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Psychology and
behavioral finance
Anchoring bias by financial analysts
on the Tunisian stock market
Ahmed Bouteska
Department of Finance and Accounting,
Faculte des Sciences Economiques et de Gestion de Tunis,
Universite de Tunis El Manar, Tunis, Tunisia, and
Psychology
and behavioral
finance
39
Received 30 August 2018
Revised 22 January 2019
20 September 2019
Accepted 23 October 2019
Boutheina Regaieg
Department of Finance and Accounting,
Faculte des Sciences Juridiques Economiques et de Gestion de Jendouba,
Universite de Jendouba, Jendouba, Tunisia
Abstract
Purpose – The purpose of this paper is to detect quantitatively the existence of anchoring bias among
financial analysts on the Tunisian stock market. Both non-parametric and parametric methods are used.
Design/methodology/approach – Two studies have been conducted over the period 2010–2014. A first
analysis is non-parametric, based on observations of the sign taking by the surprise of result announcement
according to the evolution of earning per share (EPS). A second analysis uses simple and multiple linear
regression methods to quantify the anchor bias.
Findings – Non-parametric results show that in the majority of cases, the earning per share variations are
followed by unexpected earnings surprises of the same direction, which verify the hypothesis of an anchoring
bias of financial analysts to the past benefits. Parametric results confirm these first findings by testing
different psychological anchors’ variables. Financial analysts are found to remain anchored to the previous
benefits and carry out insufficient adjustments following the announcement of the results by the companies.
There is also a tendency for an over/under-reaction in changes in forecasts. Analysts’ behavior is
asymmetrical depending on the sign of the forecast changes: an over-reaction for positive prediction changes
and a negative reaction for negative prediction changes.
Originality/value – The evidence provided in this paper largely validates the assumptions derived from the
behavioral theory particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998).
The authors conclude that financial analysts on the Tunisian stock market suffer from anchoring, optimism,
over and under-reaction biases when announcing the earnings.
Keywords Financial analysts, Tunisian stock market, Anchoring bias, Earnings per share (EPS),
Unexpected earnings
Paper type Research paper
1. Introduction
Cohen (1997) defines financial analysis as a set of concepts, methods, and instruments that
allow for an appreciation of a company’s financial conditions, the risks that affect it, and the
level and the quality of its performance. Financial analysis appears to be a rigorous
discipline, relying on rational reasoning and advanced techniques. However, proponents of
market efficiency theory have questioned the importance and effectiveness of the role of
financial analysts: if financial markets are efficient, and if the price of an asset reflects all the
relevant and available information, what is the purpose of spending time on analyzing the
intrinsic value of an asset? However, the economy is not as perfect as described by classical
financial theory. Business also involves conflicts of interest and agency relationships, and
JEL Classification — G12, G14, G17, M10
EuroMed Journal of Business
Vol. 15 No. 1, 2020
pp. 39-64
© Emerald Publishing Limited
1450-2194
DOI 10.1108/EMJB-08-2018-0052
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40
information is not available in the same way for everyone. In this context, financial analysis
can be seen as “an antidote to informational asymmetries, a moderator of opportunistic
temptations to which managers are exposed, and a moral risk reducer that affects investors”
(Cohen, 1997). Several studies have focused on the business of financial analysts as
producers of information on financial markets. The role of a financial analyst then is to
collect information and evaluate companies in order to make recommendations for
purchases or sales to both individual and institutional investors (Schipper, 1991).
Nevertheless, over the past two decades, analyst performance has fluctuated in the USA
and more recently in Europe. They often failed to prevent certain bankruptcies (Enron,
WorldCom, Lehman Brothers, etc.). Researchers blame these professionals for their lack of
rigor and the significant deviation of their forecasts from the results announced by the
companies analyzed. In addition to these accusations of their incompetence in preventing
crises, these professionals sometimes found themselves at the heart of scandals against a
backdrop of conflicts of interest and insider trading. This is the case in the Lehman Brothers
scandal, in which analysts are accused of fueling this bubble and not having anticipated the
deterioration in the financial conditions of major international groups and being
insufficiently independent of the financial institutions on which they are making forecasts.
These numerous criticisms of analysts lead us to believe that, on the one hand, they play a
key role in the functioning of capital markets and, on the other, the information produced is
not always relevant. Since then, research has focused on studying the behavior of financial
analysts as prime suppliers of information to investors, in order to better explain stock market
behavior (De Bondt and Thaler, 1990; Mendenhall, 1991). Studies show optimism, anchoring,
and under- or overreaction to new information. The primary purpose of financial analysts is to
look for anomalies in the market, that is, to ensure that the value of an asset does not deviate
too frequently from its underlying value. From this perspective, financial analysts have a role
to play in the efficiency of financial markets through their recommendations. Empirical
studies explain these deviations by financial analysts through their psychological biases. The
latter tend to be optimistic in their recommendations and are subject to anchoring. Empirical
research demonstrates that they remain anchored to previous benefits and make insufficient
adjustments following the announcement of firm results.
Given the importance of financial analysts’ forecasts in business valuation and investor
decision-making processes, as well as the smooth functioning of the stock market, on the one
hand, and observing the various work drawing attention to the importance of informational
quality of the analysts’ earnings forecasts on price formation, on the other, it is interesting to
deepen the study of financial analysts’ behavior. To shed light on the debate over financial
analysts’ role, which was fueled by the bubble of values in the telecommunications, media,
and technology sectors, we extend the empirical research on the relation between
psychological bias, analysts’ earning per share (EPS) forecasts, and price formation by
analyzing the Tunisian market. The Tunisian Stock Exchange is particularly interesting
because of the lack of evidence on earnings forecasts as well as the cognitive bias effect in
developing and emerging markets. Because of limited data availability, the existing research
uses only questionnaires or factor analysis. The literature detecting anchoring biases and its
implications on the EPS forecasts and securities price evolution is mainly focused on the
USA and developed financial markets.
The choice of the Tunisian Stock Exchange is also motivated by its specific features
that are common to emerging markets. It is tightly and poorly capitalized, with only a
small number of firms. On September 28, 2018, the Tunisian Stock Exchange listing grew
by one company, Tunisie Valeurs, bringing the total number of listed companies to
82 (Annual report of Tunisian Stock Exchange, TSE, 2018). It is also highly volatile, with
non-normal returns and information that is not always available, implying supplementary
transaction costs. This is combined with insufficient skill and qualifications by the
financial analysts. All these factors affect the performance of analysts’ earnings forecasts
and bias, as shown in a number of studies (Amir and Ganzach, 1998; Beckers et al., 2004;
Kaestner, 2005; So, 2013; Bellando et al., 2014; Ben Braham and Galanti, 2014; Lin et al.,
2015; Coën and Desfleurs, 2016, 2017; Jahidur Rahman et al., 2019). Thus, our contribution
is in extending the literature by formulating general and convincing answers to the issue
of analysts’ anchoring behavior regarding past realized earnings. A number of questions
arise. First, to what extent do the various components of a financial analyst report have
accurate informational value? Are Tunisian financial analysts subject to optimism
and anchoring bias? If so, what role does this psychological bias play in forming
the recommendations of analysts on the Tunisian financial market? Finally, what is the
impact of the earnings forecast biases on the returns of securities?
This paper is organized as follows. Section 2 offers a literature review and research
hypotheses. Section 3 presents the data and descriptive statistics. Section 4 presents the
results and discussions of the parametric analysis that reveal anchoring bias by financial
analysts. Finally, we conclude in Section 5.
2. Literature and research hypothesis
The effect of anchoring and adjustment bias on stock returns has been reported in many
studies since the 1990s. Most of this research have been carried out using data on the US
stock market and focused on individual investors. In this section, we begin by exploring the
behavioral theories explaining cognitive biases, and then we review the empirical studies on
the subject before defining our research hypotheses.
2.1 The behavioral theories explaining analysts’ cognitive biases
Classical finance and its principles of rational expectations and utility maximization have
recently been challenged by behavioral finance. This modern theory seems better able to
explain the multiple anomalies observed on financial markets. More specifically, we distinguish
the perspective theory, the postulates of mental accounting, and the theory of heuristics.
The perspective theory was formulated by the psychologists Kahneman and Tversky
(1979) under the term prospect theory. According to Gollier et al. (2003), “while classical
economic theory postulates that individuals evaluate the different states of the world in an
absolute and objective way, Kahneman and Tversky suggest that individuals evaluate
situations relatively to a point of reference in time that can be subjective” (p. 296). There are
therefore reversals of preference: decisions are not made in the same way depending on
whether they imply gains or losses relative to the reference point. The decisions of agents in
a risky environment are influenced by the way in which the choices are presented as well as
the conditions of the decision maker.
The notion of mental accounting is generally attributed to Thaler (1999). It is the set of
cognitive operations performed by individuals to organize, evaluate, and memorize their
financial operations. Work in this area is organized around three axes. The first focuses on
how results are perceived and how to make decisions before evaluating them. The second
deals with the division of activities and operations into separate accounts. The sources
and uses of funds do not appear to be apprehended globally, but separately by
individuals, who divide their expenditures and their assets into several distinct categories.
The third deals with the frequency with which the various accounts are valued and the
tolerances applied in the management of these various accounts. Thaler (1999) points out
that each of the components of mental accounting is opposed to the economic principle of
fungibility, and it is important to take this into account in order to understand the
behavior of agents. Varied proofs now exist and confirm that mental accounting
significantly influences investor behavior. In particular, the reluctance of investors to sell
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losing securities may be related to mental accounting. It is also the case for several biases
associated with the selection of information.
Finally, the theory of heuristics holds that individuals readily obey more rudimentary
intuitive rules. Tversky and Kahneman (1974) describe as heuristics these “mental
shortcuts” that make it possible to make generalizations and abstract judgments about
individuals or social categories based on a limited sample of information. The heuristics are
particularly useful when the individual is confronted with an intense cognitive load that
does not allow him to take into account all the information offered. Their function is to
reduce uncertainty and simplify the problem that agents face but lead to overestimation or
underestimation of the probability of an occurrence of an event. Tversky and Kahneman
cite three main heuristics: representation, availability and anchoring:
(1) The heuristic of representativeness refers to the tendency of individuals to generalize
what is particular in the beginning and to use it to establish general rules (Tversky
and Kahneman, 1973). Individuals tend to evaluate the occurrence of an uncertain
future event by the degree to which it resembles to a recent observed phenomenon.
(2) The availability heuristic is a reasoning mode that is based on immediately available
information (Tversky and Kahneman, 1974). Pouget (2000) suggests that “subjects
using this heuristic rule evaluate the probability of an event by the ease with which
examples or similar cases come to mind” (p. 105).
