Written by Sheikh A.
Abstract
The aim of the study is to assess the weak form of market efficiency in the context of Qatar and Saudi Arabia during the period 2001-2017. The QE General index and Tadawul All Share index are used to represent these two markets respectively. Daily data has been retrieved to capture short-term volatility in these markets and assess randomness of stock returns. The methodology of this research is based on such methods as the Kolmogorov-Smirnov test, the runs test, the autocorrelation test, the Augmented Dickey-Fuller test for a unit root and the variance ratio test. The results of the tests suggest that the Qatari stock market is not information efficient during the analysed period, which implies that daily returns could be predictable to some extent. The outcomes can be perceived reliable as all tests reported consistent results. At the same time, the Saudi stock market also demonstrated inefficiency but the results of the test were less consistent than in the context of the Qatari market. These findings have implications for investors who can exploit arbitrage opportunities to earn above average profits.
1. Introduction
The concept of efficient markets implies that investors are rewarded proportionally to the risk they take, and it is impossible to consistently outperform the market and earn abnormal returns (Fama, 1991). It is hypothesised that in efficient markets stocks will be fairly priced because all public information is already reflected in current prices. It is worth mentioning that practical implications of the efficient market hypothesis (EMH) are of utmost importance for prospective investors. The implication of the theory is that technical analysis and even fundamental analysis are worthless in helping investors earn above average returns. The investor is assumed to be better off by diversifying her portfolio or holding a market index.
It is worth noting that there are three different versions of EMH. According to the weak form of efficiency, past prices do not help predict future prices of financial assets. The implication is that technical analysis becomes useless and investors cannot rely on it. The semi-strong form of EMH, in turn, states that it is impossible to achieve abnormal returns by trading on publicly available information. As soon as new information arrives, it is instantly reflected in stock prices. Thus, fundamental analysis is also deemed to be useless. Finally, the strong form of the theory implies that stock prices reflect even private information. Therefore, a competitive edge would not be obtained even if inside information was used. However, this form of EMH lacks empirical support as it is nearly impossible to test. For this reason, most studies are focused on either the weak form or semi-strong form of EMH.
This dissertation makes a contribution to available knowledge and research by focusing on Islamic markets, which are less studied compared to developed markets.
1.1. Aim and Objectives
The aim of the dissertation is to assess the efficiency of Islamic equity markets using the case of Qatar and Saudi Arabia. The objectives are:
- To test for randomness of stock returns in Saudi Arabia and Qatar;
- To test for serial correlation in stock returns in Saudi Arabia and Qatar;
- To examine volatility of stock returns in Saudi Arabia and Qatar.
1.2. Outline
Chapter 2 of this dissertation provides a review of literature on the efficient market hypothesis and its empirical testing in different contexts. Chapter 3 outlines the methods used and data collected. Chapter 4 demonstrates the research findings and analysis. Chapter 5 provides conclusions and recommendations.
2. Literature Review
Many investors believe in possibility of outperforming the market assess allocation skills or specific information. However, according to the three forms of the EMH, this is not possible on a consistent basis (Huang and Lee, 2016). The empirical evidence obtained over the decade often supports the EMH but the results are mostly mixed (Rosch et al., 2016).
According to Guney and Comba (2015) the assumptions of the information efficiency hold to be true in the Tanzanian stock market. Using various robustness tests such as the Augmented Dickey-Fuller test (ADF) (1979), Ranks test (Harrington and Fleming, 1982) and Variance-ratio test (Chow and Denning, 1993) the scholars stressed that the stock prices tend to follow a random walk. However, their study employed aggregate share price indexes rather than individual stocks. Guney and Comba (2015) used daily data on five DSE indexes. The dataset, in turn, covered the period of 2007-2014.
