FORECASTING RETURNS FOR THE STOCK EXCHANGE OF THAILAND INDEX USING MULTIPLE REGRESSION BASED ON PRINCIPAL COMPONENT ANALYSIS

Abstract

The aim of this study was to forecast the returns for the Stock Exchange of Thailand (SET) Index by adding some explanatory variables and stationary Autoregressive Moving-Average order p and q (ARMA (p, q)) in the mean equation of returns. In addition, we used Principal Component Analysis (PCA) to remove possible complications caused by multicollinearity. Afterwards, we forecast the volatility of the returns for the SET Index. Results showed that the ARMA (1,1), which includes multiple regression based on PCA, has the best performance. In forecasting the volatility of returns, the GARCH model performs best for one day ahead; and the EGARCH model performs best for five days, ten days and twenty-two days ahead.