1

Target price forecasts: Fundamental and non-

fundamental factors

Peter Clarkson

UQ Business School, The University of Queensland and

Beedie School of Business, Simon Fraser University

p.clarkson@business.uq.edu.au

Alexander Nekrasov

The Paul Merage School of Business, University of California, Irvine

anekraso@uci.edu

Andreas Simon Graziadio School of Business and Management, Pepperdine University

andreas.simon@pepperdine.edu

Irene Tutticci

UQ Business School, The University of Queensland

i.tutticci@business.uq.edu.au

Keywords: target price forecasts, 52-week high, investor sentiment, rounding

JEL Classification: D81, D82, G12, G14, M41.

Current Version: 17 December 2015

*Acknowledgements: We appreciate helpful comments from Mark Arnold, Philip Brown, David Hirshleifer,

Russell Lundholm, John Nowland, Gordon Richardson, Siew Hong Teoh, and seminar participants at The

Australian National University, The University of British Columbia, the University of California Irvine, The

Chinese University of Hong Kong, the Hong Kong Polytechnic University, The University of Melbourne, The

University of Queensland, Simon Fraser University, the University of Western Australia, and the 2009

AFAANZ Conference.

mailto:p.clarkson@business.uq.edu.aumailto:anekraso@uci.eduhttps://exchange.uq.edu.au/owa/redir.aspx?C=9d7599d3e6474e3d9dc18e14adc23023&URL=mailto%3aandreas.simon%40pepperdine.edumailto:i.tutticci@business.uq.edu.au

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Target price forecasts: Fundamental and non-fundamental factors

ABSTRACT

This paper reveals both fundamental and non-fundamental factors play an important role in analysts’

target price formation. Analysts’ forecasts of short-term earnings and long-term growth are shown to

be important explanatory variables for target prices; equally, the following salient non-fundamental

factors are also shown to explain target price levels and especially target price biases: the 52-week

high price and recent market sentiment. Here, increases in the 52-week high and market sentiment

measures of one standard deviation correspond to increases in positive target price bias of 4.8% and

14.7%, respectively. Initially our analysis is constrained to analysts who provide long-term growth

forecasts, however, our findings are robust to the removal of this constraint and the broader set of

analysts. Our analysis reveals that analysts place greater weight on these non-fundamental factors in

settings with greater task complexity and/or resource constraints, and when they rely on valuation

heuristics as opposed to more rigorous valuation methodology, and that this greater weight is

associated with increased optimistic bias. Finally, our results show that analysts’ target prices are

useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies.

However, in an internally consistent fashion, the informativeness of target prices for future returns is

significantly reduced when greater weight is placed on either the 52-week high or recent market

sentiment in the target price formation process.

Keywords: target price forecasts, 52-week high, investor sentiment, rounding

JEL Classification: D81, D82, G12, G14, M41.

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1. INTRODUCTION

As financial market information intermediaries, equity analysts provide forecasts of a number of key

summary measures relevant to firm value.1 These summary measures are forecast on the basis of various

observable and unobservable inputs. Our study focuses on one of the key summary measures disseminated by

analysts - target price forecasts. To shed light on factors associated with analysts’ target price forecasts, we

consider the roles that both fundamental inputs and non-fundamental factors play in their derivation, their

accuracy and their association with future returns. Informed by prior research (e.g., Heath et al., 1999; Shiller,

2005; Baker and Wurgler, 2006; Baker et al., 2009), the specific non-fundamental factors we consider are the

52-week high price and recent market sentiment. Our research is motivated in part by Gleason et al. (2013) who

direct future researchers to consider analysts’ use of valuation heuristics using other judgment and decision-

making settings, as their results question the extent to which analysts rigorously adhere to methods of

fundamental valuation.

Analysts’ target price forecasts are a prediction of a stock’s future price, generally over the 12 months

following the release date (Asquith et al., 2005). This forecast is a point estimate that provides investors with a

benchmark against which to directly compare stock price in the short run.2 As Gleason et al. (2013, pg. 86)

state, target prices are more “granular, verifiable, and comparable across analysts than buy-sell

recommendations.” Research on how analysts’ form target price forecasts and the factors associated with the

accuracy of these forecasts is a growing area of researcher focus.3 One reason for why target prices have

previously been overlooked by researchers may be the expectation of a natural overlap between stock

recommendations, analysts’ forecasts of fundamentals (earnings forecasts), and target prices. However,

research indicates an element of ‘mismatch’ between target prices and stock recommendations, and this leads

to questions about the extent to which the two measures substitute for each other (Asquith et al., 2005;

Bradshaw, 2002; Gleason et al., 2013). Further, since many analysts invest additional time and effort in

1 Brown (1993) and Bradshaw (2011) provide broad reviews of the literature. 2 Target prices have become increasingly available to both researchers and individual investors. For example, finance.yahoo.com

displays consensus target price forecasts on the summary page for most companies that are followed by analysts. 3 Research on analysts’ forecasts of target prices includes studies by Bradshaw (2002), Brav and Lehavy (2003), Bradshaw et al. (2012),

Bonini et al. (2010), and Gleason et al. (2013).

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generating a target price and then publicly announce this forecast, it is likely they perceive a net benefit in doing

so.

In a survey of the content of analysts’ reports, Asquith et al. (2005) find that the three most common

methods used by analysts to determine target prices are earnings or cash flow multiples, discounted cash flow

models, and asset multiples. Their investigation reveals that price earnings multiples are used in 99% of the

analysts’ reports they examine. Prior research also provides some evidence of analysts’ forecasts of short-term

earnings and long-term growth being associated with target price (Bandyopadhyay et al., 1995; Gleason et al.,

2013). Directly, Gleason et al. (2013) show that incorporating analysts’ forecasts of earnings and long-term

growth in the residual income model or PEG ratio heuristic results in pseudo target prices that have substantial

explanatory power for actual analysts’ target prices (with adjusted R2s in excess of 50% in most years from

1997 to 2003).

Prior research has additionally established that target price announcements have information content for

investors (Brav and Lehavy, 2003; Asquith et al., 2005). Notwithstanding, two recent empirical studies by

Bonini et al. (2010) and Bradshaw et al. (2012) find the accuracy of target price forecasts to be relatively

limited.4 We might expect that if an analyst’s estimates of future earnings and long-term growth are more

accurate, they would translate to better target price forecasts. In fact, research demonstrates that forecasts of

earnings and stock recommendations have some level of accuracy and that investors who follow the investment

advice of the most accurate earnings forecasters can earn abnormal returns (Ertimur et al., 2007; Loh and Mian,

2006; Simon and Curtis, 2011). However, Bradshaw et al. (2012) find that although analysts do exhibit

persistent, differential abilities in forecasting stock prices, these differences are economically small. Gleason et

al. (2013) find superior target-price investment performance when analysts are most likely to be using rigorous

valuation techniques (the residual income model) rather than simple heuristics (the PEG ratio heuristic).

Consistent with Asquith et al. (2005), their findings also indicate that the majority of analysts use simple

heuristics and do not exhibit significant ability in forecasting future stock prices. Thus, our study contributes to

4 Bradshaw et al. (2012) show that returns based on target prices on average exceed actual returns by 15%, and absolute target price

forecast errors average 45%. Asquith et al. (2005) find that only 54% of target price forecasts from the Institutional Investor’s “All

American” team analysts are achieved at any time during the one-year forecast horizon.

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the existing research by providing a preliminary investigation of factors that may contribute to the observed bias

in target price forecasts.

There is growing evidence in the finance and accounting literature that stock prices are affected by

reference to non-fundamental factors such as the 52-week high price and past market sentiment. Thus, it is quite

possible that target price forecasts are also influenced by these factors. Equally, given the difficulty of

forecasting future stock prices and the possibility that analysts do not have sufficiently strong incentives to

spend the additional time and effort required to rigorously forecast target prices (Bradshaw et al., 2012; Gleason

et al., 2013), it is possible that an analyst may reduce a more complex task to a simpler judgment in order to

save time and effort (Gleason et al., 2013). Support for this view is also provided by Tversky and Kahneman

(1974) who suggest that individuals are likely to rely on anchors and reference points when a decision-making

task is complex. As such, we might expect a target price to be a noisy measure of fundamental value, reflecting

aspects of the analyst’s estimate of fundamental value based on some valuation process as well as salient

measures as a reference point for what price will achieve in the short run. Thus, we argue that when compared

to earnings forecasts, target price forecasts provide a direct and powerful setting to study the effect of two

prominent non-fundamental factors on important market intermediaries such as financial analysts - (1) the 52-

week high price and (2) recent market sentiment.5 Our focus on these two non-fundamental factors is supported

by Shiller (2005) who suggests that possible anchors for stock price include past prices, particularly where the

price has had a reasonable level of focus, and the nearest milestone of an index such as the Dow Jones.

