles. Returns are accumulated for each quintile portfolio for the subsequent 12 months. Table 

4 reports, for the guider and non-guider samples separately, the cumulative return to each V/P quintile 

portfolio (Q1 to Q5) and the hedged portfolio. The hedge portfolio return ( tHRet ) is computed as 

the difference in returns between the top and bottom V/P quintiles (RetQ5-RetQ1). The last row of 

the table reports the difference in hedge portfolio returns between the non-guider and guider samples. 

T-statistics are based on the time-series standard errors of the annual portfolio returns.  

 

Table 4:  
Buy-and-hold returns to the V/P-ratio portfolio strategies (G vs. N) 

V/P quintile 

portfolio returns 

Q1     

(Low V/P) 

Q2 Q3 Q4 Q5      

(High V/P) 

Q5-Q1 (

tHRet ) 

Non-guider (N) 0.10 0.14 0.11 0.14 0.21 0.12* 

(t=1.89) 

Guider (G) 0.21 

 

0.12 

 

0.14 

 

0.13 

 

0.15 

 

-0.06    

(t=-0.98)  

Difference (G-N)  0.18*** 

(t=3.01) 

*Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level. 

 
As shown in Table 4, over a 12-month interval, the average hedge portfolio return for the 

non-guider sample is 12%. With a t-statistic of 1.89, this return is significant at the 7% level.3 This 

                                                           
3 The V/P ratio portfolio returns to the non-guider sample is larger in magnitude (11.7% versus 7.1%), and slighter higher 
in statistical significance (t=1.89 versus t=1.77) than results reported in Dechow et al. Following Frankel and Lee (1998), 



Tian 

11 
 

result provides some evidence that, in the absence of forecast guidance, the forecast-based V is a 
superior measure for firm value than stock price and can therefore predict future returns through V/P 
ratios. In contrast, the average hedge portfolio return for the guider sample is -6.0%, statistically 
insignificant (t=-0.98). This result suggests that when V is estimated using guided forecasts, V becomes 
a less accurate measure for firm value and can no longer predict future returns through V/P ratios. 
The difference in the hedge portfolio returns between the non-guider and guider samples is 18%. With 
a t-statistic of 3.01, this return difference is statistically significant at the 1% level. This suggests that 
V estimated using guided forecasts have less power to predict future returns through V/P ratios than 
those estimated using non-guided forecasts.  

As a robustness check, this study uses the Fama and French three-factor model to control for 
risks and examine the risk-adjusted returns. Table 5 Panel A reports the risk-adjusted returns to the 
V/P ratio portfolio strategy for the guider and non-guider samples separately. For each sample (i.e., 
guiders and non-guiders), firms are ranked by V/P ratios and partitioned into quintiles. The average 
monthly return is computed for each V/P quintile. The monthly hedge portfolio return is defined to 
be the difference in the monthly returns between the top and bottom V/P quintiles. These monthly 
hedge portfolio returns are regressed on the three Fama and French risk factors and the momentum 
factor to obtain the risk adjusted returns. To examine the risk-adjusted return differences between the 
guider and non-guider samples, this study takes the difference in the monthly hedge returns between 
the two samples and regress these monthly return differences on the monthly Fama and French risk 
factors and the momentum factor. The estimated intercept term from the following regression model 
captures the risk-adjusted return differences between the guiders and non-guiders. Table 5 Panel B 
reports the difference in risk-adjusted returns between guiders and non-guiders. 

As shown in Panel A, the risk-adjusted return for the non-guider sample ( N

tm, ) is 1.0%, 

statistically significant at the 1% level. In contrast, the risk-adjusted return to the guider sample ( G

tm,

) is -0.6%, statistically significant at the 10% level. The difference in the risk-adjusted returns between 
the two samples, as shown in Panel B, is 1.6%, statistically significant at the 1% level. These results 
suggest that, after controlling for risks, the V/P ratio portfolio strategy for the non-guider sample is 
still able to earn a significantly higher return than that for the guider sample. Overall, the returns 
prediction results suggest that management forecast guidance tends to reduce the ability of forecast-
based V to predict future returns through V/P ratios.  
  

                                                           
this study also assesses the statistical significance of the returns using Monte Carlo simulations; results from this analysis 
show that the V/P ratio portfolio returns to the non-guider sample are statistically significant at the 1% level. 



