Description
This paper provides the first evidence for empirical tests of the impact of rational expectations as well as
behavioral biases, including among other animal spirits such as defined by Akerlof and Shiller on the
variability of trading. Using a daily data for five international capital markets in developed countries,
strong evidence is found. The hypothesis of rationality fails to determine the investors’ trading behavior.
The economy is, however, driven by behavioral biases, including more especially animal spirits
summarized in investors’ sentiments and beliefs.
2214-4625/$ – see front matter © 2015 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.aebj.2014.12.002
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
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*Abderrazak Dhaoui. Tel.: +21622622821; fax: +0-000-000-0000.
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Peer review under responsibility of Holy Spirit University of Kaslik.
What Does Matter in Economy Today: When Human Psychology Drives
Financial Markets
Abderrazak Dhaoui *
*
University of Sousse, Faculty of Economic Sciences and Management, Tunisia
A R T I C L E I N F O
Article history:
Received 17 May 2014
Received in revised form 12 December
2014
Accepted 17 December 2014
Keywords:
Optimism
Pessimism
Spontaneous reaction
Overconfidence
Rationality
Trading volume
A B S T R A C T
This paper provides the first evidence for empirical tests of the impact of rational expectations as well as
behavioral biases, including among other animal spirits such as defined by Akerlof and Shiller on the
variability of trading. Using a daily data for five international capital markets in developed countries,
strong evidence is found. The hypothesis of rationality fails to determine the investors’ trading behavior.
The economy is, however, driven by behavioral biases, including more especially animal spirits
summarized in investors’ sentiments and beliefs.
© 2015 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
1. Introduction
During the last five decades, a point of view commonly shared by many authors is that it becomes more difficult to understand how the economy really
works (Akerlof and Shiller, 2009; Dhaoui et al. (2013); Dhaoui (2013).
The efficient market and the rational expectation hypotheses loss of significance and fail in explaining the return and trading volume behavior in the major
international markets in both developed and emerging countries. The authors attribute the dysfunction of economies and markets to human psychology,
sentiments and feelings (Keynes, 1936); Akerlof and Shiller (2009); Dhaoui (2013). Variables such as overconfidence, (Daniel, Hirshleifer and
Subrahmanyam, 2001) optimism, (Haruvy, Stahl and Wilson (1999), Weinstein (1980), Otten (1989), pessimism (De Bondt and Thaler (1985); Barberis
Shleifer and Vishny (1998) or more largely animal spirits (Akerlof and Shiller, 2009) are particularly suggested to explain the disruption of evolution of
the trading volumes and of the returns.
In this regard, Akerlof and Shiller (2009), attribute the economic dysfunction to human psychology and introduce the “animal spirits” as one of
the factors influencing the economies and markets. To define the “animal spirits”, the authors enlarge the definition given by Keynes (1936) in his General
Theory and introduce confidence, fairness, corruption and association behavior, money illusion, and stories as items of animal spirits. For Keynes (1936)
the animal spirit is defined as “a spontaneous urge to action rather than inaction”.
40 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
The main aim of this paper is to provide comprehensive empirical evidence on various implications of the psychological influences by focusing
on investor behavior. Our focus on investor behavior is motivated in part by the argument of Odean (1998), Daniel, Hirshleifer and Subrahmanyam
(1998), Gervais and Odean (2001) that investor behavior should be observed in market level data, and in part by that of Daniel et al. (2001), Haruvy et al.,
(1999)Weinstein (1980), Otten (1989), De Bondt and Thaler (1995), Barberis et al. (1998), Keynes (1936), and Akerlof and Shiller (2009) that investor
beliefs and sentiments matter when making a decision in economic worlds. In this way, we provide the first evidence for empirical tests of the impact of
departures and deviations from the rationality in the financial market working based on incorporating sentiments and beliefs as human psychological
factors to supervise the changes in trading volume.
To empirically explore the psychological influences that can contribute to explain the variances in trading volume in most of international
Stock markets, we investigate the impact of both the rational expectation and the behavioral biases with an extension to Keynes (1936) and Akerlof and
Shiller (2009) on the trading volume. Expected results can give ideas about the factors likely influencing the investors’ trading behavior in the major Stock
Markets. The relation between these independent variables and the trading volume as a dependent variable serves to understand the factors approximately
influencing the way the economy works.
In this order one question remains of a great importance, that is: How does human psychology drive economies and markets? Or in other words, in which
way did the financial markets be sensitive to the investors’ beliefs and sentiments?
To find some answers to this question we investigate the specific influence of the factors describing the Rational Expectation as well as the
behavioral biases, including animal spirits’ behavior (e.g. Overconfidence, Optimism, Pessimism and Spontaneous Reactions) on the trading volume such
as considered as a financial proxy for the economy works.
In order to do this, we used a sample, including daily data for five international Stock Markets (Japan, U.S., France, U.K. and Switzerland) over the period
from June 10, 2002 to November 17, 2011. Our results show that the economy is driven by non-rational expectation. Behavioral and animal spirits’ biases
influence significantly the evolution of trading volume in the major international Stock Markets. The presence of rational investors is, however, without
impact on the process of trading in all the markets.
The remainders of this paper proceed as follows: in section 2, we present an overview of the literature on the rational expectation hypothesis
and the behavioral biases, including animal spirits. Section 3 describes the methodology and the data framework of this study. In section 4, we present and
discuss the principal results. Section 5 concludes.
2. Literature Review
For several decades, the financial and economic literature considers that investors trade rationally and that even they behave irrationally (i.e.
they would trade in a random way) the deviation from the equilibrium state caused by irrational trading can be cancelled out by an opposite irrational
trading of other irrational investors. This supposes naturally that markets are efficient and all isolated rare events cannot influence significantly the
decision-making process. This normally induces less volatility of returns and trading on the major markets. The Efficient Markets Hypothesis (hereafter
EMH) has known an impressive theoretical and empirical success since about the first decade after its conception in the mid-1960s. Especially, Michael
Jensen (1978, p. 95), argues in this field that “there is no other preposition in economics which has more solid empirical evidence supporting it than the
Efficient Markets Hypothesis”. In spite of this success, the EMH has been, however, subject to serious challenges and critics particularly when
observations have shown and confirmed the non-regular and supported the non-understandable function of economies and markets.
The incorporation of psychological factors into macro-economic models seems to confirm however the predictions of the behavioral finance
theory according to which investors cannot behave totally rationally over the full time, and the economy is for the major cases driven by human
psychology (Akerlof and Shiller, 2009).
Posner argues that economists have failed to understand how the economy works. The hypothesis of rationality largely associated with the efficient-
market hypothesis loses its significance by failing to explain the variability between the stock prices as observed in international markets and their
fundamental values and/or the excess in trading volume during about the five last decades (see, Lavoie, 2010).
The efficient markets hypothesis asserts that asset prices aggregate and reflect all relevant fundamental information. They provide, consequently, proper
signals for resource allocation. The challenge of the efficient market and rationality hypotheses dressed by many authors has incited many authors to
examine if human psychology may explain the way the economy really works.
The first work in the field has been realized by Keynes in 1936 who argued that about all of investors’ decisions breaks with the foundation of
rationality, and attributes the dysfunction of the economy to psychological factors and irrational behavior. This challenge has been confirmed more
recently when Akerlof and Shiller (2009) have explained the way the economy works in terms of human psychologies’ impacts. In the same framework,
several authors such as Akerlof and Yellen questioned in 1985 if small deviations from rationality do not really matter. Fifteen years later, Shleifer (2000),
pursued the same logic and questioned if the arbitrageurs succeed to drive irrationality out of the marketplace. Taken together these thoughts tend toward
the same conclusions that the economy is driven in the major part by human psychology and irrational investors’ behavior.
In the same line, Allais (1953), and Ellsberg (1961), demonstrated preferences that violate expected-utility theory. In 1986, Tversky and
Kahneman (1986) articulated a direct challenge to the rationality assumption itself, based on experimental demonstrations in which preferences were
affected predictably by the framing of decision problems, or by the procedure used to elicit performance. They argued that the demonstrated susceptibility
of people to framing effects violates a fundamental assumption of invariance, which has also been labeled extensionality (Arrow, 1982) and
consequentialism (Hammond, 1989).
Based on the aforementioned studies it is clearly documented that investors’ beliefs and sentiments really matter in making decision in
financial markets (trading behavior) and that investors are not fully rational. Departures from rationality are not however only random, they are in the
major cases systematic. In this line, Shleifer (2000) notes, “investors’ deviations from maxims of economic rationality turn out to be highly pervasive and
systematic”. One most problem can take place here which is, if we challenge the EMH and support the predictions of the behavioral finance, how we can
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 41
explain the equilibrium observed in the financial markets. Shleifer (2000) proposed a response to this question: “the irrational investors in the market trade
randomly. When there are large numbers of such investors, and when their trading strategies are uncorrelated, their trades are likely to cancel each other
out.”
Akerlof and Shiller (2009), in their recent work, substitute the investors’ behavior bias with the hypothesis of rationality in order to explain the
volatility in stock returns and trading volume. They consider that the dysfunction of the economy, and more specifically financial markets, is due to the
failure of the investors to expect rationally the future incomes and the behavior of stock returns. They, accordingly, attribute the dysfunction of the
economy, especially, to what they call the “Animal Spirits” bias as an extension to the General Theory developed by Keynes (1936). In this sense, they
argue that “it is necessary to incorporate animal spirits into macroeconomic theory in order to know how the economy really works”. This incites to
introduce beliefs and sentiments, which are largely excluded from theoretical and empirical models, to explain the way in which the economy really
works. Akerlof and Shiller (2009) argue in the specific framework of behavior explanation that “the current crisis bears witness to the role of such
changes in thinking. It was caused precisely by our changing confidence, temptations, envy, resentment, and illusions”.
