Momentum anomaly in the Dutch
stock market
Byrsa Amsterodamensys
Amsterdam Stock Exchange established 1602 by the Verenigde Oostindische Compagnie (VOC) –
regarded as the oldest exchange in the world. The building depicted is of Hendrick de Keyser, built in
1611 and demolished in 1838.
Abstract
In this paper the Dutch stock market from 1973-2010 is examined for the existence of the
momentum anomaly. Indeed a strategy that shortsells past losers and buys past winners earns
significant excess return. The 12 month formation and 3 months holding specification yields
1.79% monthly. Especially the loser portfolio is disproportionally weighted towards small firms,
hence transaction costs can be substantial. However there are still profitable specifications.
Performing a size-, industry- or beta-neutral momentum strategy does decrease the payoffs but
still yields significant returns and therefore do not explain the whole picture. As this evidence
contradicts the rational literature, a promising strand of explanations is presented by behavioral
finance. Likely the momentum effect is just irrational underreaction, caused by behavioral
biases, especially conservatism, representativeness and self-attribution.
Keywords: momentum anomaly, stock market, efficient market hypothesis, asset pricing
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Erasmus University Rotterdam
Erasmus School of Economics
Master thesis Financial Economics
Momentum Anomaly in the Dutch stock market
Pim Esveld - student number 308723
Supervision: Dr. Agnieszka Markiewicz and Ko-Chia C. Yu
May 17th 2010
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Table of contents
1. Introduction.....................................................................................................................................................................4
2. Literature review...........................................................................................................................................................5
2.1 Momentum findings..............................................................................................................................................5
2.2 Sources of momentum .........................................................................................................................................6
2.3 Momentum profitability......................................................................................................................................9
2.4 Explanations ......................................................................................................................................................... 10
3. Data and methodology ............................................................................................................................................. 13
3.1 Data........................................................................................................................................................................... 13
3.2 Methodology ......................................................................................................................................................... 13
4. Results............................................................................................................................................................................. 15
4.1 Momentum in the Dutch stock market ...................................................................................................... 15
4.3 Industrymomentum........................................................................................................................................... 21
4.4 Size-neutral momentum .................................................................................................................................. 22
4.5 Beta-neutral momentum ................................................................................................................................. 23
4.6 Industry-neutral momentum ......................................................................................................................... 24
5. Conclusion ..................................................................................................................................................................... 25
References .......................................................................................................................................................................... 26
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1. Introduction
For several decades researchers have reported the existence of anomalies in several markets.
We can define an anomaly as a phenomenon that cannot be explained by currently established
theory. In case of the stock market anomalies are predictable patterns in stock returns that
should not occur under conventional asset pricing models, with the market being efficient as the
underlying assumption.
In the efficient market, strategies based on past performance or other publicly available
information, cannot structurally succeed. Such a free lunch would be arbitraged away, bringing
the prices back to equilibrium. Therefore, ceteris paribus, stock picking strategies that
structurally earn excess return are anomalies. A short list of reported anomalies includes size,
value/growth, momentum, reversal, dividend, weather, calendar and insider trading. For
example, it is documented that stocks generate abnormally high returns in January and at the
days around each turn of the month. Stocks perform structurally bad on Mondays. Also on rainy
and cloudy days the returns are lower than on sunny days. On days surrounding new moon
returns are lower than with full moon.
A lot of such anomalies can be categorized as urban legend: investors trade on it and it vanished.
Others were found by chance. If lots of people search for patterns in the same dataset, there will
always be someone finding a spurious explanatory variable, for example between shoe size of
CEO’s and stock returns. Such a pattern will not survive confrontation with out-of-sample
reality. Still there is particularly one anomaly in play, that fuels an ongoing academic debate: the
momentum anomaly. A strategy that shortsells past losers and uses the proceeds to buy past
winners seems to yield return in excess of the market.
The question may arise: what does it matter? Well, of course every investor is craving for the
best risk/return tradeoff. A strategy that delivers abnormal returns structurally, is very valuable.
However, that is not the perspective in this thesis. From an academic point of view this anomaly
is important because it seems to contradict the efficient markets hypothesis (EMH). This
hypothesis states that markets are informationally efficient. Agents are rational and all past
publicly available information is incorporated into the current price. This implies that future
returns are not predictable, which seems to be violated by the momentum anomaly. Maybe all
currently available information is not yet in the price? Perhaps investors under- or overreact to
information? If anomalies are the result of behavioral biases, we can no longer speak of rational
agents, which is a huge offence on the EMH. Maybe there is an underlying source of risk, we did
not model yet. And besides, the EMH may be flawed, but do we have any alternative model
describing the stock market as a whole?
In brief: the momentum anomaly largely remains to be a puzzle for academic research. In this
master thesis I want to examine the Dutch stock market for the existence of a momentum
anomaly. The abundant literature on the subject will be reviewed, and several methodological
innovations will be taken into account. What have we learned, which parts of the anomalies have
been solved and which remain?
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2. Literature review
2.1 Momentum findings
For decades researchers report that average stock returns seem to be related to past
performance. This is called an anomaly because such strategies should not succeed structurally,
according to the EMH. It’s return cannot be explained by the Capital Asset Pricing Model of
Sharpe (1964) and Lintner (1965), who argue that the crossection of returns is linear in beta,
market risk. For this thesis especially return persistence and reversal are important. The first is
commonly called momentum, past winner continue to be winners. The second describes
winners becoming losers after some time.
Early literature on return continuation focused on a specific setup: buy winners and shortsell
losers. Levy (1967) tries 68 different trading rules and claims that a strategy that buys a stock
with a price higher than past half year average, earns abnormal return. Jensen and Bennington
(1970) are more skeptic about his results, arguing that it may be the result of selection bias. Try
68 strategies, no wonder that eventually you will find a profitable one.
Since the eighties more studies focus on the opposite setup: shortsell winners and buy losers. It
is documented that individuals have the tendency to overreact to information (e.g. Kahneman
and Tversky, 1982; Shiller, 1981). If individuals overreact, this is likely to be observable in stock
returns. Indeed, De Bondt and Thaler (1985) show that stocks that performed poorly in the past
3-5 year period, deliver higher returns in the subsequent 3-5 years. Other research has
questioned this result, arguing that the excess return is attributable to systematic risk or to the
selection of smaller stocks (e.g. Chan, 1988; Ball and Kothari, 1989). Since the outperformance
occurred only in Januaries, can it be attributed to overreaction or are there other underlying
anomalies? Chan et al. (1999) found that the winner and loser portfolios did evenly well in year
2 and 3 after formation. Later studies of e.g. Jegadeesh (1990) and Lehmann (1990) document
reversals in short terms, a week or a month. Jegadeesh and Titman (1993) find medium term
momentum and long term reversal in the U.S. stock market from 1965-1989.
It is important to note these different time horizons. The literature currently mentioned suggests
short term reversal (week/month), medium term momentum (a quartile up to a year) and long
term reversal again (3 to 5 years).
Fama and French (1996) argue that long term reversal can be consistent with a multifactor
model of returns, but they cannot explain medium term momentum. They even call momentum
the only CAPM-related anomaly not explained by the Fama-French (1993) 3-factor model. Chan,
Jegadeesh, and Lakonishok (1996) find that medium term momentum can partly be explained by
underreaction to earnings announcements, but simple stock momentum is not subsumed by
earnings momentum.
