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Is the Foreign Exchange Market Efficient - Term Paper Example

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The paper “Is the Foreign Exchange Market Efficient?” is a meaty example of a finance & accounting term paper. In an efficient market, the competing constant in the stock market reflects information realistically as well as instantly on prices, making the history significant information ineffective in forecasting the anticipated rate…
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Introduction In an efficient market, the competing constant in the stock market reflect information realistically as well as instantly on prices, making the history significant information ineffective in forecasting the anticipated rate. An efficient market must react merely to the latest information but because this is random by description, price changes, or returns in an efficient market is hard to anticipate. Fama identified three types of market efficiency namely; The weak form, semi-strong and stable form of market dynamic (Hodrick, 2014). A market is found to be weak if the historical prices are irrelevant in anticipating the future price, Semi-strong foreign exchange market efficiency exist where the entire public information is assessable such as the inflation rate, interest rate as well as the earning have no predictive power. Active form of market efficient exists where stock price portrays every information 1.0 Aims, Objectives, and Their Importance The study aim at ascertaining if it is probable to forecast security price changes due to its co fluctuation with the primary market. The aims and objective of the report are that, it tries to understand the market hypothesis, it assumption and importance as well lay down some of the concept and theories employed. Foreign exchange market efficiency is a state of affairs whereby the price of the stock exchange portrays the assessable information. According to the effort of Eugene Fama, who proposed the efficient market hypothesis (EMH), the theory stated that the information concerning the investment will not be realistic for an investor to have a hedge since, information is equally shared by all investors (Jacque, 2014). The proposition of foreign exchange market efficiency is that it is extremely hard to beat the markets and anticipate returns that are higher than normal. This can only be applicable when the market is efficient with numerous investors since no investor would take a risk where there is no actual return. An additional proposition is the direct connection with foreign exchange market efficiency such as the random walk through. The theory assumes the market price to be arbitrary and impractical to forecast. The implication is that it is much hard for the investor to realize a hedge and make enormous profits from the investment. 1.1 Importance of this issue The thought of foreign exchange market efficiency is very significant for investors since it permits them to make a more rational investment decision. The only actual method that they can get beyond reasonable profit through investment in the diverse market is taking benefit of whichever market abnormalities when they exist. In an efficient market, it is hard for the investor to realize supernormal profit nevertheless; it is a good time to benefit from them.  2.0 Conceptual or Theoretical Framework The following are theories and concept in an investment that relevant in the analysis of the issue and generating a solution to the problem (Joseph P. Daniels, 2014). The principles are relevant to the study since; it portrays some unique feature that enlighten in detailed below. 2.1 Random walk theory This is a stock market theory that the state the historical price movement of a stock or the entire market cannot be employed to forecast its prospect trends. The theory profound that the stock price changes are independent of each other as well as have a similar probability distribution. After a period, the rate maintains an increasing trend. Random walk through the state that the price assumes a random and volatile path. The chances of a stock anticipated price rising are similar to its decline. Technical and fundamental analysis is majorly a waste of time and remains unproven in outperforming the market. 2.2 Efficient Market Hypothesis Few investors are neutral on the efficient market hypothesis. An investor can either trust in it and adhere to unreceptive, broad market venture plans or abhor it and concentrate on picking stock based on growth capabilities and undervalued assets (Levi, 2009). The efficient market hypothesis state that the stock is precisely valued until anticipated events alter the valuation. It, therefore, implies that the evaluation of security is correct prior to planning event changes the assessment. Since the prospect unidentified, an adherent to the efficient market hypothesis is far much better off owning an extensive swath of stock, as well as profiting from the general increase in the market. 2.3 Rational Expectations Theory The theory state that investors in an economy will act in a manner that match to what will rationally anticipated in the prospect. An investor will invest and spend relative to what can reasonably assume will occur in the near future. Consequently, an investor will create a self-rewarding prophecy that is useful in bringing about the anticipated happening. Even though the theory has turned out to be relevant to economics, its satisfaction is uncertain. 3.0 Method or Approach The research will employ the use of qualitative data. The technique is relevant since the nature of the study calls for the use of the qualitative method as means of collecting data (Oberlechner, 2005). The data will be gathered from books, research reports, annual reports, journal articles, other publications. This will ensure that an inclusive information and data is reclaimed for the research. There are several resources necessary to gather significant data. However, it is significant to recognize the legitimacy of the wherewithal employed for the study. This study chiefly comprise of secondary data, and consequently the dependability of the information can be improved by way of validating sources.In this regards, verifying sources will be relevant in assessing the data and information for the research study. 