The “animal spirits” phenomenon can be comprehended as a non-rational behavior driven by investors. In the sense of Keynes (1936), argues
that, “most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be
taken as the results of animal spirits and not at the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities”. In this
particular framework, Keynes defines the animal spirits as “a spontaneous urge to action rather than inaction”. Akerlof and Shiller (2009), enlarge,
however, this same definition to incorporate optimism and pessimism. Accordingly, animal spirits include, in the sense of these authors, spontaneous
behavior as well as optimistic and pessimistic beliefs.
Previous literature (De Bondt and Thaler, 1985, 1987, Barberis et al., 1998, Daniel et al., 2001) relates the volatility in stock prices and the
price anomalies to the presence of under-reactions and overreactions. Under-reactions and overreactions are driven by pessimistic, optimistic, as well as
overconfident investors. In this same line, earlier studies such as that of Ciccone (2003) document that investor sentiments and behaviors play a pivotal
role in the stock market. The investor optimism and pessimism are especially reflected in stock prices. Optimistic investors in the sense of Haruvy, et al.
(1999) are “those who tend to choose the strategy which can potentially give them the highest payoff”. These authors define optimistic investors as “those
who are motivated by worst-case scenarios and hence tend to choose a secure action”. According to Weinstein (1980, 1986, 1989) and Otten (1989)
optimistic investors believe that positive events are more likely to happen to them than to others and similarly that negative events are less likely to occur
for them than for others. By extension, pessimistic investors consider that they are more likely exposed to negative events and less likely to positive events
than the others. These beliefs induce an increase in trading driven by the optimistic investors and oppositely a decrease in trading driven by pessimistic
investors. In terms of trading strategies, Chen (2013), concludes that optimistic agents trade aggressively while pessimistic ones trade conservatively.
King (2009), agrees with Akerlof and Shiller (2009), about the pivotal role that plays the “non-rational” behavior of investors such as the
sentiments of optimism or pessimism or more specifically that of what they call more largely “animal spirits”. He concludes that behavior biases are able
to explain a major part of fluctuations in the economic sphere.
Optimistic as well as overconfident investors are more prone to risky investments. They trade irrationally, and their irrational reaction can
induce abnormal volatility in trading volumes and, consequently, in stock returns. Empirical investigations show that irrational investor behaviors did not
only exist in international stock markets, but their reactions have also shown significant influences on the variability of prices (Chuang, 2010).
In the same line, Grauwe and Kaltwasser (2012), examine the impact of animal spirits on trading strategy in the foreign exchange market.
When traders do not have information about the true value of the fundamental exchange rate they need to depend on beliefs about it in order to take
positions in the market. The authors show that in a world in which there are only optimistic and pessimistic traders, the animal spirits can produce
complex dynamics in the foreign exchange market.
Specifically, optimistic investors’ underestimate their exposure to risk and exaggerate their reaction since they expect only positive results and
neglect the failure. In this vein, Shu (2010), being aware of the characteristics assigned to each type of sentiment. He argues that optimistic investors are
less patient than those who are more pessimistic and react aggressively by underestimating their exposure to risk. Oppositely, the pessimistic investors
display a high-level of risk aversion. They become more and more receding when they make a decision like an investment in risky assets which leads to a
decrease in trading volume.
Chuang, et al. (2010), use a weekly data during the period between January 1990 and December 2004 to supervise the change in investor
sentiments in Taiwan Stock Market. They find that the change in trading volume can be used as a proxy for investor sentiments. They argue that a positive
deviation of trading volume implies that investor sentiment jumps to become more optimistic and vice-versa.
In the French stock market framework, Rousseau, Germain and Vanhems (2008), argue that pessimistic investors decrease their trading volume
and avoid risky assets to prevent a loss. Psychological literature assimilates the pessimism to a statement of impotence or to an absurdity of human
existence.
Carver (2010), suggests that optimism and pessimism sentiments focus on expectancies for the future and the way that the investors confront
problems. They find that optimistic investor faces adversity differently than pessimistic one. They presume that optimistic investor uses more adaptive
ways and "commit" himself to cope with the worst scenarios. In contrast, pessimism refers to fear, doubt and stress. Then, pessimistic investor tends to be
hesitant and doubtful in the face of different challenges. The pessimistic investors have more aversion to risk and decrease their trading once they realize
negative results. However, they don’t increase their trading when they realize an abnormal gain. In opposition, optimistic and overconfident investors
increase their trading after abnormal gain, but they maintain normal trading when losses occur.
The spontaneous behavior also may play a pivotal role in explaining the volatility of trading volume and consequently, the stock returns.
Without animal spirits (considered in its restrictive definition – a spontaneous urge to action rather than inaction- as well as its largest definition adopted
in Akerlof and Shiller (2009), including optimistic and pessimistic beliefs) and considering only the hypothesis of absolute rationality, investors should,
according to Pirosc? “probably react very shy and doubtful or even they would not react at all”. In this sense and according to the Keynes (1936) General
Theory, the preferred action taken by people is to react rather than not react in real-life situation. Here, we understand that, in the framework of economic
life, the financial markets are driven by human sentiments and feelings such as optimism, pessimism and overconfidence.
42 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
Overconfident investors overweigh the precision of their own information and ignore the public available information (Daniel, et al., 1998).
They also overestimate their judgment skills and underestimate the skills of others. As a result, they overreact to private information and under-react to
public information (Odean, 1998). This asymmetric response of overconfident investors leads them to underestimate their exposition to risks and to act
aggressively, which increases their trading volume.
The argument that overconfidence influences trading volume is shared by several authors such as De Long, Shleifer, Summers, and Waldmann
(1991), Kyle and Wang (1997), Benos (1998), Odean (1998), Wang (1998, 2001), Daniel, Hirshleifer, and Subrahmanyam, Hirshleifer (1998), and Luo
Scheinkman and Xiong (2003). Particularly, Gervais and Odean (2001) developed a model predicting that overconfident investors attribute market gain to
the precision of their private information, their judgment skills and their ability to pick winning stocks, and the process of wealth accumulation leads them
to overreact following market gains. For De Bondt and Thaler (1995) the overconfidence is considered as the important behavioral factor that determines
the trading puzzle. In this regard, they argued, “the key behavioral factor needed to understand the trading puzzle is overconfidence”.
3. Data and behavioral indices
In this section, we illustrate the proxies who represent rationality, spontaneous behavior, optimism, pessimism and overconfidence and specify the
model suitable for describing their impacts on trading volume.
The sample includes daily data sets covering the period spanning June 10, 2002 to November 17, 2011 with a sample of 3448 trading and non-
trading days. Different markets in developed countries are considered to investigate the relationship between trading volume, on the one hand, and on the
other hand, rational expectation and the proxy of animal spirits and investors’ sentiment. Five international markets are considered, which are the U.S.
Stock Market (NASDAQ), the Japanese Stock Market (Nikkei 225), the U.K stock Market (FTSE100), The French Stock Market (CAC40) and the Swiss
Stock Market (SMI). Data is available online on the Yahoo Finance pages and on the website of each Stock Market. The starting date of the sample period
is determined by the availability of daily data serving to compute our variables for each country.
The remainder of this section presents the variables specifications and the estimated model. The proxies used are inspired from the work of Dhaoui
(2013), who specified for the first time the psychological proxies describing the “animal spirits” and human psychological variables affecting the investors
behavior theoretically inspired from the work of Keynes (1936), and Akerlof and Shiller (2009).
Rational expectation hypothesis asserts that investors are able to forecast the future evolution of returns considering the current state of the stock
prices. Rational expectation investor formulates his expectation however based on current information while taking into account at the same time the
forecast error he has committed over the last forecast period. Accordingly, the rational expectation proxy used in this study includes the previous realized
returns (at the time (t-1) augmented by the error term at a previous date (the date (t-1). Expected returns are done by the following relation:
R
t
Lxpcctcd
= R
t-1
+E
t-1
with R
t
Lxpcctcd
the expected return at the time t, R
t-1
the return at the time (t-1) and E
t-1
the error of expectation in the time (t-1)
which is equal to the difference between the observed return in (t-1) and the expected return at the same time: E
t-1
= R
t
-R
t-1
Lxpcctcd
. The R
t
is computed
using the following formula: R
t
= In(P
t
P
t-1
/ ), and P
t
is the stock Markets Index price at the time ‘t’. This relation illustrates that when expecting the
future returns, investors adjusted the just last returns they realized by the error term of their last expectation.
To supervise for the overconfidence sentiment we can base our analysis on the nature of connection between current trading volume and the last stock
returns. In other words, the relationship between the trading volume and overconfidence can be illustrated through the effect of previous returns (R
t-1
)
and current trading volume (I
t
). If returns increase in the just last period investors react overconfidently and increase their trading volume. This proxy is
used previously by (Khcherem and Bouri, 2009).