(3) The anchoring-adjustment heuristic is a mental process in which people agree with
their previous views or predictions to the detriment of new information (Edwards,
1968). This bias reflects the fact that individuals refer to past values and are
reluctant to revise their beliefs. This cognitive bias leads to insufficient adjustments
when new information is presented. The values that are held and that are behind the
anchoring may be past beliefs, thresholds ( for stock market indices in particular), or
even recent information.
2.2 Survey of empirical studies
The experience of De Bondt (1993) is one of the first studies showing that students have a
tendency to anchor their estimates when they make decisions about prices. In his research,
De Bondt (1993) conducted an experiment in which 27 were students asked to predict stock
prices 7 months and 13 months after the last price recorded in each graph. They were shown
six share price graphs illustrating the share price for two years. Based on the experimental
results, De Bondt (1993) concludes that most students make predictions by extrapolating the
stock price trends that they recognize from the graph (known as the “following trend” or
“extrapolation bias”). They are also inclined to make forecasts over a broader range for the
history of stock prices that have exhibited greater volatility. Furthermore, under the
influence of anchoring and adjustment bias, they bias their forecast interval. De Bondt
(1993) suggests then that two anchors were used in the experiment: the slope that students
perceive from the graph and the average stock price of the input data. Moreover, as we show
in this section, not only nonprofessionals are subject to anchoring bias.
After De Bondt (1993), further research on anchoring and adjustment in pricing has
emerged. In these studies, researchers have attempted not only to examine the existence of
anchoring and adjustment bias but also to quantify the effect of this factor on the
valuation of stock returns. Cen et al. (2013), for example, show that anchoring and
adjustment bias have a significant impact on the stock market. Using different regression
models, such as Sharpe and Campbell (2007) and Fama and French (1993), they confirm
that analysts make optimistic predictions when predictive performance (FEPS) results are
less than the median value and making pessimistic forecasts when FEPS is higher than
the median value. After the earnings announcement dates, companies with a higher
(lower) FEPS have unusually high (weak) future stock market returns. Companies with a
high FEPS over the median are also better able to engage in stock splits. Finally,
companies with divided shares face larger revisions to positive forecasts, larger
forecasting errors, and wider negative earnings surprises after the stock split than those
that did not have a division of their shares.
Contrary to Cen et al. (2013), Oomen (2011) examined the influence of anchoring bias and
adjustment without introducing prediction error as proxy. Instead, it created a model in
which the anchor and the adjustment factor are the dependent variables and used
independent variables, such as volatility of results, firm size, and analyst experience, with
two dummy variables to measure the forecasting time and the direction of earnings changes.
Oomen (2011) found that anchoring and adjustment bias with EPS of the previous year as
an anchor appears more when the change between the actual EPS and the EPS of the
previous year is positive, but when the change is negative, the consensus of the first three
forecasts of the year is the most used.
Numerous empirical studies also show that analysts are optimistic in forecasting future
earnings (Fried and Givoly, 1982; O’Brien, 1988; Francis and Philbrick, 1993; Kang et al.,
1994; Dreman and Berry, 1995). Other studies show that forecasts provided by analysts are
on average too conservative, which translates into announcement surprises that are
excessively big. Thus, several studies report underreaction by analysts (De Bondt and
Thaler, 1987; Klein, 1990; Lys and Sohn, 1990; Mendenhall, 1991). Jacquillat and Grandin
(1994) studied the performance of analysts of the French market, including their evolution
over time. Results in favor of the simultaneous existence of an under- and overreaction are
more limited and disparate. Thus Easterwood and Nutt (1999) report underreaction to bad
news but overreaction to good news. Amir and Ganzach (1998) indicate overreaction to
information in changes from one quarter to the next, on the one hand, and underreaction in
the periodic revisions of these forecasts, on the other hand.
More recent studies have documented one of the most important market anomalies:
calendar anomalies. The empirical analysis of Rossi and Fattoruso (2017), for example,
examining the impact on particular periods of civil calendars in the Italian stock market,
showed the presence of calendar effects in line with international markets. Other studies
found more mixed results. In this way, Khan et al. (2017) study the effects of calendar
anomalies (the Ramadan effect) on the Pakistani equity market, finding a minor positive
impact of Ramadan on it. Rossi and Gunardi (2018) conducted an analysis on the French
German, Italian, and Spanish stock exchanges and did not find strong proof of
comprehensive calendar anomalies. Some of these effects are country specific and unstable
in the first decade of the new millennium.
Experimental studies have also improved understanding of cognitive errors related to
the processing of information. Thus, Gillette et al. (1999) show that a public and unique
signal on the dividend is misunderstood by the market players. The authors report
underreaction by the predictions to the signal but also underreaction by prices to the signal.
This latter effect is much more pronounced. Similar results are found by Calegari and
Fargher (1997). Affleck-Graves et al. (1990) confirm the existence of cognitive biases, which
may affect forecasting, but in many cases these forecasts are better than those obtained
through the use of a self-regressive statistical model.
Research also suggests that it is particularly difficult to correct anchoring bias.
Consistent with this view, Northcraft and Neale (1987) conclude that experts are susceptible
to decision bias even within the limits of their decision framework, and experts are less
likely than amateurs to admit or understand their use of heuristics in making biased
judgments. Plous (1989), for example, shows that familiarity with a task is not sufficient to
avoid anchoring bias. Moreover, the effects of anchoring bias are not significantly
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influenced by the ease with which respondents can imagine the outcome (availability of
results), asking them to determine the most likely way to achieve the outcome (availability
of the method), or by throwing the problem in terms of how to avoid the event. Plous (1989)
also mentioned that anchoring bias exists even after various biases have been corrected
(i.e. the existence of expert opinion).
Wright and Anderson (1989) consider the effect of situational familiarity on anchoring.
They conclude that the anchoring effect is so dominant that increased familiarity with a
situation does not lead to a decrease in anchoring. Instead, they find that monetary
incentives can reduce anchoring, but the effect has only marginal statistical significance.
According to Kahneman and Tversky (1974), the gains in accuracy do not reduce the
anchoring effect. Moreover, Brewer et al. (2007) report that responsibility does not
reduce anchoring bias in predictions of physician infection. Whyte and Sebenius (1997)
provide results suggesting that groups do not make individual judgments.
Early work by Hien et al. (2014) shows that anchoring and adjustment bias affects both
male and female analysts and results in forecasting errors, which also influences market
efficiency. Campbell and Sharpe (2009) show that professional forecasters anchor their
anticipation of macroeconomic data, such as the consumer price index, on previous values,
which leads to systematic and fairly large forecast errors, and as a result markets become
inefficient. The results of the study by Waweru et al. (2008) indicate that the anchoring and
adjustment bias affected the financial decisions of institutional investors on the Nairobi
Stock Exchange. Luong and Ha (2011) described how anchoring and adjustment bias
affected investment decisions by individual investors at the Ho Chi Minh Stock Exchange.
Abraham et al. (2014) show that anchoring and adjustment bias influence the investment
decisions of listed property fund managers in South Africa. This bias may lead to judgment
errors and the potential for missed gains.
Further, Liao et al. (2013) recognized the existence of the anchoring effect in foreign
institutional investors’ momentum behavior in the Taiwan stock market. Their investment
decisions were anchored to their prior ownership and the effect grows stronger as prior
ownership increases. However, the anchoring effect does not lead to any improvement in
momentum profitability. Sometimes, momentum profitability suffers because of this effect.
The study implies that, when implementing a momentum strategy, foreign investors should
rely on past experience with a certain stock, not on how much they previously owned.
Chang et al. (2013) confirmed the informational and anchoring role played by the reference
price of the first issued share from a firm cross-listing its share in a segmented market.
Closely related, Bucchianeri and Minson (2013) find evidence for anchoring in the US real
estate market. Koskinen (2013), in his dissertation on the anchoring effect, came to a
conclusion about its presence in UK equity market. Even professional analysts were
anchored to the industry median forecast EPS as they made future forecasts. Meub and
Proeger (2015) find subjects to be less anchored when given monetary incentives and a
realistic opportunity of achieving better solutions through increased cognitive effort. Kratz
(2016) finds that anchoring to the past year annual EPS could have a negative impact on
forecast errors, and his results do not show any relationship between forecast errors and
anchoring to the industry median. Roger et al. (2018) show that anchored sell-side analysts
issue more extreme forecasts for lower-price stocks.
2.3 Development of the research hypotheses
In the empirics of this paper, we try to detect the anchoring bias on the Tunisian Stock Exchange
under the assumptions of behavioral approaches. Our research hypotheses are as follows:
H1. If financial analysts remain anchored to past benefits, the forecasts provided by
these experts indicate the same direction of change in benefits from one semester
(half-year) to the next.
H2. If the percentage of negative earnings prediction errors is greater than that of positive
one and regardless of the meaning of the earnings changes, optimism bias is verified.
H3. The effect of anchor-adjustment bias on earnings forecast revisions is stronger than
that of changes in earnings. In other words, the anchor-adjustment effect is supposed
to lead to optimism and underreaction of the analysts following the revisions in the
predicted benefits. More generally, revisions to earnings forecasts are assumed to lead
to underreaction by analysts.
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45
H4. There is net underreaction by analysts as a result of negative changes and net
overreaction following positive changes.
3. Data and descriptive statistics
3.1 The sample
The sample is composed of publicly traded companies whose market data were available
during the period 2010–2014, totaling 49 companies (Table I). Firms in our sample share a
common feature: they announced a result.
3.2 Data and variables
Financial data, such as the daily share price and the Tunindex, come from the Tunisian
Stock Exchange. In our portfolio analysis, the Tunindex is used as a market index. Both
the forecasted and the announced earnings are extracted from the “guidance stocks” of the
following intermediaries on the stock exchange: MAC sa, Tunisia Values, Amen Invest,
Cap Finance, and CGF. The variables used are unexpected earnings (UE) and surprise
unexpected earnings (SUE), EPS and the revision of earnings forecast (REV ).
3.2.1 UE and SUE. The surprise unexpected earnings variables (UE and SUE) measure
the surprise of the result announcement. Individual earnings forecasts are aggregated by
counting earnings surprises (i.e. UE) following the announcement date of earnings for each
security. These earnings surprises are based on systematic comparisons of the earnings
forecast and the realized earnings.
