Calendar Effect in Hong Kong Stock Market HSI

Subject: Finance
Pages: 20
Words: 13246
Reading time:
48 min
Study level: College


Hong Kong Stock Exchange (HKSE) is the second largest stock exchange market in Asia. The HKSE was established in the 19th century (McGuinness 2006). Today the market ranks among top ten in the market capitalisation. The market hit a record market capitalisation of US$1,063.9 trillion in 2010 and was ranked 8th place in the world (Garefalakis 2011). The free-float-adjusted stock market index of Hang Seng Index (HSI) was established in 1969 (Gao & Kling 2005). The shares traded in Hong Kong Stock Exchange HSI represent the highest index in the overall market turnover. Over 60 % of the value in HKSE is moved by HSI. The performance of Hong Kong Stock in the various sectors is significantly influenced by HSI. The 60% value moved in the HSI has high implication in the Chinese stock markets (McGuinness 2006). According to McGuinness (2006), the daily performance of the HSI generates a lot of interest in China, Asia and around the world.

Market anomalies in the equity movements affect investors’ returns and lead to inefficiencies. According to Coutts & Sheikh (2000), the leading cause of the anomalies is the calendar effect. Nikkinen, Sahlstrom and Aijo (2007) noted that month of the year or day of the week affects the stock markets indices. Studies on anomalies that relate to calendar effect are general and they lack emphasis on the significance of the calendar effect. Latif, Arshard and Farooq (2011) noted that the anomalies relating to calendar effect are due to data mining which lead to altering the normal pattern. The belief in the calendar effect causes a lot of speculation in the stock market; hence the anomalies. Anomalies slow down the activities of the markets and lead to changes in stock returns in the affected markets (Deysshappriya 2014).

Anomalies and Calendar Effects

An anomaly is an unusual occurrence. Nikkinen and Sahlstrom (2004) defined anomaly as a deviation from the usual order. In the stock markets, anomalies are an indication of lack of efficiency in the markets. Anomalies are caused by chronology of events that lead to significant and lasting effect on the market. The key trigger of stock changes is the known information. Efficient market hypothesis stipulates that stock market prices reflect the known information. The implication is that the stock market indices depend on the information available. Therefore, an investor cannot outperform equity markets by application of the already available information (Linton, Maasoumis & Whang 2005). However, studies point that there is evidence that returns in the stock exchange markets differ depending on the time, a phenomenon called calendar effect (Linton, Maasoumis & Whang 2005). The calendar effect presents the anomalies that relate to the day of the week effect or the month of the year effect or intra-monthly effect.

Day of the Week Effect

The calendar effect in relation to the day of the week postulates that expected returns and the standardised returns differ depending on the days. Many studies have been conducted to investigate closing stock indices in the five working days of the week Monday, Tuesday, Wednesday, Thursday, and Friday. Most of the studies have established anomalies. For instance, on many occasions, returns on Mondays have been found to be negative. The negativity has been attributed to the weekend effect (Al-Jarrah, Khamees & Qteishat 2011).

Month of the Year Effects

The month of the year involves the returns from January to December. The month of the year effect was discovered in 1942, when Eatchet reported anomalies that he termed as January effect (Basher & Sardosky 2006). Since then, many studies have been conducted to investigate returns across the different months. The studies have pointed to anomalies in January. Further extensive studies have indicated that the monthly changes are due to seasonal changes (Basher & Sardosky 2006).

Aims and Objectives

There are varying schools of thoughts on the significance of calendar effects. The varying schools of thoughts not only affect the investors in the theoretical days, but also present a challenge to the managers in endeavour to make accurate predictions. Studies carried on the calendar effect are specific to certain markets (Ariel 2002). Therefore, there have been tendencies to generalise the significance of the calendar effects. Ariel (2002) noted that different factors and the business environment affect stock markets just as any other trade. There is thus the need to establish specific market effect for the various stock markets. The establishment of calendar effects in specified markets will play an integral role in managing the markets based on the evidence rather than generalisations. Therefore, the aim of this research will be to find out whether calendar effects influence the Hong Kong Stock Exchange Market HSI turnover. The study will also be aimed at establishing the significance of the calendar effect on the stock returns. The scope of the study will be limited to the HSI and its various subcategories. The study objectives include:

  1. To find out the how the day of the week influences the daily returns in the Hong Kong Market HSI.
  2. To review the effects of seasonality changes in the HSI stock market
  3. To establish how the calendar effects affect the HSI investors and the general trade in the market.

The stock returns can be explained by use of the market efficient hypothesis. The hypothesis stipulates that in a particular day of trading, the stock returns are indifferent. However, the different studies that have been conducted in various markets have pointed to a trend that has established that the calendar effect has great implication on returns based on the day of the week and the month of the year. According to Gao and Kling (2005), the changes point to the abnormal returns that cannot be assumed but that are not clearly understood. This is because the studies in the different markets have shown seasonal changes. Therefore, for the top stock markets in the world such as the Hong Kong Stock Markets HSI, there is the need to determine whether the weekly effects are experienced and how they influence trade. Therefore, the first aim of the study will be based on examining the returns in the various days of the week and to investigate whether there is volatility of the HSI based on the day of the week. Even though the day of week effect has been extensively researched since 1930, there are variations that may be specific to some markets. In addition, Dimitrios and Katerina (2003) noted that the average daily returns differ from the various days of the week. Thus, it is worth noting that the changes also depend on the type of stock being traded. The subcategories of the HSI attract a lot of interest across the globe as they are dominated by the multinationals; thus, investors are keen to understand the trends. The implication of the trend can influence the involved businesspersons in the choice of the strategy of investment to be used, the selection of the portfolio and the management of the profit.

Various studies have been conducted to investigate the effects of the day of the week on the HKSE HSI. The findings from the various studies have pointed that stock returns are affected by the day of the week. The main findings have been that the day of the week depicts different patterns. For instance, some studies have pointed to negative returns on Mondays while others have pointed to Monday as having positive returns. Therefore, the objective of this study will be to explore the effects of the changes to both the management of the HSI and the investors. The findings will help the investors and managers to put in place strategies to ensure that they profit from the investments.

Research Questions

In order to achieve the objectives of the study, there is the need for research questions to guide the study. The questions will be critical in establishing the calendar effect on the Hong Kong Stock Exchange HSI. The following are the research questions:

  1. How does the day of week affect daily returns in the Hong Kong Stock Exchange Market HSI?
  2. What are the effects of the seasonality changes in the stock markets?
  3. How do the changes affect the trade in the market?

Significance of the Study

Hong Kong Stock Exchange market is strategically located in a region that has been experiencing economic boom (Garefalakis 2011). Many multinationals are eyeing the opportunities emerging in the Asian region. Due to the HKSE rankings in Asia, second after Japan Stock Exchange, it has attracted a lot of interests. The increased interest in the market calls for accurate information on the stock performance. According to Garefalakis (2011), investors are attracted to markets that are relatively stable and with accurate stock movement information. The prices in the rational markets depend on the available information (Garefalakis 2011). In the stock exchange, actions of investors determine the price movement. However, there are deviations that may occur or disappear due to periodic factors. The changes in indices due to the time factor have a significant implication on the decisions made by investors. Therefore, the manager and the stockbrokers need to have in-depth understanding of the market operations.

Market efficiency hypothesis stipulates that stock markets are rational. A comprehensive understanding of the calendar effect will help in the process of decision-making and in directing of the new market entrants. Hence, a clear correlation and trends in the market will be imperative for steering the market to greater heights. In order to guarantee efficiency, the implication of the calendar effects will present managers with the right information. The information will be applicable for strategic action planning required to maintain efficiency, competitiveness and effectiveness in the markets (Kohers et al. 2004). According to Chen and Liang (2004), fundamental analysis of stock markets is the basis for making profits.

The anomalies entail the inexplicable happenings, which arise due to speculations. According to Al-Saad and Moosa (2005), the anomalies cannot be explained using the scientific theory. However, they are based on predictable patterns that relate to the day of the week or month of the year. The most analyzed anomaly is the weekend effect, which is characterized by positive returns at the close of the week, i.e. on Fridays and a negative return experienced on Mondays. The calendar effect occurs across different stock markets in the world. Gao and Kling (2005) noted that it is a global phenomenon which is not restricted to a specific equity market.

The understanding of the actions of the calendar week will provide a framework that can be used to put in place strategic goals that will improve performance of the stock markets. For instance, the Hong Kong Stock Exchange HSI is a leading market that attracts a lot of interest both in Asia and other places that have business interest in HSI. A well-developed framework and the understanding of the mechanisms that relate to Hong Kong Stock exchange will be critical for the investors because it will help in making decisions on their direct investments in the HSI. Taking an example of the real estate stock that is a major component of the HSI, slight changes have great implications; thus, the investors should keep their eyes on the seasonality effects to avoid shocks. The findings of the calendar effects will benefit the investors in maximising the profits, which will serve to make the HSI a more vibrant stock market.