According to De Grauwe and Kaltwasser (2012), ‘Animal Spirits’ create waves of optimism and pessimism. The optimism about a given asset is
occurring when expected prices increase abnormally. However, pessimism takes place when investors estimate a dramatic decrease in prices. There are
two direct analytical methods for measuring investor sentiment. An optimistic investor expects an above average stock price level to which a standard
deviation is added at any given time. He becomes pessimist when the price falls below the average level diminished by the standard deviation. Then, the
average of individual stocks is used to calculate optimism and pessimism variables of the Stock index. The use of the average stock price and the standard
deviation is justified by the fact that investors are attracted by the return and averse to the risk. The average prices measure the former and the standard
deviation measures the latter. Following this logic, we report a strong evidence of optimistic behavior when individual stock price increases abnormally
and exceeds the average level augmented by the standard deviation (R
+o). Considering the aggregate market, the average of conditional absolute price
forms a good measure of optimism sentiment. The investor reacts as optimist if he realizes that the price increase over the medium level. In other words,
the optimism would occur when the investor realizes a gain at a just previous date. Optimistic investors overreact when they anticipate that gain reaches a
fixed level. Under this level, they react in a normal way or decide not to trade. In terms of probabilities, and with conformity to the analysis of Fischhoff et
al., (1977), Svenson (1981), and Hoffrage (2004), “when the task is easy and the probability of success is reasonably high, people tend to exhibit over-
optimism and overestimate the probabilities of good outcomes” (Chen, 2013). Let R
the average return and o
the standard deviation and let the lowest
high return they accept equal, approximately, to the average return augmented by the simple standard deviation. Optimistic investors trade aggressively
when their previous gains are higher than (R
+ o).They trade normally or rather postpone trading if they realize returns under than this level in the time (t-
1). Considering that they postpone trading the behavior of optimistic investors will be described by the following relation: F(R
t
¡0) = Hox|R
t-1
-
(R
+ o), u] with F(R
t
¡0) denotes the function describing the behavior of the investor considering his expectations for future evolution of returns giving
a minimum of gain tolerated and ? denotes the information available at time t.
Considering this function the behavior of optimistic investors can be described as follows: when R
t-1
> (R
+o) they trade, and when R
t-1
¸ (R
+
o), they postpone trading. Accordingly, their trading process will be as follows:
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 43
IroJing Bcbo:ior Jri:cn by 0ptimistic In:cstors =
_
R
t-1
-(R
+o) > u , Exccssi:c IroJing
0tbcrwisc , Postponc
The pessimism belief occurs when an investor realizes losses at a previous date. Pessimistic investors decrease their trading when they anticipate that
losses will reach a critical level. They react in a normal way when returns are higher than this level. They postpone, however, their trading when they
anticipate that losses are higher. In the same way as it is the case for the optimism, and with conformity to the analysis of Kruger (1999), Windschitl et al.
(2003), and Kruger and Burrus (2004), “when the task is very difficult with rare positive events, people often exhibit over-pessimism and overestimate the
probabilities of bad outcomes” (Chen, 2013). Let R
the average return and o the standard deviation and let the lowest loss they accept equal,
approximately, to the average return diminished by the simple standard deviation. Pessimistic investors trade in a normal way when their previous losses
are less than the target level such as done by the following: |R
-o|.
Oppositely, they postpone trading if their returns in time (t-1) rise more than this
tolerated level. Considering these arguments, the behavior of pessimistic investors will be described by the following: F(R
t
¡0) = Hox|R
t-1
-(R
-o),
u]
,
with F(R
t
¡0) denotes the function describing the behavior of the investor considering his expectations of future evolution of returns giving a
maximum of losses tolerated and ? denotes the information available at time t.
Considering this function the behavior of pessimistic investors can be described as follows: when R
t-1
> (R
-o)
they trade, and when R
t-1
¸
(R
- o), they postpone trading. Accordingly, their trading will follow this process:
IroJing Bcbo:ior Jri:cn by Pcssimistic In:cstors ·
_
R
t-1
-(R
-o) > u , Exccssi:c IroJing
0tbcrwisc , Postponc
Spontaneous reactions describe the behavior of absolute non rational investors. These latter trade in a random way without prior investigation
of the behavior of stock returns. Investors with spontaneous reactions are those without optimistic, pessimistic or overconfident sentiment. Considering the
function defining optimistic, pessimistic or overconfident investors the behavior of those with spontaneous reaction can be described by the following
relations: (R
-o) < R
t-1
< (R
+o)
Our model describes the variables which can likely explain the trading volume in the major international markets in developed countries. The
model includes five independent variables: Rational expectation, Optimism, Pessimism, Overconfidence and Spontaneous reaction (ROPOS) in order to
explain the trading volume such as measured approximately by the natural logarithm of trading volume in the date t (In(II
t
). Previous study that used the
same model is Dhaoui (2013), when investigating the sensitivity of trading volume to the “animal spirits” investor behavior using optimism, pessimism,
overconfidence and spontaneous reaction as proxies for the human psychological variables from one hand and the rational expectation to test the efficient
market hypothesis from the other hand. The estimated model is accordingly:
I
t
=×
0
+×
1
RotExpcc
t
+×
2
0ptim
t
+×
3
Pcssim
t
+×
4
0:crcon¡
t
+×
5
Spontrcoct
t
+e
t
(1)
Where:
I
t
: represents the natural logarithm of the trading volume in the time t;
RotExpcc
t
: represents the returns rationally expected by rational investors in the time t considering available information in the time (t-1);
0ptim
t
: represents the returns expected by optimistic investors in the time t considering available information in the time (t-1);
Pcssim
t
: represents the returns expected by pessimistic investors in the time t considering available information in the time (t-1);
0:crcon¡
t
: represents the returns expected by overconfident investors in the time t considering the gains they realize in the time (t-1);
Spontrcoct
t
: represents the random return that have been observed in the time (t-1) and inducing a random reaction in the time t;
e
t
: is the error term.
4. Results and discussion
In this section, we present the summary statistics and the estimates results. Table 1 shows the summary statistics of the selected variables.
Table 1- Summary statistics for returns and trading volume.
Market Variables Obs. Mean Max Min Sd Skewness Kurtosis
Japan Return
2311
-0.0001266 0.1323459 -0.1211103 0.0160059 -0.5199366 10.85016
Trading 12.3362 20.84374 8.070906 2.321057 2.932268 9.91585
US Return
1819
0.0003845 0.1184933 -0.1111493 0.0156401 -0.044101 8.409273
Trading 21.37196 25.56454 17.27309 0.2814666 -0.523498 67.95256
France Return
2311
0.0001266 0.1211103 -0.1323459 0.0160056 0.5198367 9.95241
Trading 14.33762 22. 46374 9.071106 2. 210156 2.823269 9.86514
UK Return 2263 0.0001262 0.0938424 -0.0926455 0.0128501 -0.1106145 10.65903
44 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
Trading 21.00541 21.73781 18.01672 0.3774095 -1.502721 8.069739
Switzerland Return
1786
0.0000531 0.1078764 -0.0810779 0.0123152 0.0241949 9.981337
Trading 17.9338 19.56444 16.24909 0.4080997 0.1353175 3.816379
This table shows the summary statistics for returns and trading volume. We present the summary statistics for the returns since all explanatory variables
used in the model to estimate are derivatives of the return, and the measures we used make difficult to conduct descriptive statistics for them.
Table 1 presents summary statistics for returns and trading volume for all samples related to the different stock markets we studied in this paper. Results in
this table indicate that in opposition to all other stock markets, the Japanese stock market is characterized on average by a negative closing return. The
Swiss stock market presents, however, the lowest standard deviation of returns from all the stock we studied. The highest standard deviation of returns is
observed in the Japanese stock exchange.
At the same time, results in Table 1 show that compared to the other markets, the Japanese stock market is characterized by a low volume of
transaction, but a very higher volatility in trading volume. The highest trading volume is shown in the NASDAQ stock Exchange. Considering together
summary statistics related to the Mean, the Max and the Min of trading volume, we note that the US stock market is the most liquid followed by the U.K
stock market then successively, the Swiss Stock market, the French and finally, the Japanese stock exchange.
To estimate our model for each stock market we start by testing the stationary of dependent and independent variables. For the 5 stock markets,
the outcome of ADF and Phillips-Perron unit root tests of the trading volume, optimism, pessimism, spontaneous reaction, overconfidence and rational
expectations indicators are presented in Table 2.
Table 2 presents results of the Ducky Fuller unit root test for each variable by stock markets.
Table 2- ADF and PP unit root test for dependent and independent variables.
Trading
value
Optimism Pessimism Spontaneous
reaction
Overconfidence Rational
expectation
ADF
Japan - 2.916** - 45.555*** - 39.583*** - 45.209*** - 43.369*** - 28.872***
U.S. - 15.194*** - 47.491*** - 43.981*** - 43.513*** - 45.562*** - 13.151***
France - 8.946*** - 43.903*** - 40.161*** - 44.778*** - 45.263*** - 23.636***
UK - 3.217** - 38.227*** - 33.263*** - 43.726*** - 45.008*** - 13.652***
Switzerland - 17.337*** - 39.560*** - 40.805*** - 42.753*** - 43.777*** - 43.246***
PP
Japan -3.01** -43.11*** -37.874*** -44.171*** 42.291*** -27.834***
U.S. -14.628*** -45.407*** -42.667*** -43.318*** 45.247*** -13.712***
France -7.398*** -44.013*** -40.682*** -43.016*** 44.581*** -24.307***
UK -3.166** -38.338*** -31.949*** -42.473*** 46.001*** -12.528***
Switzerland -17.006*** -38.481*** -39.360*** -40.852*** 43.267*** -44.627***
ADF denotes Augmented Dickey-Fuller unit root tests, PP refers to Phillips-Perron unit root tests. ** and *** denote rejection of the null hypothesis at the
5% and 1% levels of significance, respectively The lag length in all tests has been selected according to the Akaike Information Criteria (AIC).
Results in Table 2 show that all selected variables are stationary. All variables are at a stationary level for a critical level of 1% except the variable
“trading volume” for the cases of Japan, and the U.K. Stock markets where the variable is stationary at a critical level of 5%. Thus we can continue to
estimate our equation 1. For the 5 stock market the outcome of the estimates results are given in Table 3.
Table 3- Results of econometric regressions by stock market.
