TABLE OF CONTENTS INTRODUCTION...........................................................................................................................1 A. Graphically presenting the Compounded returns of USD/AUD.......................................1 B. Determining 68-95-99.7 Normal rule of thumb with specific evidences..........................2 C. Comparing artificially created returns with actual returns................................................4 D. Assessment for directional move with the influences of valid statistic.............................5 E. Convincing team for making investments as per 30 days of forecasted trend..................6 F. Analysing the Efficiency of FX market............................................................................7 G. Risk assessment analysis for 100 days..............................................................................9 H.Implicatinginternationalfinance’skeyvariableexchangeratefordistributional characteristics and its market efficiency...............................................................................10 CONCLUSIONS............................................................................................................................10 REFERENCES..............................................................................................................................12
INTRODUCTION Analysing the variations in the capital or currency market on an international segmentation on which there are various organisations and economies which has have influences. In relation with analysing the efficiency of market and economy there has been various models and methods which have influences in the operational gains and activities of the firm. Australia has a sound economy and the exchange rate is comparatively on the favourable state which defines economic stability in nation. In the present report there will be implication of various models and tests to analyse efficiency of AUD in compared with USD. Along with this, there will be discussion based on benefits and drawbacks of data set in the forecasted trend analysis that will bring accurate information among investors for their investing decisions. A. Graphically presenting the Compounded returns of USD/AUD To analyse the compound ate of return has been payable by S(USD/AUD) for having effective determination of market efficiency. It has been analysed by considering the 15 years of data set on daily basis, starts from 1 January 2003 to 9 August 2018. This is usually treated as the percentage of the outcomes which determines the gains and losses incurred on the capital analysis over the period(Wilson, andet.al.,2014). This is the most reliable and accurate analysis of the data set in terms of identifying the effective rate of returns in the operations. Thus, in analysing the compounded annual rate of return the data set of exchange rate of USD/AUD has been measured as seen below: yearsAnnual compound rate 20031.001167 20041.000214 20050.999987 20061.000318 20071.000466 20080.999163 20091.001087 20101.000542 20111.000038 20121.000105 20130.999428 20140.999669 20150.999616 20160.999985 20171.000313 1
20180.999703 Interpretation: Considering the outcome which determined that there have been huge movements in the interest rate outcome of the firm. Thus, as per making effective determination of all the operations on which it can be said that, Australia dollars are comparatively adequate as compared with USD. However, these outcomes determine that, there will be growth in the profitability and economic level of the country as per rise in the exchange rate. Thus, nation will retain effective revenue and the gains from exporting resources. There will be rise in the economy as compatibility with the US dollars will bring rise in economy, enhance per capita income as well as monitors operational requirements of firm. B. Determining 68-95-99.7 Normal rule of thumb with specific evidences To analyse the differences between normal and actual returns which has have bring variations in the outcomes(Kim and Yazdian, 2014). Thus, as per analysing the operational requirements and wants there will be suitable analysis of the data set after implicating Rule of thumb. The main concept behind implicating this theory is for bringing the adequate information and analysis on the data set 68.27%95.45%99.73% 2
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YearμσAverage one standard deviationAverage 2 standard deviationAverage 3 standard deviation 20030.0011671960.006677-0.005510.007844-0.012190.014522-0.018860.021199 20040.0002139930.008573-0.008360.008787-0.016930.017361-0.025510.025934 2005 - 0.0002366290.005209-0.005450.004972-0.010650.010182-0.015860.015391 20060.0003175180.005518-0.00520.005836-0.010720.011354-0.016240.016872 20070.0004663830.008245-0.007780.008712-0.016020.016957-0.024270.025202 2008 - 0.0008369620.015445-0.016280.014608-0.031730.030054-0.047170.045499 20090.0010867240.011112-0.010030.012199-0.021140.023311-0.032250.034424 20100.0005368360.008593-0.008060.00913-0.016650.017722-0.025240.026315 20113.77502E-050.008995-0.008960.009033-0.017950.018028-0.026950.027024 20120.0001049480.005754-0.005650.005859-0.01140.011614-0.017160.017368 2013 - 0.0005721720.006427-0.0070.005855-0.013430.012282-0.019850.018709 2014 - 0.0003307580.005667-0.0060.005337-0.011670.011004-0.017330.016671 2015 - 0.0003837750.007547-0.007930.007163-0.015480.014711-0.023030.022258 2016-1.4807E-050.006875-0.006890.00686-0.013770.013736-0.020640.020611 20170.0003132410.005107-0.004790.005421-0.00990.010528-0.015010.015636 2018 - 0.0002971760.005067-0.005360.00477-0.010430.009837-0.01550.014904 Histogram: 3