A number of empirical studies have linked investors’ judgment regarding investment decisions to anchors

such as the 52-week high (Baker et al., 2009; Li and Yu, 2012; Zuckerman, 2009). Empirical studies have also

shown that recent market sentiment affects stock returns at the firm and market levels (Baker and Wurgler,

2006, 2007) and helps to explain bias in analyst forecasts (Bagnoli et al., 2009; Ke and Yu, 2009; Hribar and

McInnes, 2012). Thus, in conjunction with our analysis of the level of the target price forecast, we also examine

whether these non-fundamental factors help explain ex post target price forecast errors (the difference between

5 Since reported earnings are determined by accounting which does not reflect the non-fundamental factors that affect stock price, there

is less reason to expect that analysts’ earnings forecasts will reflect non-fundamental factors since earnings forecast accuracy is rewarded.

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the target price and the 12-month-ahead stock price). From an ex post perspective, target prices may turn out to

be incorrect due to many factors including changes in expectations of future earnings. In this study, we are

interested in how the initial inputs (both fundamental and non-fundamental) relate to the forecast error. We

argue that if we can identify ex ante factors that systematically bias target price forecasts, then analysts may be

able to improve their target price estimates. Here, Tversky and Kahneman (1974) observe that although

sometimes useful, judgment heuristics lead to potentially severe systematic error. Finally, we examine the

usefulness of target price forecasts to investors by investigating the relation between target price forecasts and

future stock returns conditional on our ex ante non-fundamental variables.6

Our study contributes to the current vein of research that finds evidence of systematic bias in target price

estimates. Bradshaw (2013) provides evidence of both analyst-level incentives and country-level characteristics

influencing the level of optimistic bias, with analysts from countries with strong institutional infrastructure

displaying less optimistic bias in their target prices. Further, Dechow and You (2014) in distinguishing a

predictable noise component and a signal component in the bias observed in implied target price returns find

that the market does not quickly impound the predictable bias into share price, allowing a trading strategy based

on the predictable component to earn significant returns in the short run. Neither study considers the influence

of the non-fundamental factors we identify in ours study as proxies for common behavioral biases of investors.

Our sample consists of 26,746 target price forecasts for U.S. firms over the period 1999 to 2007

representing data from 4,148 firms and 3,518 analysts. Analyst data are sourced from Thomson Reuters I/B/E/S.

In developing our sample, we restrict our attention to analysts who report their target price forecasts in

conjunction with both long-term growth (LTG) and short-term earnings (STE) forecasts given the fundamental

role these forecasts play in the valuation process. As fewer analysts report LTG forecasts, this restriction greatly

reduces the available set of observations. Notwithstanding, we view the tradeoff as defensible under the

expectation that these forecasts also likely represent important fundamental inputs into the formulation of target

prices. While prior research has questioned the usefulness and accuracy of analysts’ LTG forecasts (e.g., Liu

6 Since target price forecasts are equivalent to forecasts of (ex-dividend) returns, a “good” target price forecast should predict future (ex-

dividend) return.

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and Thomas, 2000), more recently Jung et al. (2012) suggest that LTG forecasts positively signal the effort and

ability of the analyst in analyzing long-term prospects. In support, they find that the market responds more

strongly to recommendations issued with LTG forecasts. Equally, earlier studies by Block (1999) and Bradshaw

(2002) find that LTG forecasts are important determinant for favorable stock recommendations. Dechow and

You (2014) also include a long term growth variable in their model explaining implicit target price returns.

Inclusion of LTG forecasts in our analysis allows us to assess the extent to which the firm’s long-term prospects

are associated with target prices, thereby providing us with further insights into the nature of target prices. As

an aside, when for sensitivity purposes, we remove the restrictive requirement for LTG forecasts we find all

results based on the expanded sample to be qualitatively unaffected.

Our results indicate that, as expected, analysts’ forecasts of short-term earnings and long-term growth help

explain target price forecasts. Importantly, we also find that the non-fundamental factors are important

determinants, with higher target price forecasts being associated with both higher 52-week high prices and

higher recent market sentiment. Further, each set of factors has incremental explanatory power relative to the

other. In this sense, the results reveal a direct role for non-fundamental factors incremental to any effect that

they may indirectly have on target price through their influence on forecasts of short-term earnings or long-term

earnings growth. Further, we find that the roles played by these factors are enhanced when stocks are more

difficult to value (measured by firm size and earnings volatility), when analysts are less well positioned to make

a forecast (measured by analyst experience and brokerage size), or when analysts rely on less rigorous valuation

techniques (as revealed by the valuation model ratio (VRM) (Gleason et al., 2013).

The results from our model of ex post target price errors indicate that there is a larger positive bias when

forecasts of short-term and long-term earnings growth are high. In conjunction, we also find, in a manner

consistent with their enhanced role in the target price formation process, that higher values of the 52-week high

and recent market sentiment are associated with a larger positive bias in target price. An increase in the 52-week

high and recent market sentiment of one standard deviation corresponds to an increase in target price bias of

4.8% and 14.7%, respectively, effects that are economically meaningful when compared with the mean target

price bias of 15.6%. Further, the model incorporating just the non-fundamental factors explains more of the bias

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in target price than a model containing just the fundamentals, consistent with reliance on non-fundamental

factors producing less reliable forecasts than reliance on the fundamentals. Lastly, in a parallel fashion to our

findings for the target price forecast, the bias introduced by the 52-week high and past market sentiment

increases with greater task complexity, greater resources constraints, and when analysts rely on less rigorous

valuation techniques (as revealed by the VRM).

Finally, we find a positive association between target prices and future stock returns. However, this

association is significantly weaker when the 52-week high is high relative to the current price and when recent

market sentiment is relatively more positive. Thus, while target price forecasts appear informative for future

returns, the degree to which they are is significantly improved when the non-fundamental factors play a

relatively reduced role in the target price derivation process and thus when they make a reduced contribution to

the optimistic bias in target price.

Overall, we view our results as consistent with the nature of a target price, which is not designed to be an

accurate estimate of fundamental value but rather is analyst noisy measure of where the analyst price will go

to over the next year.7 From this perspective, roles for both fundamental and non-fundamental factors emerge.

For example, if analysts are not convinced that the stock price will reflect fundamental value in the short term,

they may adjust the target price forecast to fit recent market expectations. Alternatively, the results may reflect

analysts’ reliance on short cuts to generate a forecast for which there is possibly limited direct benefit of

estimating accurately (Bradshaw et al., 2012), or in circumstances where they are less well positioned to make

the forecast. Finally, our results also add to the general finding of prior research that analysts’ forecasts are

optimistically biased. They suggest that the optimistic bias may, at least in part, result from analysts’ relying on

past stock and market highs. Thus, the results consistently support the inference that although analysts use

fundamental inputs in deriving target price forecasts, they are also influenced by highly visible reference points

77 The common view is that target price forecasts are a prediction of price out to one year. However, the ability of analysts to on average make such longer term forecasts is questioned by the finding of Dechow and You (2014) who shows an association between consensus implied target returns and one month ahead stock returns but no association with longer returns.

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such as the 52-week stock price high and recent investor sentiment, and further that reliance on these factors

explains some of the observed bias in target prices and ultimately their usefulness as predictors of future returns.

Our study contributes to the literature in several ways. First, it documents the importance of non-

fundamental factors in the target price formation process. While much of the prior literature has focused on the

analysts’ use of fundamentals, our paper shows that the 52-week high price and recent market sentiment play

economically significant roles beyond the fundamental factors. Second, it sheds light on a key source of target

price errors, revealing the 52-week high and market sentiment as more important in explaining target price

biases than are analysts’ forecasts of earnings and long-term growth. Third, we contribute to the literature on

the usefulness of analysts’ target prices in predicting future stock returns. Prior literature finds that target prices

can be used to predict future stock returns when analysts use more rigorous valuation techniques or more

accurate earnings forecasts (Gleason et al., 2013). We find that target prices have significant explanatory power

with respect to future stock returns beyond earnings forecasts and commonly used risk proxies, but more so

when the influence of the non-fundamental factors is relatively low. Finally, our paper contributes to the

growing literature on the economic effects of non-fundamental factors. Extant research finds that non-

fundamental factors affect stock prices, the exercise of options, and mergers and acquisitions. We document the

effect of these factors on target prices issued by important market participants, financial analysts, and viewed

as informative by investors.