Stock Market Independent Variables Coef. t-statistic R-square
(Aj. R-square)
Panel A:
Japan
(optimistic
population)
Optimism 2.813 2.36**
0.2836
(0.2817)
Pessimism -1.839 -1.58
Spontaneous reaction -1.614 -2.23**
Overconfidence 4.779 3.19***
Rational expectation -0.007 -0.16
Const 2.251 4.28***
Panel B:
US
Optimism 2.891 4.65***
0.3081
Pessimism -3.990 -6.65***
Spontaneous reaction -0.426 -0.48
Overconfidence 1.091 2.93***
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 45
Rational expectation -1.091 -0.38 (0.3058)
Const 4.350 9.88***
Panel C:
France
(pessimistic
population)
Optimism 0.902 0.87
0.2279
(0.2258)
Pessimism -3.620 -3.70***
Spontaneous reaction -2.248 -3.63***
Overconfidence -1.936 -1.34
Rational expectation 0.003 0.33
Const 2.995 4.31***
Panel D:
UK
Optimism 1.953 3.21***
0.3707
(0.3690)
Pessimism -1.446 -2.31**
Spontaneous reaction 1.296 2.33**
Overconfidence 2.0573 2.01**
Rational expectation 0.001 0.08
Const 3.252 8.72***
Panel E:
Switzerland
Optimism 2.127 6.86***
0.2235
(0.2208)
Pessimism -1.896 -4.27***
Spontaneous reaction -1.725 -2.62***
Overconfidence -0.817 -0.54
Rational expectation -0.004 -0.78
Const_ 7.871 8.11
This table shows the regression estimates of the equation relating the trading volume as an endogenous variable to optimism, pessimism, spontaneous
reaction, overconfidence and rational expectation as exogenous variables. Panel A shows the results for the sample of the 225 firms included in the Nikkei
225. Panel B shows the results for the full sample of the firms listed in the Nasdaq stock market. Panel C summarizes the results for the full sample of
CAC 40 firms. Panel D shows the results for the firms listed in the FTSE100. Panel E shows the results for the firms listed in the Swiss stock market
(SMI). The first column specifies the Stock markets. Column 2 is reserved to the explanatory variables. Column 3 shows the regression coefficient.
Column 4 presents the “t-student”. And finally, column 5 shows the R-Squared and the Adjusted R-Squared.***, ** and * indicate significance at the 1, 5
and 10 percent levels respectively.
Results in Table 3 indicate for all markets, that rational expectations fail to explain the variability of the trading volume. The hypothesis of
rationality can be rejected. The rational investors are not those who control the way the economy works. The results indicate, however, that economy is
influenced largely by non-rational behavior of investors. Their animal spirits play a pivotal role. We note particularly that the increase in trading volumes
is due to the presence of non-absolute rational investors. Overconfident as well as optimistic and pessimistic investors influence significantly the degree of
fluctuation of the trading volumes in the major international markets.
In this sense, overconfidence presents a positive impact on the trading volume for the case of the Japanese, the U.S and the U.K. Markets. In
the case of the French and the Swiss Markets, the presence of overconfident investors is without effect. We notice here that these results are not surprising,
especially in the case of the Japanese markets. In fact, as an Asian population, the Japanese investors are more exposed to the overconfidence bias. In this
specific vein, we are reminded here that numerous psychologists have examined the behavior of populations and conclude that the Asian population
exhibits overconfidence in general knowledge (see Yates, Lee and Shinotsuka (1996) and Yates, Lee and Bush (1997) for more details). This implies
specifically among others that Asian investors may suffer from psychological bias from which the overconfidence bias. They underestimate their
exposition to risks and make aggressive decisions, which can be reflected in their trading volumes. Japanese investors underestimate, accordingly, their
exposition to risk and make aggressive reactions inducing an increase in trading volume. These results are consistent with the prediction of Odean (1998),
according to which the behavior of the overconfident investors consists to underestimate their exposition to risks and to act aggressively leads to an
increase in their trading volume.
The presence of investors with the spontaneous reaction influences the evolution of the trading volume significantly in the cases of the Swiss,
U.K., French and Japanese Markets. In the case of the U.S. Market, their presence is without effect. The impact is, however, significantly negative for all
the stock markets except for that of the U.K which is positive. These investors are generally less informed, and they react independently of the results they
made at a previous date. They trade not because they anticipate a favorable evolution of stock prices, but because they prefer to act rather than not to act.
Even if they are less informed, the presence of non-professional and non-rational investors induces a decrease in the trading volume. The decrease in
trading can be explained in terms of reaction of the more informed investors as a reaction to the decision made by those with spontaneous reactions. The
professional and more informed investors anticipate the reaction of those with spontaneous behavior and make opposite decisions rather than similar
decisions. They exploit their advantage of information to realize a gain at the expense of those less informed and with spontaneous reaction. These later
adjust their strategies and stop trading after the first losses they realize and wait to restart trading the following day.
The optimistic and the optimistic sentiments are not without impact on the decision process. The presence of both the more optimistic and the
more pessimistic investors induces respectively significantly positive and significantly negative impacts in the trading volumes in the case of the U.S., the
U.K. and the Swiss stock markets. For the Japanese market, the presence of pessimistic investors is without effect on the trading volume. Oppositely, for
the specific case of the French market, and oppositely to optimistic investors who are without effect, pessimistic investors influence significantly the
46 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
evolution of the trading volume. The result is also not surprising. In fact, the French population has no confidence in the future. This population is the
most pessimistic population in the world (for more details, see the BVA-Gallup Survey, January 2011). It has no confidence in politics (Cevipof Study,
January 2011), in drugs (CSA Survey, 2011), in the media (TNS Sofres-La Croix, February 2011). This implies accordingly that the French investors are
more averse to risk. They react negatively after all crises or rather a simple loss. This can explain the significant negative effect of pessimistic investors on
the trading volume in the case of the French market.
The optimistic investors have, however, a significant positive effect on trading volume, especially in the case of the Japanese market as well as
for the other market except the French one. These investors react positively after gains they realize. They underestimate their exposition to risks and are
more confident about the future. They increase their trading waiting for exploiting opportunities that can occur in the short term. Their excessive reaction
induces a significant increase in the trading volume.
Taken together these arguments can spur the point of view commonly shared by authors (Posner (2009), Akerlof and Shiller (2009), Haruvy et
al., (1999), Weinstein (1989), Otten (1989) according to which the hypothesis of rationality fails to explain how the economy works. The behavioral
biases from which the animal spirits taken in his large definition such as given by Akerlof and Shiller (2009) including the spontaneous reaction as well as
pessimism and optimism give an answer to the question of what factor explains the way the economy works.
These findings allow challenging the assumption of rationality. Human psychology exerts serious pressures on the investor’s process of making
decision inciting them to behave less than fully rationally. The way how investors think and how they feel, influences his process of decision-making. To
determine and understand the way the economy really works, the macroeconomic models have to incorporate psychological influences and investors’
cognitive and emotion. Hence, a better supervision and control of the economic variables requires seriously incorporating observable, systematic and
human departures from rationality into models of financial markets and behavior.
5. Policy implication and conclusion
The literature has considered that investors behave rationally, and ignores to incorporate into macroeconomic models the cognitive and
emotional weaknesses that may affect the investor behavior. Financial theory supposes that markets are efficient and that if market inefficiencies may
exist, they are generally not easy to exploit. In this framework, evidences do not support the ability of investors to perform the market or to produce
abnormal returns. Theoretical and empirical evidences show however that psychological influences really matter in decision making. Investors are prone
to psychological factors that drive their behavior and influence their decision in the level of financial markets.
Using data for 5 international stock markets, our findings show firstly that the hypothesis according to which investors execute rational
expectations fails to explain the way the economy works. The increase in trading volume largely observed in the major international markets in developed
countries cannot be due to the presence of absolute rational investors.
We find also that the economy is driven by behavioral biases, including especially animal spirits. In the major markets we have studied, the
increase in trading volume which is due to the presence of optimistic, pessimistic investors as well as investors with spontaneous reaction. Two
specificities characterize, however, the Japanese and the French markets. In the first, the fluctuation of the trading volume is due to the presence of both
overconfident and more optimistic investors. In opposition, the presence of more pessimistic investors influences largely the evolution of trading in the
specific case of the French market.
Taken together empirical results indicate that investors are more sensitive to psychological factors and fail to act as rational agents. The
equilibrium in financial markets is not a result of a self-regulation driven by rational behavior but by heterogeneous buy and sell orders made based on
heterogeneous non-rational forecasts. Investors are more prone to be affected by their own psychological states. This result is consistent with the
prediction of Keynes (1936), who document that “most, probably, of our decisions to do something positive, the full consequences of which will be drawn
out over many days to come, can only be taken as the results of animal spirits, a spontaneous urge to action rather than inaction, and not at the outcome
of a weighted average of quantitative benefits multiplied by quantitative probabilities.
The rejection of the hypothesis of rational expectation indicates a dysfunction of the financial market and confirms that these latter are not able
to react following fundamental predictions. Behavioral biases play however substantial role in determining the decision-making strategies. Markets are
therefore driven by human psychological factors. This specific result is consistent with the observation shown in Dhaoui (2013), confirming the important
role of behavioral components in describing the market functioning. In this vein, the authors argue that: “the behavioral based reaction induces a distorted
prevision of the price evolution given the uncertainty in investors’ beliefs and sentiments. This influences significantly the evolution of the two components
of financial markets namely returns and trading volumes. Accordingly, the abnormal changes in trading volumes and the low returns largely observed in
the major international markets can be explained among others by the reaction of non-rational investors.”
Based on the results of this study, investors are incited to incorporate in their forecasts models new components describing changes in thinking,
human psychological states, and irrational behavior.
In extension to this study, we recommend investigating the contribution of behavioral biases and the animal spirit’s behavior of investors to explain
the variances in trading across the days, the weeks, the months, the years or any other unit of time. We recommend also investigating the contribution of
these factors to explain the tremendous financial recession largely observed in the major international markets in developed as well as in emerging
countries during the last decades.
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doc_136771883.pdf
This paper provides the first evidence for empirical tests of the impact of rational expectations as well as
behavioral biases, including among other animal spirits such as defined by Akerlof and Shiller on the
variability of trading. Using a daily data for five international capital markets in developed countries,
strong evidence is found. The hypothesis of rationality fails to determine the investors’ trading behavior.
