Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets

Abstract

Abstract:The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study.

Market moods: an investor sentiment event study

“…A shorter 90-day estimation horizon from t −111 to t −21 days before the event ( t 0 ) was used, as past studies had found that forecasting performance in the smaller more volatile markets of Asia performed best with less historical in-sample data (not more) (French, 2016a; French, 2016b). The nature of markets change, as they also have in the USA and Europe, the in-sample properties would not remain constant.…”