Statistical Status of Bank Based in UAE - Statistics Project Example

7th October Analysis of statistical status of bank based in UAE The following thesis would empirically highlight one of the leading UAE public sector banks known as Union National Bank situated in Abu Dhabi. As per this papers requirement we would start this discussion by fundamentally calculating the mean and variance of the stock prices associated with the latter mentioned institution and as such pave a hypothetical analysis of a full years return on stock prices given the figures we establish during the mean-variance optimized results. According to Bloomberg international (bloomberg.com/apps/quote? ticker….) the yearly market price on specific stock indicated in the company’s archive for the year 2009/2010 ranges from 2.67 to 3.45 price (AED) while specific monthly fluctuations on a fundamental average consist of 2.71 in January, 2.83 in February, 2.67 in March, 2.97 in April, 3.01 in May, 3.02 in June, 2.88 in July, 3.07 in August, 3.21 in September, 3.33 in October, 3.45 in November and 3.37 in December. The price (AED) situated here is the mean monthly figure calculated and hence are prone to price fluctuations in between days to a relative degree of negative or positive pips. The price AED on these stocks show us two premiered fundamentals and trends which enables us to potentially establish a forthcoming prediction as to how the relative stock prices would behave in the coming annual year and how much return would an investor achieve through such calculations. The first piece of evidence regarding this is the increasing trend of stock prices valued in AED. This inclination identifies that the stock prices on any amount of investment would surly benefit the investor and hence the ADX (Average directional Index) is an up move positive directed i.e. value inclined is greater that down move while value inclined > 0 which clearly indicates that the ADX is a +DM and consequently as per J Welles Wilder a + DI on the fourteen day exponential moving average, J. Welles Wilder, Jr. (June 1978). The second most evident fact suggest two particular things, the first being that as the amount of fluctuations between the stock prices is relatively high hence the risk factor involved is on the higher side as well consequently making an escalated percentage uncertainty on the return investments. The second aspect of this hypothetical claim lies with the fact that the variance on this particular stock is altitudinous in nature i.e. to claim that while observing the February and December stock changes we may witness a decline in the Price AED hence suggesting the fact that mean variance optimization is unstable and that as the risk being higher stock prices are also sensitive to decline. To ideally calculate the standard deviation and mean variance of such figures the following mathematical methods and notations should fundamentally be applied in any given stock prices calculation cases. Walker, Helen (1931) Aggregated Mean (X) = Σ (total number of Price AED in months) 12 months (as in whole year) Calculating these figures we get the following results A.M. = 2.71+2.83+2.67+2.97+3.01+3.02+2.88+3.07+3.21+3.33+3.45+3.37 12 A.M. = 3.04 This particular figure is the average monthly stock price of the Union national bank (Abu Dhabi) for the academic year 2009/2010 but to successfully calculate the risk involved we must be mathematically aware on the full mean-variance numerical figures and as such we would potentially want to calculate the standard deviation and the variance for these given figures. Standard Deviation or σ (sigma) = Σ (X – X) 2 N –1 Where X = is the associated value from a finite set of data X = Aggregated mean N = Sample Size/ Number of observations. S.D. (σ) = (2.71-3.04)^2 + (2.83-3.04)^2 + (2.67-3.04)^2 + (2.97-3.04)^2 + (3.01-3.04)^2……………………(3.37-3.04)^2 12 - 1 S.D (σ) = 1.2031 11 S.D. = 0.1094 Further to this the standard deviation is the square root of variance hence to calculate variance we need to square the value obtained and thus by our counts the following examples should suffice to the matter. Variance = S.D.2 or σ^2 Variance = 0.1094^2 Variance = 0.0119 Judging from the values obtained we have surely determine that due to the low variance and standard deviation value obtained in our results the possibilities of getting profitable returns on an investment is likely to happen all year round but due to the market’s previous instabilities in three months in 2009/2010 calendar year we would shed some lights on other anticipated factors that could have possibly be the reasons for declined trends in those particular months. Furthermore to fully comprehend how do we arrive with price stock figures can be fully referred to the fact that Price of Stock = (Dividends Paid + Capital Gain) X Expected Return While using this interchangeably we can presume any figures pertaining to annual calculations concerning all