The economy is, however, driven by behavioral biases, including more especially animal spirits
summarized in investors’ sentiments and beliefs.
2214-4625/$ – see front matter © 2015 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.aebj.2014.12.002
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
Contents lists available at ScienceDirect
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j our nal homepage: www. el sevi er. com/ l ocat e/ aebj
HOSTED BY
Arab Society of
Faculties of Business,
Economic & Political
Sciences
*Abderrazak Dhaoui. Tel.: +21622622821; fax: +0-000-000-0000.
E-mail address: [email protected]
Peer review under responsibility of Holy Spirit University of Kaslik.
What Does Matter in Economy Today: When Human Psychology Drives
Financial Markets
Abderrazak Dhaoui *
*
University of Sousse, Faculty of Economic Sciences and Management, Tunisia
A R T I C L E I N F O
Article history:
Received 17 May 2014
Received in revised form 12 December
2014
Accepted 17 December 2014
Keywords:
Optimism
Pessimism
Spontaneous reaction
Overconfidence
Rationality
Trading volume
A B S T R A C T
This paper provides the first evidence for empirical tests of the impact of rational expectations as well as
behavioral biases, including among other animal spirits such as defined by Akerlof and Shiller on the
variability of trading. Using a daily data for five international capital markets in developed countries,
strong evidence is found. The hypothesis of rationality fails to determine the investors’ trading behavior.
The economy is, however, driven by behavioral biases, including more especially animal spirits
summarized in investors’ sentiments and beliefs.
© 2015 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
1. Introduction
During the last five decades, a point of view commonly shared by many authors is that it becomes more difficult to understand how the economy really
works (Akerlof and Shiller, 2009; Dhaoui et al. (2013); Dhaoui (2013).
The efficient market and the rational expectation hypotheses loss of significance and fail in explaining the return and trading volume behavior in the major
international markets in both developed and emerging countries. The authors attribute the dysfunction of economies and markets to human psychology,
sentiments and feelings (Keynes, 1936); Akerlof and Shiller (2009); Dhaoui (2013). Variables such as overconfidence, (Daniel, Hirshleifer and
Subrahmanyam, 2001) optimism, (Haruvy, Stahl and Wilson (1999), Weinstein (1980), Otten (1989), pessimism (De Bondt and Thaler (1985); Barberis
Shleifer and Vishny (1998) or more largely animal spirits (Akerlof and Shiller, 2009) are particularly suggested to explain the disruption of evolution of
the trading volumes and of the returns.
In this regard, Akerlof and Shiller (2009), attribute the economic dysfunction to human psychology and introduce the “animal spirits” as one of
the factors influencing the economies and markets. To define the “animal spirits”, the authors enlarge the definition given by Keynes (1936) in his General
Theory and introduce confidence, fairness, corruption and association behavior, money illusion, and stories as items of animal spirits. For Keynes (1936)
the animal spirit is defined as “a spontaneous urge to action rather than inaction”.
40 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
The main aim of this paper is to provide comprehensive empirical evidence on various implications of the psychological influences by focusing
on investor behavior. Our focus on investor behavior is motivated in part by the argument of Odean (1998), Daniel, Hirshleifer and Subrahmanyam
(1998), Gervais and Odean (2001) that investor behavior should be observed in market level data, and in part by that of Daniel et al. (2001), Haruvy et al.,
(1999)Weinstein (1980), Otten (1989), De Bondt and Thaler (1995), Barberis et al. (1998), Keynes (1936), and Akerlof and Shiller (2009) that investor
beliefs and sentiments matter when making a decision in economic worlds. In this way, we provide the first evidence for empirical tests of the impact of
departures and deviations from the rationality in the financial market working based on incorporating sentiments and beliefs as human psychological
factors to supervise the changes in trading volume.
To empirically explore the psychological influences that can contribute to explain the variances in trading volume in most of international
Stock markets, we investigate the impact of both the rational expectation and the behavioral biases with an extension to Keynes (1936) and Akerlof and
Shiller (2009) on the trading volume. Expected results can give ideas about the factors likely influencing the investors’ trading behavior in the major Stock
Markets. The relation between these independent variables and the trading volume as a dependent variable serves to understand the factors approximately
influencing the way the economy works.
In this order one question remains of a great importance, that is: How does human psychology drive economies and markets? Or in other words, in which
way did the financial markets be sensitive to the investors’ beliefs and sentiments?
To find some answers to this question we investigate the specific influence of the factors describing the Rational Expectation as well as the
behavioral biases, including animal spirits’ behavior (e.g. Overconfidence, Optimism, Pessimism and Spontaneous Reactions) on the trading volume such
as considered as a financial proxy for the economy works.
In order to do this, we used a sample, including daily data for five international Stock Markets (Japan, U.S., France, U.K. and Switzerland) over the period
from June 10, 2002 to November 17, 2011. Our results show that the economy is driven by non-rational expectation. Behavioral and animal spirits’ biases
influence significantly the evolution of trading volume in the major international Stock Markets. The presence of rational investors is, however, without
impact on the process of trading in all the markets.
The remainders of this paper proceed as follows: in section 2, we present an overview of the literature on the rational expectation hypothesis
and the behavioral biases, including animal spirits. Section 3 describes the methodology and the data framework of this study. In section 4, we present and
discuss the principal results. Section 5 concludes.
2. Literature Review
For several decades, the financial and economic literature considers that investors trade rationally and that even they behave irrationally (i.e.
they would trade in a random way) the deviation from the equilibrium state caused by irrational trading can be cancelled out by an opposite irrational
trading of other irrational investors. This supposes naturally that markets are efficient and all isolated rare events cannot influence significantly the
decision-making process. This normally induces less volatility of returns and trading on the major markets. The Efficient Markets Hypothesis (hereafter
EMH) has known an impressive theoretical and empirical success since about the first decade after its conception in the mid-1960s. Especially, Michael
Jensen (1978, p. 95), argues in this field that “there is no other preposition in economics which has more solid empirical evidence supporting it than the
Efficient Markets Hypothesis”. In spite of this success, the EMH has been, however, subject to serious challenges and critics particularly when
observations have shown and confirmed the non-regular and supported the non-understandable function of economies and markets.
The incorporation of psychological factors into macro-economic models seems to confirm however the predictions of the behavioral finance
theory according to which investors cannot behave totally rationally over the full time, and the economy is for the major cases driven by human
psychology (Akerlof and Shiller, 2009).
Posner argues that economists have failed to understand how the economy works. The hypothesis of rationality largely associated with the efficient-
market hypothesis loses its significance by failing to explain the variability between the stock prices as observed in international markets and their
fundamental values and/or the excess in trading volume during about the five last decades (see, Lavoie, 2010).
The efficient markets hypothesis asserts that asset prices aggregate and reflect all relevant fundamental information. They provide, consequently, proper
signals for resource allocation. The challenge of the efficient market and rationality hypotheses dressed by many authors has incited many authors to
examine if human psychology may explain the way the economy really works.
The first work in the field has been realized by Keynes in 1936 who argued that about all of investors’ decisions breaks with the foundation of
rationality, and attributes the dysfunction of the economy to psychological factors and irrational behavior. This challenge has been confirmed more
recently when Akerlof and Shiller (2009) have explained the way the economy works in terms of human psychologies’ impacts. In the same framework,
several authors such as Akerlof and Yellen questioned in 1985 if small deviations from rationality do not really matter. Fifteen years later, Shleifer (2000),
pursued the same logic and questioned if the arbitrageurs succeed to drive irrationality out of the marketplace. Taken together these thoughts tend toward
the same conclusions that the economy is driven in the major part by human psychology and irrational investors’ behavior.
In the same line, Allais (1953), and Ellsberg (1961), demonstrated preferences that violate expected-utility theory. In 1986, Tversky and
Kahneman (1986) articulated a direct challenge to the rationality assumption itself, based on experimental demonstrations in which preferences were
affected predictably by the framing of decision problems, or by the procedure used to elicit performance. They argued that the demonstrated susceptibility
of people to framing effects violates a fundamental assumption of invariance, which has also been labeled extensionality (Arrow, 1982) and
consequentialism (Hammond, 1989).
Based on the aforementioned studies it is clearly documented that investors’ beliefs and sentiments really matter in making decision in
financial markets (trading behavior) and that investors are not fully rational. Departures from rationality are not however only random, they are in the
major cases systematic. In this line, Shleifer (2000) notes, “investors’ deviations from maxims of economic rationality turn out to be highly pervasive and
systematic”. One most problem can take place here which is, if we challenge the EMH and support the predictions of the behavioral finance, how we can
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 41
explain the equilibrium observed in the financial markets. Shleifer (2000) proposed a response to this question: “the irrational investors in the market trade
randomly. When there are large numbers of such investors, and when their trading strategies are uncorrelated, their trades are likely to cancel each other
out.”
Akerlof and Shiller (2009), in their recent work, substitute the investors’ behavior bias with the hypothesis of rationality in order to explain the
volatility in stock returns and trading volume. They consider that the dysfunction of the economy, and more specifically financial markets, is due to the
failure of the investors to expect rationally the future incomes and the behavior of stock returns. They, accordingly, attribute the dysfunction of the
economy, especially, to what they call the “Animal Spirits” bias as an extension to the General Theory developed by Keynes (1936). In this sense, they
argue that “it is necessary to incorporate animal spirits into macroeconomic theory in order to know how the economy really works”. This incites to
introduce beliefs and sentiments, which are largely excluded from theoretical and empirical models, to explain the way in which the economy really
works. Akerlof and Shiller (2009) argue in the specific framework of behavior explanation that “the current crisis bears witness to the role of such
changes in thinking. It was caused precisely by our changing confidence, temptations, envy, resentment, and illusions”.
