Stock Market Analysis of Abu Dhabi, Qatar, and Saudi Arabia - Research Paper Example

? Stock Market Analysis of Abu Dhabi, Qatar and Saudi Arabia Literature review The GCC countries, from belatedly, have been endeavoring to fortify their capital markets by bringing out diverse modern deepens in reference to itemization, regulatory, merchandising and settlement averages in order to improve transparency and informational efficiency. This however, has led to utilizing the stock exchange indicators of the three distinctive countries i.e. Abu Dhabi, Qatar and Saudi Arabia. We ascertain that all the preceding three markets eliminates aught assumption that the returns adopt a convention statistical distribution. This however, has led to utilizing the stock exchange indicators of the three distinctive countries i.e. Abu Dhabi, Qatar and Saudi Arabia. We ascertain that all the preceding three markets eliminates aught supposition that the returns adopt a convention statistical distribution. Additionally, we get hold that hypothesis bearing on to haphazard walk and feeble form efficiency of the three GCC countries and its markets is declined for all three issues including variegation benefits, based on the correlation statistics of returns. From the analytic thinking, we restates that the necessitate for an incorporated DCC stock market should be availed since the results and suggestions have fuller entailments due to security psychoanalysis, adorning community, securities market and other regulatory authorities in their policy decisions to improve their capital market performance. However, this field research entailed market efficiency, Random walk Kolmogorov-smimov test, Runs test for randomness which was articulated to find out the market analysis of the three GCC countries. Introduction Stock exchange, act as a determinant persona in cementing the relationship between the investors and the embodied sector (Solink, 2003). In this appendage, they alleviate in marshaling the economies of people and channelize them to the increment of trade, commercialism and industrialized sectors of an economic system. In a nutshell, stock markets caper a significant purpose in capital establishment and avail fuel economical maturation in the country. Considering it from the investor’s viewpoint, securities market cognitive operation are frequently equated to operations in adventuring dens, and the investors anticipate the flop winning schemes employing infinite processes and methods at liberty. All the same, the investor’s elemental target is to rally the market in spite of the fact that most off times investors are channelized by the persuasions of faith and phobic disorder. Notwithstanding, intellectual investors like to bid dependable and adorn their hand-earned net worth optimally. Those investors look for devised entropy and coherent abstract thought backed up by technological ways and techniques, since the two blossom circumstances of sensible investors are the risk and return inherently deliver in a security, guidance on deciding the correct stock based on scientific formula aspiring a boon to the investors. Efficient Market Hypothesis (EMH) The GCC commercialize analytic thinking of the three countries will be examined in reference to effective market hypothesis (EMH) and also through Random walk hypothesis (RWH). Unlike the efficient market hypothesis, this principal sum arrogates that the stock prices align rapidly to the new information, and thus, current prices in full reflect all available information. Furthermore, the Efficient Market hypothesis is classified into three sub assumption subject on the information set involved (Farma, 1988). The efficient market possibility all the same, avers that whenever the markets are efficient, then it ought to constitute almost impossible for an investor to exceed the market on a corroborated footing. Even though defluxions will occur and there will be periods when securities are all over or depreciated, these anomalousness’s are expected to go away as quickly as they came along, thus coming through almost impossible to profit from them systematically. The weak form of market efficiency hypothecates that the current price does not meditate bazaar value and is only a contemplation of past prices. The semi-strong chassis of market efficiency speculates that the current price meditates all pronto available information. This information might let in annual accounts, yearly fillings, earning reports, declarations and additional efficiency states that the stock prices reflect all information from historical, world and private, so that no investor can actualize rate of return. Conceiving the three market analysis of the Abu Dhabi, Qatar and Saudi Arabia, we agonize that commercialize is not absolutely efficient. Anomalousness’s do subsist and there are investors and traders who surmount the market. So, the EMH has very crucial entailments for both