Download The incumbent presentation shall attempt to delineate

The impact of heterogeneous traders’ interaction on stock
returns when a subset of them is prone to herd behaviour: a
literature review of the incumbent herding literature and a
proposed method for modelling this interaction
Vasilis Kallinterakis
University of Durham
School of Economics, Finance & Business
Address (Greece):
Address (UK):
6, Rodon Street
Nea Smyrni
Athens 171 21
Tel. No.: 210 93 32 968
Howlands Farm
South Road
Durham DH1 3TQ
Tel. No.: (0044) 0777 56 82 431
The incumbent presentation shall attempt to delineate our research effort towards an
alternative articulation of the interaction between heterogeneous investors in an
arbitrarily framed stock market. The idea underlying our research lies along the lines
of argument that have long criticized the Efficient Markets Hypothesis on the grounds
of investors’ behaviour. According to the EMH, the population of investors in any
given market is assumed to be homogeneous, with respect to both its “inputs” and
“outputs”. The former pertains to the set of information available at any point in time
regarding the security/ies under consideration, while the latter refers to the way
through which investors process this information. As Fama (1970)1 argued, the EMH
presupposes that investors are capable of capturing all information relevant to a stock,
which they later process in an accurate and “rational” manner. In other words, if a
signal reaches the market, the investors are assumed to be able to decipher its
informational content precisely. Put it differently, if we let:
St = χt + et
where St is the signal arriving at period t, χt its “informative” component and et its
“noisy” component, then the rational investors of Fama’s theory should be in position
to discern between the two components and trade accordingly.
However, further studies (Shiller, 1984)2 indicated that stock returns could not be
satisfactorily explained through the EMH alone and that the concept of rationality
inherent in the EMH had to be relaxed. Theoretical considerations aside, we focus on
the crux of the problem often cited, i.e. the informational asymmetry among investors.
Some investors may have access to inside information, some may possess tools for
advanced processing of information and others may simply be uninformed regarding
both the amount and the analysis of this information. Hence, one might conjecture
that the asymmetry of background is prone to lead to asymmetry in behaviour with
knock-on effects on stock returns.
**********************************************
Our initial pathway of research orbited around the notion of rumour-mongering with
speculative intent. A caveat is important here: rumours per se are hard to define ex
ante and even if that could be achieved, we would still have to cope with the problem
of identifying them in temporal and quantitative terms; consequently, it follows that
event studies are probably inappropriate in the study of rumours, since the very
existence of the latter seems to be ill-defined (“thin air”).
However, this equipped us with the stimulus to conduct backward-looking research.
Thus, instead of searching for rumours and trying to unearth their price-impact, we
began with the assumption that any rumour, if successful (i.e. persuasive), shall
manage to mobilize a certain number of individual traders willing to follow it. A
1
Efficient Capital Markets: A Review of Theory and Empirical Work (in Session Topic: Stock Market
Price Behavior) by Eugene F. Fama. The Journal of Finance, Vol. 25, No. 2, Papers and Proceedings
of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y. December,
28-30, 1969. (May, 1970), pp. 383-417.
Shiller, R.J.: “Stock Prices and Social Dynamics”, Brookings Paper on Economic Activity, Volume
1984, Issue 2, (1984), 457-510.
2
rumour by itself does not have to be precise; according to Allport & Postman (1946)3,
the Basic Law of Rumors states that:
R~ i * a,
(where R , namely the rumour under question is a direct function of both the rumour’s
importance (i) and its ambiguity(a)). In other words, rumour-followers tend to follow
a rather “noisy” signal, the content of which they are probably unable to decompose.
All in all, this is expected to lead gradually to a convergence of their trades-since they
are “following” the same thing-and to the formation of a herd.
According to Bikhchandani & Sharma (2001)4, herding is of dual nature; it can be
either intentional or spurious. Intentional herding occurs when herd-members
disregard their prior knowledge and/or information and simply imitate the trades of
others. In the case of spurious herding, investors respond similarly to similar decision
problems (i.e. when faced with a situation for which they possess more or less the
same amount of information).
