The Profitability Of Momentum Trading Strategies: A Comparison Between .
The profitability of momentum trading strategies: A comparison between stock markets in the Netherlands and Germany Oliver Weil Master’s Thesis International Financial Management (Double Degree) University of Groningen and Uppsala University Abstract: Can momentum trading strategies beat Dutch or German stock market indices? If so, do those strategies show significant positive net returns? For the period from March 2009 to March 2016 this appears to be the case for only one out of the nine momentum trading strategies investigated with respect to the Dutch stock market and for none of those same momentum trading strategies investigated with respect to the German stock market. Furthermore, this research finds that the net momentum returns seem to be winner- instead of loser-portfolio driven and that the longer the holding period, the higher the net momentum returns realized. Keywords: Momentum, Efficient markets, The Efficient Market Hypothesis, Transaction costs JEL classifications: G10, G11, G14, G15, G19 Author: Mail: Phone number: Student number: Study program: Place and date: Supervisor: O. A. M. Weil oam.weil@gmail.com 31 610155762 s2021277 DD MSc International Financial Management Groningen, June 2nd 2017 Prof. dr. B. W. Lensink
I. Introduction Managers as well as investors are continuously trying to find ways in which they can generate significant returns. In an efficient market - a market in which security prices always fully reflect available information - the true expected return on any security is equal to its equilibrium expected value, which is, of course, also the market's assessment of its expected value (Fama 1965, 1970). Although the efficient market hypothesis has been tested widely and generally has been found consistent (Jensen, 1978), around the late 1970s, systematic deviations from theoretical expectations, so-called anomalies, were discovered (Frankfurter and McGoun, 2001). These anomalies open up the possibility of profit opportunities by using trading strategies. Research on trading strategies that go against the efficient market hypothesis distinguishes between contrarian trading strategies and momentum trading strategies. This paper focuses on momentum trading strategies. A momentum trading strategy is based on stock price momentum. The underlying expectation is that past stock performance will continue into the future. In other words, stock prices that have appreciated in the past will continue doing so in the future. The same applies to stock prices that have decreased in the past; the momentum trader assumes that they will continue doing so in the future. Making use of the anticipated price trend, a momentum trading strategy involves stocks which have performed well in the past, expecting that a positive return will be made when those stocks are sold at a later date. In the same manner, a positive return is expected to be made by the momentum trader when he short sells stocks1 that have decreased in price in the past. With respect to the momentum trading strategies the debate continues about what causes the momentum returns and, on the other hand, about whether the momentum strategy is truly profitable. With regard to the cause of the abnormal momentum returns, Fama and French (1996), Grundy and Martin (2001) and Jegadeesh and Titman (2001), show that rational models fail to explain them. Since the rational models fail to explain the abnormal momentum returns, researchers introduced the so-called behavioral models. According to Barberis and Shleifer (2003), Daniel et al. (1998) and Hong and Stein (1999), the behavioral models state that abnormal returns that arise due to a momentum trading strategy occur because of incorrect or delayed information interpretation by the investors. Discussion concerning the profitability of the momentum strategy focuses on the assumptions with respect to the true 1 Short selling is the sale of a stock that is not owned by the seller. It is driven by the assumption that a stock’s price will decrease, enabling it to be purchased back at a lower price to make a profit. 2
transaction costs involved in pursuing such a strategy. Jegadeesh and Titman (1993, 2001) find abnormal returns for the momentum trading strategy by taking into account 0.5% transaction costs. However, more recently, many researchers consider a 0.5% transaction cost too low when pursuing a momentum strategy (Agyei-Ampomah, 2007; Korajczyk and Sadka, 2004; Lesmond et al., 2004; Pavlova et al., 2011). Previous literature investigated whether momentum returns existed in the United States, the United Kingdom and internationally (e.g. De Bondt and Thaler, 1985; Doukas and McKnight, 2005; Griffin et al., 2003; Jegadeesh and Titman, 1993). To my knowledge little momentum strategy research has been done with respect to continental Europe. This paper focuses on the profitabilities (on a net basis) of momentum trading strategies with respect to the Dutch and German