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Crowded Trades: A Bayesian Remedy for Factor-Based Quants

Authors: Cheung, W. and Mishra, M.

Publisher: SSRN

Abstract:

The Fama-French factor-ranking 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 (BAF) 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 BAF.

Source: Manual