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.

1

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

2

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

3

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

4

1. IntroductionFor 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?

5

2. Literature review

2.1 Momentum findingsFor 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 momentumMany 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

6

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.

Market Specification Period Monthly returnJegadeesh & Titman (1993) US 12-3 1965-1989 1.31%Chan et al. (1996) US 6-6 1977-1993 0.73%Rouwenhorst (1998) EU 12-3 1980-1995 1.35%Jegadeesh & Titman (2001) US 6-6 1990-1998 1.39%Chen and Hong (2002) US 6-6 1928-1999 0.64%Forner and Marhuenda (2003) SP 12-3 1965-2000 1.30%Ellis & Thomas (2004) UK 6-6 1990-2003 1.40%Doukas and McKnight (2005) EU 6-6 1988-2001 0.89%Avramov et al. (2006) US 6-6 1985-2003 1.49%Agyai-Ampomah (2007) UK 12-1 1988-2003 3.71%Siganos (2007) UK 6-6 1975-2001 2.09%Rey and Schmid (2007) SW 6-6 1994-2005 3.64%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.

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.

7

SizeMost 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.

IndustryMoskowitz 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 uncertaintyJiang 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

8

systems on the trading floor generate higher momentum returns, where a priori lower continuation was expected due to lower transaction cost.

Earnings announcementsJegadeesh 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 conditionsChordia 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 ratingAvramov 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 meanRemarkably, 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 covarianceLewellen (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.

9

Macroeconomic factorsHaugen 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 profitabilityBesides 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. Agyei-Ampomah (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 strategyGrinblatt 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

10

absence of fundamental information. Under this explanation, we expect that past winners and losers will subsequently experience reversals in their stock prices.

2.4 ExplanationsThe 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

11

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.

12

“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),

13

Barberis and Shleifer (2003) and Doukas and McKnight (2005) find that conservative investors initially underreact, followed by overreaction.

3. Data and methodology

3.1 DataFirstly, 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 MethodologyThe 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

2 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.

3 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).

14

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.

15

[1]

[2]

Where Rm means the market return, Ri stands for return of stock i, denotes average return. Ifμ 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 marketFrom 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 24 36

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***

2 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***

3 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***

6 Loser 0.0043 0.0005 -0.0004 0.0006 0.0009 0.0018 0.0036 0.0045

Winner 0.0135*** 0.0144*** 0.0150*** 0.0145*** 0.0138*** 0.0123*** 0.0096*** 0.0088***

W-L 0.0092** 0.0140*** 0.0154*** 0.0139*** 0.0129*** 0.0105*** 0.0060*** 0.0043**

9 Loser 0.0030 0.0027 0.0024 0.0024 0.0030 0.0036

Winner 0.0142*** 0.0154*** 0.0161*** 0.0150*** 0.0134*** 0.0119***

W-L 0.0112** 0.0127*** 0.0137*** 0.0126*** 0.0104** 0.0082**

12 Loser 0.0028 0.0003 -0.0004 0.0003 0.0019 0.0026 0.0037 0.0046

Winner 0.0185*** 0.0186*** 0.0179*** 0.0153*** 0.0136*** 0.0121*** 0.0094*** 0.0086***

W-L 0.0157*** 0.0183*** 0.0183*** 0.0150*** 0.0118*** 0.0096*** 0.0057*** 0.0040**

16

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.

Figure 1 – Each holding months return, following the formation period of 12 months. The average line represents the normal average montly momentum returns.

-0,5%

0,0%

0,5%

1,0%

1,5%

2,0%

2,5%

1 2 3 4 5 6 7 8 9 12 18 24 30 36

Holding month

Momentum return pattern

Return certain month

Average

17

Comparison with other studiesCompared 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 cycleSeveral 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 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.

BetaAfter 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.

18

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

BetaTotal sample (n=417) 0.6418Winner-decile 0.6989Loser-decile 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 value-weighted 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.

SizeAlso 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

Average Market CapitalizationTotal sample (n=417) 678.9Winner-decile 917.5Loser-decile 320.1

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.

19

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).

LiquidityFinally 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’sAverage liquidity ratio

Total sample (n=417) 0.932Winner-decile 0.942Loser-decile 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.

SeasonalityAs 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 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.

20

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-loss-selling 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 alphaThe 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.

Profitability after transaction costsAs 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

21

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 IndustrymomentumFollowing 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 9 123 Loser 0.0010 -0.0003 0.0003 0.0012 0.0015 0.0018

Winner 0.0065*** 0.0070*** 0.0060*** 0.0052*** 0.0052*** 0.0048***W-L 0.0055* 0.0073*** 0.0057*** 0.0040* 0.0037* 0.0030

6 Loser 0.0008 0.0008 0.0011 0.0019 0.0024 0.0030Winner 0.0078*** 0.0080*** 0.0075*** 0.0073*** 0.0073*** 0.0066***W-L 0.0070** 0.0071** 0.0064* 0.0054* 0.0049 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 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

4 Moskowitz and Grinblatt (1999) use value weighted portfolio’s, but also equal weights were analyzed for robustness, generating no significant differences.