Herding research has largely been associated with two groups of professionals,
namely fund managers and financial analysts. It has been posited that this should be
attributed to several reasons, including:
a) the relative homogeneity of these subsets with respect to background and
incentives, which may indicate potential similarities in decision-making
b) the limited population-size that renders the study of these populations more
feasible and
c) the availability (albeit not always coupled with accessibility) of data
The literature underlying herd behaviour is, indeed, notably extensive and provides
the aspiring researcher with a large array of models. We shall classify those into three
different categories:
i)
Trade-based, that utilize buy- and sell-orders of funds over each stock to
estimate the existence or not of herding. The seminal study in this area was
conducted by Lakonishok, Shleifer & Vishny (1992)5, hence their model
being quoted as LSV. Relevant studies include those by Wermers (1999)6,
Kim & Wei (1999a7 & b8) and Lobao & Serra (2002)9; finally, an
Allport, G.W. & Postman L.:“An analysis of rumour“, Public Opinion Quarterly, 10 (4) , 501-517,
1946
4
Bikhchandani & Sharma:”Herd Behaviour in Financial Markets”, IMF Staff Papers, Vol 47, No3,
2001
3
Lakonishok, J., Shleifer, A. & Vishny, R.: ”The impact of institutional trading on stock prices”,
Journal of Financial Economics, 32, (1992), pp. 23-43
6
Wermers, Russ:”Mutual Fund Herding and the Impact on Stock Prices”, The Journal of Finance,
Vol. LIV, No.2, April 1999, pp. 581-622
5
Kim & Wei:” Foreign Portfolio Investors Before and During a Crisis”, National Bureau of
Economic Research Paper Series, Working Paper 6968, February 1999
8
Kim & Wei:“Offshore investment funds: monsters in emerging markets?“, National Bureau of
Economic Research Working Paper 7133, May 1999
7
Lobao, J. & Serra, A.P.:” Herding Behaviour: Evidence from Portuguese Mutual Funds”, March 11,
2002.
9
ii)
iii)
application of such models regarding herding in analysts’
recommendations can be found in the paper by Jaffe & Mahoney (1999)10.
Return-based, which employ the cross-sectional deviation of stock
returns from the market index as a proxy for herding. Studies here include
the archetypal model by Christie & Huang (1995)11 and the follow-up
model by Chang et al (2000)12; a series of papers applying this model-line
has been published during the last two years (Salmon & Hwang (2002),
Alexander & Dimitriu (2003) et al.).
Reputational, which rely upon the professional and career considerations
of managers, analysts et al. Relevant studies here are sizeable in amount;
special attention is drawn here to the seminal paper by Scharfstein & Stein
(1990)13 and the studies by Trueman (1994)14, Graham (1999)15 and Welch
(2000)16.
Results of our research so far in the herding field indicate:
-
a positive relationship between herding by fund managers and smallcap stocks. There appears to be greater uncertainty regarding these
stocks due to the limited amount of information, which tends to be
often firm-specific and ambiguous in quality - see esp. Wermers
(1999) & LSV (1992)-with knock-on effects onto their professional
considerations.
-
a positive relationship between herding and market downturns; see
Lobao & Serra (2002), Kim & Wei (1999a & b) and Chang et al
(2000). Directional asymmetry may be employed here as a potential
reason; as Chordia & Swaminathan (2000)17 and McQueen et al (1996)
argue, small stocks tend to exhibit a temporal delay in their reaction to
news vis-à-vis large stocks when news is good and no delay
(simultaneous reaction) when news is bad.
-
a positive relationship between herding and emerging markets, which
has been attributed to the informational asymmetry between overseas
and indigenous institutional investors-often coupled with positive
Jaffe, J.F & Mahoney, J.M.:”The Performance of Investment Newsletters”, Federal Reserve Bank
of New York, Staff Reports, Number 48, October 1998.
10
Christie, W.G. & Huang, R.D.: “Following the Pied Piper: Do Individual Returns Herd around the
Market?”, Financial Analysts Journal/ July-August 1995, pp.31-37.
11
Chang, E.C., Cheng, J.W. & Khorana, A.:” An examination of herd behaviour in equity markets:
An international perspective”, Journal of Banking & Finance, 24, 2000, pp. 1651-1679
13
Scharfstein & Stein: “ Herd Behaviour and Investment“, The American Economic Review, Volume
80, Issue 3, June 1990, pp. 465-479
12
Trueman:“Analyst Forecasts and Herding Behaviour”, The Review of Financial Studies, Spring
1994,Vol.7, No.1, pp.97-124
15
Graham: ”Herding among Investment Newsletters: Theory and Evidence”, The Journal of Finance,
Vol. LIV, No. 1, February 1999, pp.237-268
16
Welch: ” Herding among security analysts”, Journal of Financial Economics, 58, 2000, 369-396
17
Chordia, T. & Swaminathan, B.: “ Trading Volume and Cross-Autocorrelations in Stock Returns”,
The Journal of Finance, Vol. LV, No2, April 2000, pp.913-935.
