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We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
S. Kullback and R. A. Leibler, “On information and sufficiency,”
1951
Earlier work this paper cites.
W. F. Sharpe, “Capital asset prices: A theory of market equilibrium under conditions of risk,”
1964
Earlier work this paper cites.
J. M. Poterba and L. H. Summers, “Mean reversion in stock prices: Evidence and implications,”
1988
Earlier work this paper cites.
N. Jegadeesh and S. Titman, “Returns to buying winners and selling losers: Implications for stock market efficiency,”
1993
Earlier work this paper cites.
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,”
1994
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
L. Wasserman,
2006
Earlier work this paper cites.
R. Garnett, M. A. Osborne, S. Reece, A. Rogers, and S. J. Roberts, “Sequential Bayesian prediction in the presence of changepoints and faults,”
2010
Earlier work this paper cites.
T. J. Moskowitz, Y. H. Ooi, and L. H. Pedersen, “Time series momentum,”
2012
Earlier work this paper cites.
G. N. Bornholt, “The failure of the capital asset pricing model (CAPM): An update and discussion,”
2012
Earlier work this paper cites.
Y. Lempérière, C. Deremble, P. Seager, M. Potters, and J.-P. Bouchaud, “Two centuries of trend following,”
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in
2014
Earlier work this paper cites.
D. H. Bailey and M. L. De Prado, “The deflated sharpe ratio: correcting for selection bias, backtest overfitting, and non-normality,”
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,”
2014
Cited alongside, same era.
J. Baz, N. Granger, C. R. Harvey, N. Le Roux, and S. Rattray, “Dissecting investment strategies in the cross section and time series,”
2015
Cited alongside, same era.
2015
Cited alongside, same era.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Cited alongside, same era.
M. Abadi
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville,
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in
2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in
2017
Later among the works it cites.
C. R. Harvey, E. Hoyle, R. Korgaonkar, S. Rattray, M. Sargaison, and O. van Hemert, “The impact of volatility targeting,”
2018
Later among the works it cites.
B. Lim, S. Zohren, and S. Roberts, “Enhancing time-series momentum strategies using deep neural networks,”
2019
Later among the works it cites.
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of Transformer on time series forecasting,”
2019
Later among the works it cites.
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2016
Cited alongside, same era.
A. Y. Kim, Y. Tse, and J. K. Wald, “Time series momentum and volatility scaling,”
2016
Cited alongside, same era.
K. Daniel and T. J. Moskowitz, “Momentum crashes,”
2016
Cited alongside, same era.
C. Guo and F. Berkhahn, “Entity embeddings of categorical variables,”
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and C. Guestrin, “”Why should I trust you?” Explaining the predictions of any classifier,” in
2016
Cited alongside, same era.
B. Hurst, Y. H. Ooi, and L. H. Pedersen, “A century of evidence on trend-following investing,”
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Y.-H. H. Tsai, S. Bai, M. Yamada, L.-P. Morency, and R. Salakhutdinov, “Transformer dissection: An unified understanding for transformer’s attention via the lens of kernel,” in
2019
Later among the works it cites.
J. Sirignano and R. Cont, “Universal features of price formation in financial markets: perspectives from deep learning,”
2019
Later among the works it cites.
Z. Zhang, S. Zohren, and S. Roberts, “Deep reinforcement learning for trading,”
2020
Later among the works it cites.
B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,”
2021
Closest in time.
T. Lin, Y. Wang, X. Liu, and X. Qiu, “A survey of Transformers,”
2021
Closest in time.
B. Lim, S. Ö. Arık, N. Loeff, and T. Pfister, “Temporal fusion transformers for interpretable multi-horizon time series forecasting,”
2021
Closest in time.
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient Transformer for long sequence time-series forecasting,” in
2021
Closest in time.
K. Wood, S. Roberts, and S. Zohren, “Slow momentum with fast reversion: A trading strategy using deep learning and changepoint detection,”
2022
Closest in time.