Design and multi-objective optimization of a novel 5-DOF parallel mechanism with two double-driven chains

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a zoo of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

Automated summary

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning for high-dimensional inference. Using the post-earnings announcement drift (PEAD) literature as an example, we highlight the challenges of high-dimensional analysis due to multiple explanations, limited consensus on model design, and reliance on massive data. By identifying a small set of variables associated with momentum, liquidity, and limited arbitrage, we provide a consistent explanation for PEAD and propose a broadly applicable framework in finance.