The “animal spirits” phenomenon can be comprehended as a non-rational behavior driven by investors. In the sense of Keynes (1936), argues
that, “most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be
taken as the results of animal spirits and not at the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities”. In this
particular framework, Keynes defines the animal spirits as “a spontaneous urge to action rather than inaction”. Akerlof and Shiller (2009), enlarge,
however, this same definition to incorporate optimism and pessimism. Accordingly, animal spirits include, in the sense of these authors, spontaneous
behavior as well as optimistic and pessimistic beliefs.
Previous literature (De Bondt and Thaler, 1985, 1987, Barberis et al., 1998, Daniel et al., 2001) relates the volatility in stock prices and the
price anomalies to the presence of under-reactions and overreactions. Under-reactions and overreactions are driven by pessimistic, optimistic, as well as
overconfident investors. In this same line, earlier studies such as that of Ciccone (2003) document that investor sentiments and behaviors play a pivotal
role in the stock market. The investor optimism and pessimism are especially reflected in stock prices. Optimistic investors in the sense of Haruvy, et al.
(1999) are “those who tend to choose the strategy which can potentially give them the highest payoff”. These authors define optimistic investors as “those
who are motivated by worst-case scenarios and hence tend to choose a secure action”. According to Weinstein (1980, 1986, 1989) and Otten (1989)
optimistic investors believe that positive events are more likely to happen to them than to others and similarly that negative events are less likely to occur
for them than for others. By extension, pessimistic investors consider that they are more likely exposed to negative events and less likely to positive events
than the others. These beliefs induce an increase in trading driven by the optimistic investors and oppositely a decrease in trading driven by pessimistic
investors. In terms of trading strategies, Chen (2013), concludes that optimistic agents trade aggressively while pessimistic ones trade conservatively.
King (2009), agrees with Akerlof and Shiller (2009), about the pivotal role that plays the “non-rational” behavior of investors such as the
sentiments of optimism or pessimism or more specifically that of what they call more largely “animal spirits”. He concludes that behavior biases are able
to explain a major part of fluctuations in the economic sphere.
Optimistic as well as overconfident investors are more prone to risky investments. They trade irrationally, and their irrational reaction can
induce abnormal volatility in trading volumes and, consequently, in stock returns. Empirical investigations show that irrational investor behaviors did not
only exist in international stock markets, but their reactions have also shown significant influences on the variability of prices (Chuang, 2010).
In the same line, Grauwe and Kaltwasser (2012), examine the impact of animal spirits on trading strategy in the foreign exchange market.
When traders do not have information about the true value of the fundamental exchange rate they need to depend on beliefs about it in order to take
positions in the market. The authors show that in a world in which there are only optimistic and pessimistic traders, the animal spirits can produce
complex dynamics in the foreign exchange market.
Specifically, optimistic investors’ underestimate their exposure to risk and exaggerate their reaction since they expect only positive results and
neglect the failure. In this vein, Shu (2010), being aware of the characteristics assigned to each type of sentiment. He argues that optimistic investors are
less patient than those who are more pessimistic and react aggressively by underestimating their exposure to risk. Oppositely, the pessimistic investors
display a high-level of risk aversion. They become more and more receding when they make a decision like an investment in risky assets which leads to a
decrease in trading volume.
Chuang, et al. (2010), use a weekly data during the period between January 1990 and December 2004 to supervise the change in investor
sentiments in Taiwan Stock Market. They find that the change in trading volume can be used as a proxy for investor sentiments. They argue that a positive
deviation of trading volume implies that investor sentiment jumps to become more optimistic and vice-versa.
In the French stock market framework, Rousseau, Germain and Vanhems (2008), argue that pessimistic investors decrease their trading volume
and avoid risky assets to prevent a loss. Psychological literature assimilates the pessimism to a statement of impotence or to an absurdity of human
existence.
Carver (2010), suggests that optimism and pessimism sentiments focus on expectancies for the future and the way that the investors confront
problems. They find that optimistic investor faces adversity differently than pessimistic one. They presume that optimistic investor uses more adaptive
ways and "commit" himself to cope with the worst scenarios. In contrast, pessimism refers to fear, doubt and stress. Then, pessimistic investor tends to be
hesitant and doubtful in the face of different challenges. The pessimistic investors have more aversion to risk and decrease their trading once they realize
negative results. However, they don’t increase their trading when they realize an abnormal gain. In opposition, optimistic and overconfident investors
increase their trading after abnormal gain, but they maintain normal trading when losses occur.
The spontaneous behavior also may play a pivotal role in explaining the volatility of trading volume and consequently, the stock returns.
Without animal spirits (considered in its restrictive definition – a spontaneous urge to action rather than inaction- as well as its largest definition adopted
in Akerlof and Shiller (2009), including optimistic and pessimistic beliefs) and considering only the hypothesis of absolute rationality, investors should,
according to Pirosc? “probably react very shy and doubtful or even they would not react at all”. In this sense and according to the Keynes (1936) General
Theory, the preferred action taken by people is to react rather than not react in real-life situation. Here, we understand that, in the framework of economic
life, the financial markets are driven by human sentiments and feelings such as optimism, pessimism and overconfidence.
42 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
Overconfident investors overweigh the precision of their own information and ignore the public available information (Daniel, et al., 1998).
They also overestimate their judgment skills and underestimate the skills of others. As a result, they overreact to private information and under-react to
public information (Odean, 1998). This asymmetric response of overconfident investors leads them to underestimate their exposition to risks and to act
aggressively, which increases their trading volume.
The argument that overconfidence influences trading volume is shared by several authors such as De Long, Shleifer, Summers, and Waldmann
(1991), Kyle and Wang (1997), Benos (1998), Odean (1998), Wang (1998, 2001), Daniel, Hirshleifer, and Subrahmanyam, Hirshleifer (1998), and Luo
Scheinkman and Xiong (2003). Particularly, Gervais and Odean (2001) developed a model predicting that overconfident investors attribute market gain to
the precision of their private information, their judgment skills and their ability to pick winning stocks, and the process of wealth accumulation leads them
to overreact following market gains. For De Bondt and Thaler (1995) the overconfidence is considered as the important behavioral factor that determines
the trading puzzle. In this regard, they argued, “the key behavioral factor needed to understand the trading puzzle is overconfidence”.
3. Data and behavioral indices
In this section, we illustrate the proxies who represent rationality, spontaneous behavior, optimism, pessimism and overconfidence and specify the
model suitable for describing their impacts on trading volume.
The sample includes daily data sets covering the period spanning June 10, 2002 to November 17, 2011 with a sample of 3448 trading and non-
trading days. Different markets in developed countries are considered to investigate the relationship between trading volume, on the one hand, and on the
other hand, rational expectation and the proxy of animal spirits and investors’ sentiment. Five international markets are considered, which are the U.S.
Stock Market (NASDAQ), the Japanese Stock Market (Nikkei 225), the U.K stock Market (FTSE100), The French Stock Market (CAC40) and the Swiss
Stock Market (SMI). Data is available online on the Yahoo Finance pages and on the website of each Stock Market. The starting date of the sample period
is determined by the availability of daily data serving to compute our variables for each country.
The remainder of this section presents the variables specifications and the estimated model. The proxies used are inspired from the work of Dhaoui
(2013), who specified for the first time the psychological proxies describing the “animal spirits” and human psychological variables affecting the investors
behavior theoretically inspired from the work of Keynes (1936), and Akerlof and Shiller (2009).
Rational expectation hypothesis asserts that investors are able to forecast the future evolution of returns considering the current state of the stock
prices. Rational expectation investor formulates his expectation however based on current information while taking into account at the same time the
forecast error he has committed over the last forecast period. Accordingly, the rational expectation proxy used in this study includes the previous realized
returns (at the time (t-1) augmented by the error term at a previous date (the date (t-1). Expected returns are done by the following relation:
R
t
Lxpcctcd
= R
t-1
+E
t-1
with R
t
Lxpcctcd
the expected return at the time t, R
t-1
the return at the time (t-1) and E
t-1
the error of expectation in the time (t-1)
which is equal to the difference between the observed return in (t-1) and the expected return at the same time: E
t-1
= R
t
-R
t-1
Lxpcctcd
. The R
t
is computed
using the following formula: R
t
= In(P
t
P
t-1
/ ), and P
t
is the stock Markets Index price at the time ‘t’. This relation illustrates that when expecting the
future returns, investors adjusted the just last returns they realized by the error term of their last expectation.
To supervise for the overconfidence sentiment we can base our analysis on the nature of connection between current trading volume and the last stock
returns. In other words, the relationship between the trading volume and overconfidence can be illustrated through the effect of previous returns (R
t-1
)
and current trading volume (I
t
). If returns increase in the just last period investors react overconfidently and increase their trading volume. This proxy is
used previously by (Khcherem and Bouri, 2009).