UEi is unexpected earnings on date i, which equals the difference between the forecast
and the realized or actual EPS, as follows:
U E i ¼ EPS i E ðest i Þ;
(1)
where EPSi is the earnings per share announced on date i; E(esti) the consensus of forecasts
published by analysts the month preceding the announcement of the results on date i; Esti
Amen Bank
ICF
SIMPAR
WL
ATB
BT
BH
UBCI
BIAT
Attijari Bank
BNA
STB
BTE
ASTREE
STAR
AIR LIQUIDE
ALKIMIA
SFBT
TUNINVEST
PLAC.TSIE
SPDIT-SICAF
Tunisie Leasing
CIL
ATL
SOTETEL
MONOPRIX
SOTUVER
SIAME
SOTUMAG
ELECTROSTAR
SOTRAPIL
SIPHAT
STEQ
SOMOCER
ASSAD
GIF-FILTER
SITS
ESSOUKNA
ADWYA
TPR
POULINA GP H
ARTES
SALIM
TUNISIE RE
ENNAKL
TELNET
Attijari Lease
HEXABYTE
NBL
Table I.
Companies in
the study
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the individual forecasts published by analysts the month preceding the announcement of
the actual results.
The earnings surprise reveals whether investors underestimate or overestimate earnings
based on the mean error for each horizon. UE gives additional information by indicating the
difference between realized and forecast earnings. The date of the results announcement
corresponds to the date of publication of listed companies’ financial statements on the CMF
(Financial Market Council Tunisia) website:
SU E i ¼ ½EPS i E ðest i Þ=P i ;
(2)
where EUi is unexpected earnings at date I, and Pi is the share price preceding the
announcement of results.
3.2.2 EPS. EPS is the realized earnings per share. Ten indicators of biannual EPS are
used from 2010 to 2014.The change in earnings, ΔEPS, is the difference in realized EPS
between two semesters (half-years).
3.2.3 REV. REV is the revision in earnings forecasts prior to the announcement date. It
corresponds to the difference between two current earnings forecasts by financial analysts.
This allows us to evaluate the evolution of the forecast for each security on the market. Most
analysts adjust their forecasts and recommendations as soon as possible after earnings
announcements by companies:
REVi;t þ 1 ¼ EPS expectedi;t þ 1 –EPS expectedi;t :
(3)
3.3 Descriptive statistics
Table II reports descriptive statistics for each variable used in the regressions. The first
observation that emerges is the deviation of the variables from the normal distribution. The
asymmetry coefficient “Skewness” is different from 0, and the coefficient of flattening
“Kurtosis” is greater than 3 for all the variables. Therefore, we detect asymmetric
information on the Tunisian stock market during our period of study.
Descriptive results show also a minimal mean change in UE (−0.103), with significant
volatility. (EPS4 – EPS3) and (EPS4 – EPS1) show that the realized earnings in the second
half of 2013 on average are lower than those in the first half of 2013 and the first half of 2012.
However, the mean change in the variables (EPS4−EPS2) and (EPS4−EPS0) is positive,
which indicates higher earnings realized during the second half of 2013. The extreme values
show significant variation in the earnings realized, ranging from −4.260 to 8.545 in 2013 and
from −4.035 to 7.66 in 2012. ΔEPS is also important, ranging from a minimum variation
of −6.446 to a maximum of 8.545. The variable REV has a lower mean value (0.012) and less
volatility (0.457) than all the other variables.
The heteroskedasticity test is also performed to check for variance correlation. This
problem is detected, and White’s test is carried out to correct it.
3.4 Nonparametric analysis
Nonparametric analysis is used to detect anchoring bias by Tunisian financial analysts. Our
analysis concerns financial analysts whose mission is to estimate the fundamental value of a
stock. Our sample consists of 451 events related to 49 Tunisian listed companies during the
period from the first half of 2010 to the second half of 2014, which have a date and a value for
the result announcement, an earnings history, and a consensus earnings forecast. Forecasts of
financial analysts and announced results are biannual, i.e., ten semesters (half-years). The
choice of six-month periodicity is not arbitrary. The forecasts are published every six months
following the publication of the financial statements of listed companies.
(EPS4 – EPS3)
(EPS4 – EPS2)
(EPS4 – EPS1)
(EPS4 – EPS0)
REV
ΔEPS
Mean
−0.103083
−0.095489
0.174702
−0.256234
0.281457
0.012021
0.008415
Median
1.044965
1.649580
0.017000
−0.007000
1.400963
−0.002000
0.005000
Maximum
3.410000
8.545000
7.661000
5.973000
5.511000
4.538221
8.545000
Minimum
−4.693000
−4.260000
−4.035000
−4.651000
−4.574000
−3.882000
−6.446000
SD
−0.951318
2.405563
1.498884
1.535050
0.772714
0.457812
1.111706
Skewness
−0.951318
2.405563
2.507833
0.156634
0.772714
2.551500
1.248220
Kurtosis
11.84456
18.59162
16.22264
9.700041
9.031594
46.38314
20.43109
Jarque-Bera (Probability) 163.6924 (0.00000) 521.3977 (0.00000) 391.6572 (0.00000) 88.10286 (0.00000) 74.30625 (0.00000) 43648.65 (0.00000) 6834.575 (0.00000)
Observations
48
47
47
47
46
549
529
UE
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Table II.
Descriptive statistics
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This nonparametric analysis consists of comparing the direction of the announced EPS
change to that of the result announcement surprise. A positive announcement surprise
implies a higher realized EPS than the consensus one forecasted by financial analysts and
vice versa. The hypothesis of this analysis is that if financial analysts remain anchored to
the past earnings, the forecasts provided by these experts will indicate the same direction of
change in earnings from one semester to the other, but these forecasts will have values that
are too low. In other words, if a positive (negative) earnings change is observed from one
semester to another, a positive (negative) surprise is recorded.
Table III shows the change in the realized EPS compared to the SUE. The comparison
between the sign of these two variables allows us to confirm the hypothesis of an
anchoring bias of financial analysts to the past earnings. Our results indicate that
74.1 percent (334 events) of the earnings change is followed by a surprise in the same
direction. An announcement earnings change and surprise with the opposite sign
accounted for only 25.9 percent of the total events (117 cases). In 37.2 percent of cases
(168 events), the positive change in EPS is found to be accompanied by a positive surprise,
while in 36.8 percent of cases (166 events), the EPS negative change is related to an
announcement surprise with the same sign. There is also a negative change in EPS
followed by a positive surprise in 11.78 percent of the total (53 events). If the change in
EPS is null, the announcement surprise is found to be positive in 8 cases and negative in
0 cases. These observations confirm that financial analysts are subject to an anchoring
bias that influences their predictions.
4. Methodology
To detect anchoring by financial analysts, we were inspired by Kaestner’s (2005) model.
Thus, we used simple and multiple linear regressions to account for the sign and size of
the variables under consideration. For estimation, we employed the generalized least
squares method. This allows us to correct the problem of autocorrelation of residuals
previously detected.
Using the first test set, we tried to detect conservatism by financial analysts compared
to the previous semester. To this end, we referred to the following general linear
regression model:
UEt ¼ ai þbi ðEPSt EPSt1 Þ:
(4)
Anchoring bias assumes a positive relationship between UE and the earnings change. In
other words, if a positive (respectively negative) earning variation was accompanied by a
positive (respectively negative) announcement surprise, then the financial analysts
remained anchored to the earnings of the previous semester, and they did not adjust their
earnings. This hypothesis corresponds to a positive coefficient βi. The null hypothesis
assumes that the announcement surprise sign is independent of the sign of EPS variation.
Therefore, the absence of anchoring bias assumes that the coefficients αi and βi estimated by
the regression are not significantly different from zero. If αi is positive, then according to
Kaestner (2005) the actual earnings realized are overestimated. However, if αi is negative,
then financial analysts make excessively high estimates that are often higher than the
realized earnings, which leads to negative announcement surprises. This result leads to
the conclusion that financial analysts are too optimistic.
In general Equation (4), we use five EPS indicators: EPS0, EPS1, EPS2, EPS3 and EPS4,
which respectively depict the earnings realized in the second half of 2011, the first and the
second half of 2012, and the first and the second half of 2013. The first specification
examines whether financial analysts remained anchored to the realized EPS during the first
half of 2013 (EPS3). UE corresponds to the realized earnings and estimates for the second
Companies
Amen Bank
ATB
BT
BH
UBCI
BIAT
Attijari Bank
BNA
STB
BTE
ASTREE
STAR
ICF
AIR LIQUIDE
ALKIMIA
SFBT
TUNINVEST
PLAC.TSIE
SPDIT-SICAF
Tunisie Leasing
CIL
ATL
SOTETEL
MONOPRIX
SIMPAR
SOTUVER
SIAME
SOTUMAG
ELECTROSTAR
SOTRAPIL
SIPHAT
STEQ
Announcement date
June 9, 2010
June 3, 2010
May 20, 2010
June 18, 2010
July 5, 2010
June 14, 2010
July 23, 2010
June 7, 2010
June 28, 2010
June 30, 2010
May 31, 2010
June 15, 2010
June 21, 2010
July 5, 2010
June 15, 2010
May 12, 2010
June 30, 2010
April 16, 2010
April 29, 2010
June 24, 2010
June 10, 2010
June 15, 2010
July 8, 2010
April 14, 2010
July 1, 2010
June 21, 2010
June 15, 2010
July 5, 2010
July 15, 2010
July 9, 2010
July 12, 2010
July 9, 2010
SUE %
−3.93
−0.42
0.03
−2.52
−0.21
3.42
–
2.74
6.94
−16.09
0.83
2.12
7.90
0.12
–
−0.12
−1.86
0.88
7.30
−2.18
0.29
−0.68
–
0.83
−7.10
0.14
0.73
0.87
159.34
−2.27
6.13
2.96
2010
Semester 1
ΔEPS
2.71
−0.15
0.00
0.73
−0.50
−1.76
−0.34
0.63
0.00
0.50
2.05
6.52
−0.46
3.92
0.06
0.44
2.02
1.41
0.51
0.20
0.99
−0.09
0.27
0.72
1.76
0.21
0.22
−0.06
0.13
0.07
0.54
0.13
Semester 2
SUE %
ΔEPS
0.83
0.22
−0.02
0.10
−0.12
−0.14
0.63
−0.85
0.26
0.25
−1.43
−1.33
0.75
0.17
−3.82
−0.51
−6.16
−0.66
−3.41
−0.36
−0.96
−0.83
−1.89
−6.45
−6.75
−0.41
−0.51
−1.25
−2.32
−0.28
−2.07
−0.42
1.88
−0.41
–
–
−5.34
−0.62
−0.64
−0.24
1.13
0.28
−0.15
0.02
0.32
−0.01
−0.30
−0.56
−3.04
−1.39
−0.10
−0.20
−0.37
−0.04
−1.44
−0.01
−2.77
−0.26
−1.24
−0.30
−6.04
−0.90
−3.16
−0.37
Announcement date
June 24, 2011
June 20, 2011
July 1, 2011
September 6, 2011
July 17, 2011
July 4, 2011
July 23, 2011
September 12, 2011
June 28, 2011
September 27, 2011
June 1, 2011
July 4, 2011
August 26, 2011
June 15, 2011
June 15, 2011
June 27, 2011
June 30, 2011
May 10, 2011
April 27, 2011
June 7, 2011
June 2, 2011
July 15, 2011
July 8, 2011
May 29, 2011
July 8, 2011
June 27, 2011
June 15, 2011
August 1, 2011
July 15, 2011
July 1, 2011
July 18, 2011
July 29, 2011
2011
Semester 1
SUE %
ΔEPS
1.87
−1.64
1.90
−0.07
0.08
0.16
4.97
0.35
1.08
−0.15
1.97
1.57
3.05
−0.21
2.54
0.26
6.45
0.35
−2.56
−0.11
1.84
0.85
1.85
4.39
2.35
−0.40
0.96
0.54
−5.59
0.55
1.10
0.51
−4.26
−1.06
−1.87
–
2.84
0.29
0.57
0.16
−0.26
−0.72
3.14
−0.08
−0.05
−0.39
−0.17
−0.66
−4.89
1.04
0.06
0.06
0.96
0.02
−0.96
−0.02
20.12
−0.87
0.34
0.49
7.71
0.75
−2.42
−0.77
(continued )
Semester 2
SUE %
ΔEPS
−1.77
0.15
−3.08
−0.17
−0.09
−0.16
−6.64
−0.85
−0.88
−0.19
−2.20
−1.77
−2.88
−0.61
−2.55
−0.23
−1.61
−0.19
−4.09
−1.02
−1.77
−1.59
−2.45
−3.07
−7.01
−1.99
−0.77
−3.33
2.93
1.08
−1.08
−0.41
3.93
0.79
2.02
1.19
−2.51
−0.16
−1.05
−0.49
0.47
0.21
−2.34
−0.09
0.05
0.23
1.25
1.13
1.05
1.42
1.55
0.08
−1.20
−0.04
0.95
0.05
17.42
−0.11
0.28
−0.02
−9.83
−1.37
2.86
1.24
Psychology
and behavioral
finance
49
Table III.