As noted by Al-Saad and Moosa (2005) the presence of the calendar anomalies acts against the efficient market hypothesis. Basher and Sardosky (2006) argued that the changes caused by the calendar effect present a puzzle that the investors have to solve in order to realise profitability. In many instances, investors have attempted to take the advantage of the calendar effect over the past three decades. However, the anomalies do not disappear. In China, many past studies have pointed to the tendency of flattening. However, close analysis of various stock markets still points to a shift towards seasonal changes that result due to the Chinese New Year (Barret & Donald 2003). Thus, the anomalies tend to be towards the end of February. In addition, the empirical analysis of the days of the week points to the existence of the anomalies.

There are schools of thoughts that the calendar effects on the Hong Kong Stock Market and other large stock exchange companies is mainly due to data snooping (Ariel 2002). Therefore, clear analysis of the trends and the subsequent results will provide evidence to establish whether the concept of data snooping still exists or not. The ascertaining of the calendar effect on the stock return will also form a basis for the managers in the different sectors that are affiliated to HSI to understand the dynamics that exist in the market. For instance, if the calendar effect is a reality, managers should be in the position to come up with strategies that relate to pricing of the assets to conform to the calendar effect trends. For instance, incorporation of weekend in the future models that relate to the stock exchange. Such knowledge and its application will be a critical asset in the pricing and allocation of assets such as those that relate to real estate. In addition, the strategies will be critical in stabilising the prices of the equities and avoiding the outliers that may crop up due to the calendar effects.

Knowledge on specific calendar effects is paramount for shareholders in various companies and managers that are tasked with enacting strategies that enhance trading. Therefore, in the examination of the effect of the day of the week, the dissertation will make a significant contribution to the shareholders and the various stakeholders in the HSI. In addition, the work will be an important academic paper that will add to the existing literature on the calendar effects on the stock returns. In addition, the study will provide a new dimension that is specific to HSI and that includes explanations that relate to happenings in the market; hence, providing the descriptive aspect that has been lacking in the previous studies.

Literature Review

There is a school of thought that in the current stock markets, efficient management has eliminated the calendar effect; hence, there are no effects on the returns (Kok & Wong 2004). Another school of thought holds that calendar effects are common and have significant impacts on the returns. In this research, the focus will be to determine effects of the calendar effects on HKSE and subsequently establish whether they have any significance on the stock markets indices. The following literature review explores earlier studies on calendar effects on the stock market. The review will be critical in determining the direction of stock movements concerning calendar effects.

Day of the Week Effect

Studies carried in different international stock markets have pointed to evidence of periods affecting stock performance. The emphasis of the studies has been on the day of the week and the month of the year effect. In exclusive studies focusing on the stock markets in the Unites States, Nikkinen and Sahlstrom (2004) noted that there exist anomalies that investors exploit. The exploitation of the anomalies results in businesspersons earning abnormal returns. The findings by Nikkinen and Sahlstrom (2004) contradict the market efficiency hypothesis that stipulates that the calendar anomalies effects have disappeared. In the argument, Paul and Theodore (2006) noted that the calendar effects cannot be dismissed because the early and the current studies have pointed to a definite trend in relation to daily variations.

Kiymaz and Berument (2003) noted that stock markets are dynamic, they change with time; hence, current calendar effects should be based on current trends. However, Leshno and Levy (2002) argued that the earlier studies greatly relied on the normality assumption in which mean-variance was used as the core analysis approach. The weakness that related to the usage of the mean-variance is that higher moments tend to be ignored. Currently, nonparametric stochastic dominance (SD) approach is applied. The new approach presents an alternative that does not rely on assumption. The method is assumption-free and incorporates both the low and high moments to determine the calendar effects. Leshno and Levy (2000) concluded that the application of SD can bring a new perspective to the stock movements in relation to days of the week returns.

Thomas (2002) carried a study to investigate the calendar effects in Swedish stock markets. Thomas (2002) established that the day of the week had a significant effect on the returns. The study explored over 207 stocks in the Swedish market. Even though the sample was adequate to draw generalisation on the calendar effect, the inferences cannot be applied in other stock markets outside Sweden due to variations in business environment. In addition, the analysis used depended on the mean-variance; hence, there were assumptions that negated high moments. Dimitrios and Katerina (2003) conducted a similar study on the French stock exchange. There were anomalies observed in the study in relation to the day of the week. In the data analysed, Monday was found to have the highest volatility.

In yet another extensive study, Paul and Theodore (2006) examined the day of the week and month of the year effects in an African market. The study entailed the analysis of Ghana Stock Exchange. The cross-sectional study analysed the closing prices share indices for a duration of ten years, from 1994-2004. Monday was found to have reduced returns compared to the other business days. The trends discovered in the Ghana stock markets pointed that calendar effects are rife. Similar trends were also found in Asian markets focusing on China and India equity markets (Gao & Kling 2005). Even though the studies pointed to the trends, the studies did not quantify the significance in relation to the returns in the markets. There are still ranging debates on the market efficiency and the calendar effects implication of the particular markets.

Chen and Liang (2004) explored market anomalies in relation to daily changes in five countries. The stock exchange markets analysed included Thailand, Philippines, Singapore, Malaysia and Indonesia. The studies were carried before and after the financial crisis in Asia. In the Malaysian market, Monday presented a significant abnormal effect. However, the study concluded that there were no daily seasonal anomalies in relation to the period before and after the time of crisis. In a follow-up analysis of the study, Kok and Wong (2004) analysed the findings using the ordinary least square (OLS) approach. The results indicated that the day of the week had an effect on the Malaysian market. Negative Monday returns effects were noticed and positive Wednesday and Friday returns effects in the period before the crisis. During the crisis period running from 1996 to 1998, the anomalies were not experienced in the five markets. After the end of the crisis, Malaysia had only positive effect on Tuesday.

The findings, before and after the crisis presented a level analysis for stock movement. The movements were established in three different market environments i.e. before, during and after the crisis. The changes during and after the crisis, depicted the caution that results in normal markets due to changes (Muhammad & Rahman 2010). The relatively stable volatility in the five markets during and after the crisis related to the improved market knowledge that emerged after the hard economic times. Despite the changing patterns, the shift from negative to positive effects in the Asian markets substantiates that there was relative effect of the calendar effect in the stock markets.

In an empirical study to investigate the stock anomalies, Deysshappriya (2014) conducted an analysis of Colombo Stock Exchange in Sri Lanka. The investigation established that there was availability of the day of the week effects on the stock market. The study analysed the findings using both the OLS and GARCH models. The models detected positive effects in some days. For example, from Tuesday to Friday the positive effect was recorded while negative effects were recorded on Mondays. Higher positive magnitude was established on Friday. The effects were noticed both in the two models. The study was carried during the Sri-Lanka’s unstable times of war. The turbulent business environment marked by war and turbulent political upheavals meant that investors relied on the available information to make decisions on the shares. Due to the unstable environment, Deysshappriya (2014) noted that unrealistic prediction could not be avoided.

The negative Monday was attributed to the period after weekend in which there was scant information on the business happenings. Deysshappriya (2014) noted that the high positive Fridays was due to the trends in the proceeding days and the need to sell the share before the uncertainties of the weekend. From the study, it is arguable that the day of the week effect is based on the information available. The periods proceeding holidays were likely to experience slowdowns as the investors speculated on the market happenings. For instance, weekends precede Mondays. Similar trends in relation to yearly trends have been noticed in January. The reason for negative January is related to December festivities; hence, scarce information on the start of the year results in negative returns in January (Kiymaz & Berument 2003). In addition, Thomas (2002) noted that calendar effect that resulted in high volatility on Mondays were due to the after stock closing on Friday. The investors were certain on the opening prices on Mondays bearing in mind that weekends are generally considered stable days. The analysis of the stock after the post-war pointed that there was reduced volatility; the daily mean volatility after the war was negative. The stability was attributed to the stable economic climate.

Raj and Kumari (2006) noted that the day of the week and the month of the year variations were high during uncertainty periods compared to certain periods. Based on the findings, market anomalies should be explicitly studied and understood by investors in order to ensure that investment decisions are fruitful and guarantee positive returns. In an earlier study concerning the stock markets in Sri-Lanka, Thilakarathne (2008) found that there existed positive Friday effect and negative Monday effect. According to Thilakarathne (2008), not all markets relate to the Random Walk hypothesis, i.e. stock markets cannot be predicted. Nath and Dalvi (2005) noted that many markets across the globe have realised reduced average returns on Mondays and the high returns on Fridays. As a result, a trend has been developed in which the calendar effect has been actualised based on past trends. Nikkinen et al. (2009) pointed that the day of the week exists and has an effect on the volatility and the returns.