stock markets from March 2009 to March 2016 (hence, after the financial crisis of 2008) and also attempts to make a comparison between those profitabilities. This paper seeks to answer two research questions. The first question is: Can a momentum trading strategy yield significant positive net returns in the Netherlands that exceed the AEX index? The second question is: Can a momentum trading strategy yield significant positive net returns in Germany that exceed the MDAX index? The answers to these research questions add to the existing literature in several ways. First, this paper contributes to existing literature by analyzing a new time period (2009-2016), thus avoiding any distortions in the results caused by the financial crisis of 2008. Second, I investigate the profitability of momentum trading strategies in continental Europe, more specifically in the Netherlands and Germany. So far, most momentum trading strategy research has focused on the US and UK stock markets. Third, when calculating the net profitability of the various momentum trading strategies, I endeavor to approximate the actual transaction costs as closely as possible in order to determine whether the application of 3
momentum trading strategies is advantageous. Fourth, I investigate if, and to what extent, the profitability (on a net basis) in the Netherlands exceeds the Dutch stock market AEX index and if, and to what extent, the profitability (on a net basis) in Germany exceeds the German midcap stock market MDAX index. Finally, I investigate the profitability of momentum trading strategies (on a net basis) not only for the research period, but also with respect to six consecutive 12-month periods within the research period. This part of the research aims at providing insight in the development of the profitability of the various momentum trading strategies during the research period. The results of this research show that, while in the Netherlands five out of the nine momentum trading strategies investigated yield a positive net return that is higher than the Dutch stock market AEX index, in Germany only two out of the corresponding nine momentum trading strategies investigated yield a positive net return that is higher than the German midcap stock market MDAX index. However, only one out of the five Dutch momentum trading strategies that beat the market, yields a significant (positive) net return, while neither of the two corresponding German strategies that beat the market, is robust. With respect to all nine Dutch and German momentum trading strategies, the major part of the net momentum returns, when calculated over the research period, can be attributed to the winner portfolio. With respect to all nine Dutch and corresponding German momentum trading strategies this research shows that the longer the holding period, the higher the net momentum returns realized. For seven out of the nine momentum trading strategies the net momentum returns realized in Germany are lower than the ones realized in the Netherlands, even though for all nine momentum trading strategies applied, the German gross returns are higher than the equivalent Dutch gross returns. Finally, this research shows that for all nine momentum trading strategies the transaction costs in Germany are quite a bit higher than those in the Netherlands. However, the difference between the Dutch and German transaction costs seems to decrease as the length of the holding periods increases. The remainder of this paper is organized as follows. Section II examines the literature on trading strategies and, more in particular, about momentum strategies. Section III describes the data and methodology used for this research. Section IV provides the empirical results that are found and section V contains a number of conclusions. Finally, section VI provides the reference list and section VII consists of the Appendix. 4
II. Literature review This section contains an overview of the literature relevant for this paper. In section II.1 the efficient market hypothesis versus the contrarian and momentum trading strategies is discussed. In section II.2 the profitability of the momentum trading strategy is discussed. Section II.3 focuses on the causes of momentum profits and section II.4 reviews the literature on the correlation between Dutch and German stock market returns and between Dutch and German stock market indices. II.1. Efficient market hypothesis versus the contrarian and momentum trading strategies The efficient market hypothesis is an important theory in the world of finance and is used as starting point in much academic research. As mentioned before, in an efficient market, stock prices fully reflect available information (Fama 1965, 1970). The efficient market actually states that one cannot predict potential returns by making use of past data. Simply said, in an efficient market stocks trade at their fair value, which leads to the situation that it is impossible for managers and investors to either buy stocks that are