The remainder of this document is organized as follows. In the next section, we review the literature that

informs our expectations regarding the target price derivation process. Section 3 describes our sample data, and

Section 4 the methodology. The empirical results are presented in Section 5 and Section 6 concludes.

2. RELATED RESEARCH

A number of studies relating to investment decisions have relied on theories from psychology to explain

why prices might reflect information other than fundamental value (e.g., Baker et al., 2009; Heath et al., 1999).

Tversky and Kahneman (1974) describe three heuristics that are used by individuals to predict value:

representativeness, availability, and anchoring. Representativeness refers to the tendency of individuals to rely

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on one item because it is highly representative of (resembles) another item. Within our context, for example,

past stock price highs and market sentiment could be considered representative of future prices. Tversky and

Kahneman refer to the “illusion of validity,” in which confidence in a decision is based on the fit between a

predicted outcome and the input information. Availability refers to the tendency of individuals to rely on events

that are easily brought to mind. Characteristics that enhance availability are the familiarity of an occurrence and

how recently the event took place. Finally, anchoring refers to the tendency of individuals to make an estimate

by starting from an initial value, which is then adjusted to determine the final result.

Each of these heuristics could be used to describe the use of non-fundamental factors by analysts in their

determination of a target price forecast. Forecasting a target price involves a large number of uncertainties with

different probabilities of eventuating. Applications of financial theory often employ historic values to infer

expectations of future values because of the difficulty of generating values of future events. In this sense, past

prices and investor sentiment could be seen as representative of future prices. Further, because recent prices

and investor sentiment are easily observable by analysts, they satisfy the description of availability.

These heuristics suggest that the 52-week high and recent market sentiment may serve as reference points

or judgment anchors for estimating target price.8 Baker et al. (2009) utilize this theory in explaining merger and

acquisition activity. In particular, they identify the 52-week high as a judgment anchor. A number of the

justifications they provide for this metric as an anchor are also salient to issues faced by analysts in estimating

target prices. Specifically, the 52-week high is a value that is widely published and is the focus of analysts,

investors and corporate executives. In fact, the 52-week high price appears prominently in the summary

information on the front page of many analysts’ reports. It is often viewed as a high price that the company has

achieved and may recover through good management. Given prior research indicates positive bias in analysts’

forecast outputs and recommendations, we expect that the 52-week high could well provide an anchor point for

analysts’ target price forecasts, a point that is adjusted for other considerations such as their forecast of

fundamental value and recent market sentiment. A number of studies have found the prior year’s high price to

8 The use of these behavioural anchors may be done consciously or unconsciously by analysts in forming target price. This study does

not make this distinction. It is unlikely that the analyst would reveal that their valuation was in some way linked to the 52-week high

price unless they believed that there was some rationality to this. In fact, use of the 52-week high price as a value indicator is evident in

trading strategies of technical analysts and is known as the ‘breakout strategy’.

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be salient in investment decisions. For example, Baker et al. find it to be influential in the valuation of mergers

and acquisitions while Heath et al. find that the exercise of options by employees doubles when the share price

exceeds the prior year high. In explaining returns to momentum investing, George and Hwang (2004) suggest

that investors may anchor on the 52-week high as a reference point from which to assess the incremental value

of new information, since traders are slow to react, or overreact, to good news. Using nearness to the 52-week

high as a proxy for recent good news, they find that this is a better predictor of future returns than are past

returns, suggesting that investors focus on price levels rather than price changes. When price is close to its 52-

week high, there is a reluctance to bid it higher even where a higher price would be supported by information.

Eventually the price does increase, revealing the information. A similar behavior occurs in the event of bad

news. Thus, price is sticky at values closest to, and farthest away from, the 52-week high. Li and Yu (2012)

attempt to predict aggregate excess market returns and find that both nearness to the 52-week high and nearness

to the historical high dominate the ability of past market returns to predict future aggregate market returns, with

the strongest results for horizons of less than one year.

A burgeoning literature also provides links between analysts’ forecasts and market sentiment. Using Baker

and Wurgler’s (2007) market sentiment index, a number of studies show that sentiment explains bias in analysts’

earnings forecasts, long-term growth forecasts, and recommendations (e.g., Bagnoli et al., 2009; Ke and Yu,

2009; Hribar and McInnes, 2012). Bagnoli et al. show that analysts’ stock recommendations are correlated with

investor sentiment when analysts follow a greater number of industries and companies, and when they issue a

greater number of earnings forecasts. One possible interpretation is that analysts take short cuts in order to

produce a certain quantity of research in a limited time. However, the results also appear to suggest an

underlying belief on the part of analysts that sentiment affects asset prices or that reliance on sentiment can

substitute for a more labor intensive analysis of fundamental value. Nevertheless, while higher market sentiment

might provide a rationale for higher stock recommendations, Bagnoli et al. find that reliance on it is associated

with less profitable stock recommendations.

A few recent studies have provided evidence that investor sentiment influences short-term earnings

forecasts. For example, Hribar and McInnes (2012) find that investor sentiment affects analyst expectations

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across all stocks, with earnings forecasts becoming more optimistic when sentiment is high and less optimistic

when sentiment is low. Ke and Yu (2009) show that the translation of analysts’ forecasts into profitable stock

recommendations is adversely affected by periods of extreme investor sentiment and a high reliance on trading

commissions. They also find that institutional ownership and insider trading have a negative effect.

In a study of stock returns, Baker and Wurgler (2007) show that certain types of firms are more likely to

be sensitive to market-wide sentiment: small firms, young firms, high volatility firms, unprofitable firms, non-

dividend-paying firms, extreme growth firms, and distressed firms. They argue that such firms are more difficult

to value, which means that valuation mistakes are more likely. They also point out that stocks of such firms tend

to be the riskiest and costliest to arbitrage. This means that prices will not always reflect fundamental value.

In sum, we expect that analysts’ forecasts of fundamentals, the 52-week high price and recent market

sentiment will each be positively related to their forecasts of target price. Further, the use of non-fundamental

factors is likely to induce bias in target price; thus, we expect that a greater reliance on these factors will lead to

larger target price errors and thereby likely introduce noise into the association between the target price and

future stock returns.

3. METHOD

3.1 Target Price Forecast

To examine whether, in addition to fundamentals, analysts utilize the 52-week high stock price and recent

investor sentiment as reference points when setting their target price forecasts, we estimate the following

regression model:

TPijt = 0 + 1 STE1ijt + 2 DIFFSTEijt + 3 LTGijt + 4 52WHit + + 5 SENTt

+ γ CONTROLit + εijt (1)

where

TPijt = analyst j’s target price forecast for firm i at time t deflated by the closing price on the

trading day before the target price announcement date;

STE1ijt = analyst j’s forecast at time t of one-year-ahead earnings for firm i scaled by the closing

price on the trading day before the target price announcement date;

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DIFFSTEijt = the difference between analyst j’s forecast at time t of two-year-ahead and one-year-

ahead earnings for firm i scaled by the closing price on the trading day before the

target price announcement date;

LTGijt = analyst j’s growth rate forecast at time t for firm i’s earnings over the next three to

five years;

52WHit = the highest stock price for firm i over the 52-week period preceding the target price

announcement date t scaled by the closing price on the trading day before the target

price announcement date;

SENTt = the monthly Baker and Wurgler (2007) investor sentiment index for the month prior

to the target price announcement month;9

and CONTROL is a vector of ten control variables considered relevant to target price (discussed below). All

variables are measured at the time the target price forecast is released. The models are run as pooled time-series,

cross-sectional regressions with corrections for clustering of standard errors by firm and month (Petersen, 2009).

For ease of reference, Table 1 summarizes the independent variables of primary interest along with their

predicted signs for this and subsequent regression models.

