Constructing Trading Strategies by Applying and Adapting Q-factor Model in the Australian Market

Abstract

The Q-factor model is a quantitative pricing model similar to the Fama-French multi-factor model, which is an investment approach using investment (IA) and profitability (ROE) as the significant factors. This paper collects historical data (1987~2022) from Australian stocks and constructs long-short portfolios to investigate the effectiveness of applying this model in the Australian market. Net returns will be validated by Carhart regression. Finally, the optimal factor portfolios and trading strategies will be tested for factor substitution based on cumulative return trends and risk-return metrics. Overall, both the Q-factor model and the augmented Q-factor effectively analyzed investments in the Australian market. The difference between theoretical and practical-based long-short is mainly due to the abnormal ranking performance of individual factors, especially during the COVID-19 pandemic. In most cases, strategies based on actual data sorting generate better risk-return outcomes than theory-based approaches, suggesting that the models must be continually adapted to suit Australia's unique economic and market conditions.