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The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability.
M. M. Dacorogna, C. L. Gauvreau, U. A. Müller, R. B. Olsen, and O. V. Pictet, “Changing time scale for short-term forecasting in financial markets,” Journal of forecasting , vol. 15, no. 3, pp. 203–227, 1996
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
A. B. Geva, “Scalenet-multiscale neural-network architecture for time series prediction,” IEEE Transactions on neural networks , vol. 9, no. 6, pp. 1471–1482, 1998
1998
Earlier work this paper cites.
K.-j. Kim and I. Han, “Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index,” Expert Systems with Applications , vol. 19, no. 2, pp. 125–132, 2000
2000
Earlier work this paper cites.
V. V. Gavrishchaka and S. B. Ganguli, “Volatility forecasting from multiscale and high-dimensional market data,” Neurocomputing , vol. 55, no. 1-2, pp. 285–305, 2003
2003
Earlier work this paper cites.
M.-C. Wu, S.-Y. Lin, and C.-H. Lin, “An effective application of decision tree to stock trading,” Expert Systems with Applications , vol. 31, no. 2, pp. 270–274, 2006
2006
Earlier work this paper cites.
M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications , vol. 37, no. 1, pp. 479–489, 2010
2010
Earlier work this paper cites.
C.-H. Cheng, T.-L. Chen, and L.-Y. Wei, “A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting,” Information Sciences , vol. 180, no. 9, pp. 1610–1629, 2010
2010
Earlier work this paper cites.
C. R. Chen, J. D. Diltz, Y. Huang, and P. P. Lung, “Stock and option market divergence in the presence of noisy information,” Journal of Banking & Finance , vol. 35, no. 8, pp. 2001–2020, 2011
2011
Earlier work this paper cites.
Y. Kara, M. A. Boyacioglu, and Ö. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange,” Expert Systems with Applications , vol. 38, no. 5, pp. 5311–5319, 2011
2011
Earlier work this paper cites.
Y. Lin, H. Guo, and J. Hu, “An svm-based approach for stock market trend prediction,” in The 2013 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2013, pp. 1–7
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the arima model,” in 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation . IEEE, 2014, pp. 106–112
2014
Earlier work this paper cites.
M. Ballings, D. Van den Poel, N. Hespeels, and R. Gryp, “Evaluating multiple classifiers for stock price direction prediction,” Expert Systems with Applications , vol. 42, no. 20, pp. 7046–7056, 2015
2015
Earlier work this paper cites.
R. Aguilar-Rivera, M. Valenzuela-Rendón, and J. Rodríguez-Ortiz, “Genetic algorithms and darwinian approaches in financial applications: A survey,” Expert Systems with Applications , vol. 42, no. 21, pp. 7684–7697, 2015
2015
Earlier work this paper cites.
K. Chen, Y. Zhou, and F. Dai, “A lstm-based method for stock returns prediction: A case study of china stock market,” in 2015 IEEE International Conference on Big Data (BigData) . IEEE, 2015, pp. 2823–2824
2015
Earlier work this paper cites.
L. Li, S. Leng, J. Yang, and M. Yu, “Stock market autoregressive dynamics: a multinational comparative study with quantile regression,” Mathematical Problems in Engineering , vol. 2016, 2016
2016
Earlier work this paper cites.
L. Zhang, C. Aggarwal, and G.-J. Qi, “Stock price prediction via discovering multi-frequency trading patterns,” in Proceedings of the 23rd ACM SIGKDD , 2017, pp. 2141–2149
2017
Earlier work this paper cites.
D. M. Nelson, A. C. Pereira, and R. A. De Oliveira, “Stock market’s price movement prediction with lstm neural networks,” in 2017 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2017, pp. 1419–1426
2017
Cited alongside, same era.
B. Chovancova, M. Dorocakova, and V. Malacka, “Changes in industrial structure of gdp and stock indices also with regard to the industry 4.0,” Business and Economic Horizons (BEH) , vol. 14, no. 1232-2019-761, pp. 402–414, 2018
2018
Cited alongside, same era.
P. V. G. C. A. Casanova, A. R. P. Lio, and Y. Bengio, “Graph attention networks,” ICLR. Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Liò and Yoshua Bengio , 2018
2018
Cited alongside, same era.
H. Y. Kim and C. H. Won, “Forecasting the volatility of stock price index: A hybrid model integrating lstm with multiple garch-type models,” Expert Systems with Applications , vol. 103, pp. 25–37, 2018
2018
Cited alongside, same era.
G. Liu, Y. Mao, Q. Sun, H. Huang, W. Gao, X. Li, J. Shen, R. Li, and X. Wang, “Multi-scale two-way deep neural network for stock trend prediction.” in International Joint Conference on Artificial Intelligence , 2020, pp. 4555–4561
2020
Later among the works it cites.