The main assumption of the EMH is that asymmetric information does not exist in typical trading activities. The statement made above was tested by Kelikume (2016) in the context of Nigeria. The researcher deliberately selected this African country because of its large population on the continent and rapid development of the financial system. By means of the wavelet unit-root test (Fan and Gencay, 2010) and average monthly data on the stock index over the period 1985-2015 the researcher found that the Nigerian market was efficient. In other words, all publicly available information was already included in stock prices. Therefore, there is no such strategy that would help to generate abnormal returns for investors. As a concluding remark, Kelikume (2016) mentioned that the research had its practical implications in a sense that future investors should pursue diversification and long-term targets.
In contrast to findings of Kelikume (2016), Stakic et al., (2016) evidenced that the Serbian stock market was inefficient. The researchers utilised the dataset with information covering the period of 2006-2013. The BELEX 15 index was selected as a proxy for the market. The accuracy of the test was enhanced by application of two unit roots tests and the Runs test. The findings were consistent with the output of all tests performed. Looking more deeply into the empirical evidence obtained, the scholars argued that the real culprit of inefficiency was hidden in the overall underdevelopment of the domestic financial market coupled with unreasonably high transaction costs and lack of liquid financial tools.
Supporting evidence was obtained by Soon et al., (2015) when the efficient market hypothesis was tested in the context of the Kuala-Lumpur stock exchange. The scholars rejected the null hypothesis of existing efficiency using the GARCH model (Baillie et al., 1996). The results were held to be true for almost all sectors of the economy. However, an alternative approach based on the unit root test delivered opposite results. It is worth noting that the heteroscedasticity problem and structural breaks were ignored. In other words, the opportunities for investors to beat the market emerged. Going back to the sample constructed, the researchers obtained the monthly data on equity price indexes over the period from 1980 until 2011. This is because the information before 1980 was not publicly available.
Besides statistical tests of market efficiency, previous researchers also employed technical analysis rules to examine their ability to outperform the market. This was, for example, done by Soon and Rahim (2016) in the context of Malaysia. The approach selected was based on the technical trading strategies. As for the sample prepared, the scholars investigated almost 600 of buy recommendations. The weak form of efficiency was tested by using the profits found in selling signals. Among others, the researchers used the MACD indicator and moving average. In order to avoid bias, the execution of transactions was carried in accordance with the recommendations to buy offered by the Malaysian research houses. Using the ChartNexus software, the scholars were able to generate abnormal returns during trading simulation. It was concluded that by means of technical analysis the null hypothesis of efficient markets could be rejected. Allowing for the limitations, there were two barriers that have to be mentioned. Firstly, the approach was tested only in the context of Malaysia. Secondly, the buy recommendations were collected for 2013 only.
Malhotra et al., (2015) have attempted to test the weak form of efficiency in the context of developing countries of the Asian-Pacific region. The distinctive feature of the research performed was attributable to the testing of daily, weekly and monthly stock returns. Moreover, the data was collected for the period of 1997-2012 and included information from ten stock exchanges. The interpretation of outcome obtained from the Runs test allowed for assuming that the markets were only efficient in regards to monthly returns. The opposite was held to be true for daily and weekly returns. Considering the results of the variance ratio test, the weak form of efficiency was rejected in all three cases. To conclude, the scholars recommended holding a well-diversified portfolio in these markets in order to produce an optimal portfolio.
The countries that belong to the Gulf Cooperation Council (GCC) have also attracted the attention of researchers worldwide. Using a set of statistical tests such as the Johansen (1992) co-integration test, Jamaani and Roca (2016) argued that stock markets of Saudi-Arabia, Oman and Qatar were not the information efficient. Furthermore, the results were consistent for both individual and collective market levels. By highlighting the reasons of inefficiency, the scholars stated that this was mainly due to the absence of foreign participation in the sampled markets. From practical point of view, it is worth noting that the researchers used the information for the period of 2003-2010. It could be argued that with the extension of the observation period, the results could change dramatically. Therefore, prospective investors should treat these findings with due care.