International momentum
Many empirical researchers have studied patterns on substantially the same database of U.S.
stocks. As Rouwenhorst (1998) put it, ‘it cannot be ruled out that these apparent anomalies are
simply the outcome of an elaborate data snooping process’. He tries to address that concern by
looking at momentum in an international setting. Of course, it can be argued that international
markets are strictly not independent and thus does not rule out data snooping. But since the U.S.
studies found no relationship to common factors or conventional measures of risk, this
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argument is not applicable. Asness et al. (1996) and Richards (1996) find return momentum by
using country indices. Rouwenhorst (1998) studies differences across markets at the individual
stock level using a sample of 2,190 stocks from 12 European countries in the period 1978 to
1995, in all of which he finds momentum. Furthermore he finds that country momentum is
unimportant in explaining the continuation effect. Griffin et al. (2003) and Chui et al. (2000)
report comparable results. Doukas and McKnight (2005) find significant momentum returns in 8
out of 13 European countries.
Foerster et al. (1995) provide evidence on momentum strategies in the Canadian market. Forner
and Marhuenda (2003) find significant momentum returns in the Spanish market. Fu and Wood
(2007) find momentum in Taiwan. Also the UK market has been studied extensively. For
example, Liu et al. (1999), Hon and Tonks (2003) and Agyei-Ampomah (2007) find momentum
in UK stocks. It must be noted that Hon and Tonks’ (2003) UK sample from 1955-1996 shows
only momentum from 1976-1996. Siganos (2007) finds stronger momentum (up to 2.09 % per
month) in the UK stock market, when choosing only the top 40 winners and losers, instead of
deciles or quintiles. Rey and Schmid (2007) find returns to their momentum arbitrage portfolios
of up to 44% per annum by restricting their sample to Switzerland’s largest blue-chip stocks and
choosing only one winner and one loser stock. See table 1 for a summary of the returns,
documented by several studies with different specifications. In the following paragraph different
factors that have found to be influential on the momentum returns, will be discussed.
Table 1 – Random comparison of different returns found in different markets and specifications. The
momentum specification with e.g. 12 months formation and 3 months holding, is abbreviated by 12-3.
Market
Jegadeesh & Titman (1993)
US
Chan et al. (1996)
US
Rouwenhorst (1998)
EU
Jegadeesh & Titman (2001)
US
Chen and Hong (2002)
US
Forner and Marhuenda (2003) SP
Ellis & Thomas (2004)
UK
Doukas and McKnight (2005)
EU
Avramov et al. (2006)
US
Agyai-Ampomah (2007)
UK
Siganos (2007)
UK
Rey and Schmid (2007)
SW
Specification
12-3
6-6
12-3
6-6
6-6
12-3
6-6
6-6
6-6
12-1
6-6
6-6
Period
1965-1989
1977-1993
1980-1995
1990-1998
1928-1999
1965-2000
1990-2003
1988-2001
1985-2003
1988-2003
1975-2001
1994-2005
Monthly return
1.31%
0.73%
1.35%
1.39%
0.64%
1.30%
1.40%
0.89%
1.49%
3.71%
2.09%
3.64%
2.2 Sources of momentum
Seasonality
Jegadeesh and Titman (1993) find a striking seasonality in momentum profits. They document
that the winners outperform losers in all months except January. Therefore, momentum returns
are negative in January. They warn the reader that this seasonality could potentially be a
statistical fluke. However, when the same authors extend their time series 8 years later, they
find persistence of this effect (Jegadeesh and Titman, 2001). They find lower January losses,
when stocks priced below $5 are deleted and Nasdaq stocks are added. Jegadeesh and Titman
(1993) cite an explanation of Keim (1989), that it may be due to bid-ask spread biases.
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Size
Most studies report that the momentum sample, especially the loser sample, is disproportionally
weighted towards small and illiquid stocks (e.g. Jegadeesh and Titman, 1993; Lesmond et al.,
2004; Agyei-Ampomah, 2007). This could imply that the implementation of a momentum
strategy is not feasable, since frequent trading with illiquid and small stocks is very costly, if not
impossible. However, several studies present evidence that this is not the case. Rouwenhorst
(1998) concludes that the continuation effect is not merely a reflection of firm size. Although he
finds the momentum effect to be stronger for smaller firms, in every size category past winners
outperform losers. Hon and Tonks (2003) find that although past losers have a tendency to be
smaller than winners, only 2 out of 8 test periods are statistically significant when tested for
equality. They conclude that consequently the difference in size between loser and winner
portfolios cannot explain momentum profits. Siganos (2007) finds that the momentum returns
gradually decline as firms are larger. They find it to be due to a declining influence of the loser
portfolio. Rey and Schmid (2007) restrict themselves to Switzerland’s largest blue-chip stocks,
and still find large momentum profits. Li et al. (2009) severely question the profitability of
standard momentum strategies, but when shortlisting from each winner and loser portfolio
those stocks with the lowest transaction costs, they still find positive momentum returns.
Industry
Moskowitz and Grinblatt (1999) find evidence that the profitability of a momentum strategy is
attributable primarily to momentum in industry factors. Chen and Hong (2002) have similar
findings, also for horizons of about one year. Hou (2001) shows that this may be due to slow
information diffusion within industries. However, such an industry momentum strategy comes
with the cost that the portfolio is unlikely to be well diversified and therefore unlikely to be a
risk-free arbitrage. Asness et al. (2000), Lee and Swaminathan (2000), Grundy and Martin
(2001) find evidence that industry does not explain everything. The latter find evidence that in a
world with individual stock price momentum, even portfolios created randomly exhibit some
momentum. It depends on how industry momentum strategy is constructed, but still they find
that the difference between industry momentum and momentum in random portfolios is not
statistically significant. This is confirmed by Chordia and Shivakumar (2002), who find that
individual stock- and industry-based momentum returns are distinct and separate phenomena.
Agyei-Ampomah (2007) measure the share of each industry in their momentumportfolios.
Industries were not disproportionally weighted, at least not consistently in time.
Information uncertainty
Jiang et al. (2005) and Zhang (2006) demonstrate that firms with higher information uncertainty
show higher momentum payoffs. Information uncertainty is proxied by firm size, firm age,
return volatility, cash flow volatility and analyst forecast dispersion. Hong et al. (2000) report
that holding size fixed, momentum strategies work better among stocks with low analyst
coverage. This is consistent with the hypothesis that firmspecific information diffuses slowly
across investors. Womack (1996) shows that stocks with strong buy recommendations from
analysts typically exhibit high price momentum and stocks with strong sell recommendations
typically exhibit low price momentum. Lee and Swaminathan (2000) show that past trading
volume predicts the magnitude and persistence of momentum in the future. This suggests that
trading volume is proxies investor interest in a stock and may be related to the speed with which
information diffuses into prices. Chelley-Steeley and Siganos (2008) find that new brokerage
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systems on the trading floor generate higher momentum returns, where a priori lower
continuation was expected due to lower transaction cost.
Earnings announcements
Jegadeesh and Titman (1993) find that the winner portfolio has higher returns around
announcement date, than the loser portfolio. On a 12-36 month horizon, this pattern is exactly
opposite. Others find that unexpectedy high earnings announcements outperform unexpectedly
poor earnings (Latane and Jones, 1979; Bernard and Thomas, 1989; Bernard et al., 1995). The
superior performance persists over a period of about six months after earnings announcements.
Another study by Givoly and Lakonishok (1979) documents sluggish price responses to
revisions in analyst earnings forecasts. When using both past returns and earnings
announcements, subsequent returns at horizons of six months to a year can be even stronger
predicted (Chan et al., 1996). Fu and Wood (2007) find momentum returns in Taiwan to be
restricted to months following annual statements.