Within this model, analysts’ forecasts of fundamentals are captured by STE1, DIFFSTE, and LTG.10 Based

on the conceptual arguments underlying a valuation model and the results of prior research (e.g.,

Bandyopadhyay et al., 1995), we expect a positive association between each of these variables and the target

price forecast, TP (1, 2, 3 > 0). Alternatively, the variables designed to capture the selected non-fundamental

factors are 52WH and SENT. If analysts use the 52-week high as a reference point in forming their target price

forecasts, we would expect the coefficient on 52WH to be positive (4 > 0). Similarly, if investor sentiment has

an influence on the setting of the target price, we expect the coefficient on SENT to be positive (5 > 0). Note,

given model specification, any role played by the non-fundamental factors in the setting of the target price is

incremental to their influence on either the short-term earnings or long-term earnings growth forecasts.

As noted, our econometric model includes ten control variables considered relevant to target price

derivation. The first is a measure of whether the target price forecast is rounded to a whole dollar amount, and

9 Monthly investor sentiment is accessed from www.stern.nyu.edu/~jwurgler. The investment sentiment variable is originally defined

using annual data in Baker and Wurgler (2006) in equation (3) and denoted as SENTIMENT┴. 10 We use DIFFSTE instead of two-year-ahead earnings forecast, STE2, because STE1 and STE2 are highly correlated at around 87%.

DIFFSTE captures the incremental contribution of STE2 over STE1 and can be interpreted as a measure of short-term earnings growth.

http://www.stern.nyu.edu/~jwurgler

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for the remaining nine, four relate to analyst/brokerage characteristics and five to firm-specific characteristics.

To begin, we measure rounding as follows:

ROUNDijt = rounding, measured as a categorical variable set equal to 1 if analyst j’s target price

forecast for firm i at time t is rounded to the nearest dollar, and 0 otherwise

We include rounding following Shiller’s (2005) comment that market participants will rely on the nearest

round number when making judgments about value. Support follows from, among others, Dechow and You

(2012) who find that 46.3% of their sample EPS forecasts are rounded to the nearest five cents. They argue that

analysts are motivated to use round numbers where the costs of generating a more accurate forecast of EPS

outweigh the benefits, and suggest that rounding reflects analysts’ views of the lack of precision in forecasting.

Evidence of price clustering is also well established, with a number of rational and behavioral explanations

being advanced to explain the phenomenon.11 Rational explanations argue that market participants round

numbers in circumstances of uncertainty to reduce various costs (e.g., the resolution hypothesis by Ball et al.,

1985 and the negotiation hypothesis by Harris, 1991). Ikenberry and Weston (2007) find that the extent of

clustering on the NYSE and Nasdaq around increments of 5 and 10 cents cannot be completely explained by

rational economic theories and suggest that the phenomenon is also a function of a fundamental psychological

bias for prominent numbers. They refer to the psychology literature that finds rounding bias in the context of

various numeric-based tasks. This literature suggests that some numbers are easier to process than others, and

that this is reflected by the rounding of numbers during tasks that are time-based, involve large numbers, and

have high levels of difficulty (Shepard et al., 1975; Hornick and Zakay, 1994; Loomes, 1988). On this basis,

we predict that price clustering will also be observed in analysts’ target price forecasts, since the task of

forecasting future stock prices involves a high level of uncertainty and high information seeking costs. Thus,

we expect the coefficient on ROUND to be positive.

The analyst/brokerage characteristics we include in the model are:

REP = analyst reputation, measured as a categorical variable set equal to 1 if the analyst is

named as an “All American” team analyst by the Institutional Investor, and 0

otherwise;

11 Evidence of price clustering has been found for the US equity markets (Harris 1991; Christie and Schulz, 1994; Grossman et al.,

1996), gold markets (Ball et al., 1985; Grossman et al., 1996), foreign exchange markets (Grossman et al., 1996), and the London equity

index futures and options markets (Gwilym et al., 1998).

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EXP = analyst experience in forecasting firm i, measured as the number of years in which

the analyst has issued target price forecasts for the firm;

B_SIZE = brokerage size, measured as the number of analysts associated with a particular

broker in a given year; and

CONFLICT = a measure of possible investment banking-related conflicts of interest that may

influence analysts’ opinions which, following Ertimur et al. (2007), is based on the

Carter and Manaster (1990) rankings as updated by Loughran and Ritter (2004);

following Gleason et al. (2013), we assign a value of 1 if the brokerage firm has the

top investment banking reputational rank, 0.5 if it has a lower reputational rank, and

0 if it does not have a Carter-Manaster reputational rank.

Prior research indicates that analysts’ estimates of summary measures – forecasts of earnings and stock

recommendations – vary in accuracy and often display optimistic bias (Loh and Mian, 2006; Ertimur et al.,

2007; Bradshaw, 2002; Bradshaw et al., 2012), with various factors at both the analyst and brokerage levels

identified as contributing to this variation. We thus include the three common analyst- and broker-level variables

identified above (REP, EXP, and B_SIZE) to control for the influence of these factors on analysts’ target price

forecasts. Each has generally been positively associated with the accuracy of forecasting and recommendations

(Stickel, 1990; Park and Stice, 2000; Gleason et al., 2013). Since the optimistic bias in analysts’ forecasts and

stock recommendations has also been associated with investment banking relations, we additionally include

CONFLICT to capture any inflation in the target price induced by such bias (Lin and McNichols, 1998; Dechow

et al., 2000; O’Brien et al., 2005; Gleason et al., 2013). Note, since each of these characteristics is associated

with bias in analysts’ forecasts and stock recommendations, we expect them to be related to both the bias in,

and the level of, TP since characteristics associated with more upward bias should result in a higher target price

relative to the current market price. As such, we include the same analyst and broker controls in both the target

price model and the target price error model (discussed in the next section).

The control variables included in the model to capture firm-specific characteristics are:

β = the firm’s CAPM beta, estimated from a regression of firm returns minus the risk-free

(one-month T-bill) rate on the value-weighted market index minus the risk-free rate over

a period of 60 months preceding the target price month;

SIZE = firm size, measured as the log of market capitalization on the trading day before the target

price announcement date t;

BM = the book-to-market ratio, calculated as the book value of equity divided by the stock price

at the end of the previous fiscal year;

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RETt-1 = the past one-year return calculated as the 250-trading-day buy-and-hold return ending one

day before the target price announcement date; and

RETstd = return volatility, measured as the daily return volatility over the 250-trading-day period

ending one day before the target price announcement date.

These firm-specific characteristics are included as they have been shown to be associated with analyst

following and their ability to forecast accurately. Target price, as a forecast of value, will be a function not only

of the forecast of earnings and long-term growth but also of expectations of risk, both systematic and

unsystematic (Kerl, 2011). We include β, BM, SIZE and RETstd to proxy for a firm’s risk (Lui et al., 2007).

Firms with higher betas, higher book-to-market ratios, and smaller market capitalization are viewed as riskier

and therefore more difficult to value accurately. Also, higher risk can result in a higher discount factor being

used to discount future cash flows and hence a lower fundamental value and target price. We include past

returns, RETt-1, to control for analyst bias in target price arising from selecting past winners to follow. Hayes

(1998) argues that analysts’ incentives to gather and provide information are strongest for stocks that are

expected to perform well. Ertimur et al. (2007) provide empirical evidence that analysts initiate

recommendations following high returns and prior growth. Again, the inclusion of these firm-specific

characteristics in both the target price and target price error models is based on the argument that characteristics

associated with more upward bias should result in a higher target price forecast and hence greater target price

forecast error.