The results of Jamaani and Roca (2016) were supported by Awan and Subayyal (2016). According to the evidence obtained, the stocks of GCC member countries did not follow the random walk. Moreover, statistically significant serial correlation coefficients allowed for rejecting the null hypothesis of existing market efficiency. Comparing two research studies, Awan and Sabayyal (2016) used a shorter observation period (2011-2015). Taking into account the technical side, the scholars used the runs test and auto correlation tests.
Several studies such as Rizvi and Arshad (2016) also investigated the impact of the financial crisis on the market efficiency. The scholars focused on Asian countries and observed the trends one year before and five years after the Global financial crisis (GFC) of 2008. By means of the Multifractal de-trended fluctuation analysis (MF-DFA) (Ihlen, 2012), it was determined that the assumptions of the random walk theory were not satisfied. The impact of financial distress was severe for East Asian markets where stock prices almost halved in size. However, it is worth mentioning that the research performed was not the one without limitations. Among limiting factors there was the impact of changing trading days. In other words, Rizvi and Arshad (2016) did not account for the fact that trading days differ across sampled countries.
Delving into the forms of efficiency, the statistical evidence obtained by Pandey and Samanta (2016) pointed at the inefficiency of the Indian market. These results supported the lion share of research studies performed in the context of emerging economies. The scholars used the daily returns of the Nifty Index over the period 2008-2011. Considering the methodology selected, Pandey and Samanta (2016) used both parametric and non-parametric tests such as the ADF and the runs test. Taking into account practical implications, the investors could earn excess returns over the longer horizons by using proper investment strategies.
In contrast to Pandey and Samanta (2016), the evidence gathered by Andrianto and Mirza (2016) indicated that the Indonesian stock market followed a random walk. The result has been achieved by means of the serial correlation test coupled with the runs test. The scholars used daily prices of stocks included in the LQ45 index. However, it can be assumed that the empirical evidence is biased. This is because the observation period that Andrianto and Mirza (2016) used was quite short (2013-2014).
It is worth noting that the niche of research studies devoted to the countries-members of Organisation of Islamic conference (OIC) remains open. Using a sample of 11 member countries, Arshad et al., (2016) observed daily stock prices over a three year time period. The results were obtained by means of MF-DFA test. The researchers found that the weak form of efficiency was present in the sampled markets. Moreover, it was higher during the periods of economic booms rather than downturns.
All in all, the review of existing literature evidenced that the findings on information efficiency of stock markets is mixed. Many scientific works indicates that the stock markets are inefficient in emerging economies. Nevertheless, there were a number of studies where the empirical evidence pointed at the existence of market efficiency. However, the factors leading to efficiency were not obvious. Moreover, all previous researchers encountered various limitations. Therefore, additional analysis is required in narrow contexts and using a greater variety of methods.
3. Methodology
3.1. Data
Daily financial data is collected on two main stock indices for Qatar and Saudi Arabia. These indices are the Qatar Exchange General (QEG), which is comprised of 20 largest companies traded on the Qatari Stock Exchange, and Tadawul Stock Exchange All Share (Tad). The latter includes all stocks traded on the Tadawul Stock Exchange in Saudi Arabia. The All Share index for the Qatari stock exchange is not used because its historical values are available only from 2008 while the data on QEG is available from 2001 allowing for more observations. Thus, the period covered is March 2001 – December 2017. Daily stock prices are used in order to account for short-term volatility of the stock market assuming that investors could immediately act on new information.
3.2. Methods of Analysis
The main methods of testing the efficiency of these two stock markets are the Kolmogorov-Smirnov test of normality, the autocorrelation test, the runs test, the unit root test and the variance ratio test. These tests examine different characteristics of stock market returns and the results help to evaluate whether these series follow a random walk.
The Kolmogorov-Smirnov (K-S) test is employed to determine how significantly the distribution of the actual data differs from the theoretical normal distribution. Deviations from normality help to identify fat tails in the distribution and asymmetric stock returns. Significant deviations of each index from normality can be indicative of potential problems with market efficiency. For example, asymmetric returns and fat tails may indicate periods of panic in the market. However, deviations from normality may also be explained by technical reasons such as insufficient number of observations to fit the normal distribution. Therefore, additional stronger tests are required.