Month of the Year Effect

In order to determine the calendar effect on months, Mehdian and Perry (2002) investigated the monthly effect. In various studies carried, negative effect was found in January in the established stock markets. However, for the emerging markets, the effect was null. Hillier, Draper and Faff (2006) established similar trends in a study of different established markets in United States and Europe. Despite the indication of the calendar effects in the various markets studied, Gu (2003) pointed that there was evidence that the monthly calendar effect of January was on decline in US equity markets. Coutts and Sheikh (2000) concurred with the findings. According to Coutts and Sheikh (2000), calendar effects in the emerging markets have significant influence while in the developed markets depict stable movements. Al-Saad and Moosa (2005) noted that the January effect is not definite. The effect depends on the seasonality. For instance, in the analysis of the indices in Kuwait Stock Exchange, there was a negative July effect. The market presented a deviation from the normal January effect in other markets. Al-Saad and Moosa (2005) attributed the changes to the summer holidays.

Guler (2013) carried a study to examine January effect in stock returns targeting emerging markets. The studied markets included India, Turkey, Shanghai and Brazil. Power ratio method was applied in the study. The study established January effect varied. Some markets had the effect while other did not have the effect. Guler (2013) established the existence of January effect in China, Argentina and Turkey. In the other markets studied, the January effect was not established in the stock returns especially in Brazil and India. Earlier studies conducted by Coutts and Sheikh (2000) that investigated seasonality in Hong Kong market found that there were no seasonality changes in the stock market.

Many studies have examined the seasonality behaviour. The primary aims of the studies have been geared at bringing efficiency in the Markets. The studies that have been carried cut across different markets, from the developed markets such as Japan, United States of America, and United Kingdom to the developing markets such as Korea, Malaysia, Taiwan, Ghana, and Thailand. The studies have indicated the existence of seasonal changes in the stock markets that influence the months of the year.

Methodology and Study Design


The review of the literature that relate to calendar effects on the stock returns have pointed to the existence of anomalies in the different stock markets across the globe. Various studies on the financial economics have for a long time documented the calendar effect in different stock markets. The calendar effect results in anomalies that cause a positive return or a negative return. The changes have been termed as phenomenal seasonal anomalies (Coutts & Sheikh 2000). Market anomalies in the stock movements affect investors’ returns and lead to inefficiencies. Therefore, the following chapter covers methodology that relates to the current study.

Methodology involves the approaches that are applied in the collection of data. It also includes the approaches applied in the data analysis. According to Denk (2010), study methodology is systematic planning of actions that are applied in the collection of information. It also includes the subsequent analysis of data in a logical manner that helps in the realisation of the research purpose. Study methodology entails the use of different research designs to inform the process of the data collection (Bronfenbrenner & Evans 2000). Examples of the study designs include descriptive, cross-sectional, experimental, and explorative researches.

Kothari (2005) defined research methodology as the process applied to solve a research problem systematically. According to Kothari (2005), research methodology entails studying the various steps that are adopted by a researcher in the investigation of a problem. Study methodology includes the logic behind the steps that are employed by the researcher (Kothari 2005). Research methodology thus involves the research methods. The research methods are the techniques put in place by researchers in the process of executing a study. According to Neumann (2007), research entails moving from the unknown to the known in order to arrive at a solution to a specific issue being investigated. The process requires drawing of data to be used for the correlations. Based on the understanding, research methods can be categorised into different groups. The first group includes the methods and techniques that are used to collect data. The second research method entails the techniques for statistics that are used to establish the correlations between the unknowns and the collected data. Finally, the third method of data collection entails the process that is used to determine the accuracy of the information collected.

According to Denk (2010), the data collected should be reliable and valid. The concept brings in the issue of ethical consideration in the collection of the data by application of credible processes. Therefore, in order to execute the study and ensure that the objectives of the study are explored, and correlations determined, various research methods will be applied in the collection of the data and analysis. The current study will employ different approaches, both the qualitative and quantitative. Therefore, the study will collect data to be used for the empirical study and qualitative data that will be used to understand the various perspectives of the managers. The data collected will form the basis of knowledge.

The purpose of a scientific study is to discover answers to the research questions. According to Kothari (2005), it entails the use of scientific procedures to find out the truth, which has not been discovered. Neumann (2007) noted that the objective of a research is to gain familiarity with the issue being studied or to gain insights into a particular phenomenon. In the case of the current study, the phenomena under investigation are the anomalies that result due to changes in the calendar effects in Hong Kong Stock Exchange HSI. Therefore, the aim of the research methodology is to provide a platform for the discovery of the phenomena being studied. In addition, study methodology provides the basis for testing the hypothesis and the establishment of the causal relationship between the various variables in the study. The common variables that are tested in a study include the depended and the independent variables (Kothari 2005). For example in the study to establish the effect of the day of the week on the stock returns, the role of the research methodology is to help in obtaining the data that portrays the characteristics of the players in the stock market. In such a case, the main players are the investors and the trend they adopt to invest based on the available knowledge.

The research methodology entails different types of research that depend on the purpose of the study. The common types of the research types include the descriptive, analytical, applied, fundamental, quantitative, qualitative, conceptual, and empirical. The basic approaches applied in a research include the qualitative and the quantitative approach. The quantitative approach involves gathering of empirical data that can be subjected to numerical manipulation. On the other hand, the qualitative approach entails the type of studies that apply qualitative assessment of perceptions, opinions and behaviours (Denk 2010). The common methods that are used for data collection in the qualitative approach involve the projective techniques, focus discussions and the interviews. The use of the approaches depends on the type of the study being conducted and the nature. Therefore, in some cases, the two approaches may be combined. The study on the calendar effects can be conducted through different methods such as the analysis of the behaviour of the movements of the market indices. In the current study, the research will incorporate the two approaches in order to provide a comprehensive understanding of the various factors that influence the HSI.

There are many studies conducted in relation to calendar effects in various stock markets. Most of the studies applied quantitative approaches in which empirical data was collected and analysed. The current study will focus o the HSI returns. In addition, the returns in the listed companies will be analysed which will ensure a comprehensive understanding of the overall calendar effect. However, Neumann (2007) argued that a research should be focused on a given aspect and that mixed methods make it difficult in the drawing of the inferences. However, the integration of the returns in the HSI and critical analysis of the HSI listed companies will provide a platform in which the target users of the data will be well varnished with the implications for calendar effects. The data will be critically analysed and correlations made by use of statistical tools. For instance, the application of the multivariate regression analysis will help in striking balance in the secondary data obtained and in ensuring that the inferences drawn depict a common perspective.

Research Designs

The research design to be applied in the study will be cross-sectional study design in which empirical data on the stock indices will be collected and analysed. Cross-sectional study will help in giving the snapshots of the closing indices for the stock markets in the selected study period. By the application of cross-sectional study, the researcher will be in a position to find out the timelines of day or monthly returns. According to Denk (2010), the cross-sectional studies are carried out repeatedly to give pseudo longitudinal study. The research design will focus on the trends in the Hong Kong Stock Exchange HSI. Great emphasis will be to collect substantive empirical data that will be used for the analytical process. The empirical studies will provide opportunities in understanding the HSI indexes. The daily returns from the databases will be crucial in eliminating the bias that results in the use of other types of research. Despite the opportunities presented by the empirical data, there are limitations that are likely to be encountered in the use f the quantitative data. For instance, Kothari (2005) noted there are errors and pitfalls that relate to empirical data. Kothari (2005) argued that empirical approaches are not necessarily formal proof of the fact being investigated. The results are sometimes afflicted with uncertainty that relate to the method of analysis for the data. For instance, in the analysis of the calendar effects that relate to HSI, non parametric and parametric analysis may show substantial differences and yet similar empirical data is used for the analysis.


Sampling is the process of selecting a subset of the target population (Sans 2011). One of the key factors underpinning research methodology is to have a representative sample from which acceptable inferences can be drawn. In order to arrive at a representative sample, the population being studied should be clearly defined, the sampling frame should be clearly specified, the method to be applied in the sampling should be identified and the sample size should be determined. In the current study, the calendar effect study on HSI will incorporate both primary and secondary data. The sources of the data will be sampled using different sampling procedures. The sampling of the population to be used for primary data collection will be through purposive sampling in which the databases that collect HSI returns will be targeted. According to Sans (2011), purposive sampling is discriminatory as it gives the researcher the discretion to identify the subjects to be included in the study. As a result, there is the possibility of personal bias in the identification of the study participants. However, the method is paramount when specific information required can only be achieved from specific people. Thus, in the current study, only some databases are in position to provide specific information that relate to the phenomenon being studied; hence, the reason to settle on the method.

