Abstract
This paper suggests innovative investment strategies drawing on return seasonalities. By means of an out-of-sample study of the German stock market, we report that these long–short investment strategies earn on average raw returns up to 233 basis points per month throughout two decades from 1998 to 2017. On a monthly basis, this documents an outperformance of the corresponding Heston and Sadka (J Financ Econ 87(2):418–445, 2008) strategy by 66%. This outperformance is robust in magnitude even after adjusting for common risk factors along both the three-factor Fama and French (J Financ Econ 33(1):3–56, 1993) model and the four-factor Carhart (J Finance 52(1):57–82, 1997) model. Categorizing stocks into three risk profiles lets us conclude that long–short momentum portfolios of stocks with a low-risk profile generate robust investment performance.
Highlights
Investment strategies drawing on past return information have a long tradition in finance and are well documented in a correspondingly large body of research
The fixed lag count (FLC) strategy virtually dominates the variable lag count (VLC) strategy in all dimensions, that is in raw returns and after risk-adjustment
The blurred pattern of cross-sectional return autocorrelations in the German stock market can be exploited with the suggested lag count strategies beyond what Heston and Sadka (2008) achieve with an annual strategy
Summary
Investment strategies drawing on past return information have a long tradition in finance and are well documented in a correspondingly large body of research. The third strand of research on investment strategies drawing on the self-explanatory power of stock returns started with Lo and MacKinlay (1990) who argue that return reversals are linked to cross-sectional return autocorrelations rather than being caused by market overreaction In this vein, Heston and Sadka (2008) study the profitability of long–short equity portfolios formed on grounds of stocks’ past performance and report that the returns of winner–loser portfolios are mostly attributable to the stocks’ past performance during annually lagged months in formation periods of different lengths. We report that Heston and Sadka’s (2008) investment strategies which exploit the cross-sectional return autocorrelation at annual lags perform well in the German stock market.
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