mentioned propagations and using the mean-variance optimization techniques earlier with the use of standard deviation can potentially find specific trends in stock prices to successfully list it as hypothetical claim on individual investments. Conclusively with reference to the above-mentioned procedure if an initial investment of $ 1000 with monthly dividends of $ 11 are paid in full then the annual expected return would comprise of Expected return = 1000+121 (11 since capital gain was the 1st month investment) 12 Expected return = $ 93.42 or 9.3 % (approx) on total investment for 1 year. Apart from these mathematical evaluations of the S.D, mean and variance there is also the covariance measure that further influences market stock prices and establishes key components on this regards. Practically Covariance is a measure of how much two variables move together. This is different to variance, which measures how much a single variable moves. If two variables move together the covariance is positive. If they move in opposite directions the covariance is negative. The formula is expressed as: Cov(x,y) = Σ (Xi-X)(Yi- Y) (3) N In our case the function that we want to calculate is Cov (SPN, CAM). Like standard deviation, when calculating covariance the range of values is infinite. Correlation however ranges between -100% to +100% which makes it a better measure. The relationship between correlation and covariance is simply represented by the following formula: Correl(X,Y) = Cov(X,Y) (4) σx X σy Substituting equation [3] into [4] we arrive at Correl(X,Y) = Σ (Xi – X)(Yi –Y) {Σ (Xi – X)^2(Yi – Y)^2}^0.5 Correlation is an extremely important tool in trading for two reasons. If a trader wants to hedge a position he has to have confidence that the two stocks generally trade in the same direction so as to protect the value of the portfolio if the markets take the wrong turn. With the above specification the value obtained which ranged from different set of that were sampled calculated to be around Correl = 1.2013 X 1.3109 {1.2013 X1.3109}^0.5 Correl = 1.254 This particular value with the probability demonstration of the data that was available to be Union bank stock prices and Abu Dhabi Commercial Bank evaluated to the mentioned estimates there in but if for share price on these figures falls below 25% i.e. to say that the stock closes on around 2.28 price AED then with S.D. and Correl figures in mind the probability that the return would be higher than 15% comes to show something like this Probability = (11 X 0.1094 / 3.04) X 2.28 / 3.04 = 0.296 Coming to the qualitative speculation from where the data has been derived we would start by mentioned that the union national bank was established in 1982 as a public joint stock company. Unlike it’s modest start the UNB has escalated to becoming one of the leading domestic banking labels in all of UAE and hence plays a pivotal role in contributing and providing efficient stock control to both corporate giants plus small scale investors. It is the only publicly listed banking company that has its investment contributions from both the government of Abu Dhabi and Dubai while UNB also has leading market labeled subsidiaries comprising of Union Brokerage Company (UBC), one of the oldest brokerage firms in UAE and a leading player in the Brokerage industry while also chipping in with Al Wifaq Finance Company, a subsidiary of UNB has been established with the purpose of offering Sharia compliant financial, commercial and investing services to both organizations and individuals in compliance with the rules and principals of the Islamic Law. As part of the Bank’s vision, to be “a key player in the region”, UNB has extended its reach to Egypt through a successful acquisition of the erstwhile ACMB and currently operates in the country as UNB Egypt through a number of banking centers. UNB has ventured into Doha with a branch at the Qatar Financial Center and has also opened a representative office in Shanghai China, being the first bank from the region to do so. In 2011, UNB also received a license to operate in Kuwait. UNB is also reviewing other geographic locations for a presence or strategic alliances that will add to shareholder value (The Union National bank: updated 27th March 2011 Copyright © 2011 Union National Bank) Coming back to the analytical specifications of such an entity we periodically distribute the analysis into separate factors involving many aspect and how they would particularly influence the stock price variations in market while keeping in mind the specific variations in the industry as well. Hence the first thing up for grabs is the market sentiment in an economic market and seeing how this company values for low S.D. and mean variance values prices fluctuations in the general market would comprise of a minimal effect on stock based in this investment authority. We see that the price of the stock of a company is affected most of the time by the general market direction during a session.  