| Proper Orthogonal Decomposition (POD) analysis of CFD data for flow in an axisymmetric sudden expansion C Howard, S Gupta, A Abbas, TAG Langrish, DF Fletcher Chemical Engineering Research and Design 123, 333-346, 2017 | 19 | 2017 |
| The term structure of machine learning alpha D Blitz, MX Hanauer, T Hoogteijling, C Howard Available at SSRN, 2023 | 6* | 2023 |
| Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions C Howard Available at SSRN, 2023 | 2 | 2023 |
| To lead or to lag? Measuring asynchronicity in financial time-series using dynamic time warping C Howard, TJ Putninš, V Alexeev | 2 | 2022 |
| 3D Investing: Jointly Optimizing Return, Risk, and Sustainability D Blitz, M Chen, C Howard, H Lohre Available at SSRN, 2023 | 1 | 2023 |
| Application of proper orthogonal decomposition (POD) techniques and scale-resolving CFD simulations to study swirling flow in an axisymmetric sudden expansion C Howard, A Abbas, TAG Langrish, DF Fletcher Proc. Eleventh International Conference on CFD in the Minerals and Process …, 2015 | 1 | 2015 |
| Better Opt Out: Revisiting the Predictive Power of Options-implied Signals I Honarvar, C Howard Available at SSRN 4766424, 2024 | | 2024 |
| The Evolving Index Effect: Evidence from Australia C Howard Available at SSRN 4779993, 2024 | | 2024 |
| Beyond GMV: Raising the Bar for Evaluating Covariance Matrix Estimators MS Dom, C Howard, M Jansen, H Lohre Available at SSRN 4684926, 2024 | | 2024 |
| Causal Network Representations in Factor Investing C Howard, H Lohre, S Mudde Available at SSRN 4679870, 2023 | | 2023 |
| The Battle of the Models: Modern Takes on Traditional and Machine Learning Techniques in Empirical Finance C Howard University of Technology Sydney, 2023 | | 2023 |