undervalued or sell stocks which are overvalued. In other words, managers and investors cannot beat the market. Only in an inefficient market will the true expected returns and equilibrium expected returns not necessarily be identical (Fama, 1976). However, around the late 1970s, systematic deviations from theoretical expectations were discovered; that is, there appeared to be predictable opportunities for earning abnormal returns using rather simple trading strategies (Frankfurter and McGoun, 2001). These deviations were labeled anomalies. Anomalies are empirical results that seem to be inconsistent with maintained theories of asset-pricing behavior (Schwert, 2003). They show either market inefficiency (profit opportunities) or inadequacies in the underlying asset-pricing model. After they are recognized and studied in the academic literature, anomalies frequently seem to vanish, reverse, or decrease. This raises the question whether profit opportunities existed historically, but have since been arbitraged away, or whether the anomalies were simply statistical deviations that attracted the attention of researchers (Schwert, 2003). There have been many papers that tried to confirm the profitability of trading strategies that focus on the predictability of stock returns (Kothari, 2001). For instance, Fama and French (1995) report evidence on the book-to-market effect, De Bondt and Thaler (1985, 1987) report on the long-term contrarian effect, Jegadeesh and Titman (1993) and Rouwenhorst (1998) 5
report on the short-term momentum effect, Moscowitz and Grinblatt (1999) looked at the industry-factor effects to explain the momentum effect, Chan et al. (1996) research the use of the momentum strategy for the US stock market, while Griffin et al. (2003) show that a momentum strategy yields abnormal returns in international stocks. These articles all analyze the contrarian or the momentum strategy as trading strategies that rely on stock market anomalies. In academic literature two trading strategies may be distinguished, the contrarian trading strategy and the momentum trading strategy. Where the contrarian trading strategy involves buying historical “losers” and selling historical “winners”, the momentum trading strategy involves buying historical winners and selling historical losers. One of the most important articles that confirmed the existence of the contrarian trading strategy for the US stock market is the article by De Bondt and Thaler (1985). They show that, over a three to five year period, past winner stocks are outperformed by past loser stocks and therefore they suggest to buy the historical losers and sell short the historical winners. This contrarian trading strategy was confirmed by, amongst others, Jegadeesh (1990) and Lehmann (1990), who show that the contrarian strategy holds not only for a period of three to five years, but also for a much shorter time period, namely, a week or a month. Besides the contrarian trading strategy, the momentum trading strategy also contradicts the efficient market hypothesis. One of the most important articles that confirmed the existence of the momentum trading strategy is the one by Jegadeesh and Titman (1993). These authors show that buying stocks that performed well historically (three to twelve months) continued to perform well during the next three to twelve months. Within these next three to twelve months, the stocks appear to have a “momentum” that triggers them to keep going in an unchanged direction. The authors demonstrate that abnormal returns can be made by going short on a portfolio consisting of historically losing stocks in a certain period and taking a long position in a portfolio consisting of historically winning stocks in an equal time period. Therefore, they suggest short selling the historical losers and buying the historical winners. In the article by Jegadeesh and Titman (1993), the stocks are ranked based on their returns in the previous ‘J’-months. The formation period, which is also known as the evaluation period or the ranking period, is the period that the stocks are ranked (this period consists of J-months). In the article by Jegadeesh and Titman (1993) J varies between three, six, nine and twelve months. They chose to construct the portfolios in such a way that the winner portfolio consisted of the best performing stocks (top ten percentile) and the loser portfolio consisted of the worst performing stocks (bottom ten 6
