Abstract
Previous literature analysed the calendar effects on different stock markets. As a result, the recommendations, conclusions, and analysis revealed the significance of the day of the week anomalies associated with the calendar effects. Consequently, the response of the Heng-Seng Index demonstrates the volatility of index changes. The paper combines previous literature on index prices to analyse the calendar effects of the Heng-Seng market.
Literature from 2003 to 2012 was selected to assess the anomalies of index returns. Using the stochastic dominance test, the significance of the calendar effects on the Hong Kong stock market was analysed. Consequently, the paper correlated the significance of the Heng-Seng test on stock markets. Daily index prices of the stock markets under study were combined. Data samples tested were between 2003 and 2012. The results revealed the significance of the calendar effects of weekdays and months in the Heng-Seng market. Consequently, the result revealed a significant effect on the Heng-Seng Index on the stock markets under study.
Introduction
The calendar effect describes the volatility or anomaly in stock returns, which is influenced by the day of the week, months, and holidays (Ali & Akbar 2006). Surveys revealed the significance of the Monday and January effects of various stock returns. As a result, the calendar effect influences stock volumes, prices, and market index (Bahadur & Joshi 2004). Thus, the calendar effect describes the anomalies in stock returns. Some literature revealed that January, Monday effect, and July effects determined the stock market index of Asia and Europe (Gimba 2010). The Hong Kong stock market is influenced by the calendar effect.
In emerging markets, the Hong Kong stock market is influenced by the January effect. Data tested on the calendar effect revealed the weak form of efficiency that arose from underperformed and outperformed days or months. However, the result contradicts the market hypothesis of a different stock exchange (Lean, Smyth & Wong 2007). Consequently, stock analysis suggests the disappearing and weakening attributes of the calendar effect (Lee 2012).
Previous research used the mean-variance approach that relied on normality assumptions (Hansen & Lunde 2003). However, the results varied on the first events with the calendar effect. The errors associated with parametric analysis can be corrected with the non-parametric stochastic dominance (Harper & Jin 2012). Thus, the stochastic dominance influences valid results on the calendar effect of stock markets. The literature revealed the significance of the calendar effect on the Heng-Seng Index (Huang, Zhang & Yang 2013). As a result, the January returns dominated other calendar months.
Thus, the January effect on the Heng-Seng Index indicates the strength of day-of the week anomaly despite the inconclusive analysis (Jakpar et al. 2013). Thus, the paper will analyse the calendar effect on the Heng-Seng market using the stochastic dominance approach. Consequently, the calendar effects will be related to the stock markets under study. The Heng-Seng Index is characterised by small index funds. Fund managers track the Heng-Seng Index with three funds.
Literature Review
The researchers based their study on the theoretical concept of the stock market index. The theoretical framework suggests that stock returns are across the month of the year. As a result, researchers have correlated the theoretical concept with several empirical studies. The empirical studies revealed that the month-of-the-year effect commonly called the calendar effect stimulates stock returns. Consequently, researchers have combined empirical and theoretical studies to justify the calendar effect on stock returns. As a result, inconsistent price movement alters stock returns. Researchers believe that the price movement is influenced by the calendar effect.
The calendar effects are anomalies influenced by weekends, holidays, or end of year trade. Stock analysts believe that studies can reveal the phenomenon to assist fund managers to forecast price movements. Researchers have utilised several statistical methods to verify the calendar effect on stock markets. Consequently, literature from peer-reviewed journals has been used to analyse the calendar effects on price movement. Stock returns in January were significant across many studies on the calendar effect. Statistical methods used include a parametric approach, stochastic dominance, a linear regression model, non-parametric methods, and traditional approaches.
To investigate the validity of previous findings, on the calendar effect, researchers analysed stock markets from different countries. Consequently, the stock market index was coded to avoid experimental errors. The stock markets include Chile, Chinese, Hong Kong, Nigeria, the US, UK, Brazil, Japan, Tokyo, and Canada. As a result, the study was repeated to determine the average of the stock market index under study. Theoretical explanations have been used to explain the calendar effect on stock markets. As a result, researchers postulated hypotheses to explain the phenomenon. The hypotheses include tax loss, size of the firm, window dressing, information release, and the herding theory.