Finally, we conduct our analysis in three stages. First, we consider four variants of our primary model

(equation 1) in which we include the sets of fundamental and non-fundamental factors, and the set of control

variables, in various combinations. Second, for robustness purposes, we extend the model to alternatively

include the one-year lagged target price forecast, analyst recommendations, and the two pseudo-target prices

developed by Bradshaw (2004) and Gleason et al. (2013), one based on the residual income model (RIM) (VRIP)

and one based on PEG ratio heuristic (VPEGP). Here, we follow the procedures described in Gleason et al. to

determine each of these pseudo target prices. Third, since our arguments envisage an enhanced role for the non-

fundamental anchors in situations where stocks are more difficult to value and/or when analysts are less well

positioned to make a forecast, we re-run the model with a series of interaction effects. Specifically, we interact

both the fundamental and non-fundamental factors in turn with each of firm size (SIZE), earnings volatility

17

(EV), analyst experience (EXP), and brokerage size (B_SIZE) under the argument that smaller firms and firms

with greater earnings volatility will be more difficult to value, while analysts with less experience and from

smaller brokerages will be in a weaker position to make a forecast. In conjunction, we also run a model in which

we interact these factors with the valuation model ratio (VMR) developed by Gleason et al. As described by

Gleason et al., the VMR facilitates inferences about valuation model usage, with values of VMR exceeding one

indicative of the analysts favoring the PEG valuation heuristic and alternatively, for values less than one,

favoring RIM. Based on the findings in Gleason et al. of an improved target price process when the forecasts

are derived using a more rigorous valuation technique rather than an heuristic, we expect analyst reliance on the

non-fundamental factors to be positively related to the VMR. For this final analysis, we set the operational

notion of VMR equal to 1 if the firm’s VMR exceeds the median VMR and 0 otherwise.

3.2 Target Price Error

The fact that analysts’ target price forecasts are influenced by the non-fundamental factors does not

necessarily imply that this influence is detrimental to the target price forecast accuracy. To test whether the

influence of these factors is indeed detrimental, we estimate the following target price error model:

TPerrorijt = δ0 + δ1 STE1ijt + δ2 DIFFSTEijt + δ3 LTGijt + δ4 52WHit + δ5 SENTt

+ γ CONTROLit + υijt (2)

where all variables with the exception of TPerror are as previously defined, and the vector of control measures

includes rounding, and the same analyst/brokerage and firm-specific characteristics employed in the target price

model above (equation (1)).12 As noted, Table 1 provides a summary of the primary independent variables along

with their descriptions and predicted signs.

Following Bradshaw et al. (2012), we define target price error, TPerrorijt as the difference between the

one-year-ahead stock price (Pt+1) and the target price forecast, each scaled by the closing price on the trading

day before the target price announcement date (Pt) (i.e., TPerrort = (Pt+1/ Pt) – TPt). A negative value of TPerror

therefore implies an optimistic bias in the target price forecast. Alternatively, if a firm is delisted, Pt+1 is not

12 As explained in the previous section, both sets of characteristics have been shown to be associated with biases in analysts’ forecasts

and recommendations, so they are also likely to be related to both the level of and errors in analysts’ target prices. As we discussed in

Section 4 and revealed in Table 3, our sample target price forecasts do indeed exhibit a positive (optimistic) bias, on average.

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available. In these instances, to avoid look-ahead bias, we use the return to the firm’s shares over the period

prior to delisting (Rt+1), determining the target price forecast error as (Rt+1 + 1) – TPt.

As argued within the context of the target price forecast model above, we expect that a higher 52-week

high share price and more positive recent investor sentiment will each map into a higher target price forecast.

To the extent that these forces lead analysts to a more optimistic target price forecast, we expect a reliance on

them to result in a larger (more negative) target price forecast error. Thus, we expect the coefficients on each of

52WH and SENT to be negative (δ4 < 0 and δ5 < 0). Again, the role played by the non-fundamental factors is

incremental to any error they introduce into forecasts of short-term earnings and long-term earnings growth.

Finally, we adopt the same three-stage strategy underlying the analysis of the target price forecast as

described in the previous section. Again, the models are run as pooled time-series cross-sectional regressions

with standard errors clustered by firm and month (Petersen, 2009).

3.3 Predictability of Future Stock Returns

If our findings reveal that a reliance on the two selected non-fundamental factors, the 52-week high price

and recent market sentiment, induces biased target prices, we might expect the association between target prices

and future stock returns to decline as the influence of these measures increases. This approach follows Gleason

et al. (2012) where target price accuracy is measured as the ability of the target price to accurately forecast

future returns. If, for example, when the 52-week high price is significantly above the current stock price, it

influences analysts pulling their target price forecasts away from the fundamental value implied by the forecasts

of fundamentals, we might expect to find a weak, or perhaps even no, association between target prices and

future returns. In contrast, when the 52-week high is close to the current price, the effect of the 52-week high

on the target price forecast should be relatively small. We might then expect a relatively stronger positive

association between target prices and future returns for such stocks. In a similar fashion, we would expect that

when recent market sentiment is relatively high, there will be a larger ‘sentiment induced’ bias in target prices

and therefore a weaker relation between the target price forecast and future returns. Prior research indicates that

these non-fundamental values may be associated directly with stock price, however, our measures of 52 week

19

high and market sentiment are lagged relative to our measure of future returns. As such, we it is unclear ex ante

whether these factors will be associated with our measure of future returns.

To test the influence of the non-fundamentals on target price bias, we estimate the following regression

model of future stock returns on target prices and control variables:

RETt+1 = λ0 + λ1 TPijt + λ2 TPijt*52WHit + λ3 TPijt*SENTt +

+ λ4 STE1ijt + λ5 DIFFSTEijt + λ6 52WHit + λ7 SENTt + φ CONTROL*it + πijt (3)

where RETt+1 is the raw ex-dividend stock return measured over the 250-trading-day period following the target

price announcement day (t) using daily ex-dividend returns from CRSP, CONTROL* is a vector of control

variables limited to rounding (ROUND) and the firm-specific variables (, lnSIZE, BM, and RETt-1),13 and all

variables are as previously defined. Within the context of this model, if the target price forecast predicts future

stock price, we would expect a positive association between TP and future returns (λ1 >0). Further, if higher

values of 52WH and SENT have an increased influence on analysts thereby pulling their target price forecasts

further away from the value implied by the forecasts of fundamentals, we would expect the coefficients on the

interaction terms, TP*52WH and TP*SENT, to be negative (λ2 >0 and λ3 >0). Note, we exclude LTG because,

as Table 2 reveals, the requirement for long-term growth forecasts significantly reduces sample size and thus

the power of the returns tests.14 We estimate this model as a pooled time-series cross-sectional regression and

correct t-statistics for clustering of standard errors by firm and month (Petersen, 2009). Again, Table 1 provides

a summary of the primary independent variables and their predicted signs.

4. SAMPLE DATA

To construct our sample, analyst forecasts of target prices, earnings, and long-term growth were collected

from the Thomson Reuter I/B/E/S Detail History file over the period January 1999 to December 2007. Financial

data were then collected from CRSP and COMPUSTAT, monthly returns, daily returns, the risk-free rate and

the market value of equity from CRSP and the book value of equity from COMPUSTAT. The final sample

13 Since we do not expect that individual analyst and broker specific characteristics will help to explain firm-level future returns, we

include only firm specific controls that have been found to be associated with future stock returns. 14 Since returns tests usually have relatively low explanatory power (adjusted R2s), sample size is especially important for such tests. As

discussed in Section 5.5, our results for the returns model are robust to the inclusion or exclusion of these variables.

20

consists of 26,746 target price forecasts for 4,148 U.S. firms issued by 3,518 analysts. Table 2 presents the

sample selection criteria. As revealed, the sample size is greatly reduced by the requirement that an analyst must

also provide short-term earnings and long-term growth forecasts on the date of their target price forecast.15

Table 3 presents descriptive statistics. All variables except future ex-dividend stock returns, RETt+1, are

winsorized at the top and bottom 1% level. As revealed, the mean (median) value of the target price forecast

scaled by the closing price on the previous day (TP) is 1.228 (1.185), with a standard deviation of 0.276.16 Thus,

on average, a target price forecast is a prediction that the stock price will exceed its current level by 22.8% in

the next 12 months. The mean (median) target price forecast error, also scaled by the previous day closing price

(TPerror), is -0.156 (-0.139), with a standard deviation of 0.566. These statistics reveal a positive bias, with the

scaled target price forecast exceeding the scaled realized price by 15.6% on average.17 Finally, the mean

(median) one-year-ahead ex-dividend stock return over the sample period is 7.6% (3.5%).

Turning to the non-fundamental factors, we find that the mean 52-week high price (52WH) is 51.7% higher

than the stock price on the day prior to the target price announcement, while the median is 18.5% higher. Perhaps

coincidentally, the median value of 52WH (1.185) is the same as the median value for the target price forecast

measure, TP. However, the range for TP is narrower than the range of 52WH. These summary statistics appear

consistent with our expectation that analysts rely on past price data. The proximity of the target price forecast

to the 52-week high (PROX_52WH) is, on average, 151 days (0.414 × 365 days).

