Fetching the paper…
Reading the bibliography…
Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Mean reversion in stock prices: Evidence and implications
James M Poterba and Lawrence H Summers · 1988
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks, 1989
Ronald J. Williams and David Zipser · 1989
Earlier work this paper cites.
The sharpe ratio
William F. Sharpe · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Sequential bayesian prediction in the presence of changepoints and faults
Roman Garnett, Michael A Osborne, Steven Reece, Alex Rogers, and Stephen J Roberts · 2010
Earlier work this paper cites.
Gaussian process change point models
Yunus Saatçi, Ryan D Turner, and Carl E Rasmussen · 2010
Earlier work this paper cites.
Time series momentum
Tobias J Moskowitz, Yao Hua Ooi, and Lasse Heje Pedersen · 2012
Earlier work this paper cites.
An institutional theory of momentum and reversal
Dimitri Vayanos and Paul Woolley · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Dissecting investment strategies in the cross section and time series
Jamil Baz, Nicolas Granger, Campbell R. Harvey, Nicolas Le Roux, and Sandy Rattray · 2015
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Momentum crashes
Kent Daniel and Tobias J. Moskowitz · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Earlier work this paper cites.
Time series momentum and volatility scaling
Abby Y. Kim, Yiuman Tse, and John K. Wald · 2016
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Entity embeddings of categorical variables
Cheng Guo and Felix Berkhahn · 2016
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
A century of evidence on trend-following investing
Brian Hurst, Yao Hua Ooi, and Lasse Heje Pedersen · 2017
Cited alongside, same era.
A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Sequential neural processes
Gautam Singh, Jaesik Yoon, Youngsung Son, and Sungjin Ahn · 2019
Later among the works it cites.
Crosstransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, and Andrew Zisserman · 2020
Later among the works it cites.
Bayesian meta-learning for the few-shot setting via deep kernels
Massimiliano Patacchiola, Jack Turner, Elliot J Crowley, Michael O’Boyle, and Amos J Storkey · 2020
Later among the works it cites.
Continuous meta-learning without tasks
James Harrison, Apoorva Sharma, Chelsea Finn, and Marco Pavone · 2020
Later among the works it cites.
Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
Later among the works it cites.
(re-) imag (in) ing price trends
Jingwen Jiang, Bryan T Kelly, and Dacheng Xiu · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Universal features of price formation in financial markets: Perspectives from deep learning
Justin Sirignano and Rama Cont · 2018
Cited alongside, same era.
The impact of volatility targeting
Campbell R. Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto van Hemert · 2018
Cited alongside, same era.
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Cited alongside, same era.
Conditional neural processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, and SM Ali Eslami · 2018
Cited alongside, same era.
Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
Cited alongside, same era.
Quant gans: deep generation of financial time series
Magnus Wiese, Robert Knobloch, Ralf Korn, and Peter Kretschmer · 2020
Later among the works it cites.
Momentum turning points
Ashish Garg, Christian L Goulding, Campbell R Harvey, and Michele Mazzoleni · 2021
Later among the works it cites.
Trading with the momentum transformer: An intelligent and interpretable architecture
Kieran Wood, Sven Giegerich, Stephen Roberts, and Stefan Zohren · 2021
Later among the works it cites.
On episodes, prototypical networks, and few-shot learning
Steinar Laenen and Luca Bertinetto · 2021
Later among the works it cites.
Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren · 2021
Later among the works it cites.
Slow momentum with fast reversion: A trading strategy using deep learning and changepoint detection
Kieran Wood, Stephen Roberts, and Stefan Zohren · 2022
Later among the works it cites.
Crowded trades and tail risk
Gregory W Brown, Philip Howard, and Christian T Lundblad · 2022
Later among the works it cites.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
Later among the works it cites.
Deeptime: Deep time-index meta-learning for non-stationary time-series forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi · 2022
Later among the works it cites.
Deep inception networks: A general end-to-end framework for multi-asset quantitative strategies
Tom Liu, Stephen Roberts, and Stefan Zohren · 2023
Closest in time.
Network momentum across asset classes
Xingyue Stacy Pu, Stephen Roberts, Xiaowen Dong, and Stefan Zohren · 2023
Closest in time.
Partial index tracking: A meta-learning approach
Yongxin Yang and Timothy Hospedales · 2023
Closest in time.
Financial machine learning
Bryan T Kelly and Dacheng Xiu · 2023
Closest in time.
Spatio-temporal momentum: Jointly learning time-series and cross-sectional strategies
Wee Ling Tan, Stephen Roberts, and Stefan Zohren · 2023
Closest in time.
Robust detection of lead-lag relationships in lagged multi-factor models
Yichi Zhang, Mihai Cucuringu, Alexander Y Shestopaloff, and Stefan Zohren · 2023
Closest in time.