Active Quant: finding alpha with confidence
Blending data-driven insights, risk control and quant expertise to pursue reliable returns.


Equity factor investing has become significantly more popular in recent years. The underlying factor portfolios’ returns exhibit distinct cycles, tempting investors to try their hand at dynamically adjusting their factor allocation. However, the key question remains: how can investors best time factors to maximize returns while minimizing risk?
Factor timing is the holy grail in equity factor investing. With factors exhibiting underperformance at times, the temptation to dynamically adjust one’s factor allocation is high. Indeed, a clairvoyant factor investor could enjoy sizable outperformance relative to a static factor allocation. In our recent white paper, we explore to what extent this factor timing opportunity set can be tapped in practice. Specifically, we investigate the practical implications of factor timing by considering the role of factor momentum, valuation spreads and seasonality. Indeed, valuation spreads and seasonality signals have predictive power with regards to factor returns; yet these factor timing gains turn out to be more apparent than real as the timed strategy is lagging the benchmark after considering transaction costs. Conversely, factor momentum can add active return before costs, of which almost half can be salvaged after transaction costs.
Notwithstanding, factor momentum brings about high strategy turnover. As a consequence, one has to thoughtfully trade off the associated transaction costs (which are certain) versus the expected factor momentum alpha (which is noisy). Our findings show that a Dynamic 1/N multi-factor approach presents a prudent route to effective equity factor timing by combining low strategy turnover with the pervasive factor momentum effect while maintaining proper factor diversification.

Blending data-driven insights, risk control and quant expertise to pursue reliable returns.
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