Business conditions
Chordia and Shivakumar (2002) find that momentum payoffs are large during expansions and
non-existent during recessions. Avramov and Chordia (2005) show that the momentum payoffs
are related to business cycle variables such as the Treasury Bill yield, the term spread, and the
default spread. Griffin et al. (2003) analyses US data from 1926-2000 and find stronger
momentum in down markets. Siganos and Chelley-Steeley (2006) also find stronger momentum
in bear markets, for the UK stock market. Bauer et al. (2008) use European data and find no
empirical support that momentum is related to business cycle variations.
Credit rating
Avramov and Chordia (2005) argue that, since credit risk varies over the business cycle, it is
natural to ask whether the momentum payoffs are related to the credit risk of firms. They find
momentum to be insignificant when executed among only BB+ or higher rated firms. Avramov et
al. (2006) establish a robust link between momentum and credit rating, again momentum does
only exist among low grade firms.
High unconditional mean
Remarkably, Lo and MacKinlay (1990) point out that momentum just selects stocks having high
unconditional means. Also Conrad and Kaul (1998) and Berk et al. (1999) argue that the
profitability of momentum strategies may be completely due to cross-sectional variation in
expected returns instead of to any predictable time-series variations in stock returns. This
implies that a buy-and-hold winner minus loser strategy should earn excess return at any time
horizon beyond the formation period. This is generally not the case, and as Chen and Hong
(2002) remark, this source of momentum profits has been rejected in the literature.
Negative cross-serial covariance
Lewellen (2002) finds that the momentum in portfolios is due to future stock return being
negatively correlated with the lagged return of other stocks, so momentum profits arise from
negative cross-serial covariance. This is consistent with an overreaction hypothesis in which
certain stocks overreact to a common factor and others do not. Barberis et al. (1998) and Hong
and Stein (1999) find evidence that implies positive autocovariances in stock returns.
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Macroeconomic factors
Haugen and Baker (1996) and Chordia and Shivakumar (2002) find that momentum returns are
captured by a parsimonious set of standard macroeconomic variables. They argue this raises the
bar for the behavioral explanations of momentum. Griffin et al. (2002) find in 17 markets that
momentum profits bear basically no statistically or economically significant relation to the Chen
et al. (1986) macroeconomic factors.
2.3 Momentum profitability
Besides the search for the source of momentum profits, the question arises whether it is still
profitable after transaction costs. Statistical significance may be settled right now, but that does
not imply economic significance. At first sight this question seems only relevant for investors,
not for academics trying to explain an anomaly. Still, it is also important for the latter, since this
can explain why it is not arbitraged away. Possibly there are some limits to arbitrage.
The evidence on transaction costs is not univocal. For example, Jegadeesh and Titman (1993)
and Liu et al. (1999) assume a one-way cost of 0.5% and find that momentum is still profitable
after transaction cost. Other studies have argued that the number of tiny and illiquid stocks, the
short selling and the frequent rebalancing suggests that this average trading cost is not
representative. Also Lesmond et al. (2004) make the case for a more traditional explanation for
momentum returns: trading friction. They re-examine the studies by Jegadeesh and Titman
(1993, 2001) and conclude that transaction costs outweigh the reported momentum return.
Keim (2003) finds by examining 33 investment funds in 36 countries that returns reported in
previous studies of simulated momentum strategies are not sufficient to cover the costs of
implementing those strategies. Carhart (1997) calculates actual transaction costs and concludes
that momentum is not exploitable after those costs are taken into account. By contrast,
Korajczyk and Sadka (2004) find that for certain momentum strategies it would require a fund
size of about $5 billion for momentum profits to disappear as a result of transaction costs. AgyeiAmpomah (2007) finds that in his UK sample, small and illiquid stocks are disproportionally
weighted in the loser portfolio. From 1988-2003 transaction costs outweigh the profit for
shorter than 6m formation and holding periods, but longer periods remain to be profitable. To
summarize: although transaction costs can be substantial, it is not completely clear whether they
declare momentum profits illusory. They seem not to explain the persistence of the momentum
premium entirely.
Actual use of momentum strategy
Grinblatt and Titman (1989) find that the majority of mutual funds tend to buy past quartile
winner stocks. Grinblatt and Titman (1995) examined the investment strategies of 155 mutual
funds and found that 77% of the mutual funds in their sample used trend chasing strategies
somehow. Gompers and Metrick (2001) find current levels of institutional ownership are
negatively correlated with past twelve month stock returns, thus they conclude that large
institutions do not follow a momentum strategy. Burch and Swaminathan (2001) find that
institutions engage in momentum trading over the subsequent 3 quarters, buying winners and
selling losers, in response to past returns but not past earnings news. The success of those
mutual funds suggests that stocks indeed have some momentum in returns. Furthermore,
feedback trading/trend chasing/technical analysis is used widely to make forecasts (e.g. Taylor
and Allen 1992). Those popular trading rules can cause momentum patterns. If investors find
out some stock performed well, they buy it. It reinforces movements in stock prices even in the
9
absence of fundamental information. Under this explanation, we expect that past winners and
losers will subsequently experience reversals in their stock prices.
2.4 Explanations
The existence of the momentum anomaly presents a huge challenge for the efficient markets
hypothesis (EMH), formalized by Fama (1964) in his dissertation. The EMH states that the
market is informationally efficient, all information is already in the price. The price of each stock
is equal to it’s fundamental value. Future prices cannot be predicted by analyzing prices from the
past. Price movements are entirely determined by news (new information not incorporated in
the price yet). Hence, returns follow a random walk and it is not possible to structurally achieve
returns in excess of average market returns. The weak form EMH does not require prices to be in
equilibrium at all times, actually the EMH predicts a random split between under- and
overreaction, but investors cannot consistently profit from these random market inefficiencies.
The momentum strategy is a challenge exactly because it uses only prices from the past and can
earn structural excess return with it.
As Chan et al. (1996) put it: ‘In the absence of an explanation, the evidence on momentum stands
out as a major unresolved puzzle.’ Fama (1998) calls the short-term continuation of returns ‘an
open puzzle, but it is still rather new and further tests are in order’. Since there is disagreement
about the interpretation of this evidence, there have been numerous attempts to explain the
puzzle presented by the momentum effect. These explanations can be devided into three
categories: methodological, rational, and behavioral explanations.
Methodological explanations
“He who mines data may strike fool's gold.”(Peter Coy, Businessweek, June 1997)
The methodological explanations actually argue there is no momentum effect in reality. This
strand of literature believes that the evidence on momentum is flawed and biased. In the years
after Jegadeesh and Titman (1993) published their work on the momentum strategy, several
studies attributed it to data snooping bias. When searching extensively for patterns in a certain
dataset, one will ultimately find a significant relationship, which is just a statistical fluke (e.g.
Merton, 1988; Lo and MacKinlay, 1990; Black, 1993; MacKinlay, 1995). The data snooping
argument is by later studies proved not be true for the momentum strategy. Not only did
Jegadeesh and Titman (2001) extend their time series of their 1993 study and found persistence
out-of-sample. As has been mentioned, the momentum effect is found internationally and in
different time series and specifications. The argument that international samples are strictly
speaking not independent, and that researching international markets is not useful, does not
hold. It is found that momentum is not explained by common factors. Schwert (2003)
demonstrates that market anomalies typically disappear, reverse or attenuate following their
discovery. This is not the case for momentum. Others have argued that the momentum effect is
illusory and economically insignificant (e.g. Lesmond et al., 2004; Hanna and Ready, 2005). This
topic is already addressed in paragraph 2.3 about momentum profitability.
