Prediction of stock return by LSTM neural network

References

• Babu, C. N., and B. E. Reddy. 2014. A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing 23 (Complete):27–3927. doi:10.1016/j.asoc.2014.05.028.
   Google Scholar
• Ballings, M., V. Dirk, N. Hespeels, and R. Gryp. 2015. Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications 42 (20):7046–56. doi:10.1016/j.eswa.2015.05.013.
   Web of Science ®Google Scholar
• Baranochnikov, I. Ś. R., 2022, A comparison of LSTM and GRU architectures with the novel walk-forward approach to algorithmic investment strategy, Working papers of faculty of economic sciences, University of Warsaw, WP 21/2022 (397), https://www.wne.uw.edu.pl/application/files/3516/5945/4534/WNE_WP397.pdf.
   Google Scholar
• Bengio, Y. 2007. Scaling learning algorithms towards ai. Large Scale Kernel Machines 34 (5):1–41. doi:10.1016/j.asoc.2014.05.028.
   Google Scholar
• Bui, Q., and R. Ślepaczuk. 2021. Applying hurst exponent in pair trading strategies on nasdaq 100 index. Physica A: Statistical Mechanics and Its Applications 592:126784. https://www.sciencedirect.com/science/article/abs/pii/S037843712100964X.
   Web of Science ®Google Scholar
• Cakra, Y. E., and B. D. Trisedya. Stock price prediction using linear regression based on sentiment analysis. International Conference on Advanced Computer Science & Information Systems USA, IEEE, 2016.
   Google Scholar
• Carreira-Perpinan, M. A., and G. E. Hinton 2005. On contrastive divergence learning. proceedings of artificial intelligence & statistics R5: 33–40 . .
   Google Scholar
• Castellano Gomez, S., and R. Ślepaczuk, 2021, Robust optimization in algorithmic investment strategies, Working papers of faculty of economic sciences, University of Warsaw, WP 27/2021 (375), https://www.wne.uw.edu.pl/application/files/8616/3847/9793/WNE_WP375.pdf.
   Google Scholar
• Chai, T., R. Draxler, and A. Stein. 2015. Source term estimation using air concentration measurements and a lagrangian dispersion model – experiments with pseudo and real cesium-137 observations from the fukushima nuclear accident. Atmospheric Environment 106 (apr):241–51. doi:10.1016/j.atmosenv.2015.01.070.
   Google Scholar
• Evermann, J., J. R. Rehse, and P. Fettke. 2017. Predicting process behaviour using deep learning. Decision Support Systems 100:129–40. doi:10.1016/j.dss.2017.04.003.
   Web of Science ®Google Scholar
• Fischer, T., and C. Krauss. 2017. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270 (2):654–69. doi:10.1016/j.ejor.2017.11.054.
   Web of Science ®Google Scholar
• Grudniewicz, J., and R. Ślepaczuk, 2021, Application of machine learning in quantitative investment strategies on global stock markets, Working Papers of faculty of economic sciences, University of Warsaw, WP 23/2021 (371), https://www.wne.uw.edu.pl/files/6216/3603/4435/WNE_WP371.pdf.
   Google Scholar
• Gurjar, M., P. Naik, G. Mujumdar, and T. Vaidya. 2018. Stock market prediction using ann. International Research Journal of Engineering and Technology 5 (3):2758–61.
   Google Scholar
• Hinton, G. E. 2002. Training products of experts by minimizing contrastive divergence. Neural Computation, págs1771–1800. doi: 10.1162/089976602760128018.
   Google Scholar
• J, S. Z. et al. 2012. A review of research on deep learning. Computer Application Research 29 (8):2806–10. doi:10.48550/arXiv.1206.5538.
   Google Scholar
• Kijewski, M., and R. Ślepaczuk, 2020, Predicting prices of S&P500 index using classical methods and recurrent neural networks, Working papers of faculty of economic sciences, University of Warsaw, WP 27/2020 (333), https://www.wne.uw.edu.pl/application/files/4616/3609/7114/WNE_WP333.pdf.
   Google Scholar
• Kim, M., and H. Sayama. 2017. Predicting stock market movements using network science: An information theoretic approach. Applied Network Science 2 (1):35. doi:10.1007/s41109-017-0055-y.
   PubMedGoogle Scholar
• Li, Z. B., G. Y. Yang, Y. C. Feng, and L. Jing. 2017. Empirical test of Fama French five factor model in China’s stock market. Financial Research 6:191–206.
   Google Scholar
• Michańków, J., P. Sakowski, and R. Ślepaczuk. 2022. The comparison of LSTM in algorithmic investment strategies on BTC and SP500 index. Sensors 22:917. doi:10.3390/s22030917.
   PubMed Web of Science ®Google Scholar
• Nguyen, V., and R. Ślepaczuk. 2022. Applying hybrid ARIMA-SGARCH in algorithmic investment strategies on S&P500 Index. Entropy 24 (2):158. doi:10.3390/e24020158.
   PubMed Web of Science ®Google Scholar
• Nti, I. K., A. A. Felix, and W. B. Asubam. 2020. A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review 53 (4):3007–57. doi:10.1007/s10462-019-09754-z.
   Web of Science ®Google Scholar
• Qiu, M., Y. Song, and F. Akagi. 2016. Application of artificial neural network for the prediction of stock market returns: The case of the japanese stock market. Chaos Solitons & Fractals the Interdisciplinary Journal of Nonlinear Science & Nonequilibrium & Complex Phenomena 85:1–7. doi:10.1016/j.chaos.2016.01.004.
   Web of Science ®Google Scholar
• Roy, S. S., D. Mittal, A. Basu, and A. Abraham. 2015. Stock market forecasting using lasso linear regression model. Springