Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity

no code implementations • 21 Aug 2022 • Daniel Poh, Stephen Roberts, Stefan Zohren

Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures.

Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention

1 code implementation • 20 May 2021 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts

The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction.

Information Retrieval Learning-To-Rank +2

Building Cross-Sectional Systematic Strategies By Learning to Rank

no code implementations • 13 Dec 2020 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts

The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction.

Information Retrieval Learning-To-Rank +1

A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

no code implementations • 22 Mar 2019 • Daniel Poh, Stephen Roberts, Martin Tegnér

Secondly, in demonstrating that our model-based strategy outperforms the comparator, and can thus be employed for effective hedging in electricity markets.

BIG-bench Machine Learning Gaussian Processes +2