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

12 

 

Table 5 
Risk-adjusted returns to the V/P ratio portfolio strategies (G vs. N) 
 
 
Panel A: Risk-adjusted returns to the V/P ratio portfolio strategy for G and N separately 

Non-Guider (N) 

Variable 
Parameter 

Estimate 

Standard 

Deviation 
T value 

Intercept ( N

tm, ) 0.010*** 0.004 2.59 

Market – Risk free rate 0.989*** 0.096 10.31 

Small – Big 0.992*** 0.113 8.77 

High – Low 0.451*** 0.138 3.26 

Momentum -0.744*** 0.081 -9.17 

 

Guider (G) 

Variable 
Parameter 

Estimate 

Standard 

Deviation 
T value 

Intercept ( G

tm, ) -0.006* 0.004 -1.66 

Market – Risk free rate 

ree 

0.092 0.093 0.99 

Small – Big 0.240** 0.109 2.19 

High – Low 1.384*** 0.134 10.37 

Momentum -0.509*** 0.078 -6.49 

 
 
Panel B: Risk-adjusted return difference between guiders and non-guiders 

GN

itttt

GN

t

G

t

N

t UMDHMLSMBMktRFHretHret    4321  

Variable 
Parameter 

Estimate 

Standard 

Deviation 
T value 

Intercept ( GN

tm



, ) 0.016*** 0.006 2.90 

Market – Risk Free -0.290** 0.145 -2.00 

Small – Big -0.388*** 0.171 -2.27 

High – Low -0.585*** 0.209 -2.80 

Momentum -0.189 0.122 -1.55 

 

 
 
 



Tian 

13 
 

Conclusion 
 
 Prior valuation studies provide consistent evidence for the usefulness of analyst forecasts and 
the superior ability of forecast-based valuation models to predict firm value. This study shows that 
analyst forecasts are less useful than previously documented because of the increased management 
forecast guidance. In particular, this study examines the impact of management forecast guidance on 
the usefulness of analyst forecasts in firm valuation by empirically comparing the performance of 
intrinsic value metrics estimated using guided versus non-guided forecasts to explain stock price and 
predicting future returns through V/P ratios.  
 The results show that V estimated using guided forecasts have significantly lower correlations 
with P than those estimated using non-guided forecasts. The lower correlations cannot be explained 
by factors such as firm size, earnings performance, B/M ratio, or forecast accuracy. In addition, the 
returns to the V/P ratio portfolio strategy for the guider sample are significantly lower than those for 
the non-guider sample, and this return difference cannot be explained by the risk differences between 
the two samples. Together, these results suggest that management forecast guidance tends to reduce 
the ability of forecast-based V to track stock price and predict future returns through V/P ratios. This 
study therefore concludes that forecast guidance reduces the usefulness of analyst forecasts in firm 
valuation. 
 This study contributes to the financial analyst and expectations management literature by 
documenting the impact of management forecast guidance on the usefulness of analyst forecast in 
firm valuation. It extends the valuation literature by conditioning model performance on the quality 
of model inputs and documenting the adverse impact of management forecast guidance on valuation 
model performance. Results from this study raise awareness among researchers, practitioners, and 
investors about the pitfalls of taking analyst forecasts at face value and using them directly in firm 
valuation.  
 This study has one major limitation. In particular, this study examines only one input to the 
RIM valuation model (i.e., analyst earnings forecast), while holding other model inputs (i.e., book value 
of equity and earnings) constant. Since firm managers can manipulate other model inputs, this study 
suggest future research to further investigate the impact of management manipulation of other model 
inputs on the performance of earnings-based valuation models.  
 

References 
 

Abarbanell, J., & Bernard, V. (1991). Tests of analysts’ overreaction/undereaction to earnings 
information as an explanation for anomalous stock price behavior. Journal of Finance, 47, 
1181-1207. 

Athanasakou, V., Strong, N., & Walker, M. (2009). Earnings management or forecast guidance to 
meet analyst expectations? Accounting and Business Research, 39 (1), 3-35. 

Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. 
Journal of Accounting and Economics, 33, 173–204. 

Bartov, E., & Cohen, D.A. (2009). The “numbers game” in the pre- and post-Sarbanes-Oxley eras. 
Journal of Accounting, Auditing and Finance, 24, 505-534. 

Brown, L., & Higgins, H.N. (2005). Managers’ forecast guidance of analysts: International evidence. 
Journal of Accounting and Public Policy, 24, 280-299. 

