Constructing Trading Strategies by Applying and Adapting Q-factor Model in the Australian Market
DOI:
https://doi.org/10.54097/bsw4gm49Keywords:
Q-factor model, Australian stock market, multiple-group sorting, long-short strategy.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.
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References
Hou, K., Xue, C., & Zhang, L. (2015). Digesting anomalies: An Investment approach. Review of Financial Studies, 28(3), 650–705.
Hou, K., Mo, H., Xue, C., & Zhang, L. (2021). An augmented q-factor model with expected growth [Abnormal returns to a fundamental analysis strategy]. Review of Finance, 25(1), 1-41.
Li, Y., Zheng, W., & Zheng, Z. (2019). Deep robust reinforcement learning for practical algorithmic trading. IEEE Access, 7, 108014-108022.
Grinold, R. C., & Kahn, R. N. (2000). The efficiency gains of long–short investing. Financial Analysts Journal, 56(6), 40–53.
Chai, D., Chiah, M., & Gharghori, P. (2019). Which model best explains the returns of large Australian stocks? Pacific-Basin Finance Journal, 55, 182–191.
Banerjee, R., Cavoli, T., McIver, R., Meng, S., & Wilson, J. K. (2023). Predicting long-run risk factors of stock returns: Evidence from Australia. Australian Economic Papers, 62(3), 377–395.
Fairfield, P. M., Whisenant, S., & Yohn, T. L. (2003). Accrued earnings and growth: Implications for earnings persistence and market mispricing. The Accounting Review, 78(1), 353-371.
Balakrishnan, K., Bartov, E., & Faurel, L. (2010). Post loss/profit announcement drift. Journal of Accounting & Economics (JAE), 50(1), 20-41.
Rahman, M. L., Amin, A., & Al Mamun, M. A. (2020). The COVID-19 outbreak and stock market reactions: Evidence from Australia. Finance Research Letters, 38, 101832.
Alam, Md. M., Wei, H., & Wahid, A. N. M. (2020). COVID ‐19 outbreak and sectoral performance of the Australian stock market: An event study analysis. Australian Economic Papers, 60(3), 482–495.
Ortmann, R., Pelster, M., & Wengerek, S. T. (2020). COVID-19 and investor behavior. Finance Research Letters, 37, 101717.
Racicot, F.-É., & Théoret, R. (2016). The q-factor model and the redundancy of the value factor: An application to hedge funds. Journal of Asset Management, 17(7), 526–539.
Charoenwong, B., Nettayanun, S., & Saengchote, K. (2021). Digesting anomalies: A q-factor approach for the Thai market. Pacific-Basin Finance Journal, 69, 101647.
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