The data will cover specified time duration. The data will be accessed through data mining processes targeting period stipulated in the study. The information will mainly be from the data that contain credible information on Hong Kong Stock Exchange. The method will be very important for the study of current phenomena, as it will provide the true data for the organisation. In addition, the method is free of bias. However, in accessing the data, there are ethical issues that should be considered such as acquiring authorisation and declaration to the relevant authorities the intended use of the data.

Sample Frame

Sample frame entails the complete list of the attributes or the members of the population that are to be studied. Sampling frame represents the working population that is utilised in the study (Kothari 2005). In the case of the HSI stock exchange, the sampling frame will entail all the indices recorded during the period of the study from January 1st, 2012 to December 31st, 2014. However, the sample frame will exclude some of the indices that relate to weeks in which five days returns will not obtained. The reason for the lack of five days data could be due to holidays. According to Sans (2011), if a sample frame is taken correctly it leads to a sample that can be used for drawing inferences for the population as a whole.

Sample size

The determination of the sample size depends on the number of replication that can be applied in drawing inferences about the population/units. The type of data required for the research normally determines the sample size. Sample sizes are important in determining the precision of the study (Neumann 2007). The sample size in this study will involve the cross-sectional data covering the study period. Three year time duration from 2012 to 2014 will be used. The three-year period will ensure that monthly trends for the month of the years and the day of the week changes will be established. The sampling will be purposive because only HSI data will be targeted. In this case the focus will be n the HSI indices for the stipulated time in which public databases will be used in accessing the required data. According to Neumann (2007), the purposive sampling is used in cases where the targeted information can only be obtained from specified people. In the case of the current study the target is the HSI stock returns.

Data Collection Methods

There are varied data collection methods. According to Kothari (2005), the choice of the method to be applied in the data collection is determined by the strategy, type of variable being measured and the point of collection. Secondary data collection will be applied in the current study.

Secondary Data

Secondary data comprises the information that has been gathered and is readily available. The secondary data is normally cheaper and quickly obtainable compared to the use of primary data (Sans, 2011). Therefore, in the current study, the secondary data will be more applicable due to the difficulty in accessing the primary data. Primary data in the case will entail contacting interviews that target the managers involved in the HSI stock exchange management. This will be expensive due to travel challenges and the busy schedules of the managers. Though the primary data will provide deeper insights into the operations of the HSI, the current study will settle on the secondary data due to the economical aspect and time saving aspect. In addition, the secondary data will be paramount in providing the basis for comparisons for the data in relation to the different days of the week and seasons. Despite the advantages associated with the secondary data, some databases may contain outdated data that may not reflect the current business environment (Denk 2010). In order to avoid the challenge of using the outdated data, the current study will focus on a given stipulated time data. Hence the analysis and drawing of inferences will focus on the given time. The method to be applied in the collection of the data will be data mining.

Data Mining

The secondary data will be collected from databases (data streams) that contain Hong Kong Stock Exchange HSI daily returns. Thus the data streams will form the secondary sources of data. The data to be collected will include the recorded indices covering the period from 2012 to 2014. An authorisation will be requested from the relevant personnel. The daily stock indices will provide the basis for determining the day of the week effect and the month of the effect. Only the credible databases will be used in the study. This will ensure that only the reliable data will be collected for the study. In addition, the selection of the data to be applied to the study will be limited to the stipulated study duration. The use of credible data streams and the focus on given time will ensure that the reliability and credibility of the data is upheld. Therefore, historical data for the indices of Hong Kong Stock Exchange HSI index will be gathered. The data streams that contain daily stock returns of the listed companies will be targeted for the data collection. The data streams normally have comprehensive data, which is arranged in a chronological manner. Thus, the access of the information is easy. Furthermore, the data streams are accredited, and they receive daily data from the various stock markets. Their nature ensures that the data they have is reliable. Databases which do not consistently record the daily stock returns for HIS listed companies will be eliminated.

According to Sans (2011), the method of data collection is normally influenced by the research strategies, the point of collection, and the person to carry out the research. The main types of data collection methods to be used in the study will include secondary sources of data collection.

Data Analysis

There are different approaches to the analysis of data. Due to the challenges that relate to secondary data such as the being outdated and accuracy, the data collected will be evaluated before the analysis is conducted. This will ensure that the suitability of the data is established and that informed inferences can be drawn from the data. The key to the evaluation will be to determine the availability, relevance, accuracy and the sufficiency of the. The evaluation will be paramount in establishing whether the data to be analysed aligns with the research objectives. This will set the basis for the analysis of the empirical data from the purposively selected databases. The primary aim of the study on the Hong Kong Stock Exchange HSI is to find out whether there are calendar effects in the stock markets. According to Coutts and Sheikh (2000), the leading cause of the anomalies is the calendar effect. The month of the year or day of the week, affect the stock markets indices. Studies on anomalies that relate to calendar effect have been general, and they lack emphasis on the significance of the calendar effect. Latif, Arshard and Farooq (2011) noted that the anomalies relating to calendar effect are due to data mining that lead to altering the normal pattern. The belief in the calendar effect causes a lot of speculation in the stock market; hence, the anomalies. The calendar effects focus will be on the day of the week and month of the year. In order to achieve the objectives, non-parametric approach will be used.

Anomalies slow down the activities of the markets and lead to changes in stock returns in the affected markets (Deysshappriya 2014).The implication of the anomalies is the non-normal nature of the returns. Most of the earlier studies employed the criterion of mean-variance to analyse data which relies on the assumption of anomalies. The assumptions are aligned to the lower moments; they thus miss the higher moment’s significance. Due to the limited nature of the studies, the significance of the anomalies has not been effectively established. The non-parametric approaches to be applied for the analysis will be the stochastic dominance approach (SD) and the free-float adjusted market capitalisation. According to Barret and Donald (2003), SD endorses entire distribution of returns directly. SD has advantage over the parametric approaches when dealing with non-normal distribution. The SD does not rely on any assumption in relation to the distribution nature. Thus, it is suitably applied to any distribution.

The SD reveals the entire information for the period being studied, unlike the parametric measures that rely on the mean and variance (Lean et al. 2007). Hence, SD does not end up omitting some information especially from higher moments. Stock returns have non-normal distribution (Wong et al. 2005). SD ranks different pairs of indices to a statistical degree of confidence. Therefore, the daily stock indices for the study period will be analysed using SD. The indices include the Hang Seng Indices. Due to possible holiday effect on the stock exchange trading, the weeks with holidays will be excluded from the study. The exclusion of the weeks with less than five trading days will be aimed at upholding consistent in the data analysis.

In addition to the SD methods, free float adjusted market capitalisation weighted methodology will be applied to analyse the targeted listed companies. The method puts into account the strategic holdings. This method will provide critical information that is needed by investors because it depicts the liquidity that is related to investment in the particular stock markets. This will be very critical for the HSI investors that are involved in the real estate listed companies which are affected by slight calendar changes. The method helps in consolidating the various stock returns in order to determine the current index. Furthermore, the method has been adopted in leading stock exchange markets to determine returns for listed companies. The method calculates the current index based on current aggregate of capitalisations divided by the previous day aggregate capitalisations. The result is multiplied by the returns for the last stock returns (yesterday’s closing index).

The analysis of the data using SD and the application of statistical tools such as the SPSS, eview, and establishments of correlations will provide a clear understanding of the trends in the stock markets. The inclusion of the freefloat-ajusted market capitalisation weighted methodology will provide a new dimension that earlier studies failed to incorporate.

Primary Pilot Research

In order to determine the applicability of the methodologies, a pilot research was conducted. The research entailed collection of the data from purposively identified databases that consistently record the returns for the HSI. The databases incorporated in the study included those with accurate and reliable data covering the stipulated time. The main aim of the study was to establish the calendar effect on the Hong Kong Stock Exchange; therefore, indices for the stock performance were necessary. The collection of the stock performance relied on secondary data that was obtained from selected databases. The pilot study included accessing different stock exchange databases to determine the accessibility of the data. In the process of trying to obtain data from the identified stock exchange databases, it was difficult without getting authorisation. The authorisation included registering with the selected databases in order to obtain the passwords to login into the databases. Therefore, the pilot study was delayed for a day as there was time utilised in seeking permission to access the databases. Finally, the authorisation was given and data was collected.

Validity and Reliability


Validity measures the degree to which a research instrument collects the data that is supposed to be collected (Oladipo, Adenaike & Ojewumi 2010). For instance, in the current study the key focus was on the type of databases and the information presented that relate to the stock returns. Hence, the results obtained could be generalised. In addition, the databases were evaluated before their incorporations which enhanced their suitability for the study.