percentile). After the portfolios were constructed, they had to be held for a period of ‘K’months (again; three, six, nine and twelve months), which is named the holding period. Jegadeesh and Titman (1993) conclude that they find significant returns of 1.1% per month. Not only Jegadeesh and Titman (1993) confirmed the momentum trading strategy to be profitable in the United States, also Chan et al. (1996) did so. Jegadeesh and Titman (2001) evaluated the research they had undertaken approximately eight years earlier and found that the momentum trading strategy continued to be profitable in the United States for the period from 1990 to 1998. Furthermore, Rouwenhorst (1998) shows that also in Central Europe the momentum trading strategy generates significant positive returns. With respect to international stocks, Moskowitz and Grinblatt (1999) and Griffin et al. (2003) show that the momentum trading strategy generates significant positive returns in financial markets all over the world. More recently, Moskowitz, Ooi and Pedersen (2012) and Asness, Moskowitz and Pedersen (2013) find that momentum occurs in exchange traded futures contracts and in bonds too. II.2. Profitability of the momentum trading strategy Many researchers showed that pursuing a momentum strategy and thereby taking into account transaction costs of 0.5% per trade generated significant positive returns (Jegadeesh and Titman, 1993, 2001; Liu et al., 1999; Rouwenhorst, 1998). They came to this 0.5% transaction cost by taking an average transaction cost. However, more recently, many researchers consider a 0.5% transaction cost too low when pursuing a momentum strategy. AgyeiAmpomah (2007), Korajczyk and Sadka (2004), Lesmond et al. (2004) and Pavlova et al. (2011) consider a transaction cost of 0.5% to be too low, because regular portfolio rebalancing is needed in order to pursue a momentum strategy. One must buy the winner stocks and sell short the loser stocks every time the formation period (consisting of J-months) ends. This comes with transaction costs that, according to the previously mentioned literature, should be higher than 0.5%. Lesmond et al. (2004) even go as far as to state that a momentum strategy is not profitable at all when taking into account the necessary substantial transaction costs. This is also shown by Pavlova et al. (2011), who find that the profits of the momentum strategy vanish entirely when fully taking into account the costs of trading. On the other hand, Korajczyk and Sadka (2004) consider that, although a transaction cost of 0.5% is too low to maintain the momentum strategy, the momentum strategy is still profitable despite the higher transaction costs. Agyei-Ampomah (2007) show that the profitability of momentum strategies 7
with formation and holding periods up to six months is impeded by high transaction costs. The high transaction costs are caused by the high portfolio turnover rate. However, the trading intensity and associated costs of the momentum strategy decreases for longer formation and holding periods. Therefore, investors can profitably trade on momentum strategies with formation and holding periods of six months and more. II.3. What causes momentum profits? Although Lesmond et al. (2004), as well as Pavlova et al. (2011) clearly state that momentum profits do not occur, many other academics firmly believe that profitable momentum returns do exist. These researchers are mainly interested in the cause of the momentum returns, trying to explain the presence of these excess returns through either rational or through behavioral models. With respect to the rational models, Fama and French (1996) and Grundy and Martin (2001) show that asset-pricing models based on rationality fail to explain abnormal momentum returns. Conrad and Kaul (1998) suggest that cross-sectional diffusion in expected returns could be a valid source of the momentum returns. Jegadeesh and Titman (2001), however, claim that the findings of Conrad and Kaul (1998) are not the reason behind these profitable momentum returns. MacKinlay (1995) argues that mainly data mining drives the momentum premium, though Grundy and Martin (2001) and Jegadeesh and Titman (2001) reject his finding and state that his arguments do not completely give the explanation to momentum profits. Since rational models were not able to explain the profitable momentum returns, behavioral models were introduced in order to find an explanation. Daniel et al. (1998) state that trading because of biased self-attribution and overconfidence explains momentum investing. They argue that one will buy a stock if good news supports their own optimistic indication. This also works the other way around; one does not sell the stock if the negative indication is inconsistent with positive news. Barberis et al. (1998) propose that under-reaction is initiated by the conservatism bias over time horizons of one to fourteen months. They explain this conservatism bias as holding on to previous opinions. Thus, investors underreact to the new information that is given, causing the effects of the information on the stock price to be late. More recently, Grinblatt and Han (2005) wrote that the disposition effect is the main reason 8