The herding theory describes the behavioural framework of the calendar effect. As a result, fund managers close the business trade with higher returns. The stock returns explain the January effect on a stock market index. The researchers classified the herding theory into an irrational and rational hypothesis. The information release theory describes investors’ pressure associated with the January effect. As a result, fund managers stimulate the buying pressure in January, which influences the price movement of stock markets.
The window dressing hypothesis describes the effects of portfolio holdings on the stock market index. As a result, the calendar effect stimulates January stock returns. The size of the firm theory describes the effect of smallholdings on stock returns. As a result, small-size firms attach investors with higher stock returns. Consequently, small-size firms record higher stock returns in January.
Calendar effects: Day-of-the-week
Previous research revealed that the day-of-the-week effect differs in returns. Luo et al. (2009) revealed that the standardised returns vary across days of the week. Ng & Siklos (2012) correlated the same findings with Luo et al. They argued that the Friday effect produced a weak return on the stock market index. As a result, the Monday anomaly stimulated negative responses on the stock market index.
Month-of-the-year
Patel, Radadia & Dhawan (2012) suggested that the January effect is a significant calendar effect. Lakonishok approved the suggestion in 1988. He argued that the opening month of the year influenced many stock markets under study. Consequently, the weekday-of-the-month affected the stock market index (Rahman & Uddin 2009). The holiday effect can be classified as pre and post holidays. Pre-holidays precede the market closure, thus affecting price index and volume. Surveys revealed that pre-holidays influence the stock volume and the index price. Consequently, post-holidays affect the stock market index.
Price and volume effects
Several pieces of literature utilised different hypotheses to investigate the calendar effects of stock price and volume. The Heng-Seng stock index was analysed with four hypotheses. The hypotheses include price pressure, imperfect substitute, liquidity, and the certification hypothesis. Surveys revealed that irregular price pressure influences the Heng-Sheng price index (Whaley & Bollen 1999). In emerging markets, fund managers track the share price index using the price pressure hypothesis. The imperfect substitution hypothesis describes the elastic demand of stock markets and their substitutes.
However, studies reported that funds managers prefer local stock markets to national ones. Sariannidis, Konteos & Drimbetas (2010) revealed that the liquidity of stock affects the price index. Consequently, a positive announcement will increase the stock price index. The certification hypothesis combines the price-pressure and substitute hypothesis. The certification hypothesis assumes that the stock announcement provides market prospects. As a result, the stock price index and volume change to reflect the announcement.
Shankar & Randhawa (2006) utilised the Efficient Market Hypothesis to predict the stock demand and supply. They argued that the best prediction is influenced by the drift term. As a result, the calendar effects on the stock market price will be significant. However, the weak form efficiency is affected by the certification hypothesis. Previous reports revealed that efficient market information does not affect the Heng-Seng Index (Wong et al. 2004).
However, reports showed that stock returns vary across seasonal dates (Raj & Kumari 2011). As a result, the January effect influences many stock market indices. The study reviewed stock indices of Bombay, Amsterdam, Japan, China, Australia, Sweden, New Zealand, and the US. The analysis revealed significant changes attributed to the calendar effect. Consequently, the end of the year effect was the largest alteration in the stock indices under study.
The calendar effects are anomalies of the days of the week, weekday, and month. The days-of-the-week effect holds that one cannot generalise expected returns for all weekdays. Several studies documented findings on the calendar effect. The report revealed that the Friday effect factors the returns of the closing price on Thursday to Friday. As a result, negative returns on Mondays are called the weekend effect. Data show that the Heng-Seng index experiences high returns on Fridays.
Consequently, the Monday effect influences high volatility in the Heng-Seng stock market. Evidence from previous literature showed the abnormal distribution of the Heng-Seng market, which is influenced by the calendar effect. Stock analysts utilise investors’ trade patterns, spillover effects, and market settlement rules to explain the days-of-the-week effect. Empirical literature revealed weekday effects using low Monday returns (Brown, Chua & Mitchell 2002).