3.1 Hypothesis to be investigated The theory in the research is the effect of foreign exchange market efficiency whether the foreign exchange market is efficient and whether other factors both internal and external affect the market efficient. The information gathered would aid in ascertaining whether project management team assumed the right technique and if possible, areas that earlier correction must be made in order to get rid of project delay. This, therefore, leads to the question of identifying the right approach to gathering information for analysis. Some of the technique of gathering information is as follows; data sampling and collecting secondary information from a journal article. 3.1 Secondary data Secondary data comprise of an assortment of articles from published and peer-reviewed books and journals as well as reliable web sources that enclose information on foreign exchange market efficiency and understand the concept of investment. Therefore, will use data Australian stock exchange (ASX) to gather the data for decision-making as far as foreign exchange market efficiency is concerned. Furthermore, all the cited articles and books selected will be up to date having correspondingly used the source from websites and credible sources as well as materials from authoritative web sources (Madura, 2014). Limitation of the dissertation to secondary sources will facilitate assessment of numerous articles from diverse sources and authors on the issue of the Measuring results of market efficiency in and its effect on the prospect trend in share prices. 3.2 Data collection tools and its relevance The data for the study was collected from the Australian stock market (ASX).These entail the spot exchange rate,one-month forward rate as well as the three-month forward rate for the Australian dollar to the USD. The daily data set generated was from January 2003 to December 2006 while the forward market employed for AUD/USD assessment was the 1-month and 3-month forward rates. The OLS regression was executed, and it appreciate the Log of the real spot rate as the dependent variable (Lee, 2012). Durbin-Watson statistic is employed to identify the existence of autocorrelation in the residual from the study. The model is subsequently de-trended to achieve impartial regression estimation. 4.0 Presentation and Discussion of Results Real world example of foreign, market efficiency; Case study of Australia foreign market (ASX) in comparison with USD. Conceptual The case study of Foreign Exchange Markets efficiency in Australia examines an empirical connection between the spot price as well as the exchange rate efficiency in relation to US dollar (ASD), Japanese Yean, and Australia stock market (ASX). The research employs the monthly data from the Australian stock market (ASX).The study examines the interest rate and exchange rate connection as well as explore how it impact the forward and spot rate of Australian currency. The research test if the Australian foreign exchange (ASX) is an efficient market in relation to cointegrating relationship between the forward rate and the spot rate. Methodology of the study The research intends to appreciate the regression analysis conducted by Frenkel (1980) and Levich (1979).According to the scholars; the regression analysis is arrived at by taking the log of the future spot exchange and the present forward exchange rate. The outcome recommends that the forward exchange rate is an impartial predictor of the future spot rate. SRt+1 = ERt+1 + Ut (1) Where; {SRt+one} = is the log of the actual domestic spot exchange currency per unit of foreign currency in t + 1 period, {ERt+one} = is the log of the expected exchange rate in t + 1 period, {Ut} = is the normally distributed random error term with mean equal to zero. Assumption Investors are assumed risk neutral and can set the forward exchange rate at a point that equals the predictable future spot exchange rate. At a time t.the forward exchange is consequently equivalent to the anticipated exchange rate in t + 1. Consequently ft = ERt+1 Table 1: Summary statistics for AUD–USD Descriptive statistics Spot rate Forward rate Standard Deviation 19.24307 19.3462 Skewness -0.09345 -0.0975 Kurtosis 1.514534 1.515461 Jarque-Bera 8.405787 8.407049 Probability 0.014952 0.014943 The result of the above data analysis depicts that on average, the forward exchange rate strongly anticipate the future spot exchange rate (Oh, 2007). After rectifying for serial correlation, the anticipated coefficient of one period lagged forward rate do not fluctuate considerably. It, therefore, implies that it integrates the information, which is not taken up entirely by the forward rate. To achieve impartial regression outcome for a reason, we approximate the equation as observed in table one above. The results of the result signify that the approximated coefficient of a single period lagged forward rate does not vary considerably from one, which the assumption for the EMH holds. Table 2: Testing the Market efficiency between ASX and USD Estimated equation SRt+1 = α1 + α2 ft + Ut Currency rate Period α1 α2 DW R2 Australian dollar (AUD) 06M7-10M6 0.475* 2.731 0.989* 637.83 0.54 0.99 Japanese yen (JPY) 06M7-10M6 -0.003*29.72 0.99* 1361.3 0.74 1.00 US dollar (USD) 06M7-10M6 0.012* 4.03 0.992* 646.1 0.64 0.99 The table above depicts a countrywide regression for the model. The model is relevant since, at 5% significance level, α1 and α2 don’t diverge considerably from their theorized values.R2 point out that the regression outcome are relevant since it depict a positive result as observed above. The result recommends consequently that