According to De Grauwe and Kaltwasser (2012), ‘Animal Spirits’ create waves of optimism and pessimism. The optimism about a given asset is
occurring when expected prices increase abnormally. However, pessimism takes place when investors estimate a dramatic decrease in prices. There are
two direct analytical methods for measuring investor sentiment. An optimistic investor expects an above average stock price level to which a standard
deviation is added at any given time. He becomes pessimist when the price falls below the average level diminished by the standard deviation. Then, the
average of individual stocks is used to calculate optimism and pessimism variables of the Stock index. The use of the average stock price and the standard
deviation is justified by the fact that investors are attracted by the return and averse to the risk. The average prices measure the former and the standard
deviation measures the latter. Following this logic, we report a strong evidence of optimistic behavior when individual stock price increases abnormally
and exceeds the average level augmented by the standard deviation (R
+o). Considering the aggregate market, the average of conditional absolute price
forms a good measure of optimism sentiment. The investor reacts as optimist if he realizes that the price increase over the medium level. In other words,
the optimism would occur when the investor realizes a gain at a just previous date. Optimistic investors overreact when they anticipate that gain reaches a
fixed level. Under this level, they react in a normal way or decide not to trade. In terms of probabilities, and with conformity to the analysis of Fischhoff et
al., (1977), Svenson (1981), and Hoffrage (2004), “when the task is easy and the probability of success is reasonably high, people tend to exhibit over-
optimism and overestimate the probabilities of good outcomes” (Chen, 2013). Let R
the average return and o
the standard deviation and let the lowest
high return they accept equal, approximately, to the average return augmented by the simple standard deviation. Optimistic investors trade aggressively
when their previous gains are higher than (R
+ o).They trade normally or rather postpone trading if they realize returns under than this level in the time (t-
1). Considering that they postpone trading the behavior of optimistic investors will be described by the following relation: F(R
t
¡0) = Hox|R
t-1
-
(R
+ o), u] with F(R
t
¡0) denotes the function describing the behavior of the investor considering his expectations for future evolution of returns giving
a minimum of gain tolerated and ? denotes the information available at time t.
Considering this function the behavior of optimistic investors can be described as follows: when R
t-1
> (R
+o) they trade, and when R
t-1
¸ (R
+
o), they postpone trading. Accordingly, their trading process will be as follows:
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 43
IroJing Bcbo:ior Jri:cn by 0ptimistic In:cstors =
_
R
t-1
-(R
+o) > u , Exccssi:c IroJing
0tbcrwisc , Postponc
The pessimism belief occurs when an investor realizes losses at a previous date. Pessimistic investors decrease their trading when they anticipate that
losses will reach a critical level. They react in a normal way when returns are higher than this level. They postpone, however, their trading when they
anticipate that losses are higher. In the same way as it is the case for the optimism, and with conformity to the analysis of Kruger (1999), Windschitl et al.
(2003), and Kruger and Burrus (2004), “when the task is very difficult with rare positive events, people often exhibit over-pessimism and overestimate the
probabilities of bad outcomes” (Chen, 2013). Let R
the average return and o the standard deviation and let the lowest loss they accept equal,
approximately, to the average return diminished by the simple standard deviation. Pessimistic investors trade in a normal way when their previous losses
are less than the target level such as done by the following: |R
-o|.
Oppositely, they postpone trading if their returns in time (t-1) rise more than this
tolerated level. Considering these arguments, the behavior of pessimistic investors will be described by the following: F(R
t
¡0) = Hox|R
t-1
-(R
-o),
u]
,
with F(R
t
¡0) denotes the function describing the behavior of the investor considering his expectations of future evolution of returns giving a
maximum of losses tolerated and ? denotes the information available at time t.
Considering this function the behavior of pessimistic investors can be described as follows: when R
t-1
> (R
-o)
they trade, and when R
t-1
¸
(R
- o), they postpone trading. Accordingly, their trading will follow this process:
IroJing Bcbo:ior Jri:cn by Pcssimistic In:cstors ·
_
R
t-1
-(R
-o) > u , Exccssi:c IroJing
0tbcrwisc , Postponc
Spontaneous reactions describe the behavior of absolute non rational investors. These latter trade in a random way without prior investigation
of the behavior of stock returns. Investors with spontaneous reactions are those without optimistic, pessimistic or overconfident sentiment. Considering the
function defining optimistic, pessimistic or overconfident investors the behavior of those with spontaneous reaction can be described by the following
relations: (R
-o) < R
t-1
< (R
+o)
Our model describes the variables which can likely explain the trading volume in the major international markets in developed countries. The
model includes five independent variables: Rational expectation, Optimism, Pessimism, Overconfidence and Spontaneous reaction (ROPOS) in order to
explain the trading volume such as measured approximately by the natural logarithm of trading volume in the date t (In(II
t
). Previous study that used the
same model is Dhaoui (2013), when investigating the sensitivity of trading volume to the “animal spirits” investor behavior using optimism, pessimism,
overconfidence and spontaneous reaction as proxies for the human psychological variables from one hand and the rational expectation to test the efficient
market hypothesis from the other hand. The estimated model is accordingly:
I
t
=×
0
+×
1
RotExpcc
t
+×
2
0ptim
t
+×
3
Pcssim
t
+×
4
0:crcon¡
t
+×
5
Spontrcoct
t
+e
t
(1)
Where:
I
t
: represents the natural logarithm of the trading volume in the time t;
RotExpcc
t
: represents the returns rationally expected by rational investors in the time t considering available information in the time (t-1);
0ptim
t
: represents the returns expected by optimistic investors in the time t considering available information in the time (t-1);
Pcssim
t
: represents the returns expected by pessimistic investors in the time t considering available information in the time (t-1);
0:crcon¡
t
: represents the returns expected by overconfident investors in the time t considering the gains they realize in the time (t-1);
Spontrcoct
t
: represents the random return that have been observed in the time (t-1) and inducing a random reaction in the time t;
e
t
: is the error term.
4. Results and discussion
In this section, we present the summary statistics and the estimates results. Table 1 shows the summary statistics of the selected variables.
Table 1- Summary statistics for returns and trading volume.
Market Variables Obs. Mean Max Min Sd Skewness Kurtosis
Japan Return
2311
-0.0001266 0.1323459 -0.1211103 0.0160059 -0.5199366 10.85016
Trading 12.3362 20.84374 8.070906 2.321057 2.932268 9.91585
US Return
1819
0.0003845 0.1184933 -0.1111493 0.0156401 -0.044101 8.409273
Trading 21.37196 25.56454 17.27309 0.2814666 -0.523498 67.95256
France Return
2311
0.0001266 0.1211103 -0.1323459 0.0160056 0.5198367 9.95241
Trading 14.33762 22. 46374 9.071106 2. 210156 2.823269 9.86514
UK Return 2263 0.0001262 0.0938424 -0.0926455 0.0128501 -0.1106145 10.65903
44 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
Trading 21.00541 21.73781 18.01672 0.3774095 -1.502721 8.069739
Switzerland Return
1786
0.0000531 0.1078764 -0.0810779 0.0123152 0.0241949 9.981337
Trading 17.9338 19.56444 16.24909 0.4080997 0.1353175 3.816379
This table shows the summary statistics for returns and trading volume. We present the summary statistics for the returns since all explanatory variables
used in the model to estimate are derivatives of the return, and the measures we used make difficult to conduct descriptive statistics for them.
Table 1 presents summary statistics for returns and trading volume for all samples related to the different stock markets we studied in this paper. Results in
this table indicate that in opposition to all other stock markets, the Japanese stock market is characterized on average by a negative closing return. The
Swiss stock market presents, however, the lowest standard deviation of returns from all the stock we studied. The highest standard deviation of returns is
observed in the Japanese stock exchange.
At the same time, results in Table 1 show that compared to the other markets, the Japanese stock market is characterized by a low volume of
transaction, but a very higher volatility in trading volume. The highest trading volume is shown in the NASDAQ stock Exchange. Considering together
summary statistics related to the Mean, the Max and the Min of trading volume, we note that the US stock market is the most liquid followed by the U.K
stock market then successively, the Swiss Stock market, the French and finally, the Japanese stock exchange.
To estimate our model for each stock market we start by testing the stationary of dependent and independent variables. For the 5 stock markets,
the outcome of ADF and Phillips-Perron unit root tests of the trading volume, optimism, pessimism, spontaneous reaction, overconfidence and rational
expectations indicators are presented in Table 2.
Table 2 presents results of the Ducky Fuller unit root test for each variable by stock markets.
Table 2- ADF and PP unit root test for dependent and independent variables.
Trading
value
Optimism Pessimism Spontaneous
reaction
Overconfidence Rational
expectation
ADF
Japan - 2.916** - 45.555*** - 39.583*** - 45.209*** - 43.369*** - 28.872***
U.S. - 15.194*** - 47.491*** - 43.981*** - 43.513*** - 45.562*** - 13.151***
France - 8.946*** - 43.903*** - 40.161*** - 44.778*** - 45.263*** - 23.636***
UK - 3.217** - 38.227*** - 33.263*** - 43.726*** - 45.008*** - 13.652***
Switzerland - 17.337*** - 39.560*** - 40.805*** - 42.753*** - 43.777*** - 43.246***
PP
Japan -3.01** -43.11*** -37.874*** -44.171*** 42.291*** -27.834***
U.S. -14.628*** -45.407*** -42.667*** -43.318*** 45.247*** -13.712***
France -7.398*** -44.013*** -40.682*** -43.016*** 44.581*** -24.307***
UK -3.166** -38.338*** -31.949*** -42.473*** 46.001*** -12.528***
Switzerland -17.006*** -38.481*** -39.360*** -40.852*** 43.267*** -44.627***
ADF denotes Augmented Dickey-Fuller unit root tests, PP refers to Phillips-Perron unit root tests. ** and *** denote rejection of the null hypothesis at the
5% and 1% levels of significance, respectively The lag length in all tests has been selected according to the Akaike Information Criteria (AIC).
Results in Table 2 show that all selected variables are stationary. All variables are at a stationary level for a critical level of 1% except the variable
“trading volume” for the cases of Japan, and the U.K. Stock markets where the variable is stationary at a critical level of 5%. Thus we can continue to
estimate our equation 1. For the 5 stock market the outcome of the estimates results are given in Table 3.
Table 3- Results of econometric regressions by stock market.
