[1] |
CHONG E, HAN C, PARK F C. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies[J]. Expert Systems with Applications, 2017, 83: 187-205.
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[2] |
FISCHER T, KRAUSS C. Deep learning with long short-term memory networks for financial market predictions[J]. European Journal of Operational Research, 2017, 270: 654-669.
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[3] |
QIN Y, SONG D, CHEN H, et al. A dual-stage attention-based recurrent neural network for time series prediction[DB/OL]. [2020-03-01] https://arxiv.org/abs/1704.02971.
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[4] |
罗伯特·E·霍尔,马可·利伯曼.股票市场和宏观经济[M]// 经济学:原理与应用.2版. 北京:中信出版社, 2003.
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[5] |
DE LONG J B, SHLEIFER A, SUMMER L H, et al. Positive feedback investment strategies and destabilizing rational speculation[J]. The Journal of Finance, 1990, 45: 379-395.
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[6] |
NOFER M, HINZ O. Using Twitter to predict the stock market[J]. Business & Information Systems Engineering, 2015, 57: 229-242.
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[7] |
PENG Y, HUI J. Leverage financial news to predict stock price movements using word embeddings and deep neural networks[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1506.07220.
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[8] |
CHEN W, YEO C K, LAU C T, et al. Leveraging social media news to predict stock index movement using RNN-boost[J]. Data & Knowledge Engineering, 2018, 118: 14-24.
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BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
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BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1409.0473.
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CHO K, VAN MERRIENBOER B,GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA: Association for Computational Linguistics, 2014: 1724-1734.
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BAHDANAU D, CHOROWSKI J, SERDYUK D, et al. End-to-end attention-based large vocabulary speech recognition[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1508.04395.
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DEVLIN J, CHANG M W, LEE K, et al. BERT: Pretraining of deep bidirectional transformers for language understanding[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1810.04805.
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LEE D H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks[C]// ICML 2013 Workshop : Challenges in Representation Learning (WREPL), Atlanta, Georgia, USA, 2013.
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OLIVER A, ODENA A, RAFFEL C, et al. Realistic evaluation of deep semi-supervised learning algorithms[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1804.09170,2018.
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LU X, NI B. BERT-CNN:A hierarchical patent classier based on a pre-trained language model[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1911.06241.
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BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166.
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HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
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CHUNG J, GULCLEHRE, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1412.3555.
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JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures[C]// ICML’15: Proceedings of the 32nd International Conference on International Conference on Machine Learning. JMLR.org, 2015, 37: 2342-2350.
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[21] |
HEDAYAT A S, SLOANE N J A, STUFKEN J. Orthogonal Arrays: Theory and Applications[M]. New York: Springer, 1999.
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[22] |
KINGMA D P, BA J. Adam: A method for stochastic optimization[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1412.6980.
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[23] |
KHAIDEM L, SAHA S, DEY S R. Predicting the direction of stock market prices using random forest[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1605.00003.
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[24] |
BROWN R G, MEYER R F. The fundamental theorem of exponential smoothing[J]. Operations Research, 1961, 9(5): 673-685.
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[25] |
ZHOU G B, WU J, ZHANG C L, et al. Minimal gated unit for recurrent neural networks[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1603.09420.
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RAVANELLI M, BRAKEL P,OMOLOGO M, et al. Light gated recurrent units for Speech Recognition[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2: 92-102.)
|
[1] |
CHONG E, HAN C, PARK F C. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies[J]. Expert Systems with Applications, 2017, 83: 187-205.
|
[2] |
FISCHER T, KRAUSS C. Deep learning with long short-term memory networks for financial market predictions[J]. European Journal of Operational Research, 2017, 270: 654-669.
|
[3] |
QIN Y, SONG D, CHEN H, et al. A dual-stage attention-based recurrent neural network for time series prediction[DB/OL]. [2020-03-01] https://arxiv.org/abs/1704.02971.
|
[4] |
罗伯特·E·霍尔,马可·利伯曼.股票市场和宏观经济[M]// 经济学:原理与应用.2版. 北京:中信出版社, 2003.
|
[5] |
DE LONG J B, SHLEIFER A, SUMMER L H, et al. Positive feedback investment strategies and destabilizing rational speculation[J]. The Journal of Finance, 1990, 45: 379-395.
|
[6] |
NOFER M, HINZ O. Using Twitter to predict the stock market[J]. Business & Information Systems Engineering, 2015, 57: 229-242.
|
[7] |
PENG Y, HUI J. Leverage financial news to predict stock price movements using word embeddings and deep neural networks[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1506.07220.
|
[8] |
CHEN W, YEO C K, LAU C T, et al. Leveraging social media news to predict stock index movement using RNN-boost[J]. Data & Knowledge Engineering, 2018, 118: 14-24.
|
[9] |
BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
|
[10] |
BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1409.0473.
|
[11] |
CHO K, VAN MERRIENBOER B,GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA: Association for Computational Linguistics, 2014: 1724-1734.
|
[12] |
BAHDANAU D, CHOROWSKI J, SERDYUK D, et al. End-to-end attention-based large vocabulary speech recognition[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1508.04395.
|
[13] |
DEVLIN J, CHANG M W, LEE K, et al. BERT: Pretraining of deep bidirectional transformers for language understanding[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1810.04805.
|
[14] |
LEE D H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks[C]// ICML 2013 Workshop : Challenges in Representation Learning (WREPL), Atlanta, Georgia, USA, 2013.
|
[15] |
OLIVER A, ODENA A, RAFFEL C, et al. Realistic evaluation of deep semi-supervised learning algorithms[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1804.09170,2018.
|
[16] |
LU X, NI B. BERT-CNN:A hierarchical patent classier based on a pre-trained language model[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1911.06241.
|
[17] |
BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166.
|
[18] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
|
[19] |
CHUNG J, GULCLEHRE, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1412.3555.
|
[20] |
JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures[C]// ICML’15: Proceedings of the 32nd International Conference on International Conference on Machine Learning. JMLR.org, 2015, 37: 2342-2350.
|
[21] |
HEDAYAT A S, SLOANE N J A, STUFKEN J. Orthogonal Arrays: Theory and Applications[M]. New York: Springer, 1999.
|
[22] |
KINGMA D P, BA J. Adam: A method for stochastic optimization[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1412.6980.
|
[23] |
KHAIDEM L, SAHA S, DEY S R. Predicting the direction of stock market prices using random forest[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1605.00003.
|
[24] |
BROWN R G, MEYER R F. The fundamental theorem of exponential smoothing[J]. Operations Research, 1961, 9(5): 673-685.
|
[25] |
ZHOU G B, WU J, ZHANG C L, et al. Minimal gated unit for recurrent neural networks[DB/OL]. [2020-03-01]. https://arxiv.org/abs/1603.09420.
|
[26] |
RAVANELLI M, BRAKEL P,OMOLOGO M, et al. Light gated recurrent units for Speech Recognition[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2: 92-102.)
|