Reliability involves the consistency of the research instrument (Oladipo, Adenaike & Ojewumi 2010). The reliability for the study was achieved by use of credited databases that ensured daily returns for the targeted stock markets were obtained.

Possible Improvements

The study provided a prototype for the actual study. Key shortcoming was the collection of the secondary data. In order to avoid the challenge, authorisation for the access of the database for the Hong Kong Stock Exchange HSI will be requested in advance. In order to enhance validity and reliability of the interview, a committee of experts will be formed to develop and review the data collection instruments.

Project Management

Project management entails the process of planning the various activities that are involved in the research process, organising and controlling resources. According to Kirkland (2014), project management entails setting clear timelines that involve the procedures to be followed in order to achieve specific goals. In the research, proposal, the project management entailed the compilation of the various activities, designing and implementation of a pilot research (See Appendix I & II, the time plan and a Gantt chart). In the process of implementation of the project, there were time constraints and thus some time had to be adjusted to cater for the changes. The changes were due to unforeseen constraints. According to Randolph (2014), the primary constraint to achieving the initial time plan is due to functional challenges that relate to scope and budget allocations.

For instance, the implementation of the pilot project was not achievable as intended because the time allocated for the interviews was not adequate. In addition, the mock collection of the indices from the various selected databases was constrained because the databases could not be accessed without authorisation. As a result, there were procedures and telephone calls that had to be made in order to get authorisation for the various databases. In order to lead an efficient and successful project management, Randolph (2014) noted that optimisation of time and budgetary allocations are necessary. They should be integrated to allow achievement of the objectives that are predefined. The project management fell short of time optimisation. In order to correct the challenges, the rest of the research project will be re-evaluated. Projections will be made with adequate time allowance to integrate the remaining work. The pilot project provided insights on issues to expect in the actual project implementation. Therefore, the challenges faced during the pilot study will not be repeated during the actual study.

Data Presentation, analysis, and Interpretation


The main aim of the study was to find out whether calendar effects influence the Hong Kong Stock Exchange Market Hang Seng Index (HSI) turnover. The study was limited to the HSI and its subcategories such as the real estate. The study endeavoured to find out how the day of the week or month of the year influences the stock returns. Therefore, in order to achieve the objectives of the study, secondary sources of data were used. Data was collected from credible databases that normally record the daily returns for HSI. Thus, the chapter presents the analysed data of the daily stock returns and the turn of the month effect with the primary focus on the weekend effect and the Chinese New Year. The analysis of the data employed non-stochastic dominance (SD) approach, correlations, regressions and the mean-variance-approach (MV) approach in order to determine the calendar effect. The empirical results were be based on coefficient, p-values and F-statistics. The common SD rules are the first order SD (SSD), second order (SSD) the third order.


The data collected covered the period between January 2012 and December 2014. This ensured that data set covered many weeks and the turn of the months such as January effect and the Chinese New Year holidays. The data that was collected from the HSI stock returns databases. The dataset was analysed in terms of the day of the week, January and holiday effect. The datasets analysed were purposely selected in order to determine the influences of the day of the week. Similarly, data falling on the January was analysed in order to determine whether there existed the January effect in the Hong Kong Stock Exchange markets.

Day of the week

The secondary data collected was analysed to present the mean returns, standard deviation, kurtosis, skewness and Kolmogorov–Smirnov (K-S) which were used to examine the calendar effect for HSI. Table 1 below is a representation of the statistics.

Table 1: Summary statistics of weekday returns for Hong Kong Stock markets (HSI) for the period between 2012 and 2014.

Mon Tue Wed Thu Fri
Mean (%) -0.0801 0.0944c 0.1148c -0.0567 0.1054c
Std Dev (%) 2.15 1.48 1.78 1.61 1.51
Skewness -2.41 -0.92 1.01 -0.89 0.71
Kurtosis 29.31 17.59 14.68 5.75 4.29
K-Sa 0.14 0.08 0.10 0.09 0.08

The results pointed to a trend in which Monday had the lowest mean returns. Friday had the highest mean returns. These findings were consistent with the earlier studies that investigated the day of the week in various stock markets. On the other hand, the standard deviation of the returns showed a tendency to decrease as the week progressed. For example, Monday had the highest volatility which decreased as the days progressed. Thus, Friday had the lowest instability for the Hong Kong Stock Exchange HSI. Further analysis of the t-tests found that some days had mean returns higher than others. This pointed to the variations that are experienced on the day to day business activities. The analysis of the F-statistics indicated that standard deviations statistically varied at the confidence level of 5%.

In order to determine the comparisons, MV approach was applied. The results indicated that the mean returns for the HSI were -0.00801%. This value was lower compared to other days of the week. Specifically, the difference was significant at 10% except for the returns on Tuesday and Thursday. In addition, the standard deviation on Mondays was 2.15%, which was higher than the other days of the week. The application of the MV approach established that the days from Tuesday to Thursday dominated Monday.

In relation to Friday, the mean return was 0.1054. This was significantly higher than Monday. Therefore, the application of MV criterion pointed that the returns on Fridays dominated Monday returns. Table 2 below is an illustration of the MV approach comparisons

Table 2: MV Tests

MV test results of day-of-the-week effect for Hong Kong Market

Hong Kong Tue>Mon Wed>Mon Thu>Mon Fri>Mon Tue>Thu Fri>Thu

The table presents the MV test for the day of the week for Hong Kong for the period between 2014 and 2014. The rule applied was MV rule in which if µx > µy; thus, X >Y means X dominates Y. The significance level used for the Table 2 is 5%. Therefore, table 1 and 2 showed that there is still significant calendar effect on the HKSE HSI. This is contrary to earlier studies that pointed to the diminishing of the day of the week effect. For instance, in the literature review on page 14 a study by Coutts &Sheikh (2000) that focused on stock returns in developed markets such as the US stock exchange pointed that the day of the week had significantly diminished.

One of the core objectives of the study was to establish how the calendar effects affect the HSI investors and the general trade in the market. Based on the findings as illustrated in Tables 1 and 2, inferences cannot be drawn to determine whether the preference for investors for the days’ portfolios result in higher returns or affect the investors who are risk aversive. Thus, in order to determine how the returns affect the investors, there was the need to include the overall distribution of the returns. Hence, the SD approach was applied in order to determine the preferences of the investors. Table 3 indicates the stochastic dominance for the various days of the week for the HKSE HSI.

Table 3: Statistical Dominance Using SD

DD test results of day-of-the-week effect for Asian countries

Hong Kong Tue >1Mon

The results showed that the returns for Monday are stochastically dominated by other days of the week. The dominance pointed the day of the week effect in the Hong Kong Markets HSI. However, it is worth noting that the application of the MV criterion is usually limited to the distribution when the daily returns are normal. For the SD, the assumptions of normality are not applied. According to Barret and Donald (2003), SD endorses the entire distribution of returns directly. SD has an advantage over the parametric approaches when dealing with non-normal distribution. Therefore, it is possible that the inferences drawn from the application of MV criterion will vary from those drawn from SD approach.

Holiday Effect

The summary statistics for the different months are presented in Table

Table 4: Summary for the Turn of Months Effect

Jan Feb Mar Apr May June Jul Aug
Mean (%) -0.0097 0.2586a -0.0169 0.0902 0.0630 -0.0082 0.0781 -0.2061b
StdDev (%) 1.91 1.66 1.51 1.43 1.66 1.94 1.23 1.81
Skewness -0.65 1.27 -0.57 -0.41 -1.03 -5.62 0.06 -0.65
Kurtosis 3.69 11.60 2.80 5.35 10.75 74.36 0.55 5.14
K-Sa 0.13 0.08 0.09 0.09 0.10 0.14 0.045c 0.10
Sep Oct Nov Dec
Mean (%) -0.0156 0.2253c 0.0135 0.1053
StdDev (%) 1.62 2.22 1.47 1.54
Skewness -0.09 0.17 0.07 -0.56
Kurtosis 5.41 17.79 1.52 4.00
K-Sa 0.10 0.14 0.08 0.09

The significance levels are denoted by a, b and c. The significance levels are 1%, 5% and 10% respectively. From the returns, the returns for January are very low. The statistics point to high standard deviations for January. The kurtosis is greater than normal, and K-S are significantly high which showed returns are non-normal. The application of the MV dominance established that Hong Kong had most high dominance. Investors were found to have a preference for months of November, December, July, May, April and February. In order to establish the holiday effect, the trading days were grouped over the period from 2012 to 2014 in which two subcategories were obtained, the pre and post-holiday periods and other trading days. The mean returns and standard deviations for both the pre and post-Chinese New Year returns are illustrated in Table 5 below.