Among others Kothari et al. (1995) cite survival bias as a problem that can exaggerate predictive
power. If only the stocks that are currently listed on, say the S&P 500 index are selected, then the
dataset suffers from biased selection. The stocks that went bankrupt are not in the dataset. The
dataset contains only strong firms, that have been included in the S&P 500 once and survived
since. Thus the results are biased. In line with this methodological problem they mention the
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backfilling bias. When a stock is included in an index, all of the stock data of that firm prior to
inclusion is also backfilled. These arguments are addressed satisfactory by the methodology of
several studies, as well as in this thesis. Many researchers do not take index constituents, but all
stocks of a country. In addition to that they use active ánd inactive stocks, to correct for these
biases.
Rational explanations
“It is time, however, to ask whether this literature, viewed as a whole, suggests that
efficiency should be discarded. My answer is a solid no. (…) Instead, the alternative
hypothesis is vague, market inefficiency. This is unacceptable.” (Fama 1998)
The rational class of explanations accepts the evidence on the momentum anomaly, and tries to
reconcile its existence with the weak form EMH. This strand of literature literature generally
admits that neither the CAPM nor the Fama-French multifactor models can explain the higher
payoffs to the momentum strategy. The rational theory allows no return without risk, and thus it
is only a matter of finding a yet undiscovered risk factor.1 The unkown riskfactor has to be
incorporated in a new multifactor asset pricing model. This may lead to rejection of the CAPM,
but in their view the EMH could remain intact.
Some studies argue that differentials in expected stock returns are expected and required by
investors, because it is a compensation for bearing higher risk (Fama and French, 1992, 1993;
Ball et al., 1995; Conrad and Kaul, 1998). Chordia and Shivakumar (2002) want to stress the
rational explanation, as they believe the behavioral explanation does not provide the full
picture. They argue that persistent underreaction should present low-risk arbitrage
opportunities to rational investors, who can hold a diversified portfolio that is long on winners
and short on losers and which is constructed to have low factor risk. They believe it is strange
that this underreaction is not arbitraged away, suggesting some underlying risk factor. Avramov
and Chordia (2006) find that it may be premature to discard risk-based models to explain
momentum. The authors believe that some risk factor is in the business cycle, which could
capture the impact of momentum on the cross-section of individual stock returns.
Fama (1998) argues anomalies to be chance events, since the weak form EMH predicts a random
split between under- and overreaction. Daniel et al. (1998) find the evidence that does not
accord with this viewpoint because some of the return patterns are too strong and regular.
Momentum is present both internationally and in different time periods. Fama (1998), who
actually proposed the EMH in his dissertation in 1964, finds the rejection of the EMH
inacceptable, since studies rarely test a specific alternative to market efficiency. Market
deficiency is too vague an hypothesis. The literature has shown that, like all models, market
efficiency is a faulty description of how prices are formed, but we cannot discard it until we have
a better specific model, according to the standard scientific rule. Such an alternative, itself
potentially rejectable by empirical tests, has not been accepted yet, and therefore we cannot
reject the EMH, according to Fama.
Behavioral explanations
“If a stock has been the subject of bad news and has done poorly, they may throw it out,
even if it is now a cheap stock; they don't want prospective investors to think they pick
losers." (Birinyi, Forbes Dec. 1993)
1
Note that this may also be used as a cheap argument to dodge the burden of proof.
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“Man is neither infinite in faculties, nor in apprehension like a god. Nor is human fallibility
shed at the doorstep of the stock exchange. Psychology-based asset pricing theory has
promise of capturing this reality.” (Hirschleifer, 2001)
The behavioral strand of litarature explains the momentum payoffs by rejecting the assumption
of a rational investor, which is a major setback for the EMH. Investors have biases in trading,
which is reflected in the prices, which in turn causes certain patterns to arise. Exactly these
irrational patterns are exploited by momentum.
To start off, the intuitive appeal of the rationality assumption of the EMH is questionable. The
burden of collecting and processing information is extraordinarily high, requiring God-like
capabilities (derived from De Grauwe and Grimaldi, 2006). Also there should not occur any
trade, because of the homogeneous rational agents argument. And besides: if anyone believes in
an efficient market, it cannot be efficient, because nobody searches for inefficiencies. Not only
are there theoretical objections, also behavioral evidence attacks the rationality assumption.
Behavioral finance and psychology have documented lots of biases, of which only the most
relevant will be summarized.
Investors suffer from overconfidence bias: they put too much trust in their own predictions. This
results partly from self-attribution bias (attributing past success to own capability and past
failure to bad luck) and the hindsight bias (they tend to believe, after an event has occurred, that
they predicted it before it happened). Investors also wishfully think too rosy about future
prospects. The representativeness bias states they erroneously think they recognize patterns
from the past. They are also suffering from conservatism: they cling too tightly and for too long
to their opinion, in the face of new information. Other studies even find the stronger
confirmation bias: people tend to ignore evidence that goes against their opinion (or even
interpret the evidence in their own favour). Investors also suffer from anchoring bias: they take
an appealing value in mind, they use that as an anchor and adjust their estimate away from that
value. Finally, they search for comparable events, available in their memory, in which recent or
more pronounced events are overweighted (this list is derived from Barberis and Thaler, 2003).
These biases can be partly overlapping, but generally tend to limit the individual’s ability to
learn. In brief: people behave irrationally. Investing is done by people. Ergo, investors behave
irrationally.
But since a standard scientific rule is to judge the quality of a model by its empirical
performance, we ask ourselves the question: how does informational efficiency of the EMH stand
out in practice? There are several studies pointing out that investors do not trade fully rational.
DeLong et al. (1990) find evidence that traders extrapolate expectations. Differences in
predicted returns come as a surprise to investors (e.g. Chopra et al., 1992; Lakonishok et al.,
1994; Haugen, 1995). Chan et al. (1996) provide evidence of delayed overreaction, suggesting a
market that responds only gradually to new information. In Daniel et al. (1998) overconfident
and conservative investors overreact to private signals and underreact to public news. Hong and
Stein (1999) model the underreaction as occurring when boundedly rational agents each
observe some private information but fail to extract other agents' information from prices,
resulting in a gradual diffusion of information. This is called selective information conditioning.
Hong et al. (2000) find evidence that momentum is a by-product of initial underreaction,
because news disseminates slowly through the market. According to the authors it is thus more
likely to affect small stocks with limited analyst coverage. Among others, Barberis et al. (1998),
12
Barberis and Shleifer (2003) and Doukas and McKnight (2005) find that conservative investors
initially underreact, followed by overreaction.
3. Data and methodology
3.1 Data
Firstly, monthly data is downloaded from Datastream: factor adjusted stock prices and market
capitalization data on all stocks in the Netherlands (traded on Euronext Amsterdam)2. This
yielded 417 firms. The maximum time frame was selected, Datastream goes back to January
1973 for the Netherlands. The total number of stocks active at a certain point in time varied
from 113 to 217.
Furthermore, monthly data on the MSCI Europe index is downloaded from the MSCI Barra
website, from 1973 in local currency. This index serves as proxy for the market, to calculate beta.
There is no strictly Dutch index until in 1983 the AEX index was calculated, but this index only
covers 25 largest companies and therefore is not likely to be representative. Since the Dutch
economy is largely correlated with other European countries, the MSCI Europe index provides
an accurate proxy for the market.
Some of the stocks were denoted in Dutch Guilder (NLG), some were in Euros. Currency is not
converted, since the euro is only in place since 2001 and returns are not influenced by it. Since
Datastream is known not to be always accurate with the data. Therefore the datafile is checked
for ‘perfect’ outliers, like when return on one month is exactly 100 or 10. Those values must be a
coding error, for example a typo in the decimal. Hence they are corrected a handful times. The
dataset is not corrected for other outliers, as the risk of deleting vital information is substantial.
For example, so-called tail risk induced by take-overs and distress could be deleted. An investor
faces this risk in reality too, those outliers may represent important information about the
relationship between variables and it might be a factor in momentum returns.
3.2 Methodology
The total number of stocks active at a certain point in time varies from 217 to 113, with mean
and median equal at 164 stocks. The sample is divided into deciles based on this average
number for ease of programming in Excel, so at each point in time 16 stocks are assigned to the
winner portfolio and 16 to the loser portfolio.3
The momentum strategy is implemented with 40 specifications: 6 formation periods times 6
holding periods (plus 4 long term holding periods). The momentum specification with, say 12
months formation and 3 months holding, is abbreviated by 12-3. During the formation period
each stock’s total period return is measured, the best performing 16 stocks form the winner
portfolio and the worst 16 stock form the loser portfolio. Then the equal weighted winner
portfolio is bought and the loser portfolio is sold short during the holding period. Throughout
The Amsterdam Stock Exchange is considered the oldest in the world, established in 1602 by the Verenigde
Oostindische Compagnie or VOC (Dutch East India Company) for dealings in its printed stocks and bonds. It was
subsequently renamed the Amsterdam Bourse. Currently NYSE Euronext manages the trades.
2
Chelley-Steeley and Siganos (2005) found that momentum profits are the strongest using deciles, followed by
quintiles and then by triciles. In a later study Siganos (2007) used a fixed number too, around 2-3% of his sample
seems to be optimal. This implies that a lower number of shares may even provide equal, if not stronger, momentum,
by undertaking fewer transactions and thus lower commission cost (with larger standard deviation however).
3
13
this thesis, short term means shorter than a quartile, medium term means 3-12 months, long
term means >1 year.
Some studies also examine the returns when skipping a week or a month between the formation
and the holding period, in order to avoid some bid-ask spread, price pressure, and lagged
reaction effects (see for example Jegadeesh and Titman, 1993; Rouwenhorst, 1998). This yields
slightly higher returns. I put more energy in studying more (and shorter term) specifications
then they did (40 instead of 16).
Stocks that are delisted during the portfolio formation period are excluded from the sample at
that point but if a stock becomes delisted in the holding period the missing monthly returns are
assigned to zero.
Overlapping portfolio returns are used (in line with most studies) in order to increase the power
of the statistical tests. In any given month t, the strategy holds a series of portfolios that are
selected in the current month as well as in the previous K - 1 months, where K is the holding
period. Although I do not analyse non-overlapping returns, Hon and Tonks (2003) obtain similar
conclusions using non-overlapping and overlapping returns.
Following most studies on momentum (see for example Jegadeesh and Titman, 1993;
Rouwenhorst, 1998) the arithmetic mean return is calculated. Geometric mean is argued to be
more reliable and conservative by the very nature of a risky asset (e.g. Rey and Markus, 2007).
This involves some sophisticated technical procedures and this thesis is then incomparable with
most studies on momentum. Besides, an arithmetic mean provides the best estimate for next
period’s expected return. To evaluate the significance of the winner minus loser portfolio return
the one sample t-test is used.
The 6-6 specification is used to determine size, beta and volatility within each sample. This way
it can be examined whether the momentum portfolios are disproportionately weighted towards
certain type of stocks.
For the size/beta/industry-neutral momentum, stocks are double-sorted on size, beta and
industry. Beta is calculated using the MSCI Europe index. For industry a standard industry
classification provided by Datastream is used. For size and beta, the sample is devided into
triciles, constituting 3 size and beta classes. Then within each class momentum is executed as
mentioned above. Only within the industries having more than 20 firms, the momentum strategy
is calculated, using quintiles to increase the power of the test.
The Fama and French multifactor models have proved not to explain medium term momentum.
To try another model, alpha is calculated with the Lower Partial Moment (LPM) framework. This
measures risk quadratically over losses, it puts more weight on tail-risk, which may present a
risk-based explanation for the momentum effect. The Lower Partial Moments Framework, as
formalized by Bawa and Lindenberg (1977) can take different specifications. In this thesis
alpha’s are calculated with threshold of zero and power two and three. Post and Van Vliet (2005)
prove that Equation 1 is equal to that of Bawa and Lindenberg, and that the alfa’s can be
generated by Equation 2.
14
[1]
[2]
Where Rm means the market return, Ri stands for return of stock i, μ denotes average return. If
alpha’s are non-zero, this could mean three things, since actually a joint hypothesis is tested.
This is related Rolls critique. Either the proxy for the market does not mimick the real market, or
CAPM/LPM model does not describe the market correctly, or there really is excess return. In
order for the test to be useful, the assumption has to be made that the Rm and the CAPM/LPM are
correct.
4. Results
4.1 Momentum in the Dutch stock market
From January 1973 till February 2010 the momentum specifications have been examined. The
effective period excludes the number of months used in formation and holding, e.g. the 12-3
specification needs 12 months to form, and 3 months to hold all overlapping portfolios. Thus,
effectively this specification runs from March 1974 till February 2010. Holding periods up to 36
months are only calculated for the 6 and 12 month formation periods, to limit the time of
programming. Table 2 reports the average monthly returns.
Table 2 - Average montly returns for the period 1973-2010. The loser portfolio consists of the worst
performing decile of stocks, the winner portfolio consists of the best performing decile, measured during
formation period K. The stocks are held during the holding period J. The one sample t-test examines whether
return is significantly different from zero. Returns marked by *, ** or *** denote significance levels of 10%,
5% or 1% respectively.
K
J=
1
2
3
6
9
12
1
Loser
0.0098**
0.0059
0.0036
0.0035
0.0030
0.0036
Winner
0.0043
0.0073***
0.0086***
0.0085***
0.0092***
0.0094***
W-L
-0.0054
0.0013
0.0050**
0.0050***
0.0062***
0.0058***
Loser
0.0061
0.0030
0.0017
0.0015
0.0019
0.0021
Winner
0.0072***
0.0095***
0.0098***
0.0095***
0.0104***
0.0105***
W-L
0.0012
0.0065**
0.0081***
0.0079***
0.0085***
0.0083***
Loser
0.0057
0.0014
-0.0001
0.0006
0.0015
0.0015
Winner
0.0110***
0.0121***
0.0122***
0.0118***
0.0117***
0.0112***
W-L
0.0053
0.0107***
0.0124***
0.0112***
0.0102***
0.0097***
Loser
0.0043
0.0005
-0.0004
0.0006
0.0009
Winner
0.0135***
0.0144***
0.0150***
0.0145***
W-L
0.0092**
0.0140***
0.0154***
0.0139***
Loser
0.0030
0.0027
0.0024
Winner
0.0142***
0.0154***
W-L
0.0112**
Loser
Winner
2
3
6
9
12
24
36
0.0018
0.0036
0.0045
0.0138***
0.0123***
0.0096***
0.0088***
0.0129***
0.0105***
0.0060***
0.0043**
0.0024
0.0030
0.0036
0.0161***
0.0150***
0.0134***
0.0119***
0.0127***
0.0137***
0.0126***
0.0104**
0.0082**
0.0028
0.0003
-0.0004
0.0003
0.0019
0.0026
0.0037
0.0046
0.0185***
0.0186***
0.0179***
0.0153***
0.0136***
0.0121***
0.0094***
0.0086***
15
W-L
0.0157***
0.0183***
0.0183***
0.0150***
0.0118***
0.0096***
0.0057***
0.0040**
The most important observation is that momentum is alive and strong in the Dutch stock
market. In the second place, it is remarkable that almost none of the loser returns are
significantly different from zero, almost all winners are significantly positive. This suggests that
most of the returns to a winner-loser momentum strategy comes from buying the winners,
which indicates that the profits do not primarily rely on (costly) shorting the losers, as some
studies hypothesize (e.g. Moskowitz and Grinblatt, 1999). Only in the 1-1 specification the loser
portfolio performs very well and the winner portfolio relatively poor, causing momentum
strategy losses. This indicates mean reversion in the very short term, but only in combination
with short formation periods. This is consistent with many studies (e.g. Jegadeesh, 1990;
Lehmann, 1990). Momentum is insignificant in those short term specifications.
