With the rise of quantitative investing, factor-based strategies have become increasingly popular over the past decades. Investment managers aim to deliver alpha by tilting portfolios towards certain characteristics like value, momentum and quality. However, harvesting factor premiums in practice is filled with pitfalls and implementation shortfalls are common. This article discusses best practices in factor investing from an academic, practitioner and investor perspective.
theoretical foundations of factor investing
Factor investing has its roots in empirical asset pricing anomalies discovered in academic literature. Pioneering works like Basu (1977) on the value effect and Banz (1981) on the size effect sparked a quest for new effects. Fama and French (1993) consolidated findings into the renowned three-factor model. Factors are tied to risks inherent in different business models. For instance, distress risk explains the value premium to some extent. Understanding the economic rationales behind premiums facilitates portfolio optimization.
navigating the factor zoo
With over 400 documented effects, it has become paramount to separate the wheat from the chaff. Independent factors with strong out-of-sample evidence are rare. Methods like orthogonalization, controlling for luck through simulation and stricter t-value thresholds guard against false discoveries. Investors should focus on historically robust, economically intuitive factors with high implementation capacity.
portfolio optimization techniques
Sophisticated asset managers use multi-factor risk models for portfolio optimization. The widely adopted Barra risk model comprises country, industry and style factors. Risk models estimate the factor exposures of each asset, facilitating risk attribution along several dimensions. Portfolios can then be constructed to maximize exposures to certain factors while controlling for unintended risks.
evaluating performance attribution
With the rise of smart beta strategies, performance attribution has become crucial for manager selection. Isolating alpha from factor returns allows choosing those with true idiosyncratic skill. Macro-economic environments influence factor returns, so tactically timing factors also demonstrates skill. Lastly, innovations around alternative data and machine learning may provide a competitive edge.
Factor investing has evolved from obscure academic concepts to mainstream portfolio construction techniques. Carefully navigating the factor zoo and relentlessly focusing on robust effects is key. Beyond strategic allocation, manager skill also manifests itself through tactical factor timing, risk control and maximizing implementation efficiency.