Specifically, this research focused on estimating certainty around the predictive relationships. We make larger forecasts in factors where the certainty is higher, and vice versa. Moreover, we have also increased the overall rotation ‘amplitude’ to reflect our overall higher conviction in the rotation model. We have been able to make this improvement due to the conviction weighting of the individual factors resulting in overall higher confidence in the model, allowing us to take higher active risk from factor rotation. From a portfolio construction perspective, this model improvement reduces noise and unnecessary model turnover, and focuses alpha where pockets of predictability can be found. This is evidenced by our simulations showing increased absolute and risk adjusted return.
To arrive at the conviction metrics, we have looked for consistency across time and across regions. Validation of statistical hypotheses, as well as economic priors in these two dimensions, is a helpful way to maximise robustness and attempts to maximise the potential for the model to generalise well in the future. Our original model was designed more than ten years ago and although performing well out-of-sample, we have now had an additional ten years of data to cross-validate the model. The new data has confirmed many strong predictive relationships, but some showed weaker linkage evidence, leading to a smaller likelihood of predicting factor moves. It is worth noting that although there is significant correlation in the market sentiment and risk environment variables (mostly due to interconnectedness and globalisation of financial markets), at times we observe significant differences between regions. It is therefore important to make sure that the predictive relationships hold for individual regions as well as in aggregate.
These changes together not only improve our current model, but they have also been made with further research in mind. We hope to be able to add new conditioning variables, where identifying new relationships, and their significance, as well managing model noise and turnover will be crucial.
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