C. Comparing artificially created returns with actual returns To identify the Guage differences in the two data set for analysing the efficiency in such markets. As per analysing the outcomes there have been adequate differences and determination of all the operational needs(Barnhart and van Es, 2015). Using this technique will be helpful in demonstrating the performance made in a year of both portfolios. Below listed is the Foreign Exchange annual rate of USD/AUD which has been compared with the artificial data set. Year Average annual rate of returnartificial 20030.00116720.00212 20040.0002140.000345 2005-0.000237-0.00126 20060.00031750.00157 20070.00046640.000256 2008-0.000837-0.00094 20090.00108670.001024 20100.00053680.000635 20113.775E-053.78E-05 20120.00010490.000145 2013-0.000572-0.00064 2014-0.000331-0.00023 2015-0.000384-0.00032 2016-1.48E-05-0.00015 20170.00031320.000325 4
2018-0.000297-0.0002 Interpretation: By considering the above listed analysis on which the comparison of Actaul return and artificially created portfolios. Thus, as per analysing the outcomes the FX rate of country is comparatively near to the estimated rates. Moreover, in analysing the performance of Actual returns on which it can be said that there is need to have strategic plans which will be helpful for controlling economic condition and it will bring a stability in the performance. D. Assessment for directional move with the influences of valid statistic. It has been estimated that AUD will have economic growth in the coming period as this currency will have effective growth in the future(Jaeger and Adair, 2014). Similarly, considering the regular growth and rise in the currency level there will be effective rise in various operations. it will bring the rise in GDP, reduces inflation, unemployment in country. Along with this, there will be rise in the capital value in the international stock market and which will result in effective growth of the nation. TREND analysis: 5
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As per analysing the efficiency of the market on which it can be said that the trend determines that, there will be upward movement in the TREND line. Thus, on which there will be increment in data set as per considering the 30 days of forecast. Direction of this graph and Trend line is on upward movement which defined that there will be favourable rise in returns. E. Convincing team for making investments as per 30 days of forecasted trend To estimate the growth of currency in the next 30 days the below listed trend analysis will be helpful as per having effective outcomes and determination of all the facts(Behizadeh, 2014). Thus, this equation has been made as per considering the 15 year of data set of FX rate USD/AUD with consideration of fruitful determination of all the operations. However, it will have based on analysing the outcomes based on two hypothesis such as: Ho: μ =0 (mean return to be zero) H1: μ > 0, (mean return to be positive) 39220.74410.001885equationforecast 39230.892960.8829 39240.892990.8829 39250.893020.8830 39260.893050.8830 39270.893080.8830 39280.893110.8830 39290.893140.8831 39300.893170.8831 6
39310.89320.8831 39320.893230.8831 39330.893260.8832 39340.893290.8832 39350.893320.8832 39360.893350.8832 39370.893380.8833 39380.893410.8833 39390.893440.8833 39400.893470.8834 39410.89350.8834 39420.893530.8834 39430.893560.8834 39440.893590.8835 39450.893620.8835 39460.893650.8835 39470.893680.8835 39480.893710.8836 39490.893740.8836 39500.893770.8836 39510.89380.8836 39520.893830.8837 Interpretation:Considering the above listed graph and trend analysis of the data set on which growth of FX rate has been analysed. Thus, there will be growth in the rates of USD/AUD in the future. Therefore, the estimation has been made as per analysing and ascertaining the impacts of such data which brings positive outcomes.However, as per considering the outcomes, here alternative hypothesis has been considered which states that mean value will be positive (H1: μ > 0, (mean return to be positive)). F. Analysing the Efficiency of FX market InrelationwithanalysingtheFXmarketwithappropriaterateofreturnandthe determination of the outcomes which will be based on implicating two models such as: Random Walk Model: This is the method and model which helps in analysing a path of succession of data set and rise in the level of outcomes(Christensen and Knezek,2017). There has been estimation based on considering the true (+1) value and False (-1) on the data set. 7
Autoregressive of order model: To estimate the time series on the data set which will be adequate in making proper estimation in coming period. MAE and RMSE: Year Average annual rate of returnartificialDifferences 20030.0011671960.002120.000952804 20040.0002139930.0003450.000131007 2005-0.000236629-0.00126-0.001023371 20060.0003175180.001570.001252482 20070.0004663830.000256-0.000210383 2008-0.000836962-0.000937-0.000100038 20090.0010867240.001024-6.27238E-05 20100.0005368360.00063459.76637E-05 20113.77502E-050.00003784.98027E-08 20120.0001049480.0001454.00519E-05 2013-0.000572172-0.000642-6.98275E-05 2014-0.000330758-0.0002250.000105758 2015-0.000383775-0.0003156.87749E-05 2016-1.4807E-05-0.000145-0.000130193 20170.0003132410.0003251.17591E-05 2018-0.000297176-0.0001950.000102176 MAE0.000272441 RMSE0.000478191 8
