In recent decades, many authors have tried to forecast asset returns and explain what is lie behind of it. One of the oldest examples is the Capital Asset Pricing Model brought up by Sharpe (1964), which explains the return on assets by the correlation to the stock market. Since then, the field of research has grown considerably and the number of factors that have had a significant impact on the interpretation of the return on assets has risen to 314 in 2012 (Harvey, Liu & Zhu, 2014). Not only do these factors explain to some extent the cross – sectional aspect of equity returns, but they also help predict the direction of equities. Some of the most important factors are value (FAMA and French, 1993) and dynamics (Jegadeesh & Titman, 1993). Even if, in an efficient market, the factors should not allow to obtain risk – adjusted benefits, they generally make the study of particularly interesting subject.
Among which, over the last few decades, the study of the stock market anomalies has been one of the captivating areas in the field of financial markets. The anomaly is a factual consequence that seems inconsistent with the theory of the maintenance of market behaviour of asset prices. The researcher shows that markets are inefficient (profit opportunities) or that the models for pricing of core assets are insufficient (Schwert, 2002). However, most of these studies are concentrated in the state markets of the United States and Europe, while the Asian stock market has only a limited number of studies. Recent experience shows that anomalies such as effects on small businesses, annual turnover effects, momentum effects, weekend effects continue to appear (Frieder & Subrahmanyam, 2002; Schwert, 2002; Lucey 2005; Meneu & Pardo, 2004). With adequate previous researches have been conducted and based on the research gaps, this thesis is going to examine the presence and the behaviour of stock market anomalies in the emerging economies and this thesis is aiming to provide answers for the following research questions:
Momentums are defined as a tendency to improve the (poor) future performance of assets that have been successful in the past. This phenomenon applies to the most common anomalies in history. It is attested in developed countries and many other categories of international equities. The first Momentum effects were introduced in the academic world by Jegadeesh and Titman (1993). Over the past 20 years, it has been the subject of in-depth studies and has been recorded in different market and asset classes. Some studies attempt to explain the existence of significant excessive positive gains in incentive strategies. However, it is unclear whether these violations of market efficiency can be explained or whether they result from the rational reaction of firms or investors with real market constraints. It is extremely difficult to find an interpretation of the risk – based Momentum effect, because Momentums is perhaps one of the most difficult challenges in the theory of the intersection of the incomes of investors (Fama & French, 1996). In another option, some researchers have proposed a behavioural theory of Momentum effects.
Jegadeesh and Titman (1993) have shown that investment strategies (which are known as momentum strategies in the financial literature) have made it possible to buy stocks that have a good performance in the first 3 to 12 months and to sell stocks which have had not performed well during the same time period, which had generated about 1% of monthly profits in the US market in three to twelve months. As this is a large-scale work and some other studies have verified and expanded this finding. Explanations have been offered to interpret this momentum. A few scholars believe it that the benefits to measure are due only to the gap of data interception. Jegadeesh and Titman (2001) used data of a time period following their previous communications, which shows that motivation strategies remain cost-effective. The result can be used as evidence against the argumentation. Others argue that it is too early to reject the rational model and consider that the profitability of momentum strategies may simply be risk compensation. The possibility that the excess returns result from the zero-mobility policy is attributable to the beta capital asset pricing model of the zero-return strategy. In their view, momentum strategy returns can be attributed to risks which could not be interpreted by conventional methods, for instance, fixed asset price models and recent measures such as the Fama and French’s three-factor models (1993). Jegadeesh and Titman (1993) have attempted to interpret the movement as a return to risk. This interpretation deserves serious consideration, as the winners (who have demonstrated excellence in the past) and the losers (who have shown poor performance in the past) are ranked according to past returns. Moreover, Conrad and Kaul (1998) provided factual simulations and findings that allowed them to draw a conclusion that momentums were interpreted by horizontal differences in performance rather than by time-series models. One of the projections of this explanation is that in the post-graduation phase, the performance of the momentum policy is on average positive. Jegadeesh and Titman (2001) provided contradictory evidence that the momentum investment portfolios had been negative for the 13 to 60-month period. They attribute the results of Conrad and Kaul to small sample deviations in the trials. Lacking interpretation based on direct risk of the momentum effect resulted in a more detailed analysis of the behaviour of Investors. The result found that investors had not reacted sufficiently or that they had reacted too late to specific information from companies.