the market is efficient devoid of the risk premium (Hodrick, 2014). Nevertheless, the Durbin-Watson statistics depict that a positive correlation between the error term exist because it leaves the values of the t-statistics and R-squared less dependable, and consequently it turns out to be essential to run the regression subsequent to rectifying for serial correlation. The aim of the experiential research is to appraise the efficiency of the market. Testing the stock market using the Vector Autoregression (VAR) The procedure is possible by testing the (Augmented Dickey-Fuller) ADF test and then test the hypothesis of whether the spot rate and forward rate are cointegrated using the Vector Autoregression (VAR) model. We first run unlimited VAR on the spot and forward rate to choose and establish the order of VAR and cointegrating relationship.VAR is arrived as follows. VAR= y = + FY +Î Where Y is a m×1 vector of mutually determined variables In addition, E is a m×1 vector of unobserved variable The study is conducted in order to establish whether the AUD/USD spot rate and one month forward are non-compliance, All variables corroborate the required condition for cointegration appraisal. The test was conducted in order to select the order of VAR. The table below appraises the AUD/USD spot and one-month forward rate. The table provides that the highest value for AIC is 7067.3 as well as 7052.6 which both match to VAR of the order of one. Augmented Dickey-Fuller (ADF) Tests for AUD/USD Spot and All Forward ADF 95% ADF test statistic Critical value Variable statistic Actual exchange rate Spot rate -3.0343 -3.417 One month forward rate 2.6736 -3.417 Two months forward rate -2.3029 -3.417 From the above data analysis, the result depicts that forward rate unbiased hypothesis exist since the rate between AUD and USD exhibit a cointegrating connection between the spot rate and the forward rate. The ADF table test statistic is displayed in an absolute value with one number of lags for every test. Moreover, at 95% significance level, the null hypothesis of unit roots in relation to the alternative number of unit exist Moreover, a variable has non-significant test outcome at 5% confidence level since, it encompasses a unit root, and consequently it is non-stationary. 5.0 Conclusion and Suggestions The final objective of the research to grow the coherent consequence of efficient exchange market hypothesis to account for the standard behavioral aspect affecting the stock price (Ullrich, 2009). Devoid of the statistical importance of the outcome, the project does not act as a proof of the EMH. It is probable that supplementing the dataset, a corroboration might exist that efficient market hypothesis applies to the data as proved above in the data analysis. Nevertheless, the research of the active market cannot be comprehensive to the market as whole since, the EMH manifest itself in dissimilar situation (Richard T. Baillie, 2000). The riskier the stock will have a propensity to encounter greater price volatility at a given stage of information as compared to less risky stock. At the time of crisis, investor’s anticipation will influence the share prices in a different way as compared to the boom session. It is hard to ensure the EMH since numerous information components are unspoken, prices might change when there is the nonexistence of news, and nevertheless it might be due to investor anticipation concerning the company plans. Another factor such as the behavioral factors can affect the EMH at an individual level as well as it might have a small effect on the qualitative information existing on a stock. In this study, i employ the EMH as a tool for capturing the news sensitivity of stock so as to ascertain whether there is existence of dissimilarity in the manner underperforming as well as over performing stock will turn out to be more frighten concerning the holding where there are exposure to the news. Investors with well-performing stock will not get terrified with the news update and thus will not react aggressively, as a result; the prices will remain constant in response to an inclining frequency of the report. References Adam, S., 2008. From the Efficient Market Hypothesis to Behavioural Finance-How Investors Psychology changes. The Icfai Journal of Finance, Vol.2, No.1, pp. pp.68-76. Hodrick, R., 2014. The Empirical Evidence on the Efficiency of Forward. Newyork. Hodrick, R., 2014. The Empirical Evidence on the Efficiency of Forward and. London: Cengage Learning. Jeffrey A. Frankel, ‎.G.‎.G., 2009. The Microstructure of Foreign Exchange Markets - Page 274. New York. Kumar, R.V..&.M.S., 2007. Testing forward rate unbiasedness in India An econometric analysis of Indo-US forex market. International Research Journal of Finance and Economics, 12, pp.56-66. Lee, Y.-H..&.K.S., 2012. Efficiency tests in foreign exchange market. International Journal of Economics and Financial Issues, 2, pp.216-24. Oberlechner, T., 2005. The Psychology of the Foreign Exchange Market - Page 7. London: John Willey's & Son's. Oh, G..K.S..&.E.C., 2007. Market efficiency in foreign exchange markets. Physica. Statistical Mechanics and its Applications, 382, pp.209–12. Richard T. Baillie, ‎.C.M., 2000. The Foreign Exchange Market: Theory and Econometric. London. Sharma, A.K..&.M.A., 2006. What drives forward premia in the Indian forex market. Reserve Bank of India Occasional Papers, 27, pp.119–139. Stein, J.L., 1992. The nature and efficiency of the foreign exchange market. New York. Ullrich, C., 2009. Forecasting and Hedging in the Foreign Exchange Markets. London: Cengage Learning. Ullrich, C., 2009. Forecasting and Hedging in the Foreign Exchange Markets. London. Wickremasinghe, G., 2004. The efficiency of foreign exchange market. Melbourne: Monash University, Asian Business, and Economics Research Unit. William H. Wynne, S.I.a.F.B., 1951. State Insolvency and Foreign Bondholders. London: Yale University. Read More
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