SUE and changes in
EPS values over the
period (2010–2014)
Announcement date
July 7, 2012
July 6, 2012
June 29, 2012
September 20, 2012
June 29, 2012
July 16, 2012
July 23, 2012
September 10, 2012
June 28, 2012
September 10, 2012
May 31, 2012
June 1, 2012
June 17, 2012
June 15, 2012
Companies
Amen Bank
ATB
BT
BH
UBCI
BIAT
Attijari Bank
BNA
STB
BTE
ASTREE
STAR
ICF
AIR LIQUIDE
Table III.
August 31, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
July 9, 2010
May 28, 2010
July 1, 2010
April 1, 2010
–
–
–
–
0.10
0.20
0.35
−0.42
0.16
−0.38
0.07
−0.11
0.13
0.30
0.56
0.39
−0.18
–
1.88
–
–
2012
Semester 1
SUE %
ΔEPS
0.78
0.42
−0.08
0.18
0.69
0.17
−2.48
0.23
0.84
−0.13
2.69
1.97
−16.50
0.53
−3.42
0.23
43.36
0.08
−5.15
0.80
2.34
1.45
4.14
4.01
2.50
7.00
−0.24
3.91
−0.24
0.94
2.76
1.09
−0.41
−5.98
0.77
1.50
2.65
0.76
−1.40
3.26
0.86
–
2.59
–
–
−0.72
−2.04
−2.86
−0.48
0.00
3.60
−1.17
−0.70
−2.48
−1.66
−0.31
−3.27
–
–
–
–
–
−0.01
−0.21
−0.34
0.04
0.05
0.42
−0.13
−0.06
−0.31
−0.29
−0.10
−0.36
−0.14
−0.02
−1.49
–
–
August 31, 2011
June 6, 2011
July 15, 2011
July 22, 2011
July 15, 2011
June 6, 2011
June 8, 2011
June 15, 2011
July 2, 2011
July 5, 2011
May 19, 2011
July 15, 2011
August 1, 2011
–
–
–
–
4.42
−0.21
0.39
−0.85
0.13
−3.28
1.44
0.45
1.20
0.28
−3.21
−9.07
–
–
–
–
–
0.01
0.01
0.07
−0.15
−0.28
−0.35
−0.08
0.01
0.20
−0.15
−0.20
0.05
−0.10
0.35
−0.12
–
–
50
SOMOCER
ASSAD
GIF-FILTER
SITS
WL
ESSOUKNA
ADWYA
TPR
POULINA GP H
ARTES
SALIM
TUNISIE RE
ENNAKL
TELNET
AttijariLease.
HEXABYTE
NBL
−0.04
0.19
−0.01
0.14
0.12
0.53
−0.01
−0.03
−0.22
0.20
0.31
0.04
0.13
−0.37
−0.49
0.09
–
(continued )
−1.13
0.16
−0.43
3.00
−1.20
6.86
−1.62
−0.31
−1.70
0.36
0.77
−3.76
−1.56
–
–
–
–
EMJB
15,1
ALKIMIA
SFBT
TUNINVEST
PLAC.TSIE
SPDIT-SICAF
Tunisie Leasing
CIL
ATL
SOTETEL
MONOPRIX
SIMPAR
SOTUVER
SIAME
SOTUMAG
ELECTROSTAR
SOTRAPIL
SIPHAT
STEQ
SOMOCER
ASSAD
GIF-FILTER
SITS
WL
ESSOUKNA
ADWYA
TPR
POULINA GP H
ARTES
SALIM
TUNISIE RE
ENNAKL
TELNET
AttijariLease.
HEXABYTE
NBL
June 15, 2012
July 11, 2012
May 31, 2012
April 24, 2012
May 14, 2012
June 7, 2012
May 31, 2012
July 16, 2012
July 4, 2012
May 29, 2012
July 29, 2012
July 16, 2012
July 13, 2012
July 18, 2012
July 15, 2012
June 25, 2012
July 18, 2012
July 29, 2012
August 31, 2012
June 12, 2012
July 4, 2012
July 12, 2012
July 10, 2012
June 20, 2012
June 8, 2012
June 25, 2012
July 2, 2012
July 9, 2012
May 18, 2012
June 15, 2012
July 11, 2012
July 20, 2012
July 16, 2012
July 1, 2012
–
3.96
3.20
−0.84
2.85
2.74
0.20
−0.64
1.56
0.70
−0.02
2.35
0.63
−0.22
−1.81
18.97
2.99
1.94
3.96
−2.90
0.67
0.20
−3.76
−2.07
4.79
0.24
−0.17
2.55
3.39
1.02
−4.77
0.41
–
–
–
–
1.94
0.37
−1.00
−0.76
0.20
0.74
−0.18
0.10
−0.06
−0.57
−0.46
−0.15
0.04
−0.01
1.96
−0.07
1.05
−0.72
0.02
−0.09
0.01
−0.14
−0.06
−0.10
0.22
0.01
0.23
0.14
0.03
0.17
−0.18
0.32
1.17
−0.12
–
(continued )
Psychology
and behavioral
finance
51
Table III.
Table III.
Companies
Amen Bank
ATB
BT
BH
UBCI
BIAT
Attijari Bank
BNA
STB
BTE
ASTREE
STAR
ICF
AIR LIQUIDE
ALKIMIA
SFBT
TUNINVEST
PLAC.TSIE
SPDIT-SICAF
Tunisie Leasing
CIL
ATL
SOTETEL
MONOPRIX
SIMPAR
SOTUVER
SIAME
SOTUMAG
ELECTROSTAR
SOTRAPIL
SIPHAT
STEQ
2012
Semester 2
SUE %
−0.59
0.11
−0.63
2.62
−0.75
3.51
−7.10
1.78
−6.45
−3.10
−2.00
−0.07
−3.61
0.24
−3.06
−0.91
0.97
−2.42
−2.37
−0.47
0.87
−1.53
−1.22
0.02
−0.73
−0.36
0.21
1.16
−4.99
−1.67
−1.88
−4.88
ΔEPS
−1.29
−0.03
−0.12
0.58
−0.32
0.60
0.01
−0.01
−0.62
−0.87
−2.16
−5.28
−2.67
−1.12
−2.29
−0.31
0.33
−0.85
−0.30
−0.47
0.34
−0.07
−0.01
0.18
−1.69
0.02
0.00
0.02
−0.71
−0.15
−0.14
−0.21
Announcement date
July 10, 2013
April 24, 2013
June 21, 2013
September 6, 2013
August 7, 2013
July 1, 2013
July 9, 2013
August 6, 2013
June 28, 2013
September 25, 2013
June 14, 2013
June 3, 2013
June 17, 2013
June 14, 2013
May 20, 2013
May 31, 2013
June 12, 2013
June 21, 2013
April 26, 2013
June 13, 2013
May 30, 2013
July 1, 2013
July 29, 2013
June 11, 2013
June 20, 2013
July 18, 2013
July 8, 2013
June 24, 2013
July 15, 2013
July 1, 2013
July 18, 2013
July 29, 2013
Semester 1
SUE %
ΔEPS
0.79
1.20
0.48
0.02
0.16
0.04
3.67
−0.12
−0.80
0.25
1.49
−0.37
1.18
0.09
2.65
0.19
44.23
0.20
−0.96
0.57
1.54
2.07
1.77
5.51
2.96
0.16
0.36
−0.08
−7.66
2.56
0.13
0.56
−1.89
−0.33
0.38
0.55
2.93
0.47
−3.42
0.11
−0.30
−0.35
−0.16
0.06
–
–
−0.31
−0.16
−5.85
−0.88
0.17
0.00
−0.72
−0.01
23.55
0.00
−0.13
0.25
−1.39
0.04
5.42
0.42
6.07
0.34
Semester 2
SUE %
2.45
−1.61
−0.14
–
0.27
0.84
3.77
−12.26
−82.19
−0.90
−1.72
−0.99
−1.78
−0.57
4.83
−1.53
−1.57
−0.30
−3.13
−0.25
0.38
2.74
–
0.27
5.59
−0.37
1.06
0.68
−0.21
2.31
−14.26
2.58
2013
ΔEPS
0.23
−0.07
−0.03
–
0.07
0.88
0.49
−1.26
−4.23
−0.02
−1.72
−4.26
−1.85
−1.55
1.38
−0.57
−0.04
−0.24
−0.48
−0.21
0.25
0.05
–
0.16
8.55
−0.03
0.03
0.02
0.10
0.32
−1.19
0.21
(continued )
EMJB
15,1
52
34.19
−0.69
−0.18
3.34
0.70
−0.67
−0.49
0.07
−2.80
−1.68
0.10
1.06
−0.31
−2.12
3.05
−0.09
–
Announcement date
July 10, 2013
April 24, 2013
June 21, 2013
September 6, 2013
August 7, 2013
July 1, 2013
July 9, 2013
August 6, 2013
June 28, 2013
September 25, 2013
June 14, 2013
June 3, 2013
June 17, 2013
June 14, 2013
SOMOCER
ASSAD
GIF-FILTER
SITS
WL
ESSOUKNA
ADWYA
TPR
POULINA GP H
ARTES
SALIM
TUNISIE RE
ENNAKL
TELNET
AttijariLease.