Table 5: Pre and Post Chinese New Year Returns

Hong Kong returns
Mean return 0.00042
Standard deviation of return 0.01698
Total number of Pre and Post Chinese new year trading days (-5,+5)
Mean return 0.00173
Standard deviation of return 0.01919
Total number of other trading days
Mean return 0.00039
Standard deviation of return 0.01691

The mean returns of HSI were 0.000424. During the period of the Chinese New Year, the average returns were 0.000173. The mean return for other trading days was 0.000391. Testing of significance was carried out to determine the pre and post Chinese New Year effect. Dummy variables for five-day pre-Chinese New Year and five days post-Chinese New Year were used. The results showed that the pre-Chinese near year period had a significant positive return. Similarly, the post-Chinese year period showed positive returns. However, the returns were insignificant. For instance, the coefficient for return in pre-holiday was 0.00445 while that for the other trading days was 0.0037. In general, the findings support the perspective that the major holidays such as the Chinese New Year have an effect on the stock movements.

Table 6: Regression Result

>2 (prob)
HSI 0.114 -0.066 0.128 -0.245 -3.652 0.739

In relation to the whole period under the study, AHOL*MONt coefficient was found to be significantly negative. The values pointed to the existence of abnormal trends on the day after the weekend. The abnormal effects extended on Monday after the holiday. The results showed that the Monday effect exists after the weekend in the Hong Stock Markets. Therefore, the findings relate to the supposition that the risk aversive investors avoid trading on Mondays; hence, the low returns. On the other hand, the coefficients of BHOL*FRI t and BHOL*NO_BHOL>2 were found not to be significant. The implications are that the Friday effect is significant before the weekend.

In the period between 2012 and 2014 the returns were significantly negative as illustrated by the coefficients of AHOL*MONt. The repeated manifestation of the negative Monday returns for the different weeks affirmed an established pattern for the investors. In relation to the holiday falling before or after the Monday, Monday depicted the effect of amplifying the abnormality. Therefore, the results indicated that the abnormal returns that relate to the day of the week, and the holiday effect are still prevalent in the HSI, and they influence the investment behavior in the stock trade.

The calendar effect is a global phenomenon. Various studies conducted across different markets, both in the developing and developed worlds have pointed to the effect. For example, on page Basher and Sardosky (2006) stipulated that the changes caused by the calendar effect present a puzzle that the investors have to solve in order to realise profitability. The views by Basher and Sardosky (2006) are clerarly depicted on pages 7 and 9. Al-Saad and Moosa (2005) stated that the presence of the calendar anomalies acts against the efficient market hypothesis as outlined in the literature review on pages 6 and 15. The weekend effect contradicts the efficient market hypothesis, which stipulates that the weekend creates an opening for the investors to gain around the weekend. Due to the negative Monday, investors should take advantage of the Monday opportunity. However, this is not the case as they are frightened of the uncertainty for the week.

Summary, Discussion, Conclusion, and Recommendations


This chapter provides the summary of the whole study, discusses the findings, provides conclusions and finally draws recommendations. The discussions are in line with earlier studies that relate to the calendar effect in various markets as presented in the literature review. Therefore, the discussion presents the explanation for the calendar effect on the Hong Kong Stock Exchange Markets. It established whether there are similarities between the earlier studies and current study.

Summary of the Study

The primary aim of the study was to find out effects of calendar days on the HSI and to establish how they influence the investors’ behaviours. The calendar effect being investigated included the day of the week, January and holiday effect. The rationale for the study was influenced by the differing schools of thoughts on the calendar effects. Literature review established that there have been arguments that calendar effect has disappeared in developed stock markets such as Hong Kong HSI while others have maintained that the calendar effect is still prevalent in many major stock exchange markets. The arguments were propagated by Gu (2003), Coutts and Sheikh (2000) and Al-Saad and Moosa (2005) as found on review of literature on pages 14 to 15. For example, on page 15 of literature review; Al-Saad and Moosa noted that the stock movements were not definite. As a result, it has become difficult for investors who intend to invest in the HSI because they lack current information on the trends in the markets. Thus, the current study was paramount in providing in-depth empirical evidence. Hong Kong Stock Exchange Market has a great influence on the region. The stock market is listed as among the top equity markets across the globe. On the other hand, HSI plays a critical role in the market as most of the stock moved relates to HSI.

In the first chapter, the study explored the necessary information that related to HKSE HSI. The aims of the study, the research objectives, questions and the significance of the study were examined. The chapter provided an introduction that drew the importance of the study and its relevance. Terms such as the calendar effect, day of the week and the January effect were described.

The second chapter dwelled on literature review. The chapter was integral to the study as it provided the previous studies that have been carried in relation to the effect of the calendar effect on the stock returns. The review covered the calendar effects on both the emerging and developed markets. The reviews established that the calendar effect was real in most of the markets. However, there were studies that pointed that the calendar effect had significantly disappeared in the developed markets.

Chapter three of the study was about the study methodology. The chapter provided the philosophy that relates to the study and further outlined a detailed research design that was used in the study. The key to the research design was the target population, sampling methods, the techniques for gathering the data, and the data analysis procedures. The method of data collection as outlined in the chapter was the use of secondary data sources such as the credited databases. The data was then analysed using the statistical packages and SD approach. A pilot study was included in the chapter. The pilot study provided the researcher with a foresight of the challenges likely to be encountered during the actual study in relation to logistics and the research techniques. For example, during the pilot study, there were challenges that related to the data collection.

Chapter four was the analysis and the presentation of the data. The empirical data was operationalised using the statistical packages and SD approach. Correlations and regressions were obtained. The MV criterion was used for the comparison purposes. The analysis was critical in empirically establishing the day of the week and holiday effect.


Market anomalies result in equity movements that lead to inefficiencies in the markets. Calendar effect has been cited by many financial researchers as the primary cause of anomalies. There have been myriad of studies that have investigated the calendar effect in different stock markets. Based on the literature review findings, it was evident that anomalies influence the decisions of the investors. For example, on page 4 Farooq (2011) pointed to data mining as one of the major contributors to the alterations in the normal pattern. Similarly, on page 13 Deysshappriya (2014) outlined how the anomalies have the effect of slowing down the activities of the stock returns. The exploitation of the day of the week or turn of the month effects results to the businesspersons getting returns that are not normal. Hence, the market inefficiencies result because speculation becomes the basis of the trade.

Another study identified in the literature review that supports the current findings is by Kiymaz and Berument (2003) pages 11 and 14. The main argument by the two authors was that stock markets are not statistic. They vary with the current trends and the business operation environment. For instance, information is a major determiner of the trends inherent in any market. As a result, the information prevailing any day of the week will influence the decisions made by the investors. Days of the week such as Wednesday, Thursday and Friday are characterised by the availability of information on the market trends experienced at the start of the week and Tuesdays. This implies that the businesspersons have improved market knowledge as the days of the week progress. However, due to the speculations and risk averseness, the weekend break leads to the low returns on Mondays. Similarly, on page 14 Raj and Kumari (2006) pointed that the day of the week and the month of the year changes are associated with uncertainty periods. Deysshappriya (2014) classified the weekends and the periods after a Holiday as uncertainty period. The resulting pattern points to the fact that stock markets cannot be predicted as outlined in the Random walk hypothesis as discussed by Thilakarathne (2008) on page 14.

The findings by the various researchers relate to the current study in which the data collected and analysed verified the existence of a definite pattern. The pattern is characterised by negative returns on Mondays, positive returns on Fridays, increased returns in pre-Chinese New Year and low returns for the post-New Year period. However, the average returns for the other months are relatively stable.

Hang Seng Index is a critical pointer of the movement of the Hong Kong economy. The results for Hong Kong illustrated that there were abnormal negative Mondays while the mean returns for Friday were abnormally positive compared to other trade days. Similar observations were made in relation to the month of the year. For instance, the period before and after the Chinese New Year showed the stock returns variations. The abnormal returns depicted the existence of the weekend and holiday effect. There are many previous studies that established the Monday effect in different stocks. In the case of the Hong Kong, the Monday and Friday effect portrayed the influence of the weekends on the movements of the stocks. Thus, there is evidence of anomaly around the weekend. The investigation of the holiday effect in Hong also gave a clear picture of the anomalies in the pre- and post-holiday (Chinese New Year). This pointed that there were behaviours depicted by the investors that resulted in changes in the stock movement. The correlations exhibited the behaviours for Mondays and the preceding Fridays which pointed to a certain strategy by the investors.