In the third place we see that momentum generally increases with formation period. The best
specification is the 12-3 one, yielding 1.83% monthly, which is 22% annually. This is exactly the
same specification that often yielded the highest returns (e.g. Jegadeesh and Titman, 1993;
Rouwenhorst, 1998). The results are largely in line with other studies on the subject.
Another observation is that returns for holding periods from 3 to 12 months, top at the 3 month
holding period and then gradually decline, as losers regain strength, and winner lose strength.
This is also in line with other studies (De Bondt and Thaler, 1985; Jegadeesh and Titman, 1993),
suggesting that there is mean reversion in the long term. This is the conclusion most studies
draw from the tables.
However, when closely examining the results, it is remarkable that if the first 12 months are
deleted, monthw 13 till 36 yield exactly zero return. Losers perform equally well as winners in
the long run, which is very easily observable when presenting it in a graph, see figure 1. This
result is consistent with Chan et al. (1999). If momentum is driven by trend chasers, the returns
would reverse eventually. Since we observe that losers have about the same return as winners
(and not more), this suggests that momentum is not driver by trend chasers. It can however not
be ruled out that reversal occurs after the 36 month period.
16
Momentum return pattern
2,5%
2,0%
1,5%
Return certain month
1,0%
Average
0,5%
0,0%
1 2 3 4 5 6 7 8 9
-0,5%
12
18
24
30
36
Holding month
Figure 1 – Each holding months return, following the formation period of 12 months. The average line
represents the normal average montly momentum returns.
Comparison with other studies
Compared with international momentum studies the Netherlands generally show higher
momentum. In the first place, Jegadeesh and Titman (1993) study the American stock market
from 1965-1989, and find the 12-3 specification best performing, yielding 1.31% monthly on
average. My dataset does not go back to 1965, but from 1973-1989 in the Netherlands it yields
1.86% on average, with the same specification. Secondly, Rouwenhorst (1998) studies Europe
from 1980-1995. He also finds the 12-3 specification to be best, returning on average 1.35%
monthly. In the Netherlands I find a strong 2.16% for that period.
As far as I know, Rouwenhorst (1998) and Doukas and McKnight (2005) are the only studies
about momentum in the Dutch stock market. Comparison with those studies yields the
following. Both study the 6-6 specification for the Netherlands. Rouwenhorst from 1980-1995,
finding 1.26% average monthly return, where I find 1.43%. It must be noted that he has a
smaller sample (101 firms, instead of 417) and he converts all returns into Deutsch marks.
Doukas and McKnight study 155 Dutch stocks (excluding tiny firms, returns converted to pound
sterling) from 1988-2001. They use triciles (0.79% monthly) and quintiles (0.98%). I use deciles,
and find a significant 1.50% monthly return for that period. Several small methodological
differences may give rise to small differences, but the results are still largely in line with each
other.
Business cycle
Several studies document that momentum moves opposite to the business cycle. This is
important, because that means the momentum strategy can diversify a portfolio of stocks very
well. Beta of the momentum strategies is on average around -0.18, meaning that it has a low
market risk, but moves slightly opposite. Let’s see if we can verify that during bear/bull markets.
In the past decade Europe experienced especially 2 peaks in the business cycle. The period
March 2000 till February 2003 was pointed downward, while March 2003 till April 2007 had a
very positive trend. The credit crunch created a bearish scenario between May 2007 and January
2009. Since februari 2009 the stock market started to increase again. During these 4 periods the
17
momentum returns are measured for the 6-6 month specification. Respectively, this yielded
1.95%, 1.10%, 2.18%, -1.94% monthly. Only the credit crunch had a significant return (1%
level). Also a quick glance at other specifications, confirms a general pattern, that during
downfalls the momentum profits are higher, than for upward trends. A reason for this could be
that the stocks that performed worse during the bearish trend are sold short, but those stocks
restore much quicker than the other stocks, causing losses in the subsequent positive trend.
Beta
After having performed the momentum strategy it would be of interest to see whether the beta’s
between the portfolio’s differ. The 6-6 momentum portfolio is examined since this one is
representative for other specifications.
The beta is calculated using the MSCI Europe Index returns (local currency). Following
Rouwenhorst (1998) this can be regarded as a good proxy for the market, since most of the
exposure to the stocks in the dataset comes from the Netherlands and other European countries.
The index has been calculated since 1969, covering our full dataset. It can be argued that a Dutch
proxy would have been better, but there is no Dutch market proxy until 1983. Another line of
thought might lead to a world-proxy. This was considered, using e.g. the MSCI World index.
However, the world index captures hardly anything of the Dutch stock market exposure. The
MSCI World yielded overall lower beta’s, but in the same order, so at least the choice of
benchmark is not a crucial one. The MSCI Europe beta’s are calculated during the 12 months
prior to the holding period (see Table 3).
Table 3- Average 1 year beta of the 6-6 momentum portfolio’s
Total sample (n=417)
Winner-decile
Loser-decile
Beta
0.6418
0.6989
0.7246
As can be seen, the total sample has the lowest market risk. The winner decile has a slightly
higher beta, the loser decile has the highest. This is consistent with Jegadeesh and Titman
(1993), except for the magnitude of the numbers. Since they use NYSE/AMEX stocks and a valueweighted index of their own sample, generally higher beta’s are explainable. The message
remains the same: indeed momentum strategy systematically pick stocks with higher market
risk. But can this explain the whole momentum payoffs? That question is addressed in paragraph
3.2.
Size
Also the size of the firms is important. As has been shown in the literature review, most studies
document that momentum strategies (especially the loser portfolio) systematically selects small
firms, which are expensive to short. In the table below, average market capitalization is
reported. As can be seen, the loser decile depends heavily on small firms, whereas the winner
decile seems to make more use of larger firms, even larger than average in the sample.
Table 4 – Average size, market capitalization of 6-6 momentum portfolio’s
Total sample (n=417)
Winner-decile
Loser-decile
Average Market Capitalization
678.9
917.5
320.1
18
However, regarding size, average seems a rather misleading descriptive, since the size
distribution is quadratic. Let’s present it as follows, figure 2. It can be seen that globally the
order described by the mean holds, but for the lowest decile. The loser portfolio does not select
tiny firms that are in the total sample. Tiny firms are especially illiquid and costly to trade, but
those are not disproportionally selected by the loser portfolio.
Figure 2 – Size distribution of the 6-6 momentum portfolio’s
Hong et al. (2000) state as their first key result that once one moves past the very smallest
stocks, the profitability of momentum strategies declines sharply with firm size. This is contrary
to my finding, that when leaving out the 33% smallest firms, profits are even greater than with
the complete sample (see paragraph 3.5 for elaboration on this).
Liquidity
Finally it can be of interest to see whether the strategy selects more illiquid stocks (also difficult
and costly to trade frequently), as some studies suggest. Liquidity is measured by counting the
zero-returns, where no trading occurred, devided by the total number of returns. This ratio
ranged from 0.43 (lliquid) to 1 (perfectly liquid). The averages are presented in table 5.