14
feedback trading-see esp. Brennan & Cao (1997)18 as well as Kim &
Wei (1999a & b). For an alternative approach- reaffirming herding in
emerging markets-see also Chang et al (2000).
-
a positive relationship between an analyst’s reputation and his/her
propensity towards herding; a well-reputed professional might choose
to follow the herd if the benefits accruing from this outweigh the risks
inherent in “going-it-alone”-see Scharfstein & Stein (1990) & Graham
(1999).
-
a negative relationship between an analyst’s ability and his propensity
to herd; see Trueman (1994), Graham (1999).
-
a positive relationship between herding and the so-called “homebias”; see Feng & Seasholes (2002)19 for China; also Grinblatt &
Keloharju (2001)20 for Finland.
A rather bold conclusion deriving from our research in the field is, though, that it is
almost impossible to assert the existence of herding on the basis of any of those
models. Reputational models, for example, may be very near the notion of herding,
but then again they constitute theoretical constructions that might experience
significant difficulties in their application. Perhaps one could utilize their results to
conduct studies of separate structure and verify their findings; they, alone, represent
articulations of the herding concept. Further to that, defining herding as the situation
during which an investor disregards her personal information for the sake of adhering
to the trend may not be an explanation capable of being generalization. It might be
that an investor has no information anyway, in which case there is nothing to
disregard; he could as well be a trend-chaser (a positive feedback trader), who devises
his trading strategy contingent upon recent price sequences. As far as trying to employ
quantitative techniques is concerned, we have to admit that the problem of definitionlet alone of appropriate measurement-can only grow further21.
Put it otherwise, whether a herd is actually at works or not is hard to say, for it is
virtually impossible to control for the presence of each individual in it. Herding
presupposes some degree of intent22, the latter being subject to no enumeration;
hence, we can only use a model, like the ones mentioned previously, to indicate-at
best-the existence of herding, rather than confirm it. When professionals are
investigated for herding tendencies, the hypothesis may be valid, but then again
simple investors-often denoted as uninformed-may herd, too. In this case, it is the
18
Brennan & Cao: « International Portfolio Investment Flows », Journal of Finance, Vol. LII, No. 5,
December 1997, pp. 1851-1880
19
Feng, Lei & Seasholes, Mark S.:“ Herding and Location“, September 2002
20
Mark Grinblatt and Matti Keloharju: “How Distance, Language, and Culture influence
Stockholdings and Trades” Journal of Finance, volume LVI, No3, June 2001
21
See Bikhchandani & Sharma, ibid, for a discussion on the matter.
22
Economics is a field where such questions often appear. Take for example the case of predatory
pricing; even though it may allegedly exist, it is often hard to distinguish from competitive pricing that
a technologically efficient company might use. According to U.S. antitrust legislation, pricing alone
cannot stand as firm proof of guilt of a company, but must be accompanied by evidence of predatory
intent (thus entering the world of subjective judgement). Tacit collusion is another case: if firms in an
oligopoly follow each other’s pricing policy, then prices in that market would be expected to move in
parallel (“parallel pricing”). However, there is little that would differentiate this corporate behaviour
from a cartel, absent perhaps the explicit element of a written agreement and its enforcement. In the
US, intent is also used for collusion as well, in order to define the boundaries of legal business conduct.
possession of microdata to a full extent that might provide us with a more complete
picture as to the grounding of our assumption, notwithstanding the fact that such data
is normally hard to obtain.
Given the above, our understanding is that we have to view the issue of collective
behaviour from an alternative point. Let us assume, for a moment, that an investor
trades to the direction of past prices (i.e. buys when prices go up and sells when they
go down). By definition, this investor will adhere to positive feedback trading. Let us
now assume that a number of other traders follow this strategy. We can then safely
conjecture that the price shall begin to ascend even further, thus giving rise to a trend;
as far as the underlying trend-chasers are concerned, we may term them as a herd.
Therefore, it is of primary importance here to delineate any potential association
between the two terms.
Incumbent herding literature confirms the co-existence of the two, especially with
respect to situations entailing uncertainty. As we mentioned before in our review
findings, such cases may involve small-cap stocks, overseas markets and performance
evaluation vis-à-vis peers. However, given the flaws often encountered in the
portrayal of herd behaviour, we felt compelled to commence a research looking
backwards. Put it simple, since herding alone is hard to assert and since herding is
often coupled with (if not a byproduct of) positive feedback trading, the existence of
the latter would provide further evidence as to the existence of the former.