Lots of empirical studies have been done to test the Monday effect (Cross, 1973). The standard poor composite index revealed an average return of 0.12% on Friday, as against 0.18%. % on Monday, measuring the distinction between Friday’s closing price and Monday’s closing price. French (1984) analyses daily rates and 500 pull rates of return for all days of the week 1953 – 1977, indicating an average rate of return of -0.17% for Monday. While the average rate of return for the rest days of the week is positive and the highest for Wednesday and Friday. After studying the 17-year rates of return (1962 – 1978), Gibbons and Hess (1981) found a negative annual rate of return of 33.5% on Monday. By dividing these data into several stages, they have reached the same conclusion. Harris (1986) verified the negative results of Monday but estimated that they existed merely 45 minutes before the operation. Clare and the others (2002) studied the data of the consolidated index of the Kuala Lumpur Stock Exchange for the time period from 3 January 1983 to July 23, 1993, which indicates an average yield of -0.109% Monday. Alexakis and Xanthakis (1995) studied the weekly effects of the Greek stock market. The index was studied between 1988 and 1994 and the average rates of return on Monday and Tuesday were negative. And also they found that the widest differences in income distribution were observed on Monday. In estimating the coefficients of variation for all working days, the author concludes that on Monday, it is in Athens that the risk is highest.
Some studies have also revealed that the January yield was often much higher than other months. Rozeff and Kinney (1976) were the first to conduct study with strict standards on the January effect in the United States which investigated all NYSE shares from 1904 to 1974, using the ANOVA technique. They found an average rate of return of 3.48% in January. However, the rate of return is 0.42% less in the remaining 11 months of this year. Keim (1983) reports that, for the period of year 1963 to 1979, the average difference between the risk-adjusted rates of return of large and small firms was about 0.7% per day in January. And what is more striking is that, for the whole period from 1931 to 1978, the relationship between windfall gains and the size of daily operations was negative and more prominent in January than in other months. Keim and Stambaugh (1984) found that the Monday effect was similar for all of the large-scale portfolios. In addition, they found that the rate of return of small businesses that moved closer to closing on Monday in January was correct because of their size (Ariel, 1987). Most of the effects of the week in January occurred during January’s first five days, particularly on Monday.
Schultz (1985) used the average Dow Jones index for the period from the year of 1900 to 1917 and they found an average difference of 8% between the abnormal returns of small and large firms in the January’s first five days. In addition, after adjusting for market returns, shareholders held shares of small businesses in the first nine days of January, giving them an additional 15% return. Schwert (2002) reports annual effects for the period. It estimates that the January impact was 0.4% per day between 1980 – 1989 and 1990 – 2001, about half of the estimates for the period 1962-1979 to exist in the United States. The January effect also exists in other countries. Bhana (1985) provided evidence that the average rates of return for January were substantially Malaysia and Turkey which are higher than those for other months. Ho (1990), using daily rates of return for the period from January 1975 to November 1987, found that six-eighths of the new stock markets of Asia and the Pacific had much higher daily returns than those in other months in January. Fountas and Segredakis (2002) tested the seasonal effects (abnormal effects of January) of equity returns in 18 emerging markets between 1987 and 1995. On emerging markets, they find little evidence to support this effect. Koutianoudis and Wang (2003) studied the monthly seasonal economic impacts of the Athens Stock Exchange and found that the impact of the January market was very important. Maghayereh (2003) found no evidence of seasonal or monthly effects on the Amman Stock Exchange.
The relationship between firm-level rates of return and the value and the size of the firm also attracted the interest of previous researches. In Jegadeesh and Titman (1993 and 2001), the momentums were studied. In the first place, they divided the sample into three subsamples according to the realizable value of the firm, forming a momentum portfolio (Jegadeesh & Titman, 1993) and obtaining a clear return. For the second study, Jegadeesh and Titman (2001) divided the samples into large groups and small groups according to the median value of the New York Stock Exchange. Momentum effect was more marked for small actions. Hong’s (1999) study in more detail the relationship between momentums and size. Momentum effect is verified through dividing the sample into three portfolios of momentum investment instead of 10. Most of the authors found that the momentums were more obvious in terms of the yield of extreme actions, which might weaken the conclusions of Hong et al. (1999). However, they found that the top 30% of the market value did not have this momentum. Because most investors of institutions are content to invest in this type of stock, the momentum given to these stocks will be the most relevant for them. Moreover, Hong et al. (1999) studied the relationship between momentums and analyst coverage and then found that even when the size of the firm was controlled, the momentum effect of companies with low analyst coverage was stronger. At least one tenth of the exclusions Jegadeesh and Titman (2001) and Hong et al. (1999) reported a reversal of returns rather than a momentum. The weak momentum effects of value-weighted equity portfolios (rather than equity – weighted portfolios) also show that large equities are less momentum, Moskowitz & Grinblatt (1999), who found that volume and value-weighted equity portfolios were 9.3% and 5.2% per annum respectively.
