The January and Turn-of-The-Month Effect on Firm Returns and Return Volatility (original) (raw)
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The January effect across volatility regimes
Quantitative Finance, 2011
Using a Markov regime switching model, this article presents evidence on the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes, one with high volatility and other with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high volatility regime. In sharp contrast with most previous literature we find two major results: i) the January effect exists for all size portfolios. ii) the negative correlation between the magnitude of the January effect and the size of portfolios fails across volatility regimes. Moreover, our evidence supports a decline in the January effect for all size portfolios except the smallest, for which it is even larger.
Month-Related Seasonality of Stock Price Volatility: Evidence from the Malta Stock Exchange
Emerging Markets: Economics, 2008
This study applies different statistical tests to investigate whether monthly volatility patterns prevailing in a cross-section of stock markets are present on the Malta Stock Exchange. A January effect is detected, together with a variant of the Turn-Of-The-Month effect, in that volatility tends to increase towards the end of the month. Whilst these effects may be attributed to sources identified in previous literature, it is also shown that this seasonality is related to announce- ment patterns of listed companies.
International Journal of Business and Economic Sciences Applied Research, 2014
The current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.
The Disappearing January/Turn of the Year Effect: Evidence From Stock Index Futures and Cash Markets
Journal of Futures Markets, 2004
This study examines the returns, relative to the S&P 500, on cash indices and futures tracking smaller stocks around the turn of the year. While we control for volatility clustering, return autocorrelation in small stock indices, and other calendar effects, our main focus is the evolution of the turn of the year effect through time: in particular, whether the effect is smaller or takes place earlier subsequent to the introduction of the S&P Midcap and Russell 2000 futures in 1993. We find that evidence of a traditional turn of the year effect, in both cash and futures, is confined to the pre-1993 period. Post-1993, there are no abnormal returns during the turn of the year window as a whole. Interestingly, returns in this period remain high on the last trading day of December, but they are negative We thank an anonymous referee and the editor for valuable comments and suggestions on earlier versions of the paper. Any remaining errors are our own responsibility. often observe significant abnormal returns prior to the traditional turn of the year, i.e., in the pre-Christmas and post-Christmas windows. Taken together, our results suggest that market participants may be eliminating the turn of the year effect with the aid of two new futures contracts that are well suited to this purpose.
The Month-of-the-Year Effect in the European, American, Australian and Asian Markets
Economies, 2021
This paper examines the existence of the month-of-the-year effects in four different continents, namely Europe, Asia, America, and Oceania. Nine indexes were analyzed in order to verify differences between monthly returns from January 1990 to December 2013, followed by an examination of the January effect, Halloween effect, and the October effect, testing for statistical significance using an OLS linear regression in order to verify whether those effects offer consistent opportunities for investors. Investors with globally diversified portfolios benefit from the Halloween effect, with a 1.2% average monthly excess return in winter and spring, while the predotcom-bubble period had a better performance than the post-dotcom-bubble period. In the global post-dotcom-bubble period, there is statistical evidence for 1.60% and 1% lower average monthly returns in January (the January effect) and in months other than October (the October effect), respectively, contradicting the literature. The dotcom bubble seems to be responsible for the January effect differing from what might otherwise have been expected in the later period. There is no consistent and clear impact on continental incidence. The Halloween effect is revealed to be a fruitful strategy in the FTSE, DAX, Dow Jones, BOVESPA, and N225 indexes taken one-by-one. The January effect excess average return was only statistically significative for the pre-dotcom-bubble period for globally diversified portfolios. This paper contributes to a wider global and comparable view upon month-of-the-year effect.