At this point we feel obliged to emphasize the fact that the propensity towards
herding and/or positive feedback trading does not constitute by itself a distinguishable
feature of a given set of traders. The latter may trade each time according to their
individual perceptions relative to a stock’s properties and it is obviously difficult to
designate them as, for example, trend-followers. Any absolute characterization
pertaining to the nature of investors is prone to default due to the complexity inherent
in human nature, which may generate various results, especially in the presence of
interpersonal interaction within a precarious environment. The Efficient Markets
Hypothesis has often been attacked on such grounds, given its assumptions on
investors’ homogeneity and rationality.
Our research with respect to positive feedback trading involved the examination of
papers that develop the notion of heterogeneous traders, i.e. traders that employ
different strategies. In this respect, we should mention the papers by De Long et al
(1990) 23, Shiller (1984), Cutler et al (1990) 24 and Sentana & Wadhwani (1992) 25.
These models basically assume two categories of traders, usually designated as
“rational” and “feedback” ones. The former tend to utilize some form of rational
model to forecast future returns, while the latter rely on the previous period’s price
(s). it is from the interaction of the two that the price of a stock shall be determined
and it is this interaction that we shall try to model further.
De Long, J.B., Shleifer, A., Summers, L.H. & Waldmann, R.J.: “ Positive Feedback Investment
Strategies and Destabilizing Rational Speculation”, The Journal of Finance, Volume 45, Issue 2, June
1990, pp. 379-395.
23
Cutler, D.M., Poterba, J.M. & Summers, L.H.:” Speculative Dynamics and the Role of Feedback
Traders”, “The American Economic Review”, AEA Papers and Proceedings, pp. 63-68.
24
Sentana, E. & Wadhwani, S.:” Feedback Traders and Stock Return Autocorrelations: Evidence
from a Century of Daily Data”, “The Economic Journal”, Volume 102, Issue 411, March 1992, pp.
415-425.
25
We shall now refer to an additional tool that we chose to employ, namely the LotkaVolterra model, which was articulated during the 1920s. As we just said, we are
interested in picturing the interaction between two types of traders and the LV-model
can be utilized to this end. First of all we will provide a general expression, perhaps
the most primitive for the LV-model. According to its assumptions:
1) There are only two populations (species), namely the Predators (x) and the
Prey (y)
2) The Prey constitute the only source of food for the Predators
3) The Prey enjoys unlimited food supply from nature and face only one threat,
i.e. the Predators
4) Predators and Prey meet each other in nature, their encounters being random
5) Their interactions are jointly proportional to their respective population sizes
6) There is a single homogeneous ecosystem, e.g. a patch of land
According to the model:
dx
------- = -c * x + a* x * y
dt
dy
-------- = b * y – h* y * x
dt
(i)
(ii)
where (i) reflects the population growth rate of the predators (x) and (ii) the respective
one of the prey (y).
The constants a, b, c, h >0 where:
a = predators’ reproduction rate per 1 prey eaten
b= prey’ growth rate in the absence of predators
c= predators’ mortality rate in the absence of prey
h= predation rate coefficient
Note that the right-hand sides of these equations represent the population growth rate
for each species; we should also stress here that this is the Lotka-Volterra model
under the assumption of predatory conduct, i.e. with one species aiming at eliminating
the other. Notice also the right-hand side of both equations, where we observe the
term x * y and indicates the encounter rate of the two and stems from the law of mass
action; according to the latter, the rate of molecular collisions of two chemical species
in a dilute gas or solution is proportional to the product of the two concentrations (see
Edelstein-Keshet, 1988)26. It is this interaction that we are actually trying to insert into
the model from the beginning, in order to show that the demand of each category of
traders is influenced not only by its respective elements, but also from the demand of
its counterparts.
We assume the Sentana & Wadhwani model (S & W, hereafter) for the purpose of our
model-building; according to it, the demand functions for each category of traders are
given by the following pair of equations:
Qt =
Et-1 (rt) - z
----------------------------------μt
Yt = γ * r t-1
In equilibrium it must be that:
(1)
(2)
Qt + Yt = 1
(3)
Given the above mentioned, let us try to sketch an initial picture of the interaction at
works. The stock under question comes into life at period 0 and trading upon it
commences. Rational traders are aware of its fundamentals and are also able to
process them rationally in order to come up with next period’s price. Feedback traders
are unaware of fundamentals and if they were, they would still have no way of taking
advantage of them, since rational processing is not an assumption here for them.
