2150 words - 9 pages

5. DATA SOURCES, METHODOLOGY AND VARIABLE CONSTRUCTION

5.1. Return Calculation

There are two ways to calculate stock returns

5.1.1. Continuous Return

This is the percentage return that would be earned by an investor who bought the stock at the end of a particular day/month t-1 and sold it at the end of the following day/month. For day t and stock A, the day return R At is defined as

R At = { In (P At/P A, t-1)}*100

The stock paid a dividend in day t, the total return would be

R At = {In (P At +Divt /P A, t-1)}*100

5.1.2. Discrete Return

An alternative method to calculate stock returns is defined as

R At = {(P At/P A, t-1)-1}*100

5.1.3. Continuous Compounded versus Discrete Returns

Using 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 where Pt is the price at time t. If r1, r2,….,r12 are the returns for12 months, then the price of the stock at the end of the 12 months will be

P12 = P0 e r1 +r2 +….+r12

This representation of prices and returns allows to assume the average daily or monthly returns is r = (r1, r2,….,r12)/ 12.Since we can assume that the return data for the 12 months represent the distribution of the returns for the coming month, it follows that the continuously compounded return is the appropriate return measure, and not discretely compounded return. (Benninga, 2008)

5.2. TESTS OF RETURN PREDICTABILITY

In this research study, methodology consists of four sections based on information set of return predictability. Information set can be defined as the past history of stock prices, time patterns, market characteristics and firm characteristics .The first section consists of short-term return predictability based on past history of stock prices through non-parametric tests like run test and autocorrelation function (ACF). The second section predicts (makes forecasting) and quantifies return volatility in short-term based on past history of stock prices through a parametric test such as autoregressive integrated moving average (ARIMA) The third section explores the relationship between return predictability and time patterns (holiday effect, day-of-the-week effect and month-of-the-year effect) using ordinary least square (OLS) technique. The last section consists of firm-specific return prediction which explains market characteristics (risk premium) and firm characteristics (size and value premia) employing Capital Asset Pricing Model (CAPM) along with Fama and French three Factor Model (FF-3FM) and Augmented Fama and French three factor model(AFF-3FM) respectively.

Short-term Return Predictability

Sort-term return predictability includes both non-parametric and parametric approaches to test the weak form of stock market efficiency. In this Study, non-parametric stock market efficiency tests consist of Run test and Autocorrelation Function (Islam, 2005) along with Autoregressive Integrated Moving average (ARIMA) as a parametric test for...

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