2020
Later among the works it cites.
G. Chodorow-Reich, P. T. Nenov, and A. Simsek, “Stock market wealth and the real economy: A local labor market approach,” American Economic Review , vol. 111, no. 5, pp. 1613–1657, 2021
2021
Later among the works it cites.
W. Jiang, “Applications of deep learning in stock market prediction: recent progress,” Expert Systems with Applications , vol. 184, p. 115537, 2021
2021
Later among the works it cites.
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H. Wu, W. Zhang, W. Shen, and J. Wang, “Hybrid deep sequential modeling for social text-driven stock prediction,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018, pp. 1627–1630
2018
Cited alongside, same era.
R. Zhang, Z. Yuan, and X. Shao, “A new combined cnn-rnn model for sector stock price analysis,” in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) , vol. 2. IEEE, 2018, pp. 546–551
2018
Cited alongside, same era.
Y. Chen, Z. Wei, and X. Huang, “Incorporating corporation relationship via graph convolutional neural networks for stock price prediction,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018, pp. 1655–1658
2018
Cited alongside, same era.
Z. Li, D. Yang, L. Zhao, J. Bian, T. Qin, and T.-Y. Liu, “Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding,” in Proceedings of the 25th ACM SIGKDD , 2019, pp. 894–902
2019
Cited alongside, same era.
T. Le, B. Vo, H. Fujita, N.-T. Nguyen, and S. W. Baik, “A fast and accurate approach for bankruptcy forecasting using squared logistics loss with gpu-based extreme gradient boosting,” Information Sciences , vol. 494, pp. 294–310, 2019
2019
Cited alongside, same era.
C. Xiaoning, W. Shang, F. Jiang, and W. Shouyang, “Stock index forecasting by hidden markov models with trends recognition,” in 2019 IEEE International Conference on Big Data (BigData) . IEEE, 2019, pp. 5292–5297
2019
Cited alongside, same era.
A. Picasso, S. Merello, Y. Ma, L. Oneto, and E. Cambria, “Technical analysis and sentiment embeddings for market trend prediction,” Expert Systems with Applications , vol. 135, pp. 60–70, 2019
2019
Cited alongside, same era.
F. Feng, H. Chen, X. He, J. Ding, M. Sun, and T.-S. Chua, “Enhancing stock movement prediction with adversarial training,” International Joint Conference on Artificial Intelligence , 2019
2019
Cited alongside, same era.
F. Chen, F. Wu, J. Xu, G. Gao, Q. Ge, and X.-Y. Jing, “Adaptive deformable convolutional network,” Neurocomputing , vol. 453, pp. 853–864, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Lin, D. Zhou, W. Liu, and J. Bian, “Learning multiple stock trading patterns with temporal routing adaptor and optimal transport,” in Proceedings of the 27th ACM SIGKDD , 2021, pp. 1017–1026
2021
Later among the works it cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-sne,” J Mach Learn Res , vol. 9, no. 86, pp. 2579–2605, 2008
2021
Later among the works it cites.
U. Gupta, V. Bhattacharjee, and P. S. Bishnu, “Stocknet—gru based stock index prediction,” Expert Systems with Applications , vol. 207, p. 117986, 2022
2022
Closest in time.
M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: a literature review,” Expert Systems with Applications , p. 116659, 2022
2022
Closest in time.
Q. Zhang, P. Zhang, and F. Zhou, “Intraday and interday features in the high-frequency data: Pre-and post-crisis evidence in china’s stock market,” Expert Systems with Applications , p. 118321, 2022
2022
Closest in time.
H. Xu, L. Chai, Z. Luo, and S. Li, “Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms,” Neurocomputing , vol. 467, pp. 214–228, 2022
2022
Closest in time.
Y. Chen, F. Ding, and L. Zhai, “Multi-scale temporal features extraction based graph convolutional network with attention for multivariate time series prediction,” Expert Systems with Applications , vol. 200, p. 117011, 2022
2022
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C. Xu, H. Huang, X. Ying, J. Gao, Z. Li, P. Zhang, J. Xiao, J. Zhang, and J. Luo, “Hgnn: Hierarchical graph neural network for predicting the classification of price-limit-hitting stocks,” Information Sciences , vol. 607, pp. 783–798, 2022
2022
Closest in time.
Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A time series is worth 64 words: Long-term forecasting with transformers,” in International Conference on Learning Representations , 2023
2023
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2023
Closest in time.
T. Li, Z. Liu, Y. Shen, X. Wang, H. Chen, and S. Huang, “Master: Market-guided stock transformer for stock price forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 1, 2024, pp. 162–170
2024
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