Research Topics:

This chapter explains why standard cross-validation (CV) methods, like k-fold, fail in finance and lead to overfit models with false results.

This chapter explains why ensemble methods are effective and how to avoid common errors when applying them to finance.

This chapter explains how to perform hyper-parameter tuning for financial machine learning, emphasizing that standard cross-validation (CV) methods will fail and lead to overfitting.

We propose a novel solution for quasi-linear partial differential equations which is computationally efficient and accurate.

This chapter introduces Hierarchical Risk Parity (HRP), a machine learning-based asset allocation method designed to overcome the critical flaws of traditional quadratic optimizers, such as Markowitz’s Critical Line Algorithm (CLA).