HEXABYTE
NBL
Companies
Amen Bank
ATB
BT
BH
UBCI
BIAT
Attijari Bank
BNA
STB
BTE
ASTREE
STAR
ICF
AIR LIQUIDE
SUE %
0.87
0.65
0.09
–
–
0.08
−0.53
–
–
−4.69
1.48
0.76
0.46
0.11
1.08
−0.07
0.01
0.19
0.27
−0.23
−0.14
−0.01
−0.23
−0.36
0.00
0.01
0.06
−0.28
−0.13
0.02
−0.29
2014
Semester 1
ΔEPS
0.11
0.07
0.07
–
–
−0.11
−0.26
–
–
−1.29
1.83
3.81
1.29
0.77
July 15, 2013
June 13, 2013
July 1, 2013
August 15, 2013
July 15, 2013
June 19, 2013
August 5, 2013
June 25, 2013
July 8, 2013
July 10, 2013
June 6, 2013
June 21, 2013
July 30, 2013
July 19, 2013
July 16, 2013
April 16, 2013
July 10, 2013
−0.99
−0.01
−0.04
−0.26
−0.22
−0.06
0.11
0.02
0.30
0.12
−0.22
0.21
0.05
0.30
0.02
−0.01
−0.09
Semester 2
SUE %
ΔEPS
−1.77
−1.07
−2.56
−0.40
−0.31
−0.05
−0.75
−0.69
0.43
0.34
0.24
−0.50
1.93
−0.20
0.79
−0.25
–
0.15
−0.87
1.01
2.91
0.66
−0.95
−4.83
4.80
1.89
−0.62
−0.96
1.24
0.06
−0.27
−2.52
−0.46
−2.37
−0.93
0.60
0.49
−0.37
−0.07
2.95
−0.50
1.32
0.71
0.16
–
−0.33
−0.90
−0.41
1.14
0.61
7.23
0.99
−0.25
−3.01
−1.33
0.07
0.36
0.65
−3.25
−0.10
0.81
−1.36
−0.07
0.09
0.01
0.19
0.21
0.67
0.03
−0.01
−0.30
−0.16
0.24
−0.27
−0.01
−0.30
0.05
0.06
0.11
(continued )
Psychology
and behavioral
finance
53
Table III.
Table III.
ALKIMIA
SFBT
TUNINVEST
PLAC.TSIE
SPDIT-SICAF
Tunisie Leasing
CIL
ATL
SOTETEL
MONOPRIX
SIMPAR
SOTUVER
SIAME
SOTUMAG
ELECTROSTAR
SOTRAPIL
SIPHAT
STEQ
SOMOCER
ASSAD
GIF-FILTER
SITS
WL
ESSOUKNA
ADWYA
TPR
POULINA GP H
ARTES
SALIM
TUNISIE RE
ENNAKL
TELNET
AttijariLease.
HEXABYTE
NBL
Source: Authors
May 20, 2013
May 31, 2013
June 12, 2013
June 21, 2013
April 26, 2013
June 13, 2013
May 30, 2013
July 1, 2013
July 29, 2013
June 11, 2013
June 20, 2013
July 18, 2013
July 8, 2013
June 24, 2013
July 15, 2013
July 1, 2013
July 18, 2013
July 29, 2013
July 15, 2013
June 13, 2013
July 1, 2013
August 15, 2013
July 15, 2013
June 19, 2013
August 5, 2013
June 25, 2013
July 8, 2013
July 10, 2013
June 6, 2013
June 21, 2013
July 30, 2013
July 19, 2013
July 16, 2013
April 16, 2013
July 10, 2013
−1.16
4.61
1.96
0.38
3.79
0.34
−1.55
−0.94
–
−0.56
0.79
0.10
−0.11
0.05
0.15
−0.18
−16.36
17.37
0.50
−1.07
−0.28
−0.41
−0.65
−1.00
−0.65
0.79
1.24
1.36
−0.08
5.37
1.25
–
−0.77
−0.19
−0.20
−1.89
0.65
0.46
0.41
0.56
0.54
−0.47
−0.09
–
−0.22
−3.69
0.05
−0.01
0.00
−0.67
−0.23
−0.78
0.55
0.08
−0.09
0.01
−0.12
−0.16
−0.80
−0.09
0.00
0.29
0.26
−0.11
0.40
0.21
–
−0.18
−0.01
−0.08
−1.69
−0.50
1.83
−1.92
−2.58
−1.82
−1.04
2.72
–
1.26
2.37
1.37
5.79
9.88
−1.29
−0.49
13.57
−31.41
5.59
−0.66
−19.81
10.62
−0.28
−1.15
−0.97
1.46
2.71
−2.13
0.59
−1.86
−2.19
−5.06
1.47
−0.44
1.00
−2.24
−0.34
0.04
−0.49
0.05
−0.61
0.19
0.04
–
−0.01
−2.39
0.03
0.21
0.27
0.02
0.10
−0.29
−1.26
0.40
0.08
0.07
0.46
−0.14
0.24
0.16
0.20
0.25
−0.20
−0.34
−0.22
−0.22
−0.47
0.41
0.12
0.01
EMJB
15,1
54
half of 2013. The earnings realized during the first half of 2013 were chosen as the anchoring
value in this regression (EPS3).
Specification 1:
Psychology
and behavioral
finance
UE ¼ aþbðEPS4 EPS3 Þ:
A second specification was constructed by changing the anchoring value of the realized
EPS. This specification includes realized earnings during the second half of 2011 (EPS0).
This regression explores whether anchoring bias becomes more important over time.
Specification 2:
UE ¼ aþbðEPS4 EPS0 Þ:
If coefficient β in this regression is higher than the previous one, this proves that anchoring
bias has been amplified over the previous four semesters. The companies in our sample
were listed on the stock exchange during and after January 2011.
A third specification was performed to study the effect of the change in earnings on the
announcement surprise. UE is expressed in terms of four explanatory variables: the change
in earnings in the first half of 2013 (EPS4 − EPS3), in the second half of 2012 (EPS4 − EPS2),
in the first half of 2012 (EPS4 − EPS1), and in the second half of 2011 (EPS4 − EPS0).
Specification 3:
UE ¼ e b1 ðEPS4 EPS3 Þþb2 ðEPS4 EPS2 Þ þb3 ðEPS4 EPS1 Þþb4 ðEPS4 EPS0 Þ:
To complement this first analysis, we were inspired by the empirical work of Amir and
Ganzach (1998). The revisions in earnings forecasts and changes in past earnings are now
used and tested as psychological anchors. We emphasized the importance of forecast error
(or UE or announcement surprise) as well as its direction to highlight the effects of optimism
and under- and overreaction by financial analysts. The research methodology consists of
regressing earnings forecast errors on earning forecast revisions. The optimism bias and
over- and/or underreaction by analysts are tested using the following specification.
Specification 4:
UEt ¼ pt þbt: REVt þet ;
where t is the six-month period before the current earnings announcement; and
REVt the revision in earnings forecasts for the six-month period prior to the earnings
announcement date.
A deeper analysis is performed by examining coefficients β and δ after the ten-month
earnings forecasting revision (REV ) in the following equation.
Specification 5:
UEt ¼ pt þbt REVt DumREVPositive þdt REVt DumREVNegative þet ;
where DumREVPositive is a dummy variable that takes a value of 1 when the earnings revision
is positive, and 0 otherwise; DumREVNegative is a dummy variable that takes a value of 1 when
the earnings revision is negative, and 0 otherwise.
Regressing earnings forecast errors (UE) on changes in past earnings and revisions of
earnings forecasts in the earnings announcement will determine the importance of each
psychological anchor. First, the two variables are analyzed globally, and then the analysis is
deepened by considering the positive or negative sign of the predicted and announced
previous earnings errors.
The overall regression model is as follows.
55
EMJB
15,1
56
Specification 6:
UEt ¼ pt þbt REVt þ dt DEPSt þet :
A deeper analysis is performed by examining the specification including a positive or
negative sign of earnings forecast revisions at the earnings announcement (REV ). The
regression model that takes into account the sign of revisions in earnings is as follows.
Specification 7:
UEt ¼ pt þbt DBPAt þdt REVt DumREVPositive þlt REVt DumREVNegative þet :
5. Detection of anchoring bias by financial analysts on the Tunisian stock
market: parametric analysis
The parametric analysis considers psychological anchors, first, the past realized earnings
and, then, the revisions in earnings forecasts as well as changes in past earnings.
5.1 Past realized EPS as a psychological anchor for financial analysts
5.1.1 Analysts’ anchoring bias regarding realized EPS in the first six months of 2013.
Table IV reports estimates of the analysts’ anchoring bias regarding realized EPS at first
half of 2013. The coefficient of the change in EPS is found to be positive and significant at
the 99 percent level of confidence. The model explains 74.53 percent of this result, which
confirms the existence of a positive effect of the announcement surprise on the change in
EPS in the first half of 2013. Financial analysts remained anchored to these past realized
earnings. The intercept is negative, implying that analysts overestimated realized earnings
in this period. As a result, the negative slope of this regression confirms that forecast
earnings in the second half of 2013 were higher than the realized earnings in the first
half-year. This coefficient demonstrates optimism bias by financial analysts in the Tunisian
stock market. This effect confirms that financial analysts remained anchored to the previous
half-year in 2013. The financial analysts did not immediately adjust their estimates.
5.1.2 Analysts’ anchoring bias regarding realized EPS in the second six months of 2011.
Table V reports estimates of the analysts’ anchoring bias regarding realized EPS in the
second half of 2011. Based on these results, the coefficient of the change in EPS is
Table IV.
