Academic Research Papers

Euclidean’s investment models are the result of a decade of research into the application of machine learning to long-term equity investing. Below is a selection of peer-reviewed, published papers that describe our research.

Uncertainty-Aware Lookahead Factor Models for Quantitative Investing (2020). Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:1489-1499.

Authors: John Alberg. Euclidean Technologies, Seattle, USA. Lakshay Chauhan. Euclidean Technologies, Seattle, USA. Zachary Lipton. Carnegie Mellon University, Pittsburgh, USA. Amazon AI, Seattle, USA.

 

Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals (2018). Presented at the 31st Conference on Neural Information Processing Systems (NeurIPS 2017), Long Beach, CA, USA.

Authors: John Alberg. Euclidean Technologies, Seattle, USA. Zachary Lipton. Carnegie Mellon University, Pittsburgh, USA. Amazon AI, Seattle, USA.