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G. Risk assessment analysis for 100 days To analyse the forecasts of the data set there has been determination of Value at risk analysis which will be helpful in bringing the adequate outcomes such as: (Calculating value at risk, 2012) YearμPortfolio var historical 20030.0011675.4543710.008576 20040.000214-0.90434-0.00142 2005-0.00024-0.74525-0.00117 20060.0003180.680810.00107 20070.000466-0.55723-0.00088 2008-0.00084-0.77017-0.00121 20090.0010872.0243110.003183 20100.00053714.220760.022359 20113.78E-050.3597040.000566 20120.000105-0.18342-0.00029 2013-0.000571.7298830.00272 2014-0.000330.8618540.001355 2015-0.0003825.918510.040752 2016-1.5E-05-0.04727-7.4E-05 20170.000313-1.05406-0.00166 2018-0.0003-0.18901-0.0003 0.001572sum As per analysing the daily risks and evaluating the efficiency of the market in this regard which is required to have effective analysis which ill be based on considering various stages: Risk Overview:There will be risks relevant with the possible returns which brings the big differences and original expectance to the operations. in capital market these risks are generally higher as investors estimate the returns a firm will make them in a period. However, to analyse the risks which will based on three categories such as low risk, medium risk and high risk. Beta:Analysing the beta value bring the volatility in comparison of the data base with international market. Alpha:It demonstrates the outperformance with respect to its benchmark as well as differences in actual returns and returns on one would analyse actual efficiency. R-Square:It ascertains the similarity between the performance of the data set with the budgeted data base. 9
Standard deviation:To analyse fund’s return this method will be helpful in addressing the performance in the data base. H. Implicating international finance’s key variable exchange rate for distributional characteristics and its market efficiency. By considering the time frame on which determination of various outcomes will be based on making effective rise in the rates(Ling, 2016). Thus, there has been consideration of daily exchange rates of USD/AUD in relation with having accurate estimation of data set.Moreover, there has been various techniques which will be helpful in addressing the risks such as: Exchange rate behaviour: The exchange rate is relative price in the open economy where the purchase and sale of security can be done without considering any issues and obstacles. Impacts of productivity changes: There has been impacts on the share value of the entity as the product line and services offered by them has been changes. Considering the foreign exchange rates on which impacts of various economic factors which affects changes in valuation or returns. Therefore, applications various models bring information relevant with the rapid growth of economy or the valuation of returns. Overvaluation: Ascertaining the PPP model on which it can be determines that the currency is overvalued or undervalues. It will be beneficial in analysing all the necessary outcomes that will be helpful and adequate in making proper changes and bringing operational benefits. CONCLUSIONS On the basis of above report it can be said that, USD/AUD FE rates will have satisfactory rise in the rates in near further as per analysing the effective outcomes. This report had analysed all the outcomes such by implying various models like random walk model, compound interest, autoregressive of order model etc. Thus, implication of these models has helped in estimating the growth in rates and the positive outcomes from the data set. 10
REFERENCES Books and Journals Barnhart, T. and van Es, E., 2015. Studying teacher noticing: Examining the relationship among pre-service science teachers' ability to attend, analyze and respond to student thinking. Teaching and Teacher Education.45. pp.83-93. Behizadeh, N., 2014. Mitigating the dangers of a single story: Creating large-scale writing assessments aligned with sociocultural theory.Educational Researcher.43(3). pp.125- 136. Christensen, R. and Knezek, G., 2017. Validating the technology proficiency self-assessment questionnaire for 21st century learning (TPSA C-21).Journal of Digital Learning in Teacher Education.33(1). pp.20-31. Jaeger, M. and Adair, D., 2014, March. Two consecutive Project-Based Learning engineering designcourses-Ananalysisofportfolioassessmentresults.InInterdisciplinary Engineering Design Education Conference (IEDEC.,20144th(pp. 1-5). IEEE. Kim, Y. and Yazdian, L. S., 2014. Portfolio assessment and quality teaching.Theory Into Practice.53(3). pp.220-227. Ling, M. K., 2016. The Use of Academic Portfolio in the Learning and Assessment of Physics Students.International Journal of Assessment Tools in Education.3(2). Wilson, M. and et.al., 2014. Evaluating the validity of portfolio assessments for licensure decisions.education policy analysis archives.22(6). p.n6. Online Calculatingvalueatrisk.2012.[Online].Availablethrough:< https://financetrainingcourse.com/education/2012/07/value-at-risk-var-case-study- explanation/>. 11