Rational traders are assumed to trade every time the stock is considered by them as
underpriced, i.e. its price is below its fundamental value. Feedback traders trade on
the basis of past price-sequences.
Simply put, the problem could be expressed as follows. If a stock is underpriced, then
rational traders begin trading on it, thus boosting its price. At a certain point, be it
before or just about when the stock reaches its fundamental value, feedback traders
turn their attention to that stock (whatever the reason for that might be). Having
observed a flow of buy orders, or more appropriately, a sequence of price-rises, they
decide to buy as well. If so, we would then expect the price to rise even further, thus
diverging from its fundamental value. The behaviour of rational traders could assume
various expressions.
A proportion of them would opt for selling, since the market has grown much too
risky for them. What they essentially see is the price exhibiting a volatility, which
leads to a price overreaction. Since this enhances the uncertainty for them and since
they are not sure as to the source of this phenomenon or to its development, they
choose to unload their positions now. Part of the incumbent rational traders is
therefore expected to sell, as it feels “intimidated” by the price impact of the feedback
traders (see Kyle & Wang, 1997)27.
Leah Edelstein- Keshet:”Mathematical Models in Biology”, The Random House / Birkhäuser
Mathematics Series, 1987.
27
Kyle, A.S. & Wang, F.A.:” Speculation Duopoly with Agreement to Disagree: Can Overconfidence
Survive the Market Test?”, The Journal of Finance, Vol. LII, No. 5, December 1997, pp.2073-2090.
26
The rest of their rational peers, though, are presumed to think otherwise. What
they see is a stock being overpriced and they conjecture that this is due to the presence
of traders whose estimates about the future value of the asset are overoptimistic,
regardless of their validity. They decide, thus, to exploit this overpricing by staying in
the market and waiting for the price to rise even further, before they sell. Their selling
point is of little interest here; it could be the exhaustion of the feedback traders’
population, i.e. the lack of any further ones to boost the stock price, or maybe the slow
decline in the growth rate of returns, or perhaps the fact that their fundamental
analysis may have provided them with an indication of adverse price course.
Whatever the case, these traders behave as rational speculators (similarly to the ones
of the De Long et al model (1990)) and exploit their informational superiority.
Feedback traders are not aware of the existence of rational traders, as they are
only assumed to view the price. Rational traders may, however, contend that such a
trading practice is at works and may thus incorporate it into their demand function-at
least the speculative part of those. Let’s see how we can provide a first simple picture
of this. We have:
Yt = Et-1 (Yt) + et
where Et-1 (Yt) is the expected feedback demand by rational traders and et is an
error term.
Since Yt = γ * rt-1, the expected demand of the feedback traders is a function of
the past period price. At the end of period t-1 the rational traders, as well as the
feedback ones, know the price; feedback traders are assumed to trade towards the
same direction as past period’s price. Rational traders are betting on this contingency
and the only uncertain element here is the feedback coefficient (gamma). However,
this can be estimated, at least with respect to its sign. If the price at the end of period
t-1 is higher than at the end of period t-2, then chances are that feedback traders will
keep boosting the stock’s price. As a result, positive feedback trading will continue to
persevere and gamma will be positive. How much positive is something the rational
traders cannot estimate; the knowledge of the sign however seems to be sufficient
enough.
In follows that:
Et-1 (Yt) = γe * rt-1
where γe is an estimator of the real gamma.
Since both demands in equilibrium must equal one, we have:
Et-1 (rt) = a + μ t - γ * μ t * r t-1
Et-1 (rt) = a + μ t * ( 1 - γ * r t-1)
Et-1 (rt) = a + μ t * [1 – (γe * r t-1 – et)]
REFERENCES (in alphabetical order)
1) Allport, G.W. & Postman L.:“An analysis of rumour“, Public Opinion
Quarterly, 10 (4), 1946, pp.501-517.
2) Antoniou, A., Koutmos, G. & Pericli, A.: “Index Futures and Positive
Feedback Trading: Evidence from Major Stock Exchanges”, Working Paper,
University of Durham, Department of Economics & Finance, 1998.
3) Bikhchandani S. & Sharma S.:”Herd Behaviour in Financial Markets”,
IMF Staff Papers, Vol. 47, No3, 2001.
4) Brennan M.J. & Cao H. H.: « International Portfolio Investment Flows »,
Journal of Finance, Vol. LII, No. 5, December 1997, pp. 1851-1880.