To further illustrate the relation between TP and 52WH, we plot the density distribution of the target price

relative to the 52-week high (Figure 1, Panel A). The plot reveals a sharp spike and the absolute maximum of

the density at the 52-week high. A binomial test rejects the null hypothesis that the number of observations in

the interval that includes 52-week high equals the average of the two immediately adjacent intervals at less than

the 1% level.18 To check the robustness of this result, we also change the scale variable from the stock price to

15 Since as noted, Jung et al. (2012) find that analysts that publicise LTG forecasts are signalling effort in their recommendations, it

seems less likely that such analysts would consciously rely on non-fundamental factors and hence, this sampling constraint is likely to

skew our analyses against finding a role for the identified behavioural factors. Notwithstanding, in order to improve the generalizability

of our study we also rerun our analysis removing the constraint of the LTG forecast (see Section 5.5). 16 This figure is comparable to Asquith et al. (2005). 17 The direction and magnitude of the bias are consistent with Bradshaw et al., 2012, who find that the average target price error in their

sample is -15%. 18 The binomial test is similar to the test Burgstahler and Dichev (1997) use for the discontinuity of earnings distribution around zero.

21

the 52-week high price (Panel B) and the size of each interval from 0.01 to 0.001 (Panel C). The plots in both

Panel B and Panel C exhibit similar spikes, and absolute maximums, in the target price density at the 52-week

high.

Returning to Table 3, we find that the sample mean (median) for investor sentiment, SENT, is 0.086 (-

0.060). Thus, while the mean is suggestive of positive investor sentiment, on average, the negative median value

indicates that observations for more than one-half of the sample are derived from periods when investor

sentiment was low. Additionally, the mean value of the rounding measure (ROUND) is 0.874, indicating that

for 87.4% of our sample firms, the target price forecast is presented as an even dollar amount .

Finally, Table 3 reveals that less than 10% of our analysts are classified as “All American” team analysts

(the mean value of REP is 8.8%) and that analysts have on average approximately two years of experience

forecasting target prices for a given firm in our sample (based on their target price forecasts collected by I/B/E/S,

the mean value of EXP is 2.097). Investment banking relations for our sample brokers is also toward the lower

end, with a median value for CONFLICT is 0.500.

Lastly, Table 2 indicates we lose 169,642 observations due to the lack of a long-term growth forecast. To

assess whether there are any substantial differences between our sample and the sample of firms without LTG

forecasts, we compare summary statistics between the two samples. The untabulated results indicate that the

greatest differences relate to analyst and broker characteristics. Analysts who provide LTG forecasts tend to be

less experienced (the mean value of EXP is 2.097 years for the LTG sample and 2.600 years for the non-LTG

sample). Further, the CONFLICT measure has a mean of 0.352 for the LTG sample and a mean of only 0.277

for the non-LTG sample, suggestive of greater investment banking activity between the firm and the broker for

firms with LTG forecasts.

5. RESULTS

5.1 Univariate Correlations

Pearson pair-wise correlations for the variables used in the models are presented in Table 4. Consistent

with our expectation that analysts appeal to the 52-week high price when forecasting target price, the table

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shows an economically significant correlation of 29.2% between the TP and 52WH. The investor sentiment

measure is also positively associated with analysts’ forecasts of target price and long-term growth, and the 52-

week high price. Turning to fundamentals, the two-year-ahead growth, DIFFSTE, and long-term growth, LTG,

are positively correlated with TP, while higher forecasts of STE1 are associated with lower TP. The latter result

is likely to be driven by a trade-off between the next-year and longer-term forecasts, with higher one-year-ahead

earnings forecasts being associated with lower two-year-ahead and long-term growth forecasts. This is

consistent with the expectation that extreme earnings revert to an average level over time, and it highlights the

importance of the multivariate analysis presented in the next section.

Correlations for the control variables (not tabulated) indicate the following relations. Analyst reputation

(REP) is positively associated with experience (EXP) and broker size (B_SIZE). That is, “All American” team

analysts are more experienced and more likely to work for larger brokers. Further, analysts are more likely to

provide lower target price, DIFFSTE, and LTG forecasts when they are “All American” team analysts, have

more experience, or are from larger brokerages. Investment banking relations are associated with optimistic bias

in target price forecasts as indicated by a negative correlation between the CONFLICT measure and the target

price error.

5.2 Target Price Forecast Model Results

Table 5 presents results based on the target price forecast model (equation (1)). As described in Section

3.1, we conduct our analysis in three stages. First, we consider four variants of the model in which we include

fundamental and non-fundamental factors in various combinations. Following, for robustness purposes, we

extend the model to alternatively include a lagged target price forecast, analyst recommendations, and the two

pseudo-target price forecasts developed by Gleason et al. (2013). Finally, we run a series of models with various

interaction effects to explore whether the roles played by the non-fundamental factors are enhanced when stocks

are more difficult to value, when analysts are less well positioned to make a forecast, or when analysts rely on

less rigorous valuation techniques.

5.2.1 Primary Model Results

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Results for the following four variants of the model are presented in Panel A of Table 5: Model 1 includes

only the fundamental factors, Model 2 includes only the non-fundamental factors, Model 3 includes both sets

of factors, and finally, Model 4 includes both sets of factors along with the control variables. Here, to begin, the

adjusted R2s are 16.5% for Model 1 with only the fundamental factors, 9.8% for Model 2 with only the non-

fundamental factors, and 22.7% for the composite model (Model 3). Thus, while it appears that the fundamental

factors play a stronger role in explaining target price forecasts, the non-fundamental factors also play an

(incrementally) important role. This conclusion is supported by the Vuong test statistics (not reported)

comparing the adjusted R2s among the three models, with the null hypothesis of no difference being rejected at

the 1% level for all comparisons.19

Turning to the individual factors, given the similarity in results across models, for parsimony, we restrict

our discussion to the results for Model 4, the complete model inclusive of the control variables. For the

fundamental factors, the coefficients on STE1, DIFFSTE, and LTG are 0.986, 4.060, and 0.411, each significant

at better than the 0.001 level. Thus, as expected within a valuation framework, the target price forecast is

increasing in forecasts of both short-terms earnings and long-term earnings growth. Equally, the coefficients on

the non-fundamental factors 52WH and SENT of 0.035 and 0.038, respectively, are also each significant at better

than the 0.001 level. Thus, incremental to any indirect effect that these non-fundamental factors might have

through the fundamentals, each also appears to play a direct role in the target price formation process, with the

forecast increasing in the 52-week high (52WH) and in the level of recent investor sentiment (SENT). The

magnitude of regression coefficients indicates that an increase in 52WH and SENT of one standard deviation

corresponds to an increase in the target price (scaled by price) of 4.0% (0.035*1.151) and 2.3% (0.038*0.618),

respectively. Thus, the identified non-fundamental factors explain an economically meaningful proportion of

the overall target price variation (from Table 3, the standard deviation of TP is 27.6%).

Finally, within this model, the coefficient on ROUND is 0.029 (p < 0.001). Hence, when a target price

forecast is presented as a round number, it is 2.9% higher, on average. Of the remaining control variables,

19 Similar inference obtain when the three models are run with the control variables. Here, the adjusted R2’s are 22.8% and 16.9% for

the analogues of Models 1 and 2, and the Vuong tests now comparing Models 1, 2, and 4 are again all significant at the 1% level.

24

B_SIZE, CONFLICT, SIZE, BM, and RETstd are each significant at better than the 5% level while the other

measures are insignificant at conventional levels.

5.2.2 Additional Considerations

First, to control for unobserved analysts’ incentives and characteristics, we add the one-year lagged value

of TP (LagTP) for the same analyst and firm to the model. The results are presented as Model 5 in Panel B of

Table 5. As revealed, the lagged measure (LagTP) is highly significant (0.406, p < 0.001) and its inclusion

results in a significant increase in model adjusted R2 (0.370 relative to 0.251 for Model 4 in Panel A).

Importantly, the results for both the fundamental and non-fundamental factors are consistent with those for the

primary analysis reported in Panel A, with all coefficients remaining significant at the 0.001 level. Thus, results

and conclusion are robust to the inclusion of the lagged measure of the dependent variable as a control within

the model.