Estimates of the EPS
change in the first
semester 2013 on the
non-standardized
announcement
surprise (UE)
Table V.
Estimates of the EPS
change in the second
semester 2011 on the
non-standardized
announcement
surprise (UE)
Explanatory variable
Specification 1
UE ¼ α + β (EPS4 − EPS3)
α
β
R2
n
(EPS4 – EPS3)
−0.050469 (0.077297)
0.552802*** (0.125156)
0.745319
49
Notes: Values in parentheses are the standard errors. *,**,***Significant at 99, 95 and 90 percent
levels, respectively
Explanatory variable
Specification 2
UE ¼ α + β (EPS4 − EPS0)
α
β
R2
n
(EPS4 – EPS0)
−0.260737** (0.113486)
0.560508*** (0.134951)
0.540666
48
Notes: Values in parentheses are the standard errors. *,**,***Significant at 99, 95 and 90 percent
levels, respectively
estimated to be positive and significant at the 99 percent level of confidence. This
coefficient is slightly higher than the one obtained in the previous regression, implying
that financial analysts were more anchored to the earnings in the second half of 2011. This
finding confirms that analysts are subject to anchoring bias, which worsens over time.
The intercept estimated in this regression also has a negative sign, proving that financial
analysts tend to underestimate realized earnings over time. These results corroborate
those of Kaestner (2005), confirming that financial analysts are conservative, resulting in
underreaction to new information. These finance experts tend to adjust but insufficiently
revise their forecasts.
5.1.3 Analysts’ anchoring bias regarding realized EPS in the four previous half-years.
Table VI reports estimates of analysts’ anchoring bias regarding realized EPS in the four
previous half-years from the second half of 2011 to the first half of 2013. The coefficients
obtained for all the explanatory variables are positive. The coefficients β2, β3 and β4 are
significant at the level of 90, 99 and 95 percent, respectively, while the coefficient β1 is
statistically insignificant. This suggests that the degree of surprise in the announcement
mainly depends on the changes in earnings in the first and second half of 2012, as well as in
the second half of 2011. The model is globally significant at 89.5 percent, and its significance
increases with the inclusion of other earnings changes compared to the previous results. The
coefficient β3 is found to be the highest among the estimated coefficients. This confirms that
the major anchoring value in this model is realized earnings in the first half of 2012. This
finding means that financial analysts recalled these earnings and formulated the forecasts
for the second half of 2013 based on the earnings in the first half of 2012, while ignoring the
earnings published during the first half of 2013. Thus they offer conservative forecasts in
the announcement of earnings for 2013. This underreaction is due to their conservatism
bias. These findings support those of Kaestner (2005), who concluded that the major
anchoring value is the fourth quarter preceding the announcement of the results. According
to Kaestner, “the earnings value of the equivalent quarter of the previous fiscal year seems
to constitute the major anchor. However, a less significant anchoring to the quarter
immediately preceding the one studied is detected.”
Psychology
and behavioral
finance
57
5.2 The EPS change and the revision in earnings forecasts as psychological anchors
for financial analysts
To complete the first analysis, we highlight the optimism and the under- and overreaction of
financial analysts. Thus, the revisions in earnings forecasts and the past earnings changes
are used as psychological anchors.
5.2.1 Analysts anchoring bias to the revision of forecast earnings. Table VII reports the
results of the regression of UE on revisions in earnings forecasts. Our results indicate that
the coefficients β are positive, ranging from 0.364 to 2.391, and are statistically significant,
regardless of the period tested. This finding confirms our assumption: there is a greater
tendency toward anchoring-adjustment (underreaction) with respect to expected revisions
α
Specification 3
UE ¼ α + β1(EPS4 − EPS3) + β2(EPS4 − EPS2) + β3(EPS4 − EPS1) + β4(EPS4 − EPS0)
β1
β2
β3
β4
R2
n
−0.073415
−0.236554
0.160131*
0.599953***
0.255627**
0.895027 48
(0.049862)
(0.22773)
(0.087010)
(0.246424)
(0.130904)
Notes: Values in parentheses are the standard errors. *,**,***Significant at 99, 95 and 90 percent levels,
respectively
Table VI.
Anchoring bias by
financial analysts:
multiple regression
EMJB
15,1
58
than to changes in earnings. The forecast revisions lead to underreaction. The intercept
values are found to be negative, ranging from −0.240 to −0.403, and statistically significant
at the level of 99 percent based on period t for the full sample. This confirms the analysts’
optimism bias toward the Tunisian stock market.
Table VIII reports estimates of UE due to revisions of earnings forecast based on the sign
of the revision. Our results indicate that the coefficients β of portfolios with positive REV
and the coefficients δ of portfolios with negative REV are negative and positive,
respectively. These variables are generally significant for equity portfolios with positive or
negative earnings forecasts over the ten periods under study. The values of the coefficients
β for portfolios with positive forecast revisions before the earnings announcement range
from −0.105 to −12.805, while the values of the coefficients δ for portfolios with a negative
revision range from 0.763 to 5.540. These results show that analysts underreact to a
negative REV and overreact to a positive one. This finding confirms our hypotheses and
supports the results of other studies on international financial markets (Amir and Ganzach,
1998; Contantinou et al., 2003; Easterwood and Nutt, 1999; Easterwood et al., 1999; Amir and
Ganzach, 1998; Abarbanell and Bernard, 1992; Ali et al., 1992; Abarbanell, 1991). Globally,
analysts lowball their expectations, even though they remain optimistic about future
Semester – year
Table VII.
Estimates of the
unexpected earnings
(UE) on the
earnings forecast
revision (REV )
S1 –
S2 –
S1 –
S2 –
S1 –
S2 –
S1 –
S2 –
S1 –
S2 –
2010
2010
2011
2011
2012
2012
2013
2013
2014
2014
α
Specification 4
UEt ¼ αt + βt.REVt + ɛt
β
−0.2407* (0.1352)
−0.2928*** (0.1115)
−0.2050*** (0.1261)
−0.3139*** (0.1035)
0.1946 (0.1270)
−0.0392 (0.0956)
−0.4034* (0.2203)
−0.2300 (0.1469)
0.0498 (0.0607)
−0.0876 (0.0852)
1.7093*** (0.3456)
0.8124** (0.3598)
2.3914*** (0.8673)
1.5734** (0.7630)
1.1266*** (0.2253)
1.2719*** (0.4932)
7.6308*** (2.3524)
0.8097*** (0.2815)
0.3643** (0.1381)
0.6420*** (0.2496)
R2 (%)
F
n
72.77
9.15
24.29
20.79
38.96
12.87
37.92
13.95
12.88
11.48
136.3
5.24
17.00
13.91
33.83
7.83
29.93
7.94
6.65
6.35
53
54
55
55
55
55
51
51
47
51
Specification 5
UEt ¼ αt + βt.REVt ×Dummy_REV_Positivet + δt.REVt ×Dummy_REV_Negativet + ɛt
Semester – year
α
β
δ
R2 (%)
n
S1 – 2010
0.0125 (0.1073)
−2.2176*** (0.2489)
0.9898*** (0.3143)
80.4
53
−0.0965 (0.1238)
−1.6819* (1.0062)
1.3838*** (0.3938)
19.4
54
S2 – 2010
0.0538 (0.0998)
−3.3186*** (0.5556)
0.7632 (2.2457)
28.2
55
S1 – 2011
−0.0179 (0.0812)
−0.9806* (0.5362)
3.1183*** (0.5034)
41.1
55
S2 – 2011
Table VIII.
0.1416 (0.1157)
−1.2867*** (0.1614)
0.8228** (0.4039)
40.2
55
S1 – 2012
Estimates of the
0.1095 (0.0872)
−1.4723 (1.3369)
1.9928*** (0.3636)
20.3
55
S2 – 2012
unexpected earnings
0.1221 (0.2405)
−12.8059*** (2.9386)
5.5401*** (1.6919)
42.3
51
S1 – 2013
(UE) on the earnings
−0.0625 (0.1542)
−0.5418* (0.2869)
3.1128*** (0.9664)
23.2
51
S2 – 2013
forecast revision
−0.1324** (0.0622)
−0.2141*** (0.0465)
1.7886*** (0.4927)
26.7
47
S1 – 2014
(REV ) of portfolios
0.1086 (0.0748)
−0.1059 (0.2399)
0.2399*** (0.5161)
35.4
51
with positive revision S2 – 2014
Notes: DumREV_Positive is a dummy variable that takes avalue of 1 when the earnings revision is positive and
(REVW 0) and those
with negative revision 0 otherwise; DumREV_Negative is a dummy variable that takes avalue of 1 when earnings revision is negative
and 0 otherwise
(REVo 0)
prospects. Is this prudent behavior or intentional behavior, as suggested by Karamanou
(2001)? Our results suggest that analysts underreact (overreact) to low (high) earnings
forecast revisions, which are considered headlines with bad (good) news. Their forecasts are
biased when past earnings forecasts serve as benchmarks.
5.2.2 Analysts’ anchoring bias to changes in realized EPS and revision in forecast
earnings. Table IX reports the results of the regression of UE on both changes in past
earnings and revisions in earnings forecast at the time of the earnings announcement. Our
results confirm previous findings, namely, that optimism bias by analysts and their
underreaction when the forecasted earnings are their psychological anchors. Positive and
statistically significant values of the coefficients δ are obtained, which validate the strong
influence of past earnings forecasts as benchmarks on past realized earnings. Analysts
place more emphasis on past forecast earnings than on past realized earnings even if they
lead to underreaction. This result confirms the hypothesis of Amir and Ganzach (1998): the
benefits previously announced are psychological anchors of lesser importance than those of
forecasts of previous earnings.
Table X reports the estimates of the UE regression based on the positive or negative sign of
revisions in earnings forecasts when the results are announced. Our findings confirm the
Semester – year
S1 – 2010
S2 – 2010
S1 – 2011
S2 – 2011
S1 – 2012
S2 – 2012
S1 – 2013
S2 – 2013
S1 – 2014
S2 – 2014
α
Specification 6
UEt ¼ αt + βt.REVt + δt.ΔEPSt + ɛt
β
0.0836 (0.1392)
−0.1408* (0.0838)
−0.1951* (0.1031)
−0.1890*** (0.0481)
−0.0022 (0.0934)
0.0198 (0.0810)
0.3632 (0.2322)
−0.1492 (0.1122)
0.0516 (0.0560)
−0.0261 (0.0782)
1.7874*** (0.1354)
0.2504 (0.3142)
2.9938*** (0.6043)
−0.2331 (0.4392)
1.2535*** (0.1657)
0.8292* (0.4415)
7.4511*** (2.4481)
0.1314 (0.2604)
0.2551** (0.1160)
0.5059** (0.2266)
δ
R2 (%)
n
0.3321*** (0.1073)
0.5468*** (0.0709)
0.4975* (0.2600)
0.8918*** (0.1154)
0.3911* (0.2100)
0.2425*** (0.0826)
0.1642 (0.1114)
0.5080*** (0.1308)
0.2106* (0.1187)
0.2950*** (0.0828)
77.04
49.72
39.86
80.24
56.64
24.65
38.63
54.89
32.86
29.10
52
53
53
55
54
55
51
50
47
51
Psychology
and behavioral
finance
59
Table IX.
