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.

在最近有关将多个公司特征组合为多因素投资组合的有效方法的讨论中，自下而上和自上而下的方法有所区别。两种方法都将权重相等的特征整合在一起，而忽略了信息内容差异和企业特征之间的相关性所产生的相互作用。作者通过实现线性alpha预测框架来补充自下而上的方法来解决缺少的交互作用。自下而上与自上而下的因素投资通常在假设所有特征均等定价的情况下进行讨论，但是不同公司特征的定价影响可能差异很大。alpha预测的观点为因素投资提供了理论动力，并有助于比较自下而上和自上而下的方法在信息内容上的差异以及企业特征之间的交互作用方面的差异。考虑到公司特征之间信息内容的差异会导致信息模型高度集中的因子模型中的性能显着提高。无论采用自下而上的方法还是自上而下的方法，均等加权的特性都会导致相关的性能。考虑到公司特征之间信息内容的差异会导致信息模型高度集中的因素模型中的显着性能改进。无论采用自下而上的方法还是自上而下的方法，均等加权的特性都会导致相关的性能。考虑到公司特征之间信息内容的差异会导致信息模型高度集中的因子模型中的性能显着提高。无论采用自下而上的方法还是自上而下的方法，均等加权的特性都会导致相关的性能。