The Month of the Year Effect: Empirical Evidence from Colombo Stock Exchange
2013
Many researchers have tested whether the seasonal anomalies are present in the stock markets. Those studies have been carried out in the stock markets both in the developed and developing economies. Existence of seasonal anomalies let the investors to earn abnormal returns by trading on past information. Most common seasonal anomalies are day of the week effect, month of the year effect, holiday effect, Monday effect and Friday effect. Although information technology and regulatory mechanisms are much stronger than ever, there are strong evidences to support that seasonal anomalies exist in stock exchanges both in developing and developed countries. Furthermore, Colombo Stock Exchange has been named recently as one of the stock exchanges with higher returns in the world. Thus, it is of paramount importance identify how those returns are made of. Abnormal returns gained from anomalies cannot be justified from a risk-return standpoint. Yet it remains as an important element of stock returns. This study attempts to examine whether the month of the year effect and January effect are present in the Colombo Stock Exchange based on data from January 2000 to December 2011. For the purpose of analysis, non linear GARCH t model is employed along with other techniques due to its strong capability to detect such anomalies. Results provide evidence to support the claim that both the month of year effect and January effect exist in the Colombo Stock Exchange despite its use of modern information technology infrastructure and regulatory developments.
The MonTh-of-The-year effecT in The indian STock MarkeT: a caSe STudy on BSe SenSeX
Efficient Market Hypothesis proposes that it is not possible to outperform the market through market timing. However, research studies over the years have reported several anomalies in stock market returns. Anomalies that are linked to a particular time are called calendar effects.The month-of-the-year effect or particularly the January effect is one of such anomalies. The present study in this context has sought to address the issue of the month-of-the-year effect in Indian Stock Market represented by BSE SENSEX during the period ranging from January 2, 2004 to December 28, 2012. The GARCH(1,1)-M model has been used to model the conditional volatility. The results indicate the presence of September and November effects in the SENSEX returns during the study period. Moreover, in the volatility equation the coefficients of March, June, August, October, November and December dummy variables are negative and significant. Hence, it is confirmed that the month-of-the year effect is also present in the variance (volatility or risk) equation.
The January effect in the aftermath of financial crisis of 2008
Ekonomski pregled, 2020
The January effect is one of the most researched seasonal anomalies on the financial market. However, very few authors have looked into the January effect after the financial crisis of 2008 and even fewer have used data of individual companies instead of indexes in doing so. This paper intends to fill this void by analyzing returns of individual micro-cap companies on the three biggest stock markets New York Stock Exchange, London Stock Exchange and Tokyo Stock Exchange for a time period January 2010 to January 2017. Analysis of each individual company using simple averages and regression analysis documented that abnormally high rates of return on micro-capitalization stocks are no longer present in the stock market in the aftermath of the financial crisis of 2008. Further confirmation of disappearance of January effect is conditional on new longer datasets as they become available.
The Quarterly Review of Economics and Finance, 2009
Using improved methodology and an expanded research design, we examine whether the small firm/January effect (Keim 1983) is declining over time due to market efficiency. First, we find that January returns are smaller after 1963-1979, but have simply reverted to levels that existed before that time. Second, we show that the January effect is not limited to mature markets but also appears in firms trading on the relatively new NASDAQ exchange in the 1970s. Third, trading volume for small firms in December and January is not different from other months, implying that traders are not actively arbitraging the anomaly. Together, our results suggest that this anomaly continues to defy rational explanation in an efficient market.
Seasonality of Cross-sectional Return Volatility in the Jordan Stock Market
Universal Journal of Accounting and Finance, 2016
One important gap in finance literature is the seasonality in volatility. Just like the seasonality in stock returns it is possible that volatility may also have a pattern. Time series volatility is related to previous values and it is sticky in nature. Thus, detection of seasonality in volatility may be difficult. Therefore, we use cross-sectional volatility from daily returns of a cross-section of firms (in our case, sectors) and examine the relationship between daily cross-sectional volatility and day of the week, turn of the month and turn of the year. This paper examines how the cross-sectional volatility of the Jordanian stock market may change due to the day of the week, turn of the month and turn of the year. Results show strong evidence of reduction of volatility on Thursday compared to Sunday, and significantly lower volatility on the first three days of the month compared to the third day before the last day of the month. Thus, this finding is important for investors to better understand risk.