The autocorrelation test is frequently employed for examining the efficient market hypothesis in the weak form because it tests the ability of past returns to predict future returns. This should be impossible in efficient markets. The null hypothesis of the Box-Pierce test for serial correlation is that the autocorrelation coefficient is not different from zero. If the autocorrelation is present in the series, the p-values of the Q-statistic at all lags should be below the critical level of significance.
The runs test is used to examine randomness of changes in stock prices. This test assesses whether stock returns follow a random walk, but, unlike the autocorrelation test, it is non-parametric. If the expected number of runs, which are changes of the sign of returns, considerably differs from the actually observed number of runs, this may mean that stock returns do not change randomly. In efficient markets, it is expected that the return series would be random. This will happen if the number of runs is equal or close to the expected number of runs.
Another test that helps to detect the randomness of stock market returns is the Augmented Dickey-Fuller (ADF) test. The null hypothesis of the ADF test is that a unit root is present in data series and thus the data is non-stationary. This, in turn, would imply that the series does not follow a random walk and the market is inefficient.
The last test of the EMH employed in this study is the variance ratio (VR) test that explores whether volatility of stock market returns varies significantly in different periods. It is assumed that in efficient markets, the variance of returns in each individual period should not significantly deviate from the variance of returns in other periods. In turn, this means that if a series follows a random walk, there will be a tendency for constant variance in returns. This is shown by the p-value of the VR test statistic. If the p-value is lower than the threshold value for the chosen level of significance, the null hypothesis should be rejected which would imply that the returns in the series do not follow a random walk.
4. Results and Analysis
This chapter provides the outcomes and results of the testes of market efficiency in Qatar and Saudi Arabia. First, descriptive statistics of the series are analysed. Next, the results of the tests are provided. Finally, interpretation is provided whether the explored stock markets are efficient in the weak form.
4.1. Descriptive Statistics
The first stage of the analysis is exploring descriptive statistics of the series. The results are shown in Table 1.
The mean price of the Qatari Stock Exchange General Index (QE) was 7823.75 QAR (Qatari riyal) while its median value was 8386.59 QAR. At the same time, the maximum value of the index was 14350.5 QAR whereas the minimum value was 1183.36 QAR. Meanwhile, the standard deviation of the series is 3180.15.
As for the Tadawul All Share Index (TAD), its maximum value was 20624.84 SAR (Saudi riyal) whereas the minimum value was 2206.33 SAR. Meanwhile, the median value of the series is 6872.89 SAR and is lower than the mean value 7164.46 SAR. The standard deviation equals 3081.05, and relative to the mean price it shows approximately similar levels of volatility as the Qatari market.
4.2. Statistical Tests
4.2.1. The K-S Test
The first test applied to the Islamic stock indices is the K-S test. The outcomes of the test for the Qatari and Saudi Arabian stock indices are provided in Table 2.
The value of the maximum difference is calculated as the actual cumulative frequency distribution minus the anticipated cumulative frequency distribution. These values are equal to 0.9674 and 0.9309 for QE and TAD, respectively. Meanwhile, the critical values at the 1% significance level are estimated using the K-S tables (Zaiontz, 2018). They are equal to 0.0250 for 4,227 observations. The difference between the maximum difference and the calculated critical value of the test appeared to be positive for both indices, namely 0.9424 for QE and 0.9059 for TAD. This implies that the difference between the actual cumulative frequency distribution and the expected distribution is significantly different from zero. Thus, the initial hypothesis of the test that the data fits the expected function of the cumulative frequency distribution has to be rejected. Therefore, one can conclude that both the Qatari and Saudi stock index returns are not distributed normally. Under a pure random walk, it would be expected that stock returns would be normally distributed.
4.2.2. The Runs Test
The second test is the runs test, which explores whether signs of returns are independent and whether the number of changes of the sign is normal. The outcomes of the runs test are indicated in Table 3.
