Enhancing the momentum strategy through deep regression
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DOI: 10.1080/14697688.2018.1563707
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Cited by:
- Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
- Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
- Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
- Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
- Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
- Gabriel Borrageiro, 2022. "Sequential asset ranking in nonstationary time series," Papers 2202.12186, arXiv.org, revised Oct 2022.
- Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
- Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Building Cross-Sectional Systematic Strategies By Learning to Rank," Papers 2012.07149, arXiv.org.
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