Estimates of the
unexpected earnings
(UE) on the earnings
forecast revision
(REV ) and the
realized EPS
change (ΔEPS)
Specification 7
UEt ¼ αt + βt.ΔEPSt + δt.REVt ×DummyREV_Positive(t) + λt.REVt ×DummyREV_Negative(t) + ɛt
Semester – year
α
β
δ
λ
R2 (%) n
S1 – 2010
−0.1062 (0.1255) 0.2829*** (0.0918) −2.2444*** (0.1537) 1.1127*** (0.1894) 83.44 52
−0.0133 (0.0964) 0.5153*** (0.0808)
−1.4154* (0.7682) 0.6740** (0.3200) 54.30 53
S2 – 2010
0.0800 (0.1325) 0.4750*** (0.1314) −3.6669*** (0.7085)
1.7391* (1.0246) 42.10 53
S1 – 2011
−0.0631 (0.0576) 0.8079*** (0.0662) −1.2508*** (0.3569) 0.6551** (0.3301) 84.11 55
S2 – 2011
0.0032 (0.0812)
0.3957* (0.2319) −1.2317*** (0.0776) 1.2991*** (0.4718) 56.67 54
S1 – 2012
0.1503* (0.0769) 0.2279** (0.0904)
−1.6175* (0.9428) 1.505*** (0.3688) 30.71 55
S2 – 2012
0.1162 (0.2409)
0.0712 (0.2142) −12.5157*** (3.0624) 5.5479*** (1.6902) 42.44 51
S1 – 2013
−0.0278 (0.0810) 0.4845*** (0.1332)
−0.0341 (0.1588)
1.8524* (0.9564) 59.76 50
S2 – 2013
0.1085* (0.0574) 0.1727*** (0.0566)
0.1708 (0.1254) 1.2603*** (0.4840) 38.83 47
S1 – 2014
0.1371* (0.0777) 0.2456*** (0.0723)
0.0545 (0.2227) 2.6782*** (0.5516) 47.39 51
S2 – 2014
Notes: DumREV_Positive is a dummy variable that takes a value of 1 when the earnings revision is positive and
0 otherwise; DumREV_Negative is a dummy variable that takes avalue of 1 when earnings revision is negative
and 0 otherwise
Table X.
Estimates of the
unexpected earnings
(UE) on the earnings
forecast revisions
(REV ) and the
realized EPS change
(ΔEPS) when
revisions are positive
(REVW0) or
negative (REVo0)
EMJB
15,1
60
previous results: general optimism, strong overreaction to good news and underreaction to bad
news. These results confirm our assumptions and corroborate those of Contantinou et al. (2003),
Easterwood and Nutt (1999), Easterwood et al. (1999) and Amir and Ganzach (1998).
6. Conclusion
This paper aims to detect quantitatively the existence of anchoring bias by financial
analysts on the Tunisian stock market. Both nonparametric and parametric tests are
conducted. Nonparametric results show that the EPS variations are mostly followed by UE
surprises in the same direction, which verify the hypothesis of an anchoring bias by
financial analysts to past earnings. Parametric results confirm these findings by testing
variables for different psychological anchors. Financial analysts are found to remain
anchored to previous earnings and carry out insufficient adjustments following the
announcement of the results by companies. There is also a tendency toward over- and
underreaction to changes in forecasts. Analyst behavior is asymmetrical depending on
the sign of the forecast changes: overreaction to positive prediction changes and negative
reaction to negative prediction changes. This dual effect is particularly observed after the
Jasmine revolution in Tunisia on January 14, 2011. During this period, the country
experienced political and economic instability, leading to a loss of confidence by analysts
with the deterioration of market performance indicators.
The evidence in this paper largely validates the assumptions based on behavioral
theory, particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998).
We conclude that analysts on the Tunisian stock market are subject to anchoring,
optimism, and over- and underreaction biases when earnings are announced. This has
implications for financial decision makers, such as private investors, financial
brokers, fund managers, and financial consultants, because knowledge of relevant
biases can prevent decision makers from falling prey to them. Anchoring bias can lead
individuals to rely on values that do not represent the best information in managerial
decisions, since people tend to be anchored in the first information presented, regardless of
whether that information is financial or nonfinancial. Managers who do not seek enough
information to obtain the best estimates are no different. These biases can lead to
decisions that are not totally rational and in which even good information is used only
partially, or even ignored.
In conclusion, we emphasize that our study does not consider characteristics such as
the size of the firm, the accuracy of the forecasts, the time dimension, and the specificities
of the Tunisian stock market, its movement and volatility. Heuristics (anchoring) are also
likely to influence other areas of financial decision-making, such as investment, financing,
asset management, and dividend policy. This study can be extended to cover these areas
of corporate finance. Similarly, the design of behavioral asset evaluation models that
capture phenomena such as speculative bubbles or mimic behaviors may be the next step
in the research.
Glossary
EPS
EPSi
esti
E(esti)
Pi
UE and SUE
earnings per share.
earnings per share announced on date i.
Individual forecasts published by analysts the month preceding the
announcement of the result on date i.
Consensus of forecasts published by analysts the month preceding the
announcement of the result on date i.
Share price preceding the announcement of result on date i.
Surprise unexpected earnings variables, which measure the surprise
announcement of results on date i.
ΔEPS
EPS4 – EPS0
EPS4 – EPS3
EPS4 – EPS2
EPS4 – EPS1
REV
Earning change, which corresponds to the difference in the realized EPS
between two half-years.
Changes in earnings between the second halves of 2013 and 2011.
Changes in earnings between the second and first halves of 2013.
Changes in earnings between the second halves of 2013 and 2012.
Changes in earnings between the second half of 2013 and first half 2012.
Revision in earnings forecasts prior to the result announcement on date i,
which corresponds to the difference between two current earnings
forecasts by financial analysts.
References
Abarbanell, J. (1991), “Do analysts’ earnings forecasts incorporate information in prior stock price
changes?”, Journal of Accounting and Economics, Vol. 14 No. 2, pp. 147-165.
Abarbanell, J. and Bernard, V. (1992), “Tests of analysts’ overreaction/ underreaction to earnings
information as an explanation for anomalous stock price behavior”, Journal of Finance, Vol. 47
No. 3, pp. 1181-1207.
Abraham, G., Hall, J.H. and Cloete, C.E. (2014), “Anchoring and adjustment and herding behaviour as
heuristic-driven bias in property investment decision-making in South Africa”, No. eres2014-217,
European Real Estate Society (ERES), London.
Affleck-Graves, J., Davis, J. and Mendenhall, R. (1990), “Forecasts of earnings per share:
possible sources of analyst superiority and bias”, Contemporary Accounting Research, Vol. 6
No. 2, pp. 501-517.
Ali, A., Klein, A. and Rosenfeld, J. (1992), “Analysts’ use of information about permanent and
transitory earnings components in forecasting annual EPS”, Accounting Review, Vol. 67 No. 1,
pp. 183-198.
Amir, E. and Ganzach, Y. (1998), “Overreaction and underreaction in analysts forecast”, Journal of
Economic Behavior and Organization, Vol. 37 No. 3, pp. 333-347.
Beckers, S., Steliaros, M. and Thomson, A. (2004), “Bias in European analysts’ earnings forecasts”,
Financial Analysts Journal, Vol. 60 No. 2, pp. 74-85.
Bellando, R., Ben Braham, Z. and Galanti, S. (2014), “The performance of portfolios based on analysts’
recommendations: the Tunisian case”, Working Papers, halshs-01204662, HAL, Paris.
Ben Braham, Z. and Galanti, S. (2014), “Recommendation value on an emerging market: the impact of
analyst’ recommendations on stock prices and trading volumes in Tunisia”, Working Papers,
halshs-01015380, HAL, Paris.
Brewer, N.T., Chapman, G.B., Schwartz, J.A. and Bergus, G.R. (2007), “The influence of irrelevant
anchors on the judgments and choices of doctors and patients”, Medical Decision Making,
Vol. 27, pp. 203-211.
Bucchianeri, G.W. and Minson, J.A. (2013), “A homeowner’s dilemma: anchoring in residential real
estate transactions”, Journal of Economic Behavior and Organization, Vol. 89 No. 1, pp. 76-92.
Calegari, M. and Fargher, N. (1997), “Evidence that prices do not fully reflect the implications of current
earnings for future earnings: an experimental markets approach”, Contemporary Accounting
Research, Vol. 14 No. 2, pp. 397-433.
Campbell, S.D. and Sharpe, S.A. (2009), “Anchoring bias in consensus forecasts and its effect on market
prices”, Journal of Financial and Quantitative Analysis, Vol. 44 No. 2, pp. 369-390.
Cen, L., Hilary, G. and Wei, K.C.J. (2013), “The role of anchoring bias in the equity market: evidence
from analysts’ earnings forecasts and stock returns”, Journal of Financial and Quantitative
Analysis, Vol. 48 No. 1, pp. 47-76.
Chang, E.C., Luo, Y. and Ren, J. (2013), “Cross-listing and pricing efficiency: the informational
and anchoring role played by the reference price”, Journal of Banking and Finance, Vol. 37,
pp. 4449-4464.
Psychology
and behavioral
finance
61
EMJB
15,1
Coën, A. and Desfleurs, A. (2016), “Another look at financial analysts’ forecasts accuracy: recent
evidence from Eastern European frontier markets”, in Andrikopoulos, P. (Ed.), Handbook of
Frontier Markets: The European and African Evidence, Academic Press, London, pp. 171-189.
Coën, A. and Desfleurs, A. (2017), “Did security analysts overreact during the global financial crisis?
Canadian evidence”, in Economou, F. (Ed.), Handbook of Investors’ Behavior During Financial
Crises, Academic Press, London, pp. 169-190.
62
Cohen, E. (1997), L’Analyse Financière, 4th ed., Economica, Paris.
