Abstract
In quantitative equity fund management, the popular traditional method to gain factor exposures is to perform factor ranking. This method easily leads to some crowded ‘style islands’, where numerous fund managers trade similar portfolios. The Bayesian allocation technique creates more efficient factor-mimicking (FM) portfolios (as evident in Cheung and Mittal, 2009), and these factor style portfolios tend to be more diversified. To further distance ourselves from crowded trades, one may utilise the blending capability of the Bayesian allocation framework to span the space between those islands. More differentiation benefits could also be obtained by customising the factor models. In this article, crowdedness is quantified by the Nomura trade impact cost model, METRIC. Based on this, we show evidence of the potential benefit from applying the new portfolio construction framework. This article features: - Trading cost comparison between factor ranking and Bayesian FM portfolios in simulated crowding events; and - Evidence of cost reduction by switching from the factor-ranking to ABL portfolio construction process.
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