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Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest.
An analog of the minimax theorem for vector payoffs
D. Blackwell · 1956
Earlier work this paper cites.
Approximation to bayes risk in repeated play
J. Hannan · 1957
Earlier work this paper cites.
The combination of forecasts
J. M. Bates and C. W. Granger · 1969
Earlier work this paper cites.
The strength of weak learnability
R. E. Schapire · 1990
Earlier work this paper cites.
Aggregating strategies
V. G. Vovk · 1990
Earlier work this paper cites.
A nonparametric approach to pricing and hedging derivative securities via learning networks
J. M. Hutchinson, A. W. Lo, and T. Poggio · 1994
Earlier work this paper cites.
The weighted majority algorithm
N. Littlestone and M. K. Warmuth · 1994
Earlier work this paper cites.
Bagging predictors
L. Breiman · 1996
Earlier work this paper cites.
Experiments with a new boosting algorithm
Y. Freund, R. E. Schapire, et al · 1996
Earlier work this paper cites.
Using and combining predictors that specialize
Y. Freund, R. E. Schapire, Y. Singer, and M. K. Warmuth · 1997
Earlier work this paper cites.
Competitive on-line linear regression
V. Vovk · 1997
Earlier work this paper cites.
A game of prediction with expert advice
V. Vovk · 1997
Earlier work this paper cites.
Tracking the best expert
M. Herbster and M. K. Warmuth · 1998
Earlier work this paper cites.
Relative loss bounds for on-line density estimation with the exponential family of distributions
K. S. Azoury and M. K. Warmuth · 2001
Earlier work this paper cites.
Random forests
L. Breiman · 2001
Earlier work this paper cites.
Potential-based algorithms in on-line prediction and game theory
N. Cesa-Bianchi and G. Lugosi · 2003
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Incomplete Information and Internal Regret in Prediction of Individual Sequences
G. Stoltz · 2005
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Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
Earlier work this paper cites.
On-line regression competitive with reproducing kernel hilbert spaces
V. Vovk · 2006
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Forecasting financial time series with ensemble learning
Y. Bai, J. Sun, J. Luo, and X. Zhang · 2010
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Consumer credit-risk models via machine-learning algorithms
A. E. Khandani, A. J. Kim, and A. W. Lo · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Forecasting electricity consumption by aggregating specialized experts
M. Devaine, P. Gaillard, Y. Goude, and G. Stoltz · 2013
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International stock return predictability: what is the role of the united states?
D. E. Rapach, J. K. Strauss, and G. Zhou · 2013
Online learning with the continuous ranked probability score for ensemble forecasting
J. Thorey, V. Mallet, and P. Baudin · 2017
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Optimal learning with bernstein online aggregation
O. Wintenberger · 2017
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Fundamentals and exchange rate forecastability with simple machine learning methods
C. Amat, T. Michalski, and G. Stoltz · 2018
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Recent advances in electricity price forecasting: A review of probabilistic forecasting
J. Nowotarski and R. Weron · 2018
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Adaboost-lstm ensemble learning for financial time series forecasting
S. Sun, Y. Wei, and S. Wang · 2018
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Predicting short-term stock prices using ensemble methods and online data sources
B. Weng, L. Lu, X. Wang, F. M. Megahed, and W. Martinez · 2018
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Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and pm10 on the prev’air platform
E. Debry and V. Mallet · 2014
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Forecasting electricity consumption by aggregating experts; how to design a good set of experts
P. Gaillard and Y. Goude · 2014
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A second-order bound with excess losses
P. Gaillard, G. Stoltz, and T. van Erven · 2014
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The cross section of expected stock returns
J. Lewellen · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Cited alongside, same era.
Sequential aggregation of heterogeneous experts for pm10 forecasting
B. Auder, M. Bobbia, J.-M. Poggi, and B. Portier · 2016
Cited alongside, same era.
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Machine learning for stock selection
K. C. Rasekhschaffe and R. C. Jones · 2019
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Why does forecast combination work so well?
A. F. Atiya · 2020
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Dissecting characteristics nonparametrically
J. Freyberger, A. Neuhierl, and M. Weber · 2020
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Empirical asset pricing via machine learning
S. Gu, B. Kelly, and D. Xiu · 2020
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Shrinking the cross-section
S. Kozak, S. Nagel, and S. Santosh · 2020
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A comprehensive evaluation of ensemble learning for stock-market prediction
I. K. Nti, A. F. Adekoya, and B. A. Weyori · 2020
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Deep reinforcement learning for automated stock trading: An ensemble strategy
H. Yang, X.-Y. Liu, S. Zhong, and A. Walid · 2020
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Option price forecasting using neural networks
J. Yao, Y. Li, and C. L. Tan · 2020
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Learning multiple stock trading patterns with temporal routing adaptor and optimal transport
H. Lin, D. Zhou, W. Liu, and J. Bian · 2021
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Deep learning for mortgage risk
A. Sadhwani, K. Giesecke, and J. Sirignano · 2021
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Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting
P. H. M. Albuquerque, Y. Peng, and J. P. F. d. Silva · 2022
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Forecasting: theory and practice
F. Petropoulos, D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K. Barrow, S. B. Taieb, C. Bergmeir, R. J. Bessa, J. Bijak, J. E. Boylan, et al · 2022
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