Contantinou, P.H., Forbes, W.P. and Skerratt, L. (2003), “Analyst underreaction in the United
Kingdom”, Financial Management, Vol. 32 No. 2, pp. 93-106.
De Bondt, W. (1993), “Betting on trends: intuitive forecasts of financial risk and return”, International
Journal of Forecasting, Vol. 9 No. 3, pp. 355-371.
De Bondt, W. and Thaler, R. (1987), “Further evidence on investor overreaction and stock market
seasonality”, Journal of Finance, Vol. 42 No. 3, pp. 557-581.
De Bondt, W. and Thaler, R. (1990), “Do the security analysts overreact?”, American Economic Review,
Vol. 80 No. 2, pp. 52-57.
Dreman, D. and Berry, M. (1995), “Analysts forecasting errors and their implications for security
analysis”, Financial Analysts Journal, Vol. 51 No. 3, pp. 30-41.
Easterwood, J.C. and Nutt, S. (1999), “Inefficiency in analysts’ earnings forecasts: systematic
misreaction or systematic optimism?”, Journal of Finance, Vol. 54 No. 5, pp. 1777-1797.
Easterwood, J.C., Easterwood, C. and Nutt, S. (1999), “New evidence on serial correlation in analyst
forecast error”, Journal of Finance, Vol. 54 No. 5, pp. 1777-1797.
Edwards, W. (1968), “Conservatism in human information processing”, in Kleinmuntz, B. (Ed.), Formal
Representation of Human Judgment, Wiley, New York, NY, pp. 17-52.
Fama, E.F. and French, K. (1993), “Common risk factors in the returns on stocks and bonds”, Journal of
Financial Economics, Vol. 33 No. 1, pp. 3-56.
Francis, J. and Philbrick, D. (1993), “Analysts’ decisions as products of a multi-task environment”,
Journal of Accounting Research, Vol. 31 No. 2, pp. 216-230.
Fried, D. and Givoly, D. (1982), “Financial analysts’ forecasts of earnings: a better surrogate for market
expectation”, Journal of Accounting and Economics, Vol. 4 No. 2, pp. 85-107.
Gillette, A., Stevens, D., Watts, S. and Williams, A. (1999), “Price and volume reactions to public
information releases: an experimental approach incorporating traders’ subjective beliefs”,
Contemporary Accounting Research, Vol. 16 No. 3, pp. 437-479.
Gollier, C., Hilton, D.J. and Raufaste, E. (2003), “Daniel Kahneman et l’analyse de la decision face au
risque”, Revue d’Economie Politique, Vol. 3 No. 113, pp. 295-307.
Hien, N.D., Hung, T.D. and Giang, B.H. (2014), “The impact of the anchoring and adjustment bias on
analysts’ forecast in Vietnam stock market”, Journal of Economics and Development, Vol. 15
No. 3, pp. 59-76.
Jacquillat, B. and Grandin, P. (1994), “Performance Measurements of Analysts’ Forecasts”, Journal of
Portfolio Management Fall, pp. 94-102.
Jahidur Rahman, M., Zhang, J. and Dong, S. (2019), “Factors affecting the accuracy of analysts’
forecasts: a review of the literature”, Academy of Accounting and Financial Studies Journal,
Vol. 23 No. 3, pp. 1-18.
Kaestner, M. (2005), “Prévisions de resultat et reactions: Etude de deux sous-réactions sous l’angle du
biais d’ancrage”, Montpellier University Seminar, Montpellier, September.
Kahneman, D. and Tversky, A. (1974), “Judgment under uncertainty: heuristics and biases”,
ScienceNew Series, Vol. 185 No. 4157, pp. 263-291.
Kahneman, D. and Tversky, A. (1979), “Intuitive predictions: biases and corrective procedures”, Studies
in Management Science, Vol. 12 No. 1, pp. 313-327.
Kang, S., O’Brien, P. and Sivaramakrishnan, K. (1994), “Analysts’ interim earnings forecasts: evidence
on the forecasting process”, Journal of Accounting Research, Vol. 32 No. 1, pp. 103-112.
Karamanou, I. (2001), “Is analyst optimism intentional? Additional evidence on the existence of reporting,
and selection bias in analyst earnings forecasts”, working paper, University of Cyprus, Nicosia.
Khan, K., Nasir, M.A. and Rossi, M. (2017), “The calendar anomalies on performance and volatility of
stock market: the effects of Ramadan on Karachi stock exchange”, Global Business and
Economics Review, Vol. 19 No. 1, pp. 54-69.
Klein, A. (1990), “A direct test of the cognitive bias theory of share price reversals”, Journal of
Accounting and Economics, Vol. 13 No. 2, pp. 155-166.
Koskinen, M. (2013), “The impact of anchoring bias in the UK equity market”, School of Business,
Department of Finance, Aalto University, Helsinki.
Kratz, S. (2016), “Anchoring bias in analysts’ EPS estimates: evidence from the Swedish Stock
Market”, School of Economics and Management, Department of Business Administration,
Lund University, Lund.
Liao, L.-C., Chou, R.Y. and Chiu, B. (2013), “Anchoring effect on foreign institutional investors
momentum trading behavior: evidence from the Taiwan stock market”, North American Journal
of Economics and Finance, Vol. 26 No. 1, pp. 72-91.
Lin, Y., Huddart, S. and Louis, H. (2015), “Beyond bias and accuracy: a review of analysts’ forecast
process”, Conference Proceeding, PA, pp. 71-83.
Luong, L.P. and Ha, D.T.T. (2011), “Behavioral factors influencing individual investor’s decision
making and performance: a survey at the Ho Chi Minh Stock Exchange”, published M. Sc.
Thesis, Umea School of Business, pp. 1-103.
Lys, T. and Sohn, S. (1990), “The association between revisions of financial analysts’ earnings
forecasts and security-price changes”, Journal of Accounting and Economics, Vol. 13 No. 4,
pp. 341-363.
Mendenhall, R.R. (1991), “Evidence on the possible underweighting of earnings related information”,
Journal of Accounting Research, Vol. 29, pp. 170-179.
Meub, L. and Proeger, T.E. (2015), “Anchoring in social context”, Journal of Behavioral and
Experimental Economics, Vol. 55 No. 1, pp. 29-39.
Northcraft, G.B. and Neale, M.A. (1987), “Experts, amateurs, and real estate: an anchoring-andadjustment perspective on property pricing decisions”, Organizational Behavior and Human
Decision Processes, Vol. 39 No. 1, pp. 84-97.
O’Brien, P. (1988), “Analysts forecasts as earnings expectations”, Journal of Accounting and Economics,
Vol. 10 No. 1, pp. 53-83.
Oomen, B. (2011), “An analysis of analysts’ anchoring behavior”, master’s thesis, Erasmus University
Rotterdam, Rotterdam.
Plous, S. (1989), “Thinking the unthinkable: the effects of anchoring on likelihood estimates of nuclear
war”, Journal of Applied Social Psychology, Vol. 19 No. 1, pp. 67-91.
Pouget, S. (2000), “Rationalité limitée et marches financiers: une approche experimentale”, PhD
dissertation, Université des Sciences Sociales de Toulouse, Toulouse.
Roger, T., Roger, P. and Schatt, A. (2018), “Behavioral bias in number processing: evidence
from analysts’ expectations”, Journal of Economic Behavior and Organization, Vol. 149 No. 1,
pp. 315-331.
Rossi, M. and Fattoruso, G. (2017), “The EMH and the market anomalies: an empirical analysis on
Italian stock market”, International Journal of Managerial and Financial Accounting, Vol. 9 No. 3,
pp. 222-241.
Rossi, M. and Gunardi, A. (2018), “Efficient market hypothesis and stock market anomalies: empirical
evidence in Four European countries”, Journal of Applied Business Research, Vol. 34 No. 1,
pp. 183-192.
Schipper, K. (1991), “Commentary on analyst forecasts”, Accounting Horizons, Vol. 5 No. 4, pp. 105-121.
Psychology
and behavioral
finance
63
EMJB
15,1
64
Sharpe, S.A. and Campbell, S.D. (2007), “Anchoring bias in consensus forecasts and its effect on market
prices”, Finance and Economics Discussion Series Divisions of Research & Statistics and
Monetary Affairs, Federal Reserve Board, Washington, DC.
So, E.C. (2013), “A new approach to predicting analyst forecast errors: do investors overweight analyst
forecasts?”, Journal of Financial Economics, Vol. 108 No. 3, pp. 615-640.
Thaler, R.H. (1999), “Mental accounting matters”, Journal of Behavioral Decision Making, Vol. 12 No. 3,
pp. 183-206.
TSE (2018), “Tunisia Stock Exchange”, available at: www.bvmt.com.tn
Tversky, A. and Kahneman, D. (1973), “Availability: a heuristic for judging frequency and probability”,
Cognitive Psychology, Vol. 5 No. 2, pp. 207-232.
Tversky, A. and Kahneman, D. (1974), “Judgment under uncertainty: heuristics and biases”, Utility,
Probability, and Human Decision Making, Vol. 185, pp. 141-162.
Waweru, N.M., Munyoki, E. and Uliana, E. (2008), “The effects of behavioral factors in investment
decision-making: a survey of institutional investors operating at the Nairobi stock exchange”,
International Journal of Business and Emerging Markets, Vol. 1 No. 1, pp. 24-41.
Whyte, G. and Sebenius, J.K. (1997), “The effect of multiple anchors on anchoring in individual
and group judgment”, Organizational Behavior and Human Decision Process, Vol. 69 No. 1,
pp. 74-85.
Wright, W.F. and Anderson, U. (1989), “Effects of situation familiarity and financial incentives on use
of the anchoring and adjustment heuristics for probability assessment”, Organizational
Behavior and Human Decision Processes, Vol. 44 No. 1, pp. 68-82.
About the authors
Ahmed Bouteska, PhD, Assistant Professor of Finance, Tunis El Manar University, Faculty of
Economics and Management of Tunis, Tunisia. His research interests are in behavioral finance,
financial intermediation, financial economics, corporate finance, international finance, financial market
stability and empirical asset pricing. He is Assistant Professor and Associate Researcher at URISO
(research laboratory) of Tunis el Manar University, Tunisia. Ahmed Bouteska is the corresponding
author and can be contacted at: ahmedcbouteska@gmail.com
Boutheina Regaieg, PhD, Dean and professor of finance at University of Jendouba, Faculty of Law,
Economics and Management of Jendouba, Tunisia. Her research interests are in behavioral finance,
corporate finance, corporate governance, financial economics, international finance, financial market
stability and empirical asset pricing.
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