Several recent papers have documented that, at medium-term horizon ranging from three to twelve months, stock returns exhibit momentum. That is, past winners continue to perform well, and past losers continue to perform poorly. This kind of study starts from Jegadeesh and Titman (1993) who use a U.S. sample stock over the period from 1965 to 1989, find that a strategy that buy six-month winners and short past six-month losers earn approximately one percent per month over the subsequent six month.
Subsequent studies find some robust results. For example, Rouwenhorst (1998) obtain similar results in a sample of 12 European countries over the period 1980 to 1995.
Earlier research find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors (Jegadeesh and Titman 2001). After this, lots of researchers endeavor to investigate the reason which result in existence of the momentum effect. For example,Chui, Titman and Wei (2010) investigate how cultural differences influence the returns of momentum strategies and conclude that momentum profits are negatively related to firm size and volatility. In addition, previous work shows that average returns on common stocks are related to firm characteristics like short-term past return. And they are called anomalies, because these patterns in average returns apparently are not explained by the CAPM (Fama and French 2012). Some researchers find that the medium-term momentum in stock returns would be influenced by firm-specific information and gradually across the investing public. (Hong, Lim and Stein 2000)
From these earlier academic works, we know that the explanations of momentum effect are very various. We can categorize these into three main group. (1) risk-based and characteristic-based explanations (2) explanations invoking behavioral biases (3) explanations based on the limits of arbitrage.
Now we give some explanation to these previous findings, the first one is
“risk-5
based and characteristic-based explanations”. In early studies, Jegadeesh and Titman (1993) show that momentum is not driven by market risk and Fama and French (1996) show that their unconditional three-factor model cannot explain momentum either.
However, there are more evidence of characteristic-based explanations. For example, Hong, Lim and Stein (2000) find that small firms have more momentum and Johnson (2002) find that momentum arises from a positive relation between expected returns and firm growth rates.
The second is “explanations invoking behavioral biases”, which focus on imperfect formation and revision of investor expectations in response to new information. We can divide explanation of these theories into overreaction and underreaction. Overreaction is mainly observed by trading volume. Chan, Hameed and Tong (2000) indicates that momentum return continuation is stronger following an increase in trading volume. It’s consistent with the herding behavior theory, in which investors tend to follow the crowd in buying and selling securities. Contrary to the hypothesis of overreaction, some study find the evidence in explaining by underreaction. For example, Hong, Lim and Stein (2000) find that small firms with low analyst coverage have more momentum.
Moskowitz and Grinblatt (1999) demonstrate that industry momentum is large, which Hou, Barberis and Chen (2001) argues is due to slow information dilution within industries. Some researchers conclude the results in both of overreaction and underreaction. Grinblatt and Moskowitz (2004) discover that momentum is more prevalent for small firms with few institutional owners, growth firms, and firms with high volume.
The last one explanations based on the “limits of arbitrage”. Some studies are mainly test the influences when there are several market frictions, like transactions costs, margin accounts, short-selling buffers, and higher borrowing rates (Ali, Hwang and Trombley (2003); Hogan, Jarrow, Teo and Warachka (2004)). Some find the large
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momentum excess returns link to funding liquidity risk and funding constraints.
(Brunnermeier and Pedersen 2009)
Apart from the momentum effect in stock market, past theories also find a lot of empirical evidence in the currency momentum. Okunev and White (2003) use moving average to form the momentum portfolio and report the profits of up to 7% per year in eight currencies. Chong and Ip (2009) claim that a momentum trading strategy generated approximately 20% per year in emerging currency markets over the 20 years from 1985 to 2004. Asness, Moskowitz and Pedersen (2013) apply 10 currencies and find the currency momentum profit of over 3% per year.
The most recent and general study in monthly momentum effect is by Menkhoff, Sarno, Schmeling and Schrimpf (2012). They investigate whether currency momentum is significantly affected by (1) transaction costs, (2) business cycle risk and other traditional risk factors, and (3) different forms of limits of arbitrage. They conclude that the momentum returns are fairly sensitive to transaction cost and cannot be explained by systematic risk factors. However, the profitability of currency momentum strategies can induce arbitrage limitation, captured by idiosyncratic characteristic of the currencies involved, such as country risk and volatility of currency, for the major currency market participants.
Recently, there are some studies investigate the momentum effect in short horizon (one to four week), instead of medium horizon. In the study of Raza, Marshall and Visaltanachoti (2014), they find, based on a sample of 63 currencies, evidence of momentum effect and excess returns are around 9% per year. Momentum excess returns increase with the increase in formation period and don’t relate to the FX carry trade returns. Further, the weekly momentum excess returns are larger in the expansionary phases of the U.S. business cycle and in periods following continuous depreciation of the involved currencies, but the large excess returns are not exist during the periods of
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extreme stress, which is high volatility coupled with continuous depreciation in the currency market.
In this paper, we apply monthly data of longer time period, which is from November 1983 to October 2014, and much larger cross-section of currencies, and include both developed and emerging countries. Consequently, we can analyze the general magnitude of both cross-section and time-series aspects. We mainly continue using the portfolio-constructed method in Menkhoff, Sarno, Schmeling and Schrimpf (2012) and focus on investigating the relationship between momentum excess return and country risk to give more detail.