Stock Market Independent Variables Coef. t-statistic R-square
(Aj. R-square)
Panel A:
Japan
(optimistic
population)
Optimism 2.813 2.36**
0.2836
(0.2817)
Pessimism -1.839 -1.58
Spontaneous reaction -1.614 -2.23**
Overconfidence 4.779 3.19***
Rational expectation -0.007 -0.16
Const 2.251 4.28***
Panel B:
US
Optimism 2.891 4.65***
0.3081
Pessimism -3.990 -6.65***
Spontaneous reaction -0.426 -0.48
Overconfidence 1.091 2.93***
ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47 45
Rational expectation -1.091 -0.38 (0.3058)
Const 4.350 9.88***
Panel C:
France
(pessimistic
population)
Optimism 0.902 0.87
0.2279
(0.2258)
Pessimism -3.620 -3.70***
Spontaneous reaction -2.248 -3.63***
Overconfidence -1.936 -1.34
Rational expectation 0.003 0.33
Const 2.995 4.31***
Panel D:
UK
Optimism 1.953 3.21***
0.3707
(0.3690)
Pessimism -1.446 -2.31**
Spontaneous reaction 1.296 2.33**
Overconfidence 2.0573 2.01**
Rational expectation 0.001 0.08
Const 3.252 8.72***
Panel E:
Switzerland
Optimism 2.127 6.86***
0.2235
(0.2208)
Pessimism -1.896 -4.27***
Spontaneous reaction -1.725 -2.62***
Overconfidence -0.817 -0.54
Rational expectation -0.004 -0.78
Const_ 7.871 8.11
This table shows the regression estimates of the equation relating the trading volume as an endogenous variable to optimism, pessimism, spontaneous
reaction, overconfidence and rational expectation as exogenous variables. Panel A shows the results for the sample of the 225 firms included in the Nikkei
225. Panel B shows the results for the full sample of the firms listed in the Nasdaq stock market. Panel C summarizes the results for the full sample of
CAC 40 firms. Panel D shows the results for the firms listed in the FTSE100. Panel E shows the results for the firms listed in the Swiss stock market
(SMI). The first column specifies the Stock markets. Column 2 is reserved to the explanatory variables. Column 3 shows the regression coefficient.
Column 4 presents the “t-student”. And finally, column 5 shows the R-Squared and the Adjusted R-Squared.***, ** and * indicate significance at the 1, 5
and 10 percent levels respectively.
Results in Table 3 indicate for all markets, that rational expectations fail to explain the variability of the trading volume. The hypothesis of
rationality can be rejected. The rational investors are not those who control the way the economy works. The results indicate, however, that economy is
influenced largely by non-rational behavior of investors. Their animal spirits play a pivotal role. We note particularly that the increase in trading volumes
is due to the presence of non-absolute rational investors. Overconfident as well as optimistic and pessimistic investors influence significantly the degree of
fluctuation of the trading volumes in the major international markets.
In this sense, overconfidence presents a positive impact on the trading volume for the case of the Japanese, the U.S and the U.K. Markets. In
the case of the French and the Swiss Markets, the presence of overconfident investors is without effect. We notice here that these results are not surprising,
especially in the case of the Japanese markets. In fact, as an Asian population, the Japanese investors are more exposed to the overconfidence bias. In this
specific vein, we are reminded here that numerous psychologists have examined the behavior of populations and conclude that the Asian population
exhibits overconfidence in general knowledge (see Yates, Lee and Shinotsuka (1996) and Yates, Lee and Bush (1997) for more details). This implies
specifically among others that Asian investors may suffer from psychological bias from which the overconfidence bias. They underestimate their
exposition to risks and make aggressive decisions, which can be reflected in their trading volumes. Japanese investors underestimate, accordingly, their
exposition to risk and make aggressive reactions inducing an increase in trading volume. These results are consistent with the prediction of Odean (1998),
according to which the behavior of the overconfident investors consists to underestimate their exposition to risks and to act aggressively leads to an
increase in their trading volume.
The presence of investors with the spontaneous reaction influences the evolution of the trading volume significantly in the cases of the Swiss,
U.K., French and Japanese Markets. In the case of the U.S. Market, their presence is without effect. The impact is, however, significantly negative for all
the stock markets except for that of the U.K which is positive. These investors are generally less informed, and they react independently of the results they
made at a previous date. They trade not because they anticipate a favorable evolution of stock prices, but because they prefer to act rather than not to act.
Even if they are less informed, the presence of non-professional and non-rational investors induces a decrease in the trading volume. The decrease in
trading can be explained in terms of reaction of the more informed investors as a reaction to the decision made by those with spontaneous reactions. The
professional and more informed investors anticipate the reaction of those with spontaneous behavior and make opposite decisions rather than similar
decisions. They exploit their advantage of information to realize a gain at the expense of those less informed and with spontaneous reaction. These later
adjust their strategies and stop trading after the first losses they realize and wait to restart trading the following day.
The optimistic and the optimistic sentiments are not without impact on the decision process. The presence of both the more optimistic and the
more pessimistic investors induces respectively significantly positive and significantly negative impacts in the trading volumes in the case of the U.S., the
U.K. and the Swiss stock markets. For the Japanese market, the presence of pessimistic investors is without effect on the trading volume. Oppositely, for
the specific case of the French market, and oppositely to optimistic investors who are without effect, pessimistic investors influence significantly the
46 ARAB ECONOMICS AND BUSINESS JOURNAL 10 (2015) 39–47
evolution of the trading volume. The result is also not surprising. In fact, the French population has no confidence in the future. This population is the
most pessimistic population in the world (for more details, see the BVA-Gallup Survey, January 2011). It has no confidence in politics (Cevipof Study,
January 2011), in drugs (CSA Survey, 2011), in the media (TNS Sofres-La Croix, February 2011). This implies accordingly that the French investors are
more averse to risk. They react negatively after all crises or rather a simple loss. This can explain the significant negative effect of pessimistic investors on
the trading volume in the case of the French market.
The optimistic investors have, however, a significant positive effect on trading volume, especially in the case of the Japanese market as well as
for the other market except the French one. These investors react positively after gains they realize. They underestimate their exposition to risks and are
more confident about the future. They increase their trading waiting for exploiting opportunities that can occur in the short term. Their excessive reaction
induces a significant increase in the trading volume.
Taken together these arguments can spur the point of view commonly shared by authors (Posner (2009), Akerlof and Shiller (2009), Haruvy et
al., (1999), Weinstein (1989), Otten (1989) according to which the hypothesis of rationality fails to explain how the economy works. The behavioral
biases from which the animal spirits taken in his large definition such as given by Akerlof and Shiller (2009) including the spontaneous reaction as well as
pessimism and optimism give an answer to the question of what factor explains the way the economy works.
These findings allow challenging the assumption of rationality. Human psychology exerts serious pressures on the investor’s process of making
decision inciting them to behave less than fully rationally. The way how investors think and how they feel, influences his process of decision-making. To
determine and understand the way the economy really works, the macroeconomic models have to incorporate psychological influences and investors’
cognitive and emotion. Hence, a better supervision and control of the economic variables requires seriously incorporating observable, systematic and
human departures from rationality into models of financial markets and behavior.
5. Policy implication and conclusion
The literature has considered that investors behave rationally, and ignores to incorporate into macroeconomic models the cognitive and
emotional weaknesses that may affect the investor behavior. Financial theory supposes that markets are efficient and that if market inefficiencies may
exist, they are generally not easy to exploit. In this framework, evidences do not support the ability of investors to perform the market or to produce
abnormal returns. Theoretical and empirical evidences show however that psychological influences really matter in decision making. Investors are prone
to psychological factors that drive their behavior and influence their decision in the level of financial markets.
Using data for 5 international stock markets, our findings show firstly that the hypothesis according to which investors execute rational
expectations fails to explain the way the economy works. The increase in trading volume largely observed in the major international markets in developed
countries cannot be due to the presence of absolute rational investors.
We find also that the economy is driven by behavioral biases, including especially animal spirits. In the major markets we have studied, the
increase in trading volume which is due to the presence of optimistic, pessimistic investors as well as investors with spontaneous reaction. Two
specificities characterize, however, the Japanese and the French markets. In the first, the fluctuation of the trading volume is due to the presence of both
overconfident and more optimistic investors. In opposition, the presence of more pessimistic investors influences largely the evolution of trading in the
specific case of the French market.
Taken together empirical results indicate that investors are more sensitive to psychological factors and fail to act as rational agents. The
equilibrium in financial markets is not a result of a self-regulation driven by rational behavior but by heterogeneous buy and sell orders made based on
heterogeneous non-rational forecasts. Investors are more prone to be affected by their own psychological states. This result is consistent with the
prediction of Keynes (1936), who document that “most, probably, of our decisions to do something positive, the full consequences of which will be drawn
out over many days to come, can only be taken as the results of animal spirits, a spontaneous urge to action rather than inaction, and not at the outcome
of a weighted average of quantitative benefits multiplied by quantitative probabilities.
The rejection of the hypothesis of rational expectation indicates a dysfunction of the financial market and confirms that these latter are not able
to react following fundamental predictions. Behavioral biases play however substantial role in determining the decision-making strategies. Markets are
therefore driven by human psychological factors. This specific result is consistent with the observation shown in Dhaoui (2013), confirming the important
role of behavioral components in describing the market functioning. In this vein, the authors argue that: “the behavioral based reaction induces a distorted
prevision of the price evolution given the uncertainty in investors’ beliefs and sentiments. This influences significantly the evolution of the two components
of financial markets namely returns and trading volumes. Accordingly, the abnormal changes in trading volumes and the low returns largely observed in
the major international markets can be explained among others by the reaction of non-rational investors.”
Based on the results of this study, investors are incited to incorporate in their forecasts models new components describing changes in thinking,
human psychological states, and irrational behavior.
In extension to this study, we recommend investigating the contribution of behavioral biases and the animal spirit’s behavior of investors to explain
the variances in trading across the days, the weeks, the months, the years or any other unit of time. We recommend also investigating the contribution of
these factors to explain the tremendous financial recession largely observed in the major international markets in developed as well as in emerging
countries during the last decades.
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