5) Chang, E.C., Cheng, J.W. & Khorana, A.:” An examination of herd
behaviour in equity markets: An international perspective”, Journal of
Banking & Finance, 24, 2000, pp. 1651-1679.
6) Chordia, T. & Swaminathan, B.: “ Trading Volume and CrossAutocorrelations in Stock Returns”, The Journal of Finance, Vol. LV, No2,
April 2000, pp.913-935.
7) Christie, W.G. & Huang, R.D.: “Following the Pied Piper: Do Individual
Returns Herd around the Market?”, Financial Analysts Journal/ July-August
1995, pp.31-37.
8) Cutler, D.M., Poterba, J.M. & Summers, L.H.:” Speculative Dynamics and
the Role of Feedback Traders”, The American Economic Review, AEA
Papers and Proceedings, pp. 63-68.
9) De Long, J.B., Shleifer, A., Summers, L.H. & Waldmann, R.J.: “ Positive
Feedback Investment Strategies and Destabilizing Rational Speculation”, The
Journal of Finance, Volume 45, Issue 2, June 1990, pp. 379-395.
10) Fama, E.F.: “ Efficient Capital Markets: A Review of Theory and Empirical
Work” (in Session Topic: Stock Market Price Behavior), Journal of Finance,
Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting
of the American Finance Association New York, N.Y. December, 28-30,
1969. (May, 1970), pp. 383-417.
11) Feng, L. & Seasholes, M.S.:“ Herding and Location“, Working Paper,
University of Berkeley, Haas School of Business, September 2002.
12) Graham, J.R.:”Herding among Investment Newsletters: Theory and
Evidence”, The Journal of Finance, Vol. LIV, No. 1, February 1999, pp.237268.
13) Grinblatt M. & Keloharju M.: “How Distance, Language, and Culture
influence Stockholdings and Trades”, Journal of Finance, volume LVI, No3,
June 2001.
14) Hirshleifer, D.: “Investor Psychology and Asset Pricing”, Journal of Finance,
Vol. LVI, No4, August 2001.
15) Jaffe, J.F. & Mahoney, J.M.:”The Performance of Investment Newsletters”,
Federal Reserve Bank of New York, Staff Reports, Number 48, October 1998.
16) Keshet, Leah-Edelstein:”Mathematical Models in Biology”, The Random
House / Birkhäuser Mathematics Series, 1987.
17) Kim W. & Wei S-J. :” Foreign Portfolio Investors Before and During a
Crisis”, National Bureau of Economic Research Paper Series, Working Paper
6968, February 1999.
18) Kim, W. & Wei, S-J. :“Offshore investment funds: monsters in emerging
markets?“, National Bureau of Economic Research Working Paper 7133, May
1999.
19) Kyle, A.S. & Wang, F.A.:” Speculation Duopoly with Agreement to
Disagree: Can Overconfidence Survive the Market Test?”, The Journal of
Finance, Vol. LII, No. 5, December 1997, pp.2073-2090.
20) Lakonishok, J., Shleifer, A. & Vishny, R.: ”The impact of institutional
trading on stock prices”, Journal of Financial Economics, 32, (1992), pp. 2343.
21) Lobao, J. & Serra, A.P.:” Herding Behaviour: Evidence from Portuguese
Mutual Funds”, Working Paper, Instituto de Estudos Financeiros e Fiscais,
March 11, 2002.
22) Scharfstein, D. S, & Stein, J. C.:“ Herd Behaviour and Investment“, The
American Economic Review, Volume 80, Issue 3, June 1990, pp. 465-479.
23) Sentana, E. & Wadhwani, S.:” Feedback Traders and Stock Return
Autocorrelations: Evidence from a Century of Daily Data”, The Economic
Journal, Volume 102, Issue 411, March 1992, pp. 415-425.
24) Shiller, R.J.: “Stock Prices and Social Dynamics”, Brookings Paper on
Economic Activity, Volume 1984, Issue 2, (1984), 457-510.
25) Trueman B.: “Analyst Forecasts and Herding Behaviour”, The Review of
Financial Studies, Spring 1994,Vol.7, No.1, pp.97-124.
26) Welch, I.:” Herding among security analysts”, Journal of Financial
Economics, 58, 2000, 369-396.
27) Wermers, R.:”Mutual Fund Herding and the Impact on Stock Prices”, The
Journal of Finance, Vol. LIV, No.2, April 1999, pp. 581-622.