In a bull market, the stock price of most companies will rise and in a bear market the stock price of most companies will fall.  One can gauge the market sentiment by looking at stock indexes or its future price movement. Frank J. Fabozzi and Jack C. Francis (Dec. 1979). The performance of industry, earning results and earning guidance, new product introduction to markets or introduction of an existing product to new markets and new major contracts or major government orders are the other most influenced factors that do effect market stock prices and thus provide investors with specific stock trends to establish a particular timeline for their investments. These terminologies categorize themselves as the most reliable quantitative tools to benchmark an industry stock determination and focuses on variables that include the following. (a) The performance of the sector or industry that the company is in also plays in part in determining the stock price of the company.  Most of the times, the stock price of the companies in the same industry will move in tandem with each other.  This is because market conditions will generally affects the companies in the same industry the same way.  Of course, there are exceptions to this.  Sometimes, the stock price of a company will benefit from a piece of bad news in its competitor if the companies are competing for the same target market. (Peter Blair Henry)        (b) Objective of a company is to make profit.  Therefore, investors and traders always assess a company based on its Earning Per Share (bottom line) and Revenue (top line) and its future earning potential.  In US, companies generally report the earnings results every quarter-yearly.  A company that achieves good earning results (EPS and Revenue) expects a boost in its share price and one that delivers poor earning result shall see a beating in its share price.  Sometimes, besides reporting the EPS and Revenue for the past quarter, a company may also issue guidance (expected value) for the EPS and Revenue in coming quarter or coming years.  This is also closely monitored by investors and is an important factor that will affect the company stock price. (c) The introduction of new product to market is seen as a revenue enhancer for a company.  This also applies to an existing product that breaks into new markets.  Sometimes, the prospect of a new product introduction suffices to improve the stock price of a company. (Claudia Bird Schoonhoven, Kathleen M. Eisenhardt and Katherine Lyman) (d) New major contracts or major government order is expected to see a bull run in its stock price.  Those companies that fail in the contract bidding normally experience the fate of sell-off in its stocks. (John Y. Campbell, Robert J. Shiller) Asset prices are commonly believed to react sensitively to economic news. Daily experience seems to support the view that individual asset prices are influenced by a wide verity of unanticipated events and that some events have a more persuasive effect on asset prices than do others. Consistent with the ability of investors to diversify modern financial theory has focused on persuasive or systematic influences on likely sources of investment links. The general conclusion of theory is that an additional component of long run return is required or obtained whenever a asset is influenced by systematic economic news and that no extra reward can be earned by bringing diversifiable risks Nai-Fu Chen, Richard Roll and Stephen A. Ross (Jul., 1986). References: The Bloomberg Channel: Markets yearly figures. http://www.bloomberg.com/apps/quote?ticker=UNB:UH J. Welles Wilder, (June 1978). New Concepts in Technical Trading Systems. Greensboro, NC: Trend Research. ISBN 978-0894590276. Walker, Helen (1931). Studies in the History of the Statistical Method. Baltimore, MD: Williams & Wilkins Co. pp. 24–25. The Union National bank: updated 27th March 2011 Copyright © 2011 Union National Bank. (http://www.unb.co.ae/english/index.aspx Mutual Fund Systematic Risk for Bull and Bear Markets: An Empirical Examination Frank J. Fabozzi and Jack C. Francis The Journal of Finance Vol. 34, No. 5 (Dec., 1979), pp. 1243-1250 Peter Blair Henry, Do stock market liberalizations cause investment booms?, Journal of Financial Economics, Volume 58, Issues 1-2, 2000, Pages 301-334, ISSN 0304-405X, 10.1016/S0304-405X(00)00073-8. (http://www.sciencedirect.com/science/article/pii/S0304405X00000738) Claudia Bird Schoonhoven, Kathleen M. Eisenhardt and Katherine Lyman Administrative Science Quarterly Vol. 35, No. 1, Special Issue: Technology, Organizations, and Innovation (Mar., 1990), pp. 177-207 (article consists of 31 pages) John Y. Campbell, Robert J. Shiller; NBER Working Paper No. 8221 Issued in April 2001 Nai-Fu Chen, Richard Roll and Stephen A. Ross The Journal of Business Vol. 59, No. 3 (Jul., 1986), pp. 383-403 (article consists of 21 pages) Read More