Second, we add two measures to identify whether the target price forecast is accompanied by a

recommendation, and if so, the nature of the recommendation. Specifically, we set IncREC equal to 1 if the

target price forecast is accompanied by a recommendation, and 0 otherwise. We then capture the nature of the

recommendation using RECOMM, set equal to 1 for a sell recommendation, 2 for a hold, 3 for a buy, and 4 for

a strong buy. If there is no recommendation (IncREC = 0), we set RECOMM to 0. While there is some question

as to whether recommendations lead target price or target price lead recommendations, as previously discussed,

overall the research indicates that target prices provide a more ‘granular’ indicator to shareholders of an

analysts’ view of the stock than a recommendation. The results indicate that when accompanied by a

recommendation, the target price forecast is significantly higher; the coefficient on IncREC is 0.265 (p < 0.001).

Further, the results indicate that higher target price forecasts are associated with better recommendations; the

coefficient on RECOMM is 0.121 (p < 0.001). Importantly, given our central focus, the coefficients on the

fundamental and non-fundamental factors remain highly significant (p < 0.001) and have a similar magnitude

to that reported for Model 4 in Panel A. Of note, inclusion of the two recommendation measures leads to a

significant increase in model adjusted R2 (to 0.326 from 0.251 for Model 4).

Third, as an alternative approach to address issues surrounding model specification, we add the two pseudo

25

target prices constructed by Gleason et al. (2013) to the model, one based on the residual income model (RIM)

(VRIP) and one based on PEG ratio heuristic (VPEGP). Here, while the coefficient on each measure is positive

and significant at better than the 1% level, their inclusion does not alter our base conclusions and adds relatively

little to the model’s explanatory power. Specifically, the model adjusted R2 increases only marginally with the

inclusion of the pseudo target price forecasts (to 0.256 from 0.251 in Model 4). Further, each of the fundamental

and non-fundamental factors remains significant in the predicted direction at better than the 0.001 level. Thus,

results and conclusions are also robust to the inclusion the two pseudo target prices constructed by Gleason et

al. within our econometric model.20

5.2.3 Interactive Model Results

Panel C of Table 5 presents results for a series of five interactive models designed to provide insights into

the settings where the non-fundamental factors are most important in the development of the target price

forecast. Here, the first two sets of columns present results for the models where the fundamental and non-

fundamental factors are interacted with firm-related characteristics (size and earnings volatility), the third and

fourth sets of columns present results where the factors are interacted with analyst and brokerage characteristics

(analyst experience and brokerage size), and the fifth set of columns where they are interacted with the VRM

ratio designed to capture the relative use by analysts of the RIM valuation model versus the less rigorous PEG

valuation heuristic (Gleason et al., 2013).

The results are largely (although not universally) consistent with expectations. To illustrate, consider first

the results for the model interacted with firm size presented in the first set of columns. Here, the coefficients on

the STE1 and LTG interaction terms are 0.770 and 0.091, respectively, while the coefficients on the 52WH and

SENT interaction terms are -0.032 and -0.017. In each instance, the coefficient is statistically significant at better

than the 0.01 level. Thus, on balance, for larger firms which are arguably easier for analysts to value given their

better information environments, analysts place greater weight on the fundamental factors and reduced weight

on the non-fundamental factors.

20 When we alternatively include the two pseudo target prices in the model in lieu of the fundamental factors, our conclusions regarding

a role for the non-fundamental factors is unaltered. The coefficients on each of these factors remains significant at better than the 0.001

level. Additionally, the coefficients on each of the two pseudo target prices is also significant at better than the 0.001 level.

26

A similar conclusion follows from the results for the model interacted with analyst experience presented

in the third set of columns. Here, the coefficients on STE1 and LTG are positive and significant while the

coefficient on 52WH is negative and significant. Thus, again consistent with expectations, on balance analysts

with greater experience place greater weight on the fundamental factors and reduced weight on the non-

fundamental factors. As a final illustration, for the model interacted with VMR presented in the fifth column,

the coefficient on LTG is negative and significant (-0.355, p = 0.005) while the coefficient on 52WH (0.020, p

= 0.021) is positive and significant. Thus, when analysts are more likely to use valuation heuristics rather than

a rigorous valuation model, they are more likely to refer to the 52WH.

5.3 Target Price Forecast Error Model Results

Table 6 presents results for the target price error model (equation (2)) designed to examine whether

analysts’ ex ante forecasts of fundamentals and reference to non-fundamental anchors each help to explain the

optimistic bias in target price forecasts documented in Table 3. The format of the presentation parallels that

used for the discussion of the target price forecast results above. Panel A presents results for the same four

variants of the model, Panel B extends the model to include alternatively one-year lagged target price error, the

two recommendation measures, and the two pseudo target prices developed by Gleason et al. (2013) and Panel

C presents results for the same five interactive models.

5.3.1 Primary Model Results

To begin, the results for the primary models presented in Panel A reveal the adjusted R2s to be 5.4% for

the model with only the fundamental factors (Model 1), 10.5% for the model with only the non-fundamental

factors (Model 2), and 13.8% for the composite model (Model 3). Here again, the test statistics for the Vuong

tests (not reported) indicate that the null hypothesis of no difference in adjusted R2s is rejected at the 1% level

for all pairwise comparisons.21 These results reveal two points of note. First, the non-fundamental factors play

a stronger role in explaining target price forecast error than do the fundamental factors. Second, there is a role

21 The same inferences pertain when the models are run the control variables. The adjusted R2’s are 8.2% and 13.6% for the analogues

of Models 1 and 2, respectively, with the Vuong tests among Models 1, 2, and 4 again significant at the 1% level for all comparisons.

27

for both sets of factors, given that the composite model exhibits a statistically significant increase in explanatory

power over either separate factor model.

Turning to the individual factors, again given the similarity of results across models, we focus on the

complete model, Model 4. For the fundamental factors, the coefficient on STE is insignificant (0.246, p = 0.353)

while in contrast, the coefficients on DIFFSTE (-2.274, p < 0.001) and LTG (-0.461, p < 0.001) are both negative

and significant. These findings are consistent with analysts overweighting longer-term measures when

forecasting target prices and/or with the documented analyst optimism in longer-term forecasts (Frankel and

Lee, 1998; Hughes et al., 2008; Gode and Mohanram, 2012), each of which would result in a more optimistic

target price forecast (recall, more negative values of TPerror indicate greater optimism). For the non-

fundamental factors, as expected all coefficients are negative and statistically significant. The coefficients on

52WH and SENT are -0.042 and -0.238, respectively, each significant at better than the 0.001 level. Thus, the

results indicate that there is a larger optimistic bias in the target price forecast when the 52-week high price is

higher and when recent market sentiment is more positive. Again, this effect is incremental to any effect that

the non-fundamental factors might have through the forecasts of fundamentals. The magnitude of the regression

coefficients indicates that an increase in 52WH and SENT of one standard deviation corresponds to an increase

in the target price optimistic bias (scaled by price) of 4.8% (0.042*1.151) and 14.7% (0.238*0.618. Hence, the

identified factors explain an economically significant proportion of the overall variation in target price errors

and the average optimistic bias (as Table 3 reveals, the standard deviation of TPerror is 56.6% and the mean

value of TPerror is -15.6%).

Finally, for the control variables, the coefficient on ROUND is -0.137 (p < 0.001) which corresponds to an

increase in the target price optimistic bias of 13.7%, on average, when the target price forecast is presented as a

round number. Of the remaining control variables, EXP, B_SIZE, and β are each significant at better than the

5% level while the other measures are insignificant at conventional levels.

5.3.2 Additional Considerations

The first column of Panel B in Table 6 reveals that when the target price error model is extended to include

the one-year lagged measure of TPerror, results for both the fundamental and non-fundamental factors are

28

consistent with those reported in Panel A for the primary analysis. For the fundamental factors, the coefficient

on STE1 remains insignificant while the coefficients on DIFFSTE and LTG remain negative and highly

significant. Equally, for the non-fundamental factors, the coefficient on each remains negative and highly

significant. Additionally, the coefficient on the lagged TPerror measure is negative and highly significant, and

the model adjusted R2 increases modestly to 0.181 from 0.151 reported for Model 4 in Panel A. Thus, results

and conclusions are robust to the inclusion of the lagged measure of the dependent variable.