The number of runs for the Saudi Arabian stock index is larger than the number of runs for the Qatari stock index, namely 1894 against 1723, relatively. This means that the Saudi stock market is more volatile as the number of changes in sign is larger. The number of positive returns on the Qatari stock index is 2266 compared to 2257 positive returns in the Tadawul stock index. At the same time, the expected number of runs is 2103 and 2104 for QE and TAD, respectively. This means that the actual number of runs is lower than the expected number of runs for both indices. The difference is -380.50 and -210.76 for QE and TAD respectively. This, in turn, shows that both indices underreact to news arriving in the markets, and the underreaction of the QE is stronger compared to the TAD. However, the test allows for indicating not only the deviation of the expected and actual number of runs but also its statistical significance. The null hypothesis of the test is that the changes in sign of the stock returns are random. In this case, the p-value of the Z-score should be higher than the critical value for the chosen level of significance. The p-values of the Z-scores are lower than 0.01 for both indices. This means that both indices do not follow a random walk, which indicates market inefficiency.
4.2.3. Autocorrelation Test
Next, the autocorrelation test is run. Its null hypothesis states that data observations are independent from the previous values. In this case, serial correlation in the sample should be statistically insignificant and if a relationship with prior values of returns is indicated, it is random and cannot be predicted consistently. This would imply that the index values follow a random walk. The results of the test can be seen in Table 4.
As the daily data are employed for the analysis, a sufficient depth of lags should be attained to exclude the influence of particular days of the week. The number of lags chosen for this study is 20. As the number of observation is quite large, the Box-Pierce Q-statistic was used for the test. Its p-value is the criterion which shows whether the estimated autocorrelation is statistically significant. To meet the condition of independency from previous values, the p-value should be higher than the chosen level of significance such as the 1% significance level. The results of the test show that the p-values of the Box-Pierce Q-statistic are 0.00000 and 0.00001 for QE and TAD, respectively. The null hypothesis of the test is that there is no serial correlation. Since the p-values are so low, this null hypothesis is rejected for both markets. As a result, it can be concluded that according to the autocorrelation test the stock returns in Qatar and Saudi Arabia do not exhibit random behaviour as predicted by the efficient market hypothesis.
4.2.4. The Unit Root Test
The next test is the ADF unit root test. It allows for checking whether the series are stationary, i.e. have a constant mean and variance over time. This would imply that the series are non-random, and this non-randomness can be exploited by investors to earn abnormal profits. The results of the ADF unit root test are shown in Table 5.
The assessment of the unit root presence can be made in two ways, namely by estimating the p-value of the test and comparing test statistics for the chosen significance level against the critical value. The null hypothesis of this test is that the series are non-stationary. In order to reject this hypothesis, the p-value should be lower than 0.01 for the 1% significance level. One can see that the p-values are equal to 0.2159 and 0.2454 for QE and TAD, respectively. That shows that both p-values are higher than the significance level and the null hypothesis of the test cannot be rejected for both indices. Thus, the data are non-stationary and the stock indices do not follow a random walk. To confirm this, the test statistics are compared to their critical values. One can see that the absolute value of the test statistic for both indices does not exceed the critical value. This confirms the results that both return series have a unit root.
4.2.5. Variance Ratio Test
The last test applied to these stock indices is the variance ratio test. The results are listed below.
First, the periods with different number of lags are chosen. Then, two types of the test are estimated. The “Common Tests” investigates whether the null hypothesis is true for all periods. At the same time, the “Individual Tests” exhibit the outcomes of testing for the period with the specific number of lags.
The p-values of the z-scores for QE are lower than the 1% significance level. This shows that the null hypothesis has to be rejected and the index values do not follow a random walk. However, the p-values of the test for TAD are lower than the 1% and 5% significance levels. This implies that the TAD index returns could be random.
4.3. Summary of the Analysis
The summary of conducted tests shows whether the EMH has been accepted (+) or rejected (-) using different tests (Table 7).