investors and authorities that determines and ascendance the market. Random Walk hypothesis (RWH) Research in different markets Random walk hypothesis comprises three classifiable sub-hypothesis including the comparative studies, explore in different markets and statistical tools enforced during market analysis. As far as Random walk supposition is concerned, it is consequently realized that the hypothesis dissents both in developed and developing markets globally. The developed markets are ascertained to be weak -form efficient connoting that, successive returns are autonomous and abide by random walk. These results of weak-form efficiency are affirmed considering a low degree of serial correlation statistics and transaction cost. Completely the three countries market analysis backs up the proposal that price alterations are random and past changes are not useful in forecasting future price changes particularly after transactions cost are taken into consideration. It is habitual that weak-form forms of market analysis cannot channelize the investors with whatsoever clean-cut merchandising rules to make aberrant profits. Comparative studies Comparative analyses forms of Random walk hypothesis examine case-by-case and efficiency of market place. This can be established within the three countries market analytic thinking to conk out the growth analytic thinking and absolved prediction to both traders and investors in particular at the brink of actualizing benefit maximization. All the same, Statistical tool enforced becomes a requirement statistical examining tool since it enables one to analyze the cogency of weak-form EMH and the RWH. In the meantime, the statistical test falls under two classifiable groups (Ross, 2002). Statistical tools applied The first group implicates a comparability of risk return results for trading or filter rules that make investment conclusions based on past market information against results from a mere buy and accommodate strategy. The second group implies statistical tests of independency between rates of return. Autocorrelation and runs tests are the most popular ones in this group since it depicts the isolation deviation from random walk exemplar and can be used to key out cycles that are responsible for inefficiencies described in a given serial publication. In addition to statistical model, discrepancy ratio test is commonly used since it is used to examine the entropy in a time series of stock price taking into chronicle the problem arising within the market analysis. An overview of capital Markets in GCC countries The evolution of stock market started back in the year 1970 and therefore availed in various GCC countries. This was due to the basis of regulating stock exchange worldwide for the benefits of the traders as well as investors. The following table gives a summary of the commencement of organized stock market activities in the three distinctive GCC countries. Table1 Emergence of capital Markets in GCC Countries Country Stock Market Year of commencement Qatar Doha Securities Market 1997 Abu Dhabi Abu Dhabi Sec. Market 2000 Saudi Arabia Tadawul Saudi Stock Market 1989-2012 Through this inauguration, the stock market in the GCC and its market capitalization has increased from $120 million to 2000 as well as to $ 522 billion in the year 2004 as per availed research. It is believed that the aggregate volume of shares traded similarly increased from 7.9 billion to 50.9 billion in the same period , with cumulative value of the shares increasing from $ 22.9 billion to 551.9 billion respectively. However, Abu Dhabi, Qatar and Saudi Arabia portrays similar financial systems, which mainly consist of the central bank, commercials banks, insurance companies, stock broking firms, stock exchanges among other financial institutions (Working, 1960). Major characteristics of the GCC markets GCC markets relish moderate stock exchange intensity compared to their United States of America and European counterparts. The day by day average volume compasses between 2 million US dollars to 4 billion US dollars. Furthermore, most of the GCC countries have put surveillance mechanism in place to contain the stock market operations. Passable powers are enthroned with regulatory authorities to protect and safeguard investor’s interests. GCC markets put a lot of limitations on foreign possession. Through the analysis, we substantiate those larger markets, notably those of Qatar, Saudi Arabia and Abu Dhabi place considerable obstructions in the way of foreign investiture. GCC markets are acknowledged for high excitability. These markets have witnessed accrue from their blossom prices a few times. A sample fall from the peak prices in 2009-2012. Figure 1 GCC Markets-Extent of Fall from peak prices in 2008-2012 To avail the stock market report, statistical method of data collection were used to determine the both parametric and non-parametric models tests for