22

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 Montly returnLarge Loser 0.0044

Winner 0.0094***

W-L 0.0050**

Medium Loser 0.0009

Winner 0.0134***

W-L 0.0125***

Small Loser 0.0156*

Winner 0.0149***

W-L -0.0007

All ex. small Loser 0.0012

Winner 0.0120***

W-L 0.0108***

All ex. medium Loser 0.0143

Winner 0.0141***

W-L -0.0001

All ex. Large Loser 0.0121

Winner 0.0163***

W-L 0.0042

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

23

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 momentumWe 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 specificationBeta Montly return

High Loser 0.0008

Winner 0.0129***

W-L 0.0121***

Medium Loser -0.0009

Winner 0.0132***

W-L 0.0141***

Low Loser 0.0036

Winner 0.0123***

W-L 0.0087

All ex. low Loser -0.0015

Winner 0.0144***

W-L 0.0159***

All ex. medium Loser 0.0015

Winner 0.0147***

W-L 0.0131*

All ex. high Loser 0.0013

Winner 0.0159***

24

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 returnIndust. goods Loser 0.0058

Winner 0.0110***W-L 0.0052*

Pers. goods Loser 0.0010Winner 0.0079***W-L 0.0069*

Technology Loser 0.0048 Winner 0.0096*W-L 0.0048

Real Estate Loser 0.0015 Winner 0.0068***W-L 0.0053***

Food and bev. Loser 0.0074***Winner 0.0086***W-L 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) profits amount to a significant 6.42% annually. This suggests that momentum is not driven by industry factors, as Moskowitz and Grinblatt (1999) argue.

25

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 methodology in this paper did not allow for empirically testing the hypotheses on investor behavior. This would be an interesting avenue for further research.

26

References

Agyei-Ampomah, S., 2007, The post-cost profitability of momentum trading strategies: Further evidence from the UK, European Financial Management 13, 776–802.

Anderson, L.R, Gerlach, J.R. and Ditraglia, F.J., 2005, Yes, Wall Street, there is a January effect, evidence from laboratory auctions, working paper.

Asness, C.S., Liew, J.M. and Stevens, R.L., 1996, Parallels between the cross- sectional predictability of stock returns and country returns, working paper.

Asness, C.S., Porter, R.B. and Stevens, R.L., 2000, Predicting stock returns using industry-relative firm characteristics, working paper.

Ball, R. and Kothari, S.P., 1989, Nonstationary expected returns: implications for tests of market efficiency and serial correlation in returns, Journal of Financial Economics 25, 51-74.

Barberis, N. and Shleifer, A., 2003, Style investing, Journal of Financial Economics 68(2), 161-200.

Barberis, N., Shleifer, A., and Vishny, R., 1998, A model of investor sentiment, Journal of Financial Economics 49, 307-343.

Bauer, R., Cosemans, M. and Schotman, P., 2008, Conditional asset pricing and stock market anomalies, European Financial Management, forthcoming.

Bawa, V. and Lindenberg, E.B., 1977, Capital market equilibrium in a mean-lower partial moment framework, Journal of Financial Economics 5, 189–200.

Berinyi, G., 1993, The window dressing anomaly, in: Forbes 20-12-1993.

Bernard, V.L. and Thomas, J.K., 1989, Post-earnings-announcement drift: Delayed price response or risk premium?, Journal of Accounting Research 27, 1-36.

Bernard, V.L., Thomas, J.K. and Wahlen, J., 1995, Accounting-based stock price anomalies: separating market inefficiencies from research design flaws, working paper.

Black, F., 1986, Noise, The Journal of Finance 41, 529-543.

Burch, T.R. and Swaminathan, B., 2001, Are institutions momentum traders?, working paper.

Chan, K. C., 1988, On the contrarian investment strategy, Journal of Business 61, 147-163.

Chan, K. C. and Lakonishok, J., 1997, Institutional Equity Trading Costs: NYSE Versus Nasdaq, The Journal of Finance 52(2), 713-735.

Chan, K. C., Jegadeesh, N. and Lakonishok, J., 1996, Momentum Strategies, The Journal of Finance 51, 1681-1713.

Chan, K.C., Jegadeesh, N. and Lakonishok, J., 1999, The profitability of momentum strategies, Financial Analysts Journal 55, 80–90.

Chelley-Steeley, P. and Siganos, A., 2008, Momentum profits in alternative stock market structures, Journal of Multinationals Financial Management 18, 131–144.

Chen, N., Roll, R., and Ross, S.A., 1986, Economic forces and the stock market, Journal of Business 59, 383–403.