investors underreact to information. They explain this effect as the trend of investors to sell shares of which the price has increased, while keeping the shares that have decreased in price. II.4. Correlation between Dutch and German stock market returns and between Dutch and German stock market indices The Netherlands and Germany have close economic, political, social and cultural ties. Economically, Germany is particularly important to the Netherlands. It is its main trading partner, not only for imports, but also for exports2. According to Eun and Resnick (1984), who examined multiple stock markets around the world, the stock market returns of Germany and the Netherlands (and Switzerland) had the highest correlation. Furthermore, Bertero and Mayer (1990) examined share price movements for 23 countries globally. Of the 23 markets examined they report four groups of countries whose stock market indices were particularly closely correlated. One of these groups consisted of Switzerland, Germany and the Netherlands. Likewise, Roll (1992), who examined 24 stock markets worldwide regarding the behavior of international stock market indices, shows that the correlation between Germany and the Netherlands is the highest (again, together with Switzerland). All these findings clearly indicate that German and Dutch stock market returns and stock market indices are highly correlated. 2 gen-met-nederland/inhoud/duitsland (accessed on January 19th 2017) 9
III. Data and methodology This section contains the data and methods used in my research. Section III.1 provides an overview of the framework of my research. Section III.2 explains the source of the data used in my research. Section III.3 provides the research methods used to calculate the returns of the momentum trading strategy. Section III.4 provides the research methods used to calculate the transaction costs and section III.5 provides the research method used to calculate correlations. III.1. Framework of this research In this paper I analyze the profitability of the momentum trading strategy with respect to the Dutch and German stock markets. In this context I also compare the development of those profitabilities. The research period runs from March 2009 to March 2016, thus avoiding any distortions caused by the financial crisis of 2008. This paper makes use of the momentum trading strategy method as developed by Jegadeesh and Titman (1993, 2001), but deviates from their method as follows: First, with respect to the sample, while I copy their method for the Dutch stock market by taking into account all the stocks listed on the Amsterdam Stock Exchange, I do not copy their method for the German stock market. For the German stock market I make use of a subsample of stocks listed on the Frankfurt Stock Exchange. The reason for this is that otherwise the samples would not be comparable in terms of companies’ market capitalization. Second, with respect to portfolio size, where Jegadeesh and Titman (1993, 2001) employ a portfolio size constituted by the best performing 10% or the worst performing 10% of the stocks in their sample (consisting of NYSE and AMEX stocks), this paper employs a portfolio size of 20 stocks for the winner portfolio and 20 stocks for the loser portfolio. Third, with respect to the calculation of transaction costs, these costs are taken into account in line with a method developed by Lesmond et al. (2004). However, while those authors assume a turnover rate of a 100%, this research follows Barber and Odean (2000), who apply the actual turnover rate when calculating transaction costs. III.2.Data The data used in this research relates to stocks that are traded on the Amsterdam Stock Exchange (AEX) and the Frankfurt Stock Exchange (Deutsche Börse AG) during the period from March 2009 to March 2016. For the Amsterdam Stock Exchange I collect data on all stocks, but for the Frankfurt Stock Exchange I only collect data on a sub-sample of the 10
Frankfurt Stock Exchange, which roughly equals the average market capitalization of the Amsterdam Stock Exchange. I do this in order to make the Dutch sample and the German sample comparable. It is important to do so, as Doukas and McKnight (2005), Hong et al. (2000) and Liu et al. (1999) all found that there exists a strong relationship between companies’ market capitalization and momentum profitability. The data for both samples used in my research is extracted from DataStream Advance 5.1. All warrants and investment-trusts are eliminated from the two samples. Furthermore, as both samples include surviving and non-surviving stocks, it can be said that the survivorship bias is ruled out (Agyei-Ampomah, 2007). The survivorship bias is a form of the sample selection bias that arises when a sample only includes funds (stocks) that survive until the end of the research period (Carpenter and Lynch, 1999). Finally, in order to prevent this research from being biased regarding the month-end effect (Thaler, 1987), the data is deliberately collected on the 15th day of the month. With respect to the Amsterdam Stock Exchange, during the period from March 2009 to March 2016, all in all 382 different stocks were traded. The maximum number of stocks represented consists of 313 and the minimum number of stocks represented consists of 288. On average 296 stocks are available for analysis. As for the Frankfurt Stock Exchange, during the period from March 2009 to March 2016 all in all 322 stocks were traded, with a maximum of 321 and a minimum of 265. On average 284 stocks are available for analysis. The data collected from DataStream Advance 5.1 comprises the total monthly returns, also known as the total return index (RI), as well as the stock (share) price, the market capitalization (MV) and the ask and bid price (PA, respectively PB) of all the stocks in the two samples. Furthermore, to be able to compare the momentum trading strategy returns with the Amsterdam Stock Exchange index and the Frankfurt Stock Exchange index, DataStream Advance 5.1 is also used to extract the monthly total returns of these indices. 11