According to Paul and Theodore (2006) the behaviours of investors can be attributed to the risk averseness of the stock exchange investors. For instance, if investors note an anomaly on Friday, they may end up selling their stocks on Fridays. The repeated negative Mondays are thus a response to a trend that the investors have identified and hence tend to move a lot of stock on the other days of the week. The day of the week and the resultant weekend effect has remained to be a mystery. In an endeavor to explain the trend, Hirshlifer and Shumway (2010) noted that investors may have positive feeling before the weekend. The mood may lead to their decision to buy or sell stocks. According to Kamstra, Kramer and Levi (2003), the positive mood dispels the apparent risk aversion. However, it is worth noting that the explanations are theory based. Hence, there is the need for qualitative studies to expound on assumptions of the weekend feelings.

The analysis of the data pointed to the existence of weekend effect in the Hang Seng Index. Previous studies that explored the subject in the Asian stock markets found that the day of the week and holiday had influence on the stock returns. Thus, the findings are an indication of a trend that has not disappeared. This is contrary to finding outline in literature review on page by 12 Chen and Liang (2004) that noted that the calendar effect had disappeared. In addition, the evidence of the calendar anomalies in the Hang Seng Index is contrary to the Efficient Market Hypothesis. The hypothesis stipulates that that stock returns are not influenced by the day of the week or the time of the year. However, the day of the week and the holiday effect presented a phenomenon of negative Mondays, positive Fridays and abnormal returns in the period around the Chinese New Year. The abnormalities affect the investment strategies employed by the investors in the market.

In the period of the investigation, there was a significant effect of the day of the week. These related to literature findings by Gao and Kling (2005), Al-Saad and Moosa (2005), and Guler (2013). According to Gao and Kling (2005) the turn of the week is an anomaly that exists in global stock markets. The anomalies affect the investment behavior of the investors. According to Al-Saad and Moosa (2005), the seasonality changes and the turn of the week cause the stock returns to shift. For example, in the start of the week, the returns are negative while mid and the close of the week the returns are usually positive. In relation to the turn of the month, Guler (2013) investigated various stock markets in Asia. The investigation concentrated on the major holidays such as the Chinese New Year and the January effect. The findings agree with the current study that has reported day of the week and holiday anomalies in the HSI.

The seasonal patterns are very critical for the investors, managers and the researchers in the financial markets. The differences in the returns especially the day of the week pointed to the varying confidence of the investors. These could be caused by the available information in the market. The unfavourable information causes the negative Mondays during the weekend as pointed earlier in literature (Coutts & Sheikh 2000). The perspective is again supported by Thilakarathne (2008) who argued that negative Monday effect results due to lag of financial reports over the weekends. Therefore, as the week progresses, there is favourable information on the stock movement that prompts the investors to move their stocks. On Fridays as the stock closes, the investors capitalise on the trends recorded from Monday to Thursday. This serves as crucial information that contributes to positive Friday returns.

In relation to the Monthly effect, there are many previous studies that have pointed to the anomalies. In the case of the HSI, the datasets analysed during the pre and post holiday season also pointed to anomalies. These findings related to earlier findings discussed in the literature review by Mehdian and Perry (2002) that established that the average returns on December are normally higher than the other months of the year. The reason attributed to the trend is that investors are likely to sell their stocks as the financial period closes in order to reinvent their investments with positive returns at the start of the year.

The study also verified the volatility that is associated with the day of the week. Despite the existence of the efficient market hypothesis, these anomalies have enormous implications for the investors. These findings support some earlier studies that explored stock markets stability. For example, on page 14 of the literature review a research by Guler (2013) pointed that stability of markets plays a critical role in fostering confidence among the stakeholders. Thus, the uncertainty of the trend over the weekend results in the high volatility as the week starts. Therefore, the HSI returns pointed to the trend adopted by the investors. To ensure stability, the players in the stock markets can avail favourable information to boost the investors’ confidence.

The current study was empirical; there is thus the need for a qualitative study to explore the personal factors that may influence the investors to favour certain calendar days. Based on the findings, the study verified the existence of the calendar effect on the Hong Kong exchange HSI markets. Furthermore, the anomalies normally influence the decisions of the investors based on the day of the week or turn of the month investments. In general, it is true that both the turn of the month and day of the week have a profound effect on the HSI.

Explanation of the Monday Effect

There are many factors that lead to the Monday market effect. Vijay (2004) provided various views to explain the Monday effect. Vijay (2004) attributed the Monday effect to the closure of the market for two days. For instance, there are many unexpected events that may take place over the weekend. As a result, the investors intentionally trade on Fridays afternoon in order to cushion themselves from the negative Monday returns. In addition, the weekend effect relates to the behavior of short sellers. According to Deysshappriya (2014), the speculations lead to the unhedged short sales. Many investors prefer the short sellers’ behaviours because they have limited losses. Therefore, the stocks are characterized by close monitoring that during the trading hours. Therefore, the weekend gap implies lack of information on the markets. The resultant effect is the negative Mondays and positive Fridays. According to Vijay (2004), the short sellers are risk averse and hence like to close their businesses at the end of the week. Deysshappriya (2014) added that weekend acts as a natural break because it is a long non-trading period contrary to the other days of the week. Therefore, the lack of trade over the weekend makes the short sellers move the stocks at the end of the week to avoid speculations. The speculations are opened as the week starts which results in the weekend effect.


The stock market anomalies are critical for investors. The day of the week and the monthly effect are the prevalent anomalies that affect the HSI. From the analysis of the data, the study achieved the outlined study objectives. The main study objectives were:

  1. To find out the how the day of the week influences the daily returns in the Hong Kong Market HSI.
  2. To review the effects of seasonality changes in the HSI stock market
  3. To establish how the calendar effects affect the HSI investors and the general trade in the market.

The study determined that the day of the week plays a critical role in determining the movements of the stocks. The study verified the effect brought about by the day of the week in the Hong Kong Market Stock Exchange HSI. The findings of the current study noticed the negative Monday effect which is followed by positive Fridays. The weekend and holiday break leads investors to rely highly on the scant information and stock forecasts that may not be realistic. Specifically, the positive returns on Fridays are due to the need by the businesspersons to sell the stocks based on the current information before the weekends. Hence, it is very true that there are definite patterns that determine the movement of stocks in Hong Kong Stock Markets HSI.

The third aim of the study was to establish whether the calendar effect affects the investment behaviour of the businesspersons in the HSI. In order to achieve the objective, the study drew correlations. The empirical results pointed to the existence of a pattern that was skewed towards the turn of the week and the months of the year. These empirical findings thus pointed that there was an effect of the calendar effects on the investment behavior. Various reasons were accorded to the behaviour. The reasons are in line with past qualitative studies that studied the calendar effects. There were explanations provided in the current study to verify the third objective. However, there is need for independent qualitative studies to be conducted to establish the reasons for the investors’ behaviours with specific focus on the Hong Kong Stock Markets.

The financial studies have documented both the January, new year and weekend effect for many years. The implication of the weekend effect is the negative returns that exist on Mondays and abnormal positive returns that are recorded at the close of the week. The effect is general, and it is attributed to investor behaviours globally. Despite the existence of the anomalies, financial economists still provide arguments that align with the efficient market hypothesis. Literature reviews have supported the existence of the calendar effects while others have supported the perspective that the effect has significantly disappeared. Therefore, this study aimed at exploring the empirical evidence intended to determine the existence or disappearance of the calendar effects with the main focus on the Hong Kong stock exchange HSI.


The paper examined the calendar effect on Hong Kong Stock Exchange by use correlations and regressions. The findings established significant effect of Chinese New Year and the day of the week. The analysis and the literature review pointed to the existence of research gaps that need to be addressed so that stakeholders have in-depth information on the calendar effects. For example, there is the need for comprehensive research to cover both the qualitative aspects that lead to the investment behaviors such as short seller tendencies. Furthermore, there is the need for researchers that deeply investigate the New Year effects. From the current study, there were no special considerations given to the prevailing market environment during the Chinese New Year periods. This is because there could be external factors that led to the changing trends. Therefore, future studies should take into considerations the New Year anomalies before, during and after the financial crisis.

In the global arena, Hong Kong Stock Exchange market is one of the top markets. Therefore, a financial crisis in Europe and other parts of Asia could have a direct impact on the returns. For example, in the study sample, the period between 2012 and 2014 there was a financial crisis that affected the Eurozone. Therefore, the anomalies experienced during the period are likely to have been influenced by the crisis and hence they may be more or less significance compared to anomalies when there are no financial crises. In addition, future studies should dwell on the calendar effect based on the specific day of the week. For example, the Chinese New Year could be on a specific day of the week or the turn of a particular month. This could affect the returns. Many studies have not explored such a phenomenon; thus, a research on effect will provide valuable information to the investors and help the other stockholders in establishing the market efficiencies. In general, it is evident that day of the week, and monthly effect still influences the stock movements in HSI. Therefore, the study recommends that businesspersons should have adequate knowledge of the anomalies for them to make informed decisions as they invest.