27

Chen, J. and Hong, H., 2002, Discussion of "Momentum and Autocorrelation in Stock Returns", The Review of Financial Studies 15(2), 565-573.

Chordia, T. and Shivakumar, L., 2002, Momentum, business cycle, and time-varying expected returns, The Journal of Finance 57(2), 985–1019.

Chui, A., Titman, S. and Wei, J., 2000, Momentum, ownership structure and financial crises: an analysis of Asian stock markets, working paper

Core, J.E., Guay, W.R., Richardson, S.A and Verdi, R.S., 2006, Stock Market Anomalies: What Can We Learn from Repurchases and Insider Trading?, Review of Accouting studies 11, 49-70.

Daniel, K.D., Hirshleifer, D.A. and Subrahmanyam, S., 1998, A theory of overconfidence, self-attribution, and security market under- and overreactions, Journal of Finance 53, 1839-1886.

De Bondt, W. F. M. and Thaler, R., 1985, Does the stock market overreact?, The Journal of Finance 40, 793-805.

De Bondt, W. F. M. and Thaler, R., 1987, Further evidence of investor overreaction and stock market seasonality, The Journal of Finance 42, 557-581.

De Grauwe, P. and Grimaldi, M., 2006, Exchange rate puzzles: a tale of switching attractors, European Economic Review 50(1), 1-33.

Doukas, J.A. and McKnight, P.J., 2005, European momentum strategies, information diffusion, and investor conservatism, European Financial Management 11 (3), 313–338.

Dyl, E., 1977, Capital gains taxation and year end stock market behavior, The Journal of Finance 32, 165-175.

Eakins, S. and Sewell, S., 1993, Tax-loss selling, institutional investors, and the January effect: a note, Journal of Financial Research 16, 377-384.

Ellis, M. and Thomas, D.C., 2004, Momentum and the FTSE 350, Journal of Asset Management 5, 25–36.

Fama, E.F., 1998, Market efficiency, long-term returns, and behavioral finance, Journal of Financial Economics 49, 283-306.

Fama, E.F. and French, K.R., 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3–56.

Fama, E.F. and French, K.R., 1996, Multifactor explanations of asset pricing anomalies, The Journal of Finance 51, 55–84.

Fama, E.F. and French, K.R., 2008, Dissecting anomalies, The Journal of Finance 63(4), 1653-1678.

Foerster, S., Prihar, A. and Schmitz, J., 1995, Back to the future, Canadian Investment Review 7, 9-13.

Forner, C. and Marhuenda, J., 2003, Contrarian and momentum strategies in the Spanish stock market, European Financial Management 9, 67-88.

Fu, H., 2009, Return reversals and momentum: a complementary rhythm in Taiwan, working paper.

28

Fu, H. and Wood, A., 2009, Momentum in Taiwan: seasonality matters!, Applied Economics Letters, forthcoming.

Givoly, D. and Lakonishok, J., 1979, The information content of financial analysts' forecasts of earnings: some evidence on semi-strong inefficiency, Journal of Accounting and Economics 1, 165-185.

Gompers, P. and Metrick, A., 2001, Institutional investors and equity prices, Quarterly Journal of Economics 116, 229-260.

Griffin, J. M., Ji, X. and Spencer, M. J., 2003, Momentum investing and business cycle risk: evidence from pole to pole, The Journal of Finance 58, 2513-2548.

Grinblatt, M. and Titman, S., 1989, Mutual fund performance: an analysis of quarterly portfolio holdings, Journal of Business 62, 394-415.

Grinblatt, M., Titman, S. and Wermers, R., 1995, Momentum investment strategies, portfolio performance, and herding: a study of mutual fund behavior, American Economic Review 85, 1088-1105.

Hanna, J.D. and Ready, M.J., 2005, Profitable predictability in the cross section of stock returns, Journal of Financial Economics 78(3), 463-505.

Hirshleifer, D., 2001, Investor psychology and asset pricing, The Journal of Finance 56, 1533-1597.

Hon, M. and Tonks, I., 2003, Momentum in the UK stock market, Journal of Multinational Financial Management 13, 43–70.

Hong, H. and Stein, J.C., 1999, A unified theory of underreaction, momentum trading and overreacting in asset markets, The Journal of Finance 54, 2143-2184.

Hong, H., Lim, T. and Stein, J.C., 2000, Bad news travels slowly: Size, analyst coverage and the profitability of momentum strategies, The Journal of Finance 55, 265–295.

Jegadeesh, N., 1990, Evidence of predictable behavior of security returns, The Journal of Finance 45, 881-898.

Jegadeesh, N. and Titman, S., 1993, Returns to buying winners and selling losers: implications for stock market efficiency, The Journal of Finance 48, 65–91.

