
New research debunks the “Virtue of Complexity in Return Prediction” in finance
In "Simplified: A Closer Look at the Virtue of Complexity", Professor Daniel Buncic of Stockholm Business School has released a new study that disputes the findings of one of the most hotly debated papers in quantitative finance this summer. See the articles in the Financial Times and Bloomberg on this topic. Daniel Buncic's research challenges the claim by Kelly, Malamud, and Zhou (2024, KMZ) in "The Virtue of Complexity in Return Prediction" that models with thousands of parameters trained on a short span of financial data can outperform simpler benchmark models. While KMZ’s research has attracted considerable attention in both academia and industry, Daniel Buncic highlights key methodological flaws that undermine its broader implications for systematic investing. In particular, he shows that the supposed "Virtue of Complexity" is an artifact of two implementation choices made by KMZ which make the performance of simpler models appear much worse than they really are. First, KMZ restrict the intercept term in their prediction models to zero. Second, they employ a paradoxical aggregation scheme in the construction of the complex model’s performance measures that no investment professional would (or could) ever implement. These two design choices lead to the seemingly poor performance of simple models.










