CALENDAR EFFECTS IN INTERNATIONAL STOCK MARKETS

Calendar Effects in International Stock Markets

Calendar Effects in International Stock Markets

Our paper examines calendar effects in Shanghai stock market? particularly monthly and daily effects. Using individual stock returns? we observe the change of the calendar effect over time. In Shanghai and Shenzhen? the year end effect was strong in 1991 but disappeared later. As the Chinese year-end is in February? the highest returns can be achieved in March and April. Studying daily effects? we found that Fridays are protable. Chinese investors are amateur speculator" who often embezzles business fund for private trading; thus? these funds are used for short term speculations before they are paid back prior to weekends.

Capital market efficiency has been a very popular topic for empirical research since (Simon 2001 pp 116) introduced the theoretical analysis of market efficiency and proclaimed the Efficient Market Hypotheses (Fama 1970 383-417). Subsequently? a great deal of research was devoted to investigating the randomness of stock price movements for the purpose of demonstrating the efficiency of capital markets. Since then? all kinds of calendar anomalies in stock market return have been documented extensively in the Nance literature. 

To analyze monthly and daily effects in stock returns? we use the market index of the Shanghai and Shenzhen stock exchanges? which is common in the literature. However? to measure the changes of calendar anomalies over time relying on index data is insufficient due to data availability. Obviously? having at best 13 observations for every months since the reopening of the stock exchanges in the 1990s makes it a risky venture to estimate changes of monthly effects over the 13 years (Collins and Hussey 2003 pp 74). Hence? we use in addition individual stock returns of all stocks listed on both exchanges since the restart of security trading in China. This increases the number of observations dramatically? and one obtains precise estimates for the shift of monthly patterns over time (Tinic; Barone-Adesi and West 2007 51-64).

Descriptive statistics

Descriptive statistics of market returns for different months underline that on a first glance monthly effects are nearly negligible. Table 1 summarizes the average returns as well as the upper and lower boundaries of a 95% confidence interval (Saunders; Thornhill & Lewis 2007 189). When we look at the whole period from 1990 to 2002? the confidence intervals of average monthly returns include in all cases the zero return. Therefore? a clear positive or negative effect cannot be confirmed. Nevertheless? two points are worth mentioning: we just have 12 years and? hence? at best 12 observations for every month; strong assumptions like no serial dependency are required to derive the confidence intervals. The subsequent section deals with the latter issue by using more elaborate techniques? namely regression analyses and ARIMA models (Smirlock and Starks 2006 197-210). To overcome the problem concerning the low number of observations? individual monthly stock returns of all listed companies are used (Aggarwal; Hiraki and Rao 2000 249-263). Furthermore? this increase in the number of observations allows estimating the shifts of the monthly pattern over time. 

Regression analysis

The starting point of our analysis is the hypothesis of an efficient market; hence? randomness of returns can be assumed. Accordingly? we state that market returns follow a geometric ...