Two Best Paper Awards at the Hong Kong Fintech Conference
© 2022 EPFL
Professors Damir Filipovic and Semyon Malamud were both each awarded a Best Paper Award at the 2022 Hong Kong Conference for Fintech, AI, and Big Data in Business for their research on maching learning.
Prof. Damir Filipovic: "Stripping the Discount Curve-a Robust Machine Learning Approach" (Authors: Damir Filipovic - EPFL and Swiss Finance Institute, Markus Pelger - Standford University and Ye Ye - Standford University)
Abstract:
We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks. Our method achieves substantially smaller out-of-sample yield and pricing errors, while being robust to outliers and data selection choices. We attribute the superior performance to the optimal trade-off between flexibility and smoothness, which positions our method as the new standard for yield curve estimation.
Prof. Semyon Malamud: "The Virtue of Complexity in Machine Learning Portfolios" (Authors: Bryan Kelly - Yale University, Semyon Malamud - EPFL and Swiss Finance Institute and Kangying Zhou - Yale University)
Abstract:
We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising “virtue of complexity”: Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.