The results of testing the efficiency of the Qatari stock market demonstrate that the returns of the QE are not random. The result is reliable as all tests provided the same evidence. As for the Tadawul Stock Exchange, all tests reject the random walk hypothesis and EMH, except for the VR test. Nevertheless, there is more evidence against the EMH in the Tadawul Stock Exchange considering that four tests rejected the hypothesis and only one test accepted it.
The rejection of the random walk hypothesis for the two stock exchanges shows that stock markets in Qatar and Saudi Arabia are not efficient in the weak form. This implies that these markets provide arbitrage opportunities for investors.
5. Discussion and Conclusion
This chapter provides a discussion of the results by comparing them with those reported in the literature review. Next, conclusions are formulated. Finally, the limitations of the study are indicated and recommendations for future research are provided.
5.1. Discussion
The findings of the current study coincide with those of Jamaani and Roca (2016) and Awan and Subayyal (2016). It was evidenced in the literature review that both papers explored quite short time periods, namely 2003-2010 and 2011-2015, respectively, and that an extension of the analysed time interval would have provided more reliable results. The current study used the observation period 2001-2017 and gained the same results as previous researchers. This means that the Qatari and Saudi stock markets have been information inefficient in the 21st century. Another interesting observation is that Awan and Subayyal (2016) employed the same techniques, namely the runs test and autocorrelation test for analysis. Thus, the current study expanded the arsenal of the tests that could be used. This improves the quality of the analysis and reliability of the attained results. At the same time, Jamaani and Roca (2016) employed the Johansen cointegration test, which was not employed in this dissertation, but they received the same results. The researchers argued that the reason for inefficiency is that foreign capital is limited in Islamic equity markets. One can agree with this reasoning and add that further development of stock markets in the GCC region can improve their efficiency.
The conclusion that emerging stock markets are inefficient partly coincides with those of Malhotra et al. (2015) whose object of interest was the Asian-Pacific region. The researchers found that, in overall, stock markets of that region were inefficient based on the analysis of daily returns. However, the results were different when weekly and monthly returns were used. As the current analysis did not capture weekly and monthly data, full comparison cannot be made. On the other hand, Malhotra et al. (2015) applied only the runs test compared to the broad arsenal of methods employed in the current analysis.
A comparison of the results of this dissertation with available evidence from other developing stock markets shows that the obtained outcomes coincide with those of Stakic et al. (2016) who found the Serbian stock market to be inefficient in the weak form. Moreover, the authors made the same conclusion that this inefficiency was because of the overall underdevelopment of the stock market in Serbia. At the same time, the analysis by Kelikume (2016) of the Nigerian stock market showed that it was efficient. Unlike the current study, the author made a conclusion that all incoming information was quickly absorbed by the stock prices of the Nigerian companies. Thus, the results of the study are mainly consistent with the previously attained results but there are a few exceptions as shown above.
5.2. Conclusions
The aim of the study was to estimate whether the stock markets of Qatar and Saudi Arabia were information efficient based on the evidence from the period 2001-2017. The General index comprised of 20 largest companies was chosen to represent the Qatari stock market while the Saudi Arabian stock market was represented by the Tadawul All Share index. The latter covers all stocks listed on the stock exchange. Daily stock prices were employed for the tests.
The main methods used for examining the weak form efficiency of the markets included the K-S test, the runs test, the autocorrelation test, the ADF unit root test and the VR test. The results of have shown that the stock market in Qatar is not efficient as the general index values do not follow a random walk. The result can be considered reliable since all five tests employed in the study showed the same results. As for the Saudi Arabian stock market, the first four tests rejected the random walk hypothesis while the results of the VR test surprisingly confirmed this hypothesis. However, considering that there is more evidence against the EMH in Saudi Arabia, it is concluded that this market is not information efficient, either.
5.3. Limitations and Recommendations
The first limitation of the study is that each method of testing the EMH is not perfect on its own. Although the number of tests employed in this dissertation was large enough, some new insights might be revealed from adding the Johansen cointegration test and GARCH models to the available set. The former could provide evidence on long-term equilibrium in the market and the GARCH models would be able to take into account possible volatility clustering in data.
