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We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time.
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The Impact of Volatility Targeting
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Enhancing Time-Series Momentum Strategies Using Deep Neural Networks
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, and Stephen Roberts · 2019
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Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations
Nick Baltas and Robert Kosowski · 2020
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Deep Learning for Portfolio Optimization
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Momentum Turning Points
Ashish Garg, Christian L Goulding, Campbell R Harvey, and Michele Mazzoleni · 2021
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Time Series Forecasting with Deep Learning: A Survey
Bryan Lim and Stefan Zohren · 2021
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Building Cross-Sectional Systematic Strategies By Learning to Rank
Daniel Poh, Bryan Lim, Stefan Zohren, and Stephen Roberts · 2021
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Calculated (or Derived) based on data from CRSP Daily Stock 2022
The University of Chicago Booth School of Business Center for Research in Security Prices (CRSP) · 2022
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Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
Daniel Poh, Bryan Lim, Stefan Zohren, and Stephen Roberts · 2022
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Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection
Kieran Wood, Stephen Roberts, and Stefan Zohren · 2022
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Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
Kieran Wood, Sven Giegerich, Stephen Roberts, and Stefan Zohren · 2023
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