Table 5 – Average liquidity ratio for 6-6 momentum portfolio’s
Average liquidity ratio
Total sample (n=417)
Winner-decile
Loser-decile
0.932
0.942
0.935
The numbers do not differ much, though the winner sample might be somewhat more liquid. It is
anyway not observable in this dataset that the momentum portfolios select systematically
illiquid stocks.
Seasonality
As can be seen in figure 3 losers generally have positive returns in the first half of the year, and
negative returns in the second half. Winner returns are generally higher. Momentum returns are
only substantially negative in January and May, which is in line with other studies (e.g. Jegadeesh
19
and Titman 1993). The largely positive return for both losers and winners in January is
remarkable, the January effect. Losers even outperform winners in that month, which yields a
negative winner-minus-loser return.
Figure 3 – Average returns each month, 6-6 month specification, 1973-2010
The January effect is first documented by Wachtel (1942), and ever since a stream of studies
about the January effect has come by. The most studied explanation is the well known tax-lossselling hypothesis of Dyl (1977) followed by e.g. Roll (1983), Jones et al. (1991) and Eakins and
Sewell (1993). In the first place it is strange that this effect is still found, and that rational
investors do not arbitrage it away. Secondly, among others Fu (2009) has found evidence against
this hypothesis (in his case in the Taiwan stock market), since in Taiwan there is no tax on
capital gains. The same holds for the Dutch stock market, where capital gains have only been
taxed for just a while after World War II. In the sample period 1973-2010, this tax was not
applicable. Furthermore, the January effect has also been found in countries where the tax year
does not begin with January (e.g. Brown et al., 1983). Also window dressing has been proposed
as an explanation (fund managers do not want to present to their clients that they had losers in
their portfolio, so they sell them before year end). Or is it just psychology: the ‘...widespread
hope that the new year will prove better than the old’ (Wachtel 1942)? Remarkably, Anderson et
al. (2005) even found the January effect in laboratory. This suggests a behavioral explanation
(against the EMH). Since explaining the January effect lies not in the scope of the thesis, I will
leave this puzzle to further research by only observing that the January effect is also present in
the Dutch stock market, and that it has a slight negative influence on the momentum profits.
Therefore it does not explain the positive overall momentum returns.
Lower partial moments alpha
The alpha according to the CAPM is on average 0.0093 for the momentum winner-loser
specifications. According to the standard LPM (power two) it is slightly higher (0.0097).
However, with LPM power three (taking into account skewness) alpha has decreased to 0.0019
(all alpha’s significant at the 1% level). This suggests that the momentum strategy suffers from
skewness, which lowers excess return for momentum strategies. Still, the fact that even LPM
with skewness taken into account, yields significant excess return, means that this does not
explain momentum in total.
20
Profitability after transaction costs
As has been shown in the literature review, there is much debate on the profitability of
momentum strategies. Such a strategy requires high turnover with short selling small and less
liquid stocks. Jegadeesh and Titman (1993) assume 0.5% one way transaction cost (as in
Rouwenhorst 1998). They calculate annual cost of around 10%. Several studies find this number
to low. Chan and Lakonishok (1997) find that average round-trip cost is 0.9% for large
capitalization stocks and 3.31% for small capitalisation stocks on the NYSE.
Profitability of the 6-6 specification can now be determined. Annualized return is 18%.
According to the estimate of Jegadeesh and Titman (1993), in my dataset still 8% return net of
transaction cost remains. According to the estimate of Chan and Lakonishok (1997) exactly all of
the return is consumed by the transaction cost. Other specifications have higher or lower
turnover, so their transaction costs will be different. It greatly depends on the estimate of
transaction costs, whether momentum is profitable. Still, the 12-3 specification has much higher
annual returns, this specification may be profitable, even with conservative estimates.
Transaction costs can be reduced by lowering turnover, shortlisting from the total sample only
the less costly-to-trade stocks, choosing longer holding periods, choosing smaller portfolios.
Finally with derivatives the same level exposure can be achieved at lower cost. My personal view
is that momentum strategies definitely can be used for return enhancement, if it is adapted to
reduce transaction costs. It must however be noted that the aim of this thesis is not to find a
profitable momentum strategy, but to identify and disentangle the momentum effect in the
Dutch stock market.
4.3 Industrymomentum
Following Moskowitz and Grinblatt (1999) who find evidence that the profitability of a
momentum strategy is attributable primarily to momentum in industry factors, in this
paragraph it is examined whether there is industrymomentum in the Dutch stock market.
The strategy is performed as follows. The general industry classification from Datastream yields
14 different industries. Each industry’s performance is measured over formation period K. Then
the best 3 industries are equally weighted4 into the winner portfolio, the worst 3 industries form
the loser portfolio. The winner portfolio is bought and the loser portfolio is sold short, and held
during the holding period J. Table 6 summarizes the return on this strategy.
Table 6 – Average montly returns on industrymomentum from 1973-2010.
K
J=
1
2
3
6
9
12
3
Loser
Winner
W-L
0.0010
0.0065***
0.0055*
-0.0003
0.0070***
0.0073***
0.0003
0.0060***
0.0057***
0.0012
0.0052***
0.0040*
0.0015
0.0052***
0.0037*
0.0018
0.0048***
0.0030
6
Loser
Winner
W-L
0.0008
0.0078***
0.0070**
0.0008
0.0080***
0.0071**
0.0011
0.0075***
0.0064*
0.0019
0.0073***
0.0054*
0.0024
0.0073***
0.0049
0.0030
0.0066***
0.0036
There is significant momentum effect among industries. The best specification is the 3-2 or the
6-2 strategy. For the 6-6 specification, Moskowitz and Grinblatt (1999) found a mean return of
Moskowitz and Grinblatt (1999) use value weighted portfolio’s, but also equal weights were analyzed for robustness,
generating no significant differences.
4
21
0.0043, which is slightly lower. Again especially the winning industries contribute to the
momentum profits, all loser industries have returns of about zero. The returns of losers and
winners converge in the longer term, momentum profits decline from the third holding month
onwards. In general, the returns to the industrymomentum strategy are about half of those of
the stock momentum, suggesting that industry factors are not a very likely explanation.
Furthermore, a portfolio of equalweighted industries is less diversified than a normal
stockmomentum portfolio. Therefore, performing industrymomentum in practice would be a
suboptimal choice, but it is however interesting from an academic perspective. Conrad and Kaul
(1998) argue that industry momentum profits should be significantly smaller than those for
individual equities because the cross-sectional variation in mean industry returns is smaller
than that for individual stock returns. This is indeed the case (contrary to the findings of
Moskowitz and Grinblatt, 1999). However, before it can be concluded that momentum just
selects stocks with higher unconditional mean, those stocks should then show momentum in
every subsequent period. As is shown in figure 1, this is not true. It is more likely that the
formation of industryportfolios just dilutes the strong momentum of individual stocks.
4.4 Size-neutral momentum
As we have seen previously, there are large differences in size between the portfolios. The
question arises whether size can explain the momentum profits. Perhaps momentum profits are
confined to just a specific size. Or possibly momentum does not exist among equally sized stocks,
since the dispersion of returns will be smaller. In order to control for size, the total sample is
double-sorted: first on size, then on return. There are 3 size classes: the first tricile are the small
firms, the second tricile are the medium firms, the last tricile are the large firms. Within each size
class ordinary momentum is performed, in the 6-6 specification. The results can be found in
Table 7.
Table 7 – Average monthly returns for the 6-6 specfication.
