5. DATA SOURCES, METHODOLOGY AND CONSTRUCTION OF VARIABLES5.1. Calculating YieldThere are two ways to calculate stock yield5.1.1. Continuing Return This is the percentage return that would be earned by an investor buying the stock at the end of a given day/month t-1 and selling it at the end of the next day/month. For day t and stock A, the daily return R At is defined as R At = { In (P At/PA, t-1)}*100 The stock paid a dividend on day t, the total return would be R At = {In ( P À + Divt /PA, t-1)}*1005.1.2. Discrete Return An alternative method for calculating stock returns is defined as R At = {(P At/PA, t-1) -1}*1005.1.3. Continuous Compound Returns and Discrete ReturnsUsing the continuously compounded rate of return, it is assumed that Pt = Pt-1 ert where rt is the rate of return during the period (t-1,t) and Pt is the price at time t . If r1, r2,….,r12 are the returns for 12 months, then the stock price at the end of the 12 months will beP12 = P0 e r1 +r2 +….+r12This representation of prices and returns allows us to assume The average daily or monthly returns are r = (r1,r2,….,r12)/ 12. Since we can assume that the return data for the 12 months represents the distribution of returns for the upcoming month, it follows It follows that continuously compounded performance is the appropriate performance measure, not discretely compounded performance. (Benninga, 2008)5.2. TESTING FOR PREDICTABILITY OF RETURNSIn this research study, the methodology consists of four sections based on a set of information on predictability of returns. The information set can be defined as stock price history, temporal patterns, market characteristics, and company characteristics. The first section includes the predictability of short-term returns based on past highs of the paper..... .r ARIMA Model, the next step is to see if the selected model is appropriate. A test of the chosen model is to see if the estimated model residuals are white noise, such that the chosen model fits the data reasonably well. The Box and Jenkins (BJ) methodology suggests certain diagnostic checks to determine whether or not an estimated model is statistically appropriate; if “Yes”, go to the last step, i.e. forecasting; if “No”, go back to the first step and repeat the procedure from parameter identification and estimation to diagnostic control until there is a good final model.5.2.2.2.1. D) Step 4: Forecasting One of the reasons ARIMA models are widely accepted is their success in forecasting. The forecasts made by this method are more authentic than those made from other econometric models, especially for short-term forecasts (Gujarati, 2003).