In order to answering the research questions, the methodology is carefully chosen and a secondary data analysis has been selected. To specify the data sample of the stock market for the emerging economies which is defined by World Bank it is distinguished from the highly developed, medium emerging and weak emerging countries. Thus, this way will limit the country sample to the emerging economies. This resulted in samples from countries: Argentina, Brazil, Chile, China, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Peru, South Africa, Philippines, Poland, Czech Republic, Russian Federation, Republic of Korea, Thailand, Turkey and Venezuela. In order to maintain the list of countries corresponding to the MSCI Emering Market index (MEM model), this model will be used in this document as baseline and reference model for investment portfolios, to which will be added the following countries: Egypt, Israel, Jordan, Morocco, Pakistan. In these countries, at least one action is publicly traded (average) in the sample mem during the sampling period. This increase brought the total number of these countries to more than a dozen. Data from these countries are extracted from the data flow database to structure the return and anomaly variables. The sample included publicly traded or exchange traded stocks to reduce the risk of distortion. The number of stocks selected during the first cycle was around significant, which ensured that only ordinary shares in the domestic market were taken into account, with the exception of investment. The selection process is based on the combined processes of the ordinary share analysis. The components and weights of the Morgan Stanley Capital International Emerging Markets Index were retrieved from the data base.
Controlling the market behaviour requires organizations to commit towards the development of certain ideologies that enhance the outcomes of events in the workplace. Many individuals prefer to associate with corporations that understand their needs and are willing to develop viable solutions, which address the issues affecting people’s lifestyles. One of the most common technique that can be used to evaluate the performance of an organization is to evaluate the measures that have been put in place by the corporation (Breaban & Noussair, 2017). Importantly, the ability of a company to overcome various issues revolving around its operational performance can be affected by aspects such as government policy among others. When financial markets record certain outcomes, it is impossible for a corporation to denounce the impact of the findings on its approach in the business environment. While there are anomalies that influence the direction taken by a company, the asset price should reflect the expected market prices for the commodity.
There is a limited research on the performance of financial markets in the world because of the impact of the studies on the perspectives of individuals towards life. Mainly, exploring the research gaps in the modern corporate world enables the public to make informed decisions that guide their investment in the market. One of the aspects that should be considered by individuals revolves around the need to understand the possibilities that can occur in the contemporary world. Specifically, understanding how the key anomalies identified in a stock market can affect the concept of growth and development will enable investors to make informed decisions that improve the outcomes of events in their modern world. Besides, the public should be informed about the ability to utilize the anomalies to predict the stabilization of an emerging stock market. In this research, the key focus of the concept is defined by the research gaps that are yet to be explored in the contemporary world.
Different scholars have identified the approaches that can be used to enhance the performance of an organization in the modern world. When undertaking a process of research, it is imperative for individuals to identify the techniques that can be utilized by the public to explore their potential. Investors should be informed about the changes that can take place in the contemporary world and their possible impact on the outcomes of events in the business environment. The ability to measure market gaps and make informed decisions differentiates individuals from their competitors in a move that is likely to enhance the outcomes of events (Raja, Pamina, Madhavan, & Kumar, 2019). Using the rational model exposes individuals to momentum strategies that are believed to achieve risk compensation. In many stock markets, the Friday’s closing price influences the moves made by investors on Monday, an aspect that has yet been referred to as the Monday-Effect. Hence, it is imperative for the public to understand the different issues that can be approved using solutions developed by an organization.
The use of questionnaires and online polls play an important role in the development of solutions that can be used to address the changing needs of individuals. By using the above research approaches, it is possible to realize the desired credibility to influence the perspectives of individuals in the modern world. Relying on existing data enables the researchers to evaluate the current approaches and techniques used by different investors in stock markets around the world (Chen, Gao, He, Jiang, & Xiong, 2019). The Emerging Market Index (EMI) has been used in this research to identify the correlation between investment decisions made by individuals and stock market predictions. When executing the tasks, researchers are expected to maintain the outlined standards of operation that guide individuals to understand the perfect concepts that can be used in the financial markets.
Conclusion
Even if, in an efficient market, the factors should not allow to obtain risk – adjusted benefits, they generally make the study of particularly interesting subject. In this regard, financial markets receive various entries that influence the outcomes of events in the modern world. Equity returns determine the profitability of an organization and its ability to overcome set objectives in the business environment. In an efficient market, investors are supposed to make profits and loss statements depending on the behavior of the market. However, unanticipated challenges expose organizations to difficult situations that compel company managers to make certain decisions that may affect the outcomes of events in the business environment. Stock market anomalies influence the decisions of investors because of their ability to encourage fear, which hinders them from realizing their potential. During this period, it is possible for an individual to realize his or her position in the market to overcome issues affecting their ability to make informed decisions. Contrary to popular opinion, inefficient markets reflect the actual performance of an organization and enable company managers to make informed decisions in their immediate environment. From this observation, corporations should invest heavily in Research and Development (R&D) to develop viable solutions that can be used by an organization to overcome the various issues affecting the perspectives of investors towards the market behaviour.
