Similar examples may be developed for quality and momentum factors. By bringing in multiple dimensions, they also are better able to adapt to changing market conditions. Momentum may be broken down into its “good and bad” components, whereby price changes driven by fundamentals and earnings news are considered positive attributes while unexplained price changes are not. The recent drawdown from the Nov. 9, 2020, momentum crash following the COVID-19 vaccine announcement would have been largely avoided by separating momentum into its two components. Quality factors may be adjusted for forward-looking metrics such as long-term growth rates or changing ESG scores. New techniques such as machine learning and the incorporation of unstructured data may be employed to assist in the development of better forward-looking factor definitions. These techniques enable quantitative approaches to use information previously considered only by fundamental managers.
For investment strategies that employ multiple factors, weighting is another important decision. Many quantitative strategies do this by embracing long-term weights derived from historical returns, risks and correlations. Other managers have employed a more dynamic approach based on a best-fit analysis over rolling time periods. In either case, however, these approaches assume that the past is prologue to the future. In the case of BP, a dynamic approach would eventually downweight the factor, but only after the factor has underperformed for a period of time. By optimizing on the recent past, one must assume that factor efficacy changes slowly and in predictable ways. Both the quant crisis of 2007 and the recent dominance of megacap stocks suggest this assumption does not always hold true.
An alternative approach for weighting factors is to estimate their current risk premiums based on today’s prices rather than historic returns. While auction markets provide useful information for real-time pricing of fixed-income factors, the same cannot be said for equity factors. Advanced statistical techniques such as Ridge regression, however, may be employed to disaggregate today’s stock prices into their factor components. By doing this, managers can weight factors based on expected rather than historical returns. This approach avoids the problems associated with fitting models to the past or relying on uncertain beliefs about the future.
Will BP perform well in the future? Today, the answer may be yes, but under what conditions will its success continue and for how long? The valuations of asset-light companies may be contracting in today’s market but aren’t their futures still bright if their fundamentals stay strong? Market pricing provides valuable information to answer these important questions.
Innovation has always been critical for success in competitive fields such as investing. For investors who recognize the value of factor investing as a means to create portfolios with desirable return and risk characteristics, multidimensional forward-looking factors are an important tool to achieve consistent results as …….