analysis. Randomness technique has been employed within the stock market data collection and manipulation in regards to the changes of successive price. Considering the results below, we find that the randomness forms of data collection and manipulation consists of conversion of the time series data, which in our case is for the period of January to December , the year 2012 for both three GCC countries into logarithmic data and then computing the return percent using Microsoft Excel Data and Methodology Hypothesis There are two distinguishing guesses that were sampled in this study. The first Ho analyzes whether the stock returns abide by a normal distribution or not. Null Hypothesis Ho represents the stock returns in the Gulf markets follow a normal distribution while the Alternative Hypothesis Ha represents the stock returns in the Gulf markets which do not follow a normal distribution. The reason as to why the normality was examined was due to the fact that unless the time serial publication of data is normal, parametric examination such as sequential correlation test cannot be enforced. So, first normality test would be implemented and if the statistical distribution proved to be normal, serial correlation test would be used. Otherwise, runs test which ignores normality assumptions would be applied. Normality was screened using Kolmogorov-Smimov test. The second Ho, examines whether the stock returns follow a random walk during the study period. At this particular moment, the Null Hypothesis Ho represents the stock returns in the Gulf markets which are taken randomly during the study period. An alternative hypothesis Ha therefore represents the stock returns in the Gulf markets which are randomly taken during the study period. Data FINCORP, one of the leading brokerage firm and Asset management companies in Muscat Oman offered the information needed for the study. This company has an embodied Research variance that garners data bearing on to the GCC and other Middle East markets. Our data are accompanying all the three GCC markets. Our data begins from January 2008 to December 2012. It habituates the daily prices of all the three exponents for which were collected by FINCORP. The country, exponent, period covered and the number of reflections are given in the table 2 below Table 2 Data Details SI. No Stock Market Country Index Period from Period to Number of observations 1 Qatar UAE DFM 02-Jan-08 19-Jan-12 900 2 Abu Dhabi UAE ADSM 02-Jan-08 19-Jan-12 1398 3 Saudi Saudi Arabia TASI 01-Jan-08 18-Jan-12 1455 Statistical Methods As far as analytical tools are implicated, we use both parametric and non-parametric tests for analytic thinking. As adverted in the literature critical review, there are several examinations that are used to test for haphazardness. We use, run test for randomness. This method is widely used to examine whether there is randomness in sequential price changes. First, conversion of the time serial publication data into logarithmic data and so ciphering return percent were done using Microsoft Excel. SPSS which is one of the accountancy software was also used to calculate runs test and Kolmogorov-Smirnov test. Although the researcher designated to use Auto correlation function and partial Auto correlation function using SPSS, he could not do so as the statistical distribution failed the normality test in all the three cases. An abbreviated explanation of the tools used inclines in the following lines. Non-Parametric Tests i. The one-sample kolmogorov-Smirnov test subroutine compares the ascertained cumulative statistical distribution function for a variable with an assigned abstract statistical distribution, which may be convention, uniform, Poisson, or exponential function. The Kolmogorov-Smirnov Z is figured from the largest deviation between the observed and theoretical cumulative distribution functions. This blimey of it tests whether the reflections could reasonably have come from the conditioned distribution. ii. The runs test for randomness As expressed earlier, in order to examine for weak-form efficiency, we employ runs test as it does not compel returns to be ordinarily distributed. This allows a solid alternative to parametric sequential correlation tests in which distributions are arrogated to be normally test, the only alternative runs test for haphazardness was used. A run is delimitated as a chronological sequence of indistinguishable symbols which are complied or refined by different symbols or no symbol at all. The number of runs is reckoned as a sequence of the price changes of the same sign such as; ++,--,00. When the anticipated number of run is importantly dissimilar from the ascertained number of runs, the test eliminate the null hypothesis that the daily returns are argotic. As delineated by various assimilators, a lower than anticipated number of runs points market’s over reaction to entropy, afterwards reversed, while higher number of runs contemplates a dawdled response to information. Either position would evoke an opportunity to make extra returns. In the meantime, to perform a runs test, both the expected runs and the existent runs are calculated for the sample returns. The expected number of runs is represented by: E (r) ={ n+2nanb}/n Where n represents the number of observations, na and nb respectively represent observations above and below the sample mean or median, and r represents the observed number of runs. The standard error can therefore be written as: ? (r) =[{2nanb(2nanb-n)}/n2]1/2 The asymptotic (and approximately normal) Z-statistic can be written as follows: Z(r) = {r-E(r)}/ ? (r) The null supposition for this test is for temporal independence in the serial publication (or weak-form efficiency). As returns are not commonly distributed, the presence of morphologic breaks or outlets in the series can prejudice the test employing a mean and a median as the base. The latter can bear authentic results when outliers exist. Parametric Test Auto-correlation and Partial Auto correlation function Auto correlation and partial auto correlation occasions are authentic appraises for examination of either habituation or independence of stochastic variable in a serial publication as well as compute the price commutes at different lagged 1,2,3 time periods. This test is used very popularly and the test expects the returns to be normally administered. So, prior to enforcing the test, outliers in the distributions need to be abstracted in this test, the sequential correlation coefficient measures the relationship between the values of a chance variable at time t and its value during the previous period. Auto correlation test certify on whether the coefficients of correlation are significantly different from zero. For a large sample, the Ljung-Box statistic abides by the chi-square distribution with m degrees of freedom. LB = n(n+2)?mµ-1(pn2µn-k)~x2 Where pn2µ = Auto correlation coefficients at lag k N=Sample size Variables used First, the daily share price index of the individual sample markets was converted into logarithmic form using fx function (statistics sub-menu). Then the logarithmic values were used to compute daily market return percent. The variables used were as follows: Log Rjt = In{[pt-pt-1]/pt-1} Where Log Rjt = Daily Logarithmic Market return percent of index, j and time period t Log Pjt = Logarithmic market index, j at time period, day t, Log pt-1 = Logarithmic market index j at time period, day t-1 For example, considering the index values of Abu Dhabi stock market on the following dates Date: 16.01.2012 2342.83 Dirham Log value 3.369741 Date: 19.01.2012 2337.83 Dirham Log value 3.368813 3.367941-3.368813 So, Based on the log values, the return would be = -------------------------- 3.368813 = -0.000258845 Empirical Results and Discussion a. Descriptive statistics of daily market return of the sample indices Table 3 presents the descriptive statistics of the daily returns percent which has been done after conversion to logarithm form. Interestingly, all the three markets have reported positive mean returns during the study period. Dubai with a mean of 0.00022 has registered the highest return followed by the Abu Dhabi and finally Qatar. It is realized that Dubai however, stands at high risk market after determining its standard deviation translating to 0.00294, which is the highest of all results of the other two GCC markets. Standard deviation is used to measure the volatility of the markets and through its analysis; it is advisable for a given market within GCC market to deliver low standard deviation which triggers reasonable returns at the end of the year. Another interesting aspect is that all of the three GCC markets have leptokurtic with Kurtosis higher than 2.58. The distribution of returns has flatter tails than normal distributions patterns. Through this observation we administer that the GCC markets do not follow normal distribution patterns at all. Table 3 Descriptive statistics of daily market return of the sample indices (%) Test Qatar Abu Dhabi Saudi Arabia Mean 0.00022 0.00011 0.00013 Median 0.00026 0.00008 0.00017 Variance 0.000 0.000 0.000 Std. Deviation 0.00294 0.00136 0.00178 Minimum -0.01904 -0.00761 -0.01067 Maximum 0.01462 0.00784 0.01019 Range 0.03365 0.01545 0.02088 In. Quar. Range 0.00229 0.00067 0.00123 Skewness -0.318 0.262 -0.789 Kurtosis 4.698 6.611 8.722 Observations 900 1398 1455 Observations given are (n-1) as the first year is taken as the base year in the computation of daily return % Results of Kolmogorov-Smirnov Normality Test As started earlier in the methodology, the K-S test examines whether the returns follow a normal distribution or not. The test results are presented in Table 4. The results reveal that all the three GCC markets have had asymmetric distributions during the study period. The P-Value is invariably significant with 0.000 Read More