Al-Jarrah, I. M., Khamees, B. A and Qteishat, I. H 2011, ‘The turn of the month anomaly in Amman stock exchange: evidence and implications’, Journal of Money, Investment, and Banking, vol. 21, no. 1, pp. 5-11.

Al-Saad, K and Moosa, I 2005, ‘Seasonality in Stock Returns: Evidence from an Emerging Market’, Applied Financial Economics, vol. 15, no. 2, pp. 63-71.

Ariel, R. A 2002, A Monthly Effect in Stock Returns, Journal of Financial Economics, vol. 18, no. 1, pp.161-174.

Barrett, G and Donald, S 2003, ‘Consistent Tests for Stochastic Dominance’, Econometrica, vol. 71, no. 1, pp. 71-104.

Basher, S. A and Sadorsky, P 2006, ‘Day of the week effect in emerging stock markets’. Applied Economics Letters, vol. 13, no. 1, pp. 621–628.

Bronfenbrenner, U and Evans, G. 2000, ‘Developmental Science in the 21st Century: emerging Questions, Theoretical Models, Research Designs and Empirical Findings’, Social Development, vol. 9, no. 1, pp.115-125.

Chen, Y and Liang, B 2004, ‘Timing Ability in the Focus Market of Stock markets’, Journal of Finance, vol. 56, no. 6, pp. 2871-2901.

Coutts, J.A and Sheikh, M.A 2000, ‘The January Effect and Monthly Seasonality in the All Gold Index on the Johannesburg Stock Exchange 1987-1997’, Applied Economics Letters, vol. 7, no. 1, pp. 489-492.

Denk, T 2010, ‘Comparative multilevel analysis: proposal for a methodology’, International Journal of Social Research Methodology, vol.13, no.1, pp. 29-39.

Deysshappriya, N 2014, ‘An Empirical Investigation on Stock Market Anomalies: the Evidence from Colombo Stock Exchange in Sri Lanka’, International Journal of Economics and Finance, vol. 6, no.6, pp.177-187.

Dimitrios, G and Katerina, C 2003, ‘Market Anomalies’, Journal of Financial Economics, vol. 32, no. 2, pp. 87-92.

Gao, L. and Kling, G 2005, ‘Calendar Effects in Chinese Stock Market’, Journals of Economics and Finance, vol. 6, no. 1, pp.75-88.

Garefalakis, A 2011, ‘Determinant Factors of Hong Kong Stock Market’, International Research Journal of Finance and Economics, vol. 62, no. 1, pp. 52-59.

Guler, S 2013, ‘January effect in stock returns: evidence from emerging markets’, Interdisciplinary Journal of Contemporary Research in Business, vol. 5, no. 4, pp. 641-648.

Gu, A.Y 2003, ‘The declining January effect: evidence from U.S. Equity Markets’, The Quarterly Review of Economics and Finance, vol. 43, no. 1, pp. 395-404.

Hillier, D., Draper, P and Faff, R 2006, ‘Do precious metals shine? An investment perspective’, Financial Analysts Journal, vol. 62, no. 2, pp.42-62.

Hirshleifer, D and Shumway, T 2001, ‘Good day sunshine: stock returns and the weather’, Journal of Finance, vol. 58, no. 1, pp.1009-1032.

Kamstra, M, Kramer, L and Levi, M 2003, ‘Winter blues: a SAD stock market cycle’, American Economic Review, vol. 93, no. 1, pp.324-343.

Kiymaz, H. and Berument, H 2003, ‘The day of the week effect on stock market volatility and volume: international evidence’. Review of Financial Economics, vol. 1, no. 1, pp. 363-380.

Kirkland, C 2014, ‘Project Management: a problem-based approach’, Project Management Journal, vol. 45, no. 1, pp. 63-92.

Kohers, G., Kohers, N., Pandey, G and Kohers, T 2004, ‘The disappearing day of the week effect in the world’s largest equity markets’, Applied Economic Letters, vol. 11, no. 1, pp. 167-171.

Kok, K.L and Wong, Y.C 2004, ‘Time-of-the-month anomaly in ASEAN equity markets’. International Business and Finance, vol. 2, no. 1, pp.137-145.

Kothari, C 2005, ‘Contemporary methods of quantitative data collection and analysis in literacy research’, Reading Research Quarterly, vol. 39, no. 1, pp.94-112.

Latif, M., Arshard, S and Farooq, S 2011, ‘Market efficiency, market anomalies, causes, evidences, and some behavioural aspects of market anomalies’, Research Journal of Finance and Accounting, vol. 2, no. 10, pp. 3-12.

Lean, S., Hooi, H., Smyth, R and Wong, W 2007, ‘Revisiting calendar anomalies in Asian stock markets using a stochastic dominance approach’, Journal of Multinational Financial Management, vol. 17, no. 2, pp.125-141.

Leshno, M and Levy, H 2002, ‘Preferred by “All” and Preferred by “Most” Decision Makers: Almost Stochastic Dominance’. Management Science, vol. 48, no. 1, pp.1074-1085.

Linton, O., Maasoumis, E and Whang, Y 2005, ‘Consistent Testing for Stochastic Dominance under General Sampling Schemes’, Review of Economic Studies, vol. 72, no. 2, pp. 735- 765.

McGuinness, P. B 2006, ‘Turn-of the-month returns effects for small cap Hong Kong stocks’. Applied Economics Letters, vol. 13, no. 14, pp. 891-898.

Mehdian, S and Perry, M. J 2002, ‘Anomalies in US equity markets: a re-examination of the January effect’. Applied Financial Economics, vol. 12, no. 2, pp.141-145.

Muhammad, N and Rahman, N 2010, ‘Efficient market hypothesis and market anomaly: evidence from day-of-the week effect of Malaysian exchange’, International Journal of Economics and Finance, vol. 2, no. 2, pp. 35-42.

Nath, C. N and Dalvi, M 2005, ‘Day-of-the-week effect and market efficiency: evidence from Indian equity market using high frequency data of national stock exchange’, Journal of Emerging Market in Finance, vol. 11, no. 2, pp. 5–25.

Neumann, W. L 2007, Social Research Methods: Qualitative and quantitative approaches. Journal of Research, vol. 3, no. 2, pp.35-44.

Nikkinen, J and Sahlstrom, P 2004, ‘Scheduled domestic and US macroeconomic news and stock valuation in Europe’, Journal of Multinational Financial Management, vol. 14, no. 1, pp. 201-215.

Nikkinen, J., Sahlstrom, P and Aijo, J 2007, ‘Turn-of-the-month and intra-month effects: explanation from the important macroeconomic news announcements’, Journal of Futures Markets, vol. 27, no. 2, pp.105-126.

Nikkinen, J., Sahlstrom, P., Takko, K and Aijo, J 2009, ‘Turn-of-the-month and intra-month anomalies and US macroeconomic news announcement on the thinly traded Finnish Stock Market’. International Journal of Economics and Finance, vol. 1, no. 1, pp. 3-11.

Oladipo, S., Adenaike, F and Ojewumi, A 2010, ‘Establishing the reliability and validity scale’, IFE Psychologia, vol. 18, no. 2, pp. 1-23.

Paul, A and Theodore, P 2006, Modeling and forecasting volatility of returns on the Ghana Stock Exchange using GARCH Models’, Journal of Emerging Market in Finance, vol. 4, no. 2, pp.115-132.

Raj, M and Kumari, D 2006, ‘Day-of-the-week and other market anomalies in the Indian stock market’, International Journal of Emerging Markets, vol. 1, no. 3, pp. 235-246.

Randolph, S 2014, ‘Maximizing project value: a project manager’s guide’, Project Management Journal, vol. 45, no. 2, pp.22-41.

Sans, W 2011, ‘Sampling methods and market Surveillance’, Economic Quality Control, vol. 26, no. 2, pp. 2-11.

Thilakarathne, P. M. C 2008, ‘Seasonality effect of emerging stock markets: evidence from Sri Lanka’. Journal of Multinational Financial Management, vol. 2, no. 1, pp. 97-107.

Thomas, H 2002, ‘Trends and calendar effects in stock returns’, Australian Technical Analysts Association Journal, vol. 3, no. 1, pp. 222-234.

Vijay, S 2004, Beyond the random walk: a guide to stock market anomalies and Low-Risk Investing, Oxford University Press, New York.

Wong, W., Thompson, H., Wei, S. and Chow, Y 2005, ‘Do winners perform better than losers do? A Stochastic Dominance Approach’, International Journal of Emerging Markets, vol. 3, no. 2, pp. 135-246.