Interpretability, transparency, and auditability of machine learning (ML)-driven investment
has become a key issue for investment managers as many look to enhance or replace …

Investment decisions can benefit from incorporating an accumulated knowledge of the past
to drive future decision making. We introduce Continual Learning Augmentation (CLA) which …

… In this article, Dan Philps provides an introduction to automated machine learning and its …
Philps also offers the interesting perspective that complexity is not simply a technical …

Temporal continual learning (TCL) is introduced in this thesis as an extension of continual
learning (CL). While traditional CL has been applied to sequential tasks, extending CL to TCL …

LSTMs promise much to financial time-series analysis, temporal and cross-sectional
inference, but we find that they do not deliver in a real-world financial management task. We …

We present an easily applied method of risk-adjusting reduced-form models for changes in
systematic risk over the credit cycle. Using an empirical approach, we model the probable …

In Interpretable, Transparent, and Auditable Machine Learning: An Alternative to Factor
Investing , from the Fall 2021 issue of The Journal of Financial Data Science, Daniel Philps of …

We present a novel process for generating synthetic datasets tailored to assess asset allocation
methods and construct portfolios within the fixed income universe. Our approach begins …

Investment decisions can benefit from incorporating an accumulated knowledge of the past
to drive future decision making. We introduce Continuous Learning Augmentation (CLA) …

…  Ram Gopal, Daniel Philps, & Tillman Weyde 2023 … Dan Philps is a veteran
quantitative investment manager and a widely published artificial intelligence (AI) …