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

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.