A dynamic investigation into the predictability of Australian industry stock returns

This thesis involved an empirical investigation of the predictability of Australian industrial stock returns using a dynamic state-space framework. The systematic risks of industrial portfolios were examined in a stochastic market- model. The systematic risks of industry portfolios are found to be stochastic processes. Most of the industry groups have time-varying systematic risks that are mean-reverting to their stable or moving long-term mean. However, the investment and financial services, alcohol and tobacco, gold, insurance and media industry groups have rather random systematic risks. The time-varying market model provides a better explanation of the portfolio returns than the single-index model since it captures the stochastic properties of market risk. Further, a Bayesian dynamic-forecasting model was employed to examine the explanatory power of a set of economic and financial variables. The unanticipated components of the term-structure variable, the interest-rate variable and the aggregate-dividend-yield variable were shown to be significant in explaining the industry portfolio excess returns. The comparison between multivariate analysis and univariate analysis strongly indicates that the correlations within industries are critical in the investigation of the predictability of returns. In the out-of-sample analysis, a maximally predicted portfolio (MPP) was constructed based on the updated economic and financial information; however, the predictability of the MPP did not exceed that of a naive forecast.