Next, the second column of Panel B reveals that the optimistic bias in target price forecasts is greater when

the target price forecast is accompanied by a recommendation, and then if the recommendation is better. The

coefficient on IncREC is -0.262 (p < 0.001) and the coefficient on RECOMM is -0.117 (p < 0.001). Further and

of central interest, the coefficients on DIFFSTE and LTG, and on both of the non-fundamental factors (52WH

and SENT), remain negative and highly significant (p < 0.001), while the coefficient on STE remains

insignificant. Here, the model adjusted R2 increasaes modestly to 0.168 from 0.151 for Model 4.

Finally, the third column of Panel B reveals that results and conclusions are also robust to the addition of

the two pseudo target prices to the model. With the addition of VPEGP and VRIP, the model adjusted R2

increases only slightly to 0.152 (from 0.151). As with the primary analysis reported in Model 4 of Panel A, the

coefficients on each of DIFFSTE and LTG remain negative and highly significant, as do the coefficients on each

of the three non-fundamental factors.

5.3.3 Interactive Model Results

Panel C of Table 6 presents results for the series of the five interactive target price error models. Here, the

results essentially parallel those for the interactive target price models (Panel C of Table 5) and are largely (but

again not universally) consistent with expectations. For example, the coefficient on 52WH is positive and

significant within the models interacted with firm size, analyst experience, and brokerage size. Thus, for firms

that are larger, and for analysts with more experience analysts and those from larger brokerage firms, this factor

contributes less to the optimistic bias in target price forecasts (consistent with analysts having referred less to

the 52WH in making their forecasts).

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Continuing, for the model interacted with earnings volatility, the coefficient on SENT is negative and highly

significant, consistent with analysts placing greater weight on recent market sentiment for firms with greater

earnings volatility. Within this model, the coefficient on 52WH is also negative but insignificant. Finally, for

the model interacted with the valuation model ratio (VMR) presented in the fifth column, the coefficient on

52WH is negative and highly significant, indicative that with the use of valuation heuristics as opposed to

rigorous valuation methodology, the increased emphasis on the 52-week high price leads to greater optimism in

the target price forecast.

5.4 Returns Analysis Results

As the final step, we consider the usefulness of target price forecasts by examining their association with

subsequent stock returns, measured as the ex-dividend stock return over the 250-trading-day period following

the target price announcement day. Of direct interest, in conjunction we also examine whether the influence of

two of our anchors, the 52-week high and investor sentiment, affects the usefulness of the target price forecast.

The results for this final step in our investigation into target price forecasts are presented in Table 7. In

terms of our primary focus, Model 1 includes only TP, Models 2 and 3 add the interaction terms between TP

and each of the non-fundamental factors individually, and finally Model 4 presents the full model. All models

include the sets of fundamental and non-fundamental factors, as well as the set of control variables. Here, across

all models, as might be expected within a valuation framework, the coefficients on the fundamental factors,

STE1 and DIFFSTE, are uniformly positive and significant at better than the 0.001 level, a finding consistent

with the notion that short-term earnings forecasts provide explanatory power for future returns. For the non-

fundamental factors, the coefficient on 52WH is insignificant across all models and the coefficient on SENT is

consistently negative and significant at better than the 0.001 level. The finding for SENT is consistent with

recent market sentiment being negatively associated with future stock returns (Baker and Wurgler, 2007).

Finally, model adjusted R2s range from 7.0% to 7.7%.

Turning directly to evidence regarding the usefulness of target price forecasts for predicting future returns,

across all models the coefficient on TP is positive and significant at better than the 0.001 level. Thus, in a general

sense, it appears that target price forecasts can be used as a reference point for developing profitable trading

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strategies. For example, in Model 4 (the complete model), its coefficient is 0.194 (p < 0.001). This coefficient

indicates that an increase in TP of one standard deviation corresponds to an increase in the future one-year ex-

dividend return of 5.4% (0.194*0.276).

Importantly, however, the results for the interaction terms also indicate that the strength of the association

is conditional on the levels of the 52-week high and recent market sentiment, with the coefficients on the

interaction terms with 52WH and SENT each negative and significant. For example, in Model 4, the coefficient

on TP*52WH is -0.041 (p = 0.002) and the coefficient on TP*SENT is -0.086 (p = 0.038). Thus, the target price

forecast, TP, appears to be most informative in terms of future returns when 52WH and SENT are relatively low,

and its informativeness decreases as these measures increase. Here, intuitively, these findings fit well with the

findings reported in Tables 5 and 6 that higher values of both 52WH and SENT map into higher target price

forecasts and ultimately, into larger optimistic bias in the forecast.

Thus, overall, the results for this part of our investigation suggest that the target price forecast is

incrementally informative for future stock returns over short-term earnings forecasts, but more so when the

influence of the non-fundamental factors, the 52-week high and recent market sentiment, on the target price

formation process is relatively more muted. When the 52-week high stock price is high relative to the current

price and/or when recent market sentiment is relatively more positive, both instances where the earlier results

suggest that these non-fundamental factors play a greater role in the development of the target price forecast

and ultimately manifest in greater optimistic forecast bias, we find the informativeness of the target price

forecast for future returns to be significantly diminished.

5.5 Additional Considerations

This section reports results from a series of tests designed to reinforce arguments and conclusions based

on our primary analyses reported in the previous sections. First, arguments supportive of a role for the 52-high

in the target price formation process imply that its influence should increase with proximity to the target price

forecast date since the more recent it is, the more likely the analyst will be able to bring it to mind in determining

the target price. To consider the effect of proximity, we interact all measures within both the target price and

target price errors models with proximity (PROX_52WH), measured as minus one times the number of days

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between the occurrence of the 52-week high price and the target price forecast date, scaled by 365. The results,

reported in Panel A of Table 8, indicate that the weight placed on the 52WH variable increases with proximity

in both models. From the first set of columns for the target price model, the coefficient on 52WH*PROX_52WH

is 0.047 (p = 0.014) while from the second set of columns for the target price error model, the coefficients on

52WH*PROX_52WH is -0.062 (p = 0.019). Of note, in conjunction with the relatively greater weight on the

52WH measure, the weight on SENT is significantly reduced with increased proximity, as is the weight placed

on the long-term growth measure (LTG). Thus, as suggested, with increased proximity, analysts appear to rely

more heavily on the 52-week high price in developing their target price forecast but in an apparent tradeoff rely

less on recent market sentiment.

Second, we investigate whether the association between target price and the fundamental factors is affected

by the level of either 52WH or SENT. For example, larger values of either could lead analysts to reduce the

weight they place on the fundamentals. The results are reported in Panel B of Table 8. For the target price model,

the weight placed on STE1 is reduced when either 52WH or SENT is high while the weight placed on longer-

term forecasts is increased. One possible interpretation is that analysts increase the weight on longer-term

forecasts in order to “support” the higher target price forecast that derives with higher values of 52WH or SENT.

Interestingly, for the 52WH interaction model, the coefficient on SENT*52WH is insignificant whereas for the

SENT interaction model, the coefficient on 52WH*SENT is negative and significant, suggestive that 52WH

becomes less important when SENT is high but alternatively, the role of SENT is unaffected in the face of a high

52WH. Finally, for the target price error model, on balance the results follow logically from those for the target

price model with the effect of STE1 on target price optimism reduced and the effect of LTG increased when

52WH and SENT are high.

Third, to reinforce arguments and conclusions that the non-fundamental factors incrementally and directly

affect target price forecasts rather than just through the valuation inputs, we ran regressions with each of the

valuation inputs as the dependent variable (STE1, DIFFSTE, and LTG) and then each of the pseudo prices

deflated by the closing price on the trading day before the target price announcement date as the dependent

variable (VRIP/P and VPEGP/P). Each model included the two non-fundamental factors and the control

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variables. The results (not tabulated) reveal that none of the non-fundamental factors has a consistently positive

effect on either the fundamental forecasts, with the effects often negative or insignificant. For the pseudo price

models, only SENT has a positive association with VRIP/P or VPEGP/P. Opposite expectations, the 52WH is

negatively associated with VPEGP/P which appears the most likely valuation model used by analysts. Thus,

overall, these results reaffirm our conclusion that the non-fundamental factors directly affect target price

forecasts rather than acting only indirectly through the fundamentals.

Finally, to examine whether our results and conclusions are robust to several of our design choices, we

undertake the following additional sensitivity analysis. First, we re-run the analyses reported in Tables 4 and 5

without LTG. This allows for a substantia