What driver performance of machine learning

 

A recent paper, "What drives the performnce of mchine learing factor strategies?" seeks to disentangle two key ingredients in modeling: expanding the dataset and allowing for flexible functional forms. Now, as expected, as you move closer to a realistic setting, you find that the value of both deteriorates. However, this research finds that the value of an expanded dataset is more persistent than the functional form employed. While many may think that machine learning is a form of holy grail for investing, the reality is that real-world constraints and transaction costs are key drivers of performance. Reality indicates that adding nonlinear complexity does not add value, whereas being non-sparse is beneficial.