However, negative returns on Mondays affect various stock markets. Documented studies showed significant effects on the daily stock market using literature on the calendar effect. Consequently, studies corroborate the findings of Heng-Seng markets by documenting significant negative returns for Monday effects. However, the sensitivity test reveals conflicting results of the stock’s daily effect (Ho & Tsui 2004).
The January effect is an irregularity in stock markets, which has many assumptions (Chow, Yung & Zhang 2013). Coutts & Sheikh (2000) reported that the January effect on the stock market is influenced by the end-year transactions. Reports suggested that the January effect is influenced by the Dow Jones industrial average (Draper & Paudyal 2002). Empirical studies on the January effect in the Heng-Seng index revealed significant returns in January (Gongmeng, Firth & Jeong-Bon 2000). January has a higher significance compared to other months. Thus, the January effect exists in developed markets such as the Heng-Seng Index. Stock analysts suggest the decline in Hong Kong small stocks, which are influenced by the January effect (Gongmeng, Lee & Rui 2001).
Many market analysts have studied the phenomenon of price movement and stock returns. Previous samples suggested that the price movement was affected by stock anomalies. Thus, the term calendar effect describes the irregularities associated with the price movement. Changes associated with the calendar effects include the day-of-the-week effect, day-of-the-month effect, weekdays-of-the-month, and month-of-the-year effect. Ozenbas (2006) examined price movement caused by the day-of-the-week effect on Tokyo’s stock market. The analysis was conducted without statistical deductions.
However, the results revealed that the the-day-of-the-week effect stimulated negative returns. He attributed the price movement to the weekend effect on stock value. Schwert (1989) compared the stock index of Dow Jones with the Chinese stock market and suggested that the Monday effects influenced the price movement. Choi & Cook (2006) utilised different statistical analyses to show the significance of the weekend effect on stock value. Surveys, conducted on the Chinese, Japanese, and US stock markets revealed the consistent change in stock value. As a result, the price movement was associated with the day-of-the-week effect.
Most literature employed several standards and non-standard means of analysing the calendar effect. As a result, the validity of the calendar effect can be used to determine price changes in the stock value. Haug & Hirschey (2006) used the bootstrap procedure to verify the calendar effects on the Hong Kong stock market. The author used a non-conventional approach to reveal the significance of the calendar effect. Chakrabarti et al. (2012) explained the findings of previous literature with their assessment of seven European stock markets. The findings revealed that the price movement is influenced by the calendar effect.
However, most authors arrived at the same conclusion using different statistical and non-conventional approaches. Stock analysts argue that the consistency of the findings using traditional and non-conventional approaches may affect the validity of the calendar effects on price movement.
Previous studies revealed that the month-of-the-year effect stimulates price movement of the stock market. The month-of-the-year effects are associated with January, July, and December. Surveys revealed that January effects were significant compared to another month of the year. Shrestha (2004) studied twelve stock markets across Asia to conclude that the January effect was higher than other months. Yungsan & Shin (2000) suggested that the January effect was associated with the small-firm effect. He revealed that small organisations achieve higher returns in January.
The finding was repeated on the Heng-Seng stock index using a non-parametric test to reveal the significance of the January effect. Kiymaza & Berument (2006) revealed that the average price movement associated with January effects had a 95 per cent level of significance. Kucuksille (2012) and Maghayereh (2003) explained the significance of the January effect using the stochastic dominance approach. However, some authors utilised the linear regression approach to suggest the calendar effect on stock markets. Andersen, Bollerslev & Cai (2000) rejected the variability of the calendar effect using heteroskdasticity.
He argued that the error term affected the variability of the calendar effect. To understand price movement associated with the calendar effect, surveys have been conducted on many stock markets. Hansen, Lunde & Nason (2005) revealed that the interaction between stock value and exchange rates was not significant. Mehdian & Perry (2002) argued that the Chinese exchange rate did not influence the stock price. He concluded that the calendar effect was significant on the stock price index. Maghayereh (2003) suggested that the stock price showed a diverse outlook. McGuinness (2005) utilised the linear regression model to reveal the relationship between the calendar effects and price movements.
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