III.3. Research method used calculating the results of the momentum trading strategy In my research, I replicate the method by Jegadeesh and Titman (1993) with respect to constructing the momentum portfolios. That method - also known as the J-month/K-month strategy - is the one most broadly used in the literature. The stocks in the sample are ranked based on their returns over the formation period J and are held for a period of K. The stocks with the highest returns are called winners and the stocks with the lowest returns are called losers. Here I replicate Jegadeesh and Titman (1993) again, by starting with the holding period K one month after the formation period J has ended. By skipping one month I avoid the effects of bid-ask price pressure and lagged reaction effects, which are found in Jegadeesh (1990) and Lehmann (1990). Agyei-Ampomah (2007) shows that the profitability of momentum strategies with formation and holding periods of up to six months is impeded by high transaction costs. Those high costs are caused by the high portfolio turnover rate. However, the trading intensity and associated costs of the momentum strategy decreases for longer formation and holding periods. Therefore, he concludes that investors can profitably trade on momentum strategies with formation and holding periods of six months and more. In view of the above, I replicate Agyei-Ampomah (2007) and take formation and holding periods of six, nine and twelve months. By doing so I deviate from Jegadeesh and Titman (1993), who take formation and holding periods of three, six, nine and twelve months. I take the monthly total returns extracted from DataStream Advance 5.1, to calculate the returns over the formation period J (J 6, 9, 12). For this calculation I use the following equation: 𝑅!, !!!,! !"!" ! !"!(!!!) !"!(!!!) (1) In equation 1, 𝑅!, !!!,! is the return of stock 𝑎 for the formation period J, so from time 𝑡 𝐽 to time 𝑡. 𝑅𝐼!" is the total return index of stock 𝑎 at time 𝑡 and 𝑅𝐼!,(!!!) is the total return index of stock 𝑎 at time 𝑡 𝐽. Total return stands for the total growth in value of a stock on 12
the assumption that dividends are used to acquire additional stocks3. I use equation 1 to determine the winner portfolio and the loser portfolio. By using equation 1 to calculate the returns, this research should be comparable with wellknown papers such as Agyei-Ampomah (2007), Chan et al. (1996), Jegadeesh and Titman (1993) and Rouwenhorst (1998). The stocks are ranked based on their returns in the J period. In this research, the winner portfolio consists of the best performing 20 stocks and the loser portfolio consists of the worst performing 20 stocks. These winner and loser portfolios are equally weighted after the formation period at time t and they are held for a period of K months (K 6, 9, 12). By constructing equally weighted (winner and loser) portfolios I again follow, among others, Jegadeesh and Titman (1993). In order to avoid the effects of bid-ask price pressure and lagged reaction effects, the holding period will begin one month after the end of the formation period (Jegadeesh, 1990; Lehman, 1990). Using equation 2, I calculate the return per stock, for both the winner and the loser portfolio, for a holding period of K months, using three different holding periods (K 6, 9, 12). In my research each K-6 will mean that the return of the stock is calculated for a period of six months, starting from time t 1 up to and including t 7, K-9 for a period of nine months, starting from time t 1 up to and including t 10 and K-12 for a period of twelve months, starting from time t 1 up to and including t 13. Furthermore, just as in Jegadeesh and Titman (1993) and in Agyei-Ampomah (2007), the returns of the portfolios are calculated on an overlapping holding period basis. 3 http://findb.aalto.fi/docs/Datastream/datastream time series walkthrough.pdf (accessed on January 26th 2017) 13
In order to calculate the returns per stock over the holding period K, the
the momentum strategy for the US stock market, while Griffin et al. (2003) show that a momentum strategy yields abnormal returns in international stocks. These articles all analyze the contrarian or the momentum strategy as trading strategies that rely on stock market anomalies.
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