Another limitation of this dissertation is that only two stock markets were chosen among the GCC countries. This choice was determined by the fact that these two markets are arguably the most developed in the region. However, addition of new markets could have revealed some differences in the anlalysis. This would also allow for better generalisation of the results. Thus, future studies can be recommended to conduct similar tests in the context of the United Arab Emirates, Oman, Kuwait and Bahrain in order to allow for that comparability.
Another way in which the study can be improved is to expand the tests to data with different frequency such as weekly and monthly data. This was done in some of the studies reviewed in Chapter 2. It is valid to note that if a market is not information efficient based on daily data, it could be efficient in the context of the weekly or monthly data as there is more time for investors to react to all incoming information.
Finally, it is recommended that a cross-country analysis of EMH should be conducted where different regions will be represented. The matter is that market conditions in the GCC countries are more or less homogenous whereas inclusion of emerging economies from other regions such as South East Asia would reveal more interesting results. It can also be added that a comparison of the efficiency tests in the emerging stock markets and advanced markets could be useful.
References
Andrianto, Y., and Mirza, A. R. (2016) A Testing of Efficient Markets Hypothesis in Indonesia Stock Market. Procedia-Social and Behavioral Sciences, 219(1), pp. 99-103.
Arshad, S., Rizvi, S. A. R., Ghani, G. M., and Duasa, J. (2016) Investigating stock market efficiency: A look at OIC member countries. Research in International Business and Finance, 36(2), pp. 402-413.
Awan, U. F., and Subayyal, M. (2016) Weak Form Efficient Market Hypothesis Study: Evidence from Gulf Stock Markets. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2787816 [Accessed on 4 October 2017].
Baillie, R. T., Chung, C. F., and Tieslau, M. A. (1996) Analysing inflation by the fractionally integrated ARFIMA-GARCH model. Journal of applied econometrics, 11(1), pp. 23-40.
Birău, F. R. (2015) Emerging capital market efficiency: a comparative analysis of weak-form efficiency in Romania and Hungary in the context of the global financial crisis. AI & SOCIETY, 30(2), pp. 223-233.
Broock, W. A., Scheinkman, J. A., Dechert, W. D., and LeBaron, B. (1996) A test for independence based on the correlation dimension. Econometric reviews, 15(3), pp. 197-235.
Burns, P. (2002). Robustness of the Ljung-Box test and its rank equivalent. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=443560 [Accessed on 4 October 2017].
Chow, K. V., and Denning, K. C. (1993) A simple multiple variance ratio test. Journal of Econometrics, 58(3), pp. 385-401.
Dickey, D. A., and Fuller, W. A. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), pp. 427-431.
Dismuke, C., and Lindrooth, R. (2006) Ordinary least squares. Methods and Designs for Outcomes Research, 93(2), pp. 93-104.
Ernest, W. C., Jnr, S. D., and Kofi, S. A. (2016) Macroeconomic variables and stock market performance of emerging countries. Journal of Economics and International Finance, 8(7), pp. 106-126.
Fan, Y., and Gençay, R. (2010) Unit root tests with wavelets. Econometric Theory, 26(05), pp. 1305-1331.
Fama, E. F. (1991) Efficient capital markets: II. The Journal of Finance, 46(5), pp. 1575-1617.
Guney, Y., and Komba, G. V. (2016) Testing for the Weak-Form Market Efficiency of the Dar Es Salaam Stock Exchange. Forthcoming: Handbook of Frontier Markets.
Harrington, D. P., and Fleming, T. R. (1982) A class of rank test procedures for censored survival data. Biometrika, 69(3), pp. 553-566.
Ho, S. L., Xie, M., and Goh, T. N. (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Computers & Industrial Engineering, 42(2), pp. 371-375.
Huang, Y. K., and Lee, C. H. (2016) Tests of Market Efficiency and Causality for the Greater China Stock Markets. Applied Science and Management Research, 3(1), pp. 209-216.