Brown, R.M., & Pinello, A.S. (2007). To what extent does the financial reporting process curb 
earnings surprise games? Journal of Accounting Research, 45(5), 947-981.  



USEFULNESS OF ANALYST FORECASTS IN FIRM VALUATION 

14 

 

Burgstahler, D., & Eames, M. (2006). Management of earnings and analysts' forecasts to achieve 
zero and small positive earnings surprises. Journal of Business Finance and Accounting, 33, 633-
652. 

Coller, M., & Yohn, T.L. (1997). Management forecasts and information asymmetry: An 
examination of bid-ask spreads. Journal of Accounting Research, 35(2), 181-191. 

Dechow, P.M., Hutton, A.P., & Sloan, R.G. (1999). An empirical assessment of the residual income 
valuation model. Journal of Accounting & Economics, 26, 1–34. 

Easton, P., & Sommers, G. (2007). Effect of analysts’ optimism on estimates of the expected eate of 
return implied by earnings forecasts. Journal of Accounting Research, 45, 983-1015 

Fama, E., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal 
of Financial Economics 33: 3. 

Frankel, R., & Lee, C.M.C. (1998). Accounting valuation, market expectation, and cross-sectional 
stock returns. Journal of Accounting and Economics, 25, 283–319. 

Gaio, C., & Raposo, C. C. (2014). Corporate governance and earnings quality – international 
evidence. Journal of Accounting and Finance: 14 (3), 52-74.  

Graham, R. J., Harvey, R. C, & Rajgopal, S. (2005). The economic implications of corporate 
financial reporting. Journal of Accounting and Economics, 40, 3-73. 

Hutton, A., Miller, G.S., & Skinner, D. (2003). The role of supplementary statements with 
management earnings forecasts. Journal of Accounting Research, 41, 867-890. 

Koh, K., Matsumoto, D.A., & Rajgopal, S. (2008). Meeting or beating analyst expectations in the 
post-scandals world: Changes in stock market rewards and managerial actions. Contemporary 
Accounting Research, 25 (4), 1067-1098. 

Kasznik, R., & Lev, B. (1995). To warn or not to warn: management disclosures in the face of an 
earnings surprise. The Accounting Review, 70 (1), 113-134. 

Kasznik, R., & McNichols, M. (2002). Does meeting earnings expectations matter? Evidence from 
analyst forecasts revisions and share prices. Journal of Accounting Research, 40, 727–759. 

Kross, W.J., Ro, B.R., & Suk, I. (2011). Consistency in meeting or beating earnings expectations and 
management earnings forecasts. Journal of Accounting and Economics, 51, 37-57. 

Lansford, B., Lev, B., & Tucker, W. J. (2013). Causes and consequences of disaggregating earnings 
guidance.  Journal of Business, Finance and Accounting, 40: 26-54. 

Lang, M.H., & Lundholm, R.J. (1996). Corporate disclosure policy and analyst behaviour. The 
Accounting Review, 71 (4), 467-492. 

Lee, C.M.C., Myers, J., & Swaminathan, B. (1999). What is the intrinsic value of the Dow? The Journal 
of Finance, 54, 1693–1741.    

Matsumoto, D. (2002). Management’s incentive to avoid negative earnings surprises. The Accounting 
Review, 77, 483–514. 

Ohlson, J.A. (1995). Earnings, book values, and dividends in equity valuation. Contemporary Accounting 
Research 11, 661–687.  

Richardson, S., Teoh, S., & Wysocki, P. (2004). The walk-down to beatable analyst forecasts: the role 
of equity issuances and insider trading incentives. Contemporary Accounting Research 21, 885–
924. 

Sohn, B.C. (2012). Analyst forecast, accounting conservatism and the related valuation implications. 
Accounting and Finance 52, 311-341. 

Xie, Y. (2011). The market effects of breaking a string of meeting or beating analysts' expectations: 
downward revision of future cash flows or increase in cost of equity capital? Journal of Business 
Finance and Accounting 38 (1)&(2), 95-11. 

 



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Yao Tian is an Assistant Professor at San Jose State University. Dr. Tian obtained her Ph.D. degree 

in Accounting at the University of Waterloo in 2008. She served as an Assistant Professor at the 

University of Alberta from 2008-2011. Dr. Tian has published seven research papers in leading 

accounting and finance journals, including The Accounting Review and Journal of Business, Finance, and 

Accounting. 

 