Jegadeesh, N. and Titman, S., 2001, Profitability of momentum strategies: an evaluation of alternative explanations, The Journal of Finance 56, 699–720.

Jensen, M. and Bennington, G., 1970, Random walks and technical theories: some additional evidence, The Journal of Finance 25, 469-482.

Jiang, G., Lee, C.M.C. and Zhang, Y., 2005, Information uncertainty and expected returns, Review of Accounting Studies 10, 185-221.

Johnson, T.C., 2002, Rational momentum effects, The Journal of Finance 57(2), 585-608.

Jones, S., Lee, W., and Apenbrink, R., 1991, New evidence on the January effect before personal income taxes, The Journal of Finance 46, 1909-1924.

29

Kahneman, D. and Tversky, A., 1982, Intuitive prediction: biases and corrective procedures, in: Kahneman, D., Slovic, P. and Tversky, A. (eds.), Judgement under uncertainty: heuristics and biases, Cambridge University Press, London.

Keim, D.B., 1989, Trading patterns, bid-ask spreads, and estimated security returns: The case of common stocks at calendar turning points, Journal of Financial Economics 25, 75-98.

Keim, D. B., 2003, The cost of trend chasing and the illusion of momentum profits, working paper.

Korajczyk, R.A. and Sadka, R., 2004, Are momentum profits robust to trading costs?, The Journal of Finance 59(3), 1039-1082.

Kothari, S.P., Shanken, J. and Sloan R.G., 1995, Another look at the cross-section of expected stock returns, The Journal of Finance 50(1), 185-224.

Krugman, P., 1993, What do we need to know about the international monetary system?, working paper.

Latane, H.A. and Jones, C.P., 1979, Standardized expected earnings 1971-1977, The Journal of Finance 34 (3), 717-724.

Lehmann, B., 1990, Fads, martingales and market efficiency, Quarterly Journal of Economics 105, 1-28.

Lesmond, D.A., Schill, M.J. and Zhou, C., 2004, The illusory nature of momentum profits, Journal of Financial Economics 71, 349–380.

Levy, R., 1967, Relative strength as a criterion for investment selection, The Journal of Finance 22, 595-610.

Lewellen, J., 2002, Momentum and autocorrelation in stock returns, Review of Financial Studies 15, 533–563.

Li, X., Brooks, C. and Miffre, J., 2009, Low-cost momentum strategies, Journal of Asset Management 9, 366–379.

Lintner, J., 1965, The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, Review of Economics and Statistics 47, 13–37.

Lo, A.W. and MacKinlay, A.C., 1990, When are contrarian profits due to stock market overreaction?, Review of Financial Studies 3, 175–205.

MacKinlay, A.C., 1995, Multifactor models do not explain deviations from the CAPM, Journal of Financial Economics 38, 3-28.

Moskowitz, T.J. and Grinblatt, M., 1999, Do industries explain momentum?, The Journal of Finance 54 (4), 1249-1290.

Post, T. and Van Vliet, P., 2005, Empirical tests of the mean-semivariance CAPM, working paper.

Rey, D. and Schmid, M., 2007, Feasible momentum strategies: evidence from the Swiss stock market, Financial Markets Portfolio Management 21, 325–352.

Richards, A.J., 1996, Winner-loser reversals in national stock market indices: can they be explained?, The Journal of Finance 52, 2129-2144.

30

Roll, R., 1983, Vas ist das? The turn-of-the-year-effect and the return premia of small firms, Journal of Portfolio Management 9, 18-28.

Rouwenhorst, K.G., 1998, International momentum strategies, The Journal of Finance 53, 267–284.

Schwert, G.W., 2003, Anomalies and market efficiency, in: Constantinides, G.M., Harris, M. and Stulz, R. (eds.), The Handbook of the Economics of Finance, 939–972.

Sharpe, W.F., 1964, Capital asset prices: a theory of market equilibrium under conditions of risk, The Journal of Finance 19(3), 425-442.

Shiller, R.J., 1981, Do stock prices move too much to be justified by subsequent changes in dividends?, American Economic Review 71, 421-436.

Siganos, A., 2007, Momentum returns and size of winner and loser portfolios, Applied Financial Economics 17, 701–708.

Siganos, A. and Chelley-Steeley, P., 2006, Momentum profits following bull and bear markets, Journal of Asset Management 6, 381–388.

Singal,V., 2004, Beyond the random walk: a guide to stock market anomalies and low risk investing, Oxford University Press, Oxford.

Taylor, M.P. and Allen, H., 1992, The use of technical analysis in the foreign exchange market, Journal of International Money and Finance 11(3), 304-314.

Womack, K.L., 1996, Do brokerage analyst recommendations have investment value?, The Journal of Finance 51, 137–167.

Zhang, X.F., 2006, Information uncertainty and stock returns, The Journal of Finance 61, 105-137.