how to calculate correlation coefficient of returns?
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Start by simplifying the bottom of the equation by multiplying the two standard deviations. Then, divide the covariance on the top by your result. The solution is your correlation coefficient. The coefficient is represented as a decimal between -1 and 1, rather than as a percentage.
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Generally, NPV can be calculated with the formula NPV = ⨊(P/ (1+i)t ) – C, where P = Net Period Cash Flow, i = Discount Rate (or rate of return), t = Number of time periods and C = Initial Investment.
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Calculating Daily Return as a Percentage To calculate your daily return as a percentage, perform the same first step: subtract the opening price from the closing price. Then, divide the result by the opening price. Finally, multiply the result by 100 to convert to a percentage.
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Correlation Coefficient Equation The correlation coefficient is determined by dividing the covariance by the product of the two variables' standard deviations. Standard deviation is a measure of the dispersion of data from its average.
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The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement.
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['C = Future sum.', "i = Interest rate (where '1' is 100%)", 'n= number of periods.']
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['For example, there are two lists of data, and now I will calculate the correlation coefficient between these two variables.', 'Select a blank cell that you will put the calculation result, enter this formula =CORREL(A2:A7,B2:B7), and press Enter key to get the correlation coefficient.']
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['Earning Yield. Shares are valued on the basis of expected earning and the normal rate of return. Under this method, value per share is calculated using the below formula:', 'Dividend Yield. Under this method, shares are valued on the basis of expected dividend and the normal rate of return.']
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['Calculate the average (mean) price for the number of periods or observations.', "Determine each period's deviation (close less average price).", "Square each period's deviation.", 'Sum the squared deviations.', 'Divide this sum by the number of observations.']
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For instance, say your nominal return is 10% and inflation was 4%. You'd take 1.10 and divide it by 1.04, getting 1.0577. Subtract 1 and turn the result into a percentage, and that means that your real after-tax return was 5.77%.
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['On the Data tab, in the Analysis group, click Data Analysis. ... ', 'Select Correlation and click OK.', 'For example, select the range A1:C6 as the Input Range.']
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['Analyze>Scale>Reliability Analysis.', 'Select Statistics.', 'Check “Intraclass correlation coefficient”.', 'Make choices as you decided above.', 'Click Continue.', 'Click OK.', 'Interpret output.']
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['Analyze in the Analysis section of the upper toolbar.', 'Select Correlation in the Column analyses section and select the two columns that you need for the analysis: Click OK.', 'Select Compute r between two selected data sets in the Compute correlation between which pair of columns section.']
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['To calculate the expected return of a portfolio, you need to know the expected return and weight of each asset in a portfolio.', "The figure is found by multiplying each asset's weight with its expected return, and then adding up all those figures at the end."]
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['Yield Statistic = (Actual Rooms Revenue) / (Potential Rooms Revenue)', 'Yield Statistic = ((Rooms Nights Sold) / (Rooms Nights Available)) * ((Actual Average Room Rate) / (Potential Average Rate))', 'Yield Statistic = Occupancy Percentage * Achievement Factor.']
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['FV represents the future value of the investment.', 'PV represents the present value of the investment.', 'i represents the rate of interest earned each period.', 'n represents the number of periods.']
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Covariance is calculated by analyzing at-return surprises (standard deviations from the expected return) or by multiplying the correlation between the two variables by the standard deviation of each variable.