Ihlen, E. A. (2012) Introduction to multifractal detrended fluctuation analysis in Matlab. Fractal Analyses: Statistical And Methodological Innovations And Best Practices, 3(4), pp. 97.
Jamaani, F., and Roca, E. (2015) Are the regional Gulf stock markets weak-form efficient as single stock markets and as a regional stock market? Research in International Business and Finance, 33(2), pp. 221-246.
Johansen, S. (1992) Cointegration in partial systems and the efficiency of single-equation analysis. Journal of Econometrics, 52(3), pp. 389-402.
Kelikume, I. (2016) New evidence from the efficient market hypothesis for the Nigerian stock index using the wavelet unit root test approach. The Journal of Developing Areas, 50(5), pp 185-197.
Kim, O. (2016). Market Efficiency and Arbitrage Opportunities for Russian Depositary Receipts Cross-Listed on the London Stock Exchange. Available at: http://www.worldscientific.com/doi/abs/10.1142/S0219091516500077 [Accessed on 5 October 2017].
Malhotra, N., Tandon, K., and Tandon, D. (2015). Testing the Empirics of Weak Form of Efficient Market Hypothesis: Evidence from Asia-Pacific Markets. IUP Journal of Applied Finance, 21(4), pp. 18.
Oztekin, A., Kizilaslan, R., Freund, S., and Iseri, A. (2016) A data analytic approach to forecasting daily stock returns in an emerging market. European Journal of Operational Research, 253(3), pp. 697-710.
Pandey, S., and Samanta, A. (2016) Weak-form Efficiency in Inidian Stock Market Index. International Journal of Management Research and Reviews, 6(7), pp. 904.
Petersen, M. A. (2009) Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22(1), pp. 435-480.
Rizvi, S. A. R., and Arshad, S. (2016) How does crisis affect efficiency? An empirical study of East Asian markets. Borsa Istanbul Review, 16(1), pp. 1-8.
Robinson, C. J., and Bangwayo-Skeete, P. (2016) Semi-Strong Form Market Efficiency in Thinly Traded Markets: Evidence from Stock Price Reaction to Major National and International Events. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2845385 [Accessed on 6 October 2017].
Rösch, D. M., Subrahmanyam, A., and van Dijk, M. A. (2016) The Dynamics of Market Efficiency. Available at: https://static1.squarespace.com/static/531afe04e4b0b17f973f5962/t/5775767f6b8f5bde4cb3805a/1467315842130/market_efficiency_dynamics_full_paper_21June2016.pdf [Accessed on 5 October 2017].
Said, A., and Harper, A. (2015) The Efficiency of the Russian Stock Market: A Revisit of the Random Walk Hypothesis. Academy of Accounting and Financial Studies Journal, 19(1), p. 42.
Shaikh, S. A. (2016) Analysis and Test of Market Efficiency: A Case Study of KSE. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2713276 [Accessed on 5 October 2017].
Shawtari, F. A., Salem, M. A., Hussain, H. I., and Hawariyuni, W. (2016) Long Run Relationship between Macroeconomic Indicators and Stock Price: The Case of South Africa. Available at: http://www.icommercecentral.com/open-access/long-run-relationship-between-macroeconomic-indicators-and-stock-price-the-case-of-south-africa.php?aid=66383 [Accessed on 3 October 2017].
Soon, S. V., Baharumshah, A. Z., and Chan, T. H. (2015) Efficiency Market Hypothesis in an Emerging Market: Does It Really Hold for Malaysia? Jurnal PAigurusan, 42, pp. 31-42.
Stakić, N., Jovancai, A., and Kapor, P. (2016). The efficiency of the stock market in Serbia. Journal of Policy Modeling, 38(1), pp. 156-165.
Zaiontz, C. (2018) Kolmogorov-Smirnov Table, Available at: http://www.real-statistics.com/statistics-tables/kolmogorov-smirnov-table/, [Accessed on 3 October 2017].
