Bottom-up versus top-down factor investing: an alpha forecasting perspective

Abstract

In a recent discussion about efficient ways to combine multiple firm characteristics into a multifactor portfolio, a distinction was made between the bottom-up and top-down approach. Both approaches integrate characteristics with equal weights and ignore interaction effects from differences in informational content and correlations between the firm characteristics. The authors complement the bottom-up approach for the missing interaction effects by implementing a linear alpha forecasting framework. Bottom-up versus top-down factor investing is typically discussed using the assumption that all characteristics are equally priced, but the pricing impact of different firm characteristics can vary tremendously. The alpha forecasting perspective provides a theoretical motivation for factor investing and helps to compare the bottom-up and top-down approach with regard to the difference of informational content and interaction effects between firm characteristics. Taking into account the difference in informational content between firm characteristics leads to significant performance improvement in factor models with a high concentration of informational content. Equally weighted characteristics result in related performance irrespective of whether the bottom-up or top-down approach is applied.

Factor investing: alpha concentration versus diversification

“…The weighting strategy for can be applied to build bottom-up multifactor portfolios. The OOP perspective has been applied by Zurek and Heinrich (2021) to show the benefits of bottom-up versus top-down factor strategies. According to this approach, we can apply multiple factor-specific firm characteristics and build multifactor portfolios within a one-step approach.…”

“…With value, growth, momentum, quality and low-volatility, our study includes five well-known factors which corresponds to the factor setting of Zurek and Heinrich (2021). Each factor is based on several firm characteristics, with the multifactor portfolio comprising overall 16 firm characteristics.…”

“…Statistical outliers are adjusted by the method of DeMiguel et al (2020) by setting equal all observations greater (less) than the defined threshold to the third (first) quartile plus (minus) three times the interquartile range. Zurek and Heinrich (2021) have shown that the Alpha-Con approach tilts the factor exposure towards those factors that exhibit firm characteristics with the highest informational content. Since the diversification approaches can have a disrupting effect on the factor exposures, it is important to compare the ICs of each firm characteristic.…”