Size
Large
Medium
Small
All ex. small
All ex. medium
All ex. Large
Montly return
Loser
0.0044
Winner
0.0094***
W-L
0.0050**
Loser
0.0009
Winner
0.0134***
W-L
0.0125***
Loser
0.0156*
Winner
0.0149***
W-L
-0.0007
Loser
0.0012
Winner
0.0120***
W-L
0.0108***
Loser
0.0143
Winner
0.0141***
W-L
-0.0001
Loser
0.0121
Winner
0.0163***
W-L
0.0042
22
All of the size-sorted portfolio’s show lower momentum returns than in the total sample. The
large firms exhibit some momentum, yielding a significant 0.5% per month. The medium firms
show large momentum, of 1.25% per month, approaching the level of the total sample. Within
the small sample there is no significant momentum effect. Some studies indicate that most of the
returns come from small firms, but that does not hold in this sample. When deleting the small
firms, a strong monthly momentum return of 1.08% remains. The opposite is true actually, when
deleting the large firms, momentum is not significant anymore.
Thus, momentum is not explained by size at all. A momentum strategy that depends on the (less
expensive to trade) largest part of the stocks can earn fine returns. It is remarkable however,
that momentum returns show an inverted U-shape relationship with size. If momentum would
be explained by some size-related risk factor, we would expect momentum to be higher among
small firms. This is not the case.
In the view of Hong and Stein (1999) momentum is due simply to slow diffusion of private
information, which especially occurs among small firms with low analyst coverage. Among
smaller firms momentum profits will be higher under that hypothesis. They also argue that
among smaller firms, momentum profits mainly come from shorting the losers, because bad
news of small firms is swept under the carpet and thus diffuses slowest. Clearly this hypothesis
is not confirmed in my data. Among small firms, losers and winners perform equally well, so
there is no significant momentum effect among them.
4.5 Beta-neutral momentum
We have already observed that a momentum strategy systematically picks higher beta stocks.
This is partly logical: it selects stocks that had extraordinary returns over the past quartiles, so
their beta will most of the time be higher. The question is whether this higher market risk
(denoted by beta) can account for the excess returns of the strategy. Since stocks with the same
beta have the same expected return according to the CAPM, the dispersion in returns within a
subsample of the same beta should be small, hence momentum returns should be low.
The subsamples are stratified based on the beta with respect to the MSCI Europe index. Beta is
calculated over the past year and is then devided in triciles. In Table 8 the returns to beta neutral
portfolios can be found.
Table 8 – Average monthly returns for the 6-6 specification
Beta
High
Medium
Low
All ex. low
Montly return
Loser
0.0008
Winner
0.0129***
W-L
0.0121***
Loser
-0.0009
Winner
0.0132***
W-L
0.0141***
Loser
0.0036
Winner
0.0123***
W-L
0.0087
Loser
-0.0015
Winner
0.0144***
W-L
0.0159***
23
All ex. medium
All ex. high
Loser
0.0015
Winner
0.0147***
W-L
0.0131*
Loser
0.0013
Winner
0.0159***
W-L
0.0146***
Only the low beta subsample does not yield significant momentum returns. The returns are
often even higher than the total sample. For example, when we exclude high beta stocks,
momentum returns are still higher than the total sample. This indicates that the strategy may
select higher beta stocks, but is not confined to it.
If the momentum profits would be related to differences in expected returns, they will be less
when they are implemented on stocks within each subsample, because then the dispersion of
returns will be very low. This is not the case, again suggesting that momentum returns are not
due to cross-sectional differences in return. This result again confirms that stocks show some
autocorrelation in idiosyncratic returns, because profits remain also after splitting the total
sample up.
4.6 Industry-neutral momentum
To address the hypothesis of Moskowitz and Grinblatt (1999) that momentum is subsumed by
an industry-related factor, industry-neutral subsamples are constructed, based on the general
industry classification provided by Datastream. Note that this is a different setup then in
paragraph 3.2, where winning industries are bought and losing industries are sold short. The
industries are not all suitable, since they comprise of too few firms. The following firms
(number) are studied: industrial goods (94), personal and household goods (47), technology
(41), real estate (26), food and beverages (24). The winning quintile of stocks is bought, the
losing quintile is sold short. Then ordinary momentum strategy is performed, using the 6-6
month specification. The results can be found in Table 9.
Table 9 – Momentum returns to industry-neutral strategy.
Industry
Monthly return
Indust. goods
Loser
Winner
W-L
0.0058
0.0110***
0.0052*
Pers. goods
Loser
Winner
W-L
0.0010
0.0079***
0.0069*
Technology
Loser
Winner
W-L
0.0048
0.0096*
0.0048
Real Estate
Loser
Winner
W-L
0.0015
0.0068***
0.0053***
Food and bev.
Loser
Winner
W-L
0.0074***
0.0086***
0.0011
Although the power of this test can be questioned for the smallest samples, still we can see some
weak momentum profits within sectors. For example, in the industrial goods sector (94 firms)
24
profits amount to a significant 6.42% annually. This suggests that momentum is not driven by
industry factors, as Moskowitz and Grinblatt (1999) argue.
5. Conclusion
"It appears that the market has simply not done its arithmetic." (Krugman, 1985)
This thesis contributes to research in that the Dutch stock market is not researched for
momentum to this extend. Also, compared to other studies, an abundant amount of
specifications (40) and time span (37 years) is studied.
A trading strategy that shortsells past losers, and uses the proceeds to buy past winners, earns
excess return in the Dutch stock market from 1973-2010. The best strategy is (in line with other
studies) the 12-3 specification, yielding on average 22% annually.
Especially the first 3 holding months contribute to the momentum effect, from 4-12 months the
returns gradually decline as winner and loser return converges. From 12 months onwards
winners and loser perform equally well. Mean reversals (negative returns in the long run) are
not evident in my dataset. There is especially a extraordinary positive return for both losers and
winners in January, making momentum profits insignificant in that month.
The portfolio of losers are disproportionally weighted toward small firms, whereas winner
stocks are on average larger than the total sample. However, this does not explain the
momentum profits, since even in size-neutral subsamples there is momentum. The portfolio of
losers (and also slightly for winners) selects higher beta. Again, this does not explain the total
picture, since within beta-neutral samples there is momentum. No significant difference in
liquidity among losers/winners/total sample is found. There is some industrymomentum, but
since the returns are much lower, it seems that this just dilutes pure stock momentum. Among
industry-neutral portfolios there is some weak momentum, indicating that also industry cannot
explain the momentum profits.
Also transaction costs are taken into account. They consume a large part of the returns
(especially for the shorter specifications). Estimates however diverge, but using conservative
estimates some specifications can earn excess return. Especially after some turnover-reducing
steps or with the use derivatives momentum undoubtedly can be used to enhance returns or
diversify large investment portfolios since momentum beta is slightly negative, indicating that it
mirrors the business cycle.
This evidence strongly suggests that rational explanations (risk factors) fail to explain
momentum. To date trading remains to be a human (fallible) activity. It is more likely that agents
just underreact to news, because they suffer from conservatism bias. They alter their opinions
slowly in the face of new information. Also the representativeness bias (people erroneously see
patterns) can account for this trend following behavior. My hunch is that also ego-defense
mechanisms may be of influence: portfolio managers don’t want prospective investors to think
they pick losers, even if it may be rational to do so, because past losers are now cheap. However,
they can blame bad luck when a selected past winner, results in losses. This is in line with the
self-attribution bias. Behavioral finance presents some challenging explanations to the efficient
markets hypothesis. A task for the field is to develop a strong alternative model. The data and
25
methodology in this paper did not allow for empirically testing the hypotheses on investor
behavior. This would be an interesting avenue for further research.
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