The Declining January Effect: Experience of Five G7 Countries

ABSTRACT The January effect exhibits a pronounced declining trend for both large and small firm stock indices for the last few decades and the effect is disappearing in major equity indices of Canada, France, Germany, Japan and United Kingdom. The downward trend is more apparent for the UK indices. The anomaly is more stellar with large stocks in UK, but with smaller stocks in France and Germany. The January effect is positively connected to real GDP growth, risk free rate of interest, and return of the year, and it is negatively related to inflation and market volatility. The power ratio method provides a consistent way to reveal the relative contribution of January return in the year. Finding the pattern of changes in the anomaly has implications for investment strategies. INTRODUCTION The January effect--or the abnormally large returns on common stocks in most months of January--has been one of the most intriguing issues in financial economics since 1976. Wachtel (1942) provided the first academic reference to a January seasonal in stock returns. 34 years later, Rozeff and Kinney (1976) pointed out that common stock returns in January are significantly larger than those in other months, and that the anomaly is related to small firms. Reinganum (1981), Keim (1983), and Roll (1983) reaffirm that the January effect is more pronounced in small firms. If this is the case, the January effect may decline as firms become larger. Kohers and Kohli (1991) provided evidence that the January effect is not related to small firm effect. There are several explanations for the January effect. Stoll and Whaley (1983) attribute the anomaly to transaction costs. Chang and Pinegar (1989, 1990) and Kramer (1994) suggest seasonality in risk premium or expected returns. Ritter (1988) hypothesizes tax-loss selling effects. Haugen and Lakonishok (1988) suggest window dressing. Ogden (1990) relates the January effect to year-end transactions of cash or liquidity. Kohers and Kohli (1992) and Kramer (1994) connect the anomaly to business cycle, and Ligon (1997) reports that higher January returns relate to higher January trading volume and lower real interest rates. Existing literature does not consider the dynamics of the effect, as previous researchers report constant coefficients of dummy variables, or average returns of the month, for their relatively short sample periods. And obviously with these methodologies, one type of observations would overweigh the other if the number of years with an abnormal January is greater than the number of years without it, or the effect is extremely strong in certain years. If the January effect exhibits an increasing or declining trend, or is disappearing in certain markets, then the trend may indicate some changes in the factors discussed above or changes in the impacts of these factors on the effect. And there may exist some unidentified factors or new factors that affect the abnormal return in January. In this study, a power ratio method is developed to calculate the effect in each individual year for sufficiently long time periods, in order to explore the dynamics and trend of the January effect of major stock indices of Canada, France, Germany, Japan and United Kingdom. The indices include the Canadian TSE (Toronto Stock Exchange) 35 from 1987, and TSE 300 from 1970, the French CAC 40 from 1987 and SBF 250 from 1970, the German DAX 30 and FAZ Aktien 100 from 1970, the Japanese Nikkei 225 from 1950, and the British FT 30 and FT 700 from 1976. All the data is through year 2000. The purpose of using the time periods is to reveal the trend with the limit of data availability. The Nikkei 250 is price weighted but using it does not overstate the effect of small stocks on returns because there is no small stock in it. All the other indices are value weighted. Using value weighted indices makes the effect of large stocks on returns more apparent. Using the indices for the study avoids issues related to portfolio formation, such as size-beta correlation, size-price correlation, and survivorship. …