Background
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 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 (Deshappriya 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 Ratchet 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 endeavouring 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:
- To find out how the day of the week influences the daily returns in the Hong Kong Market HSI.
- To review the effects of seasonality changes in the HSI stock market
- 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:
- How does the day of week affect daily returns in the Hong Kong Stock Exchange Market HSI?
- What are the effects of the seasonality changes in the stock markets?
- 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 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 effects 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
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
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 free float-adjusted 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
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
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.
References
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.
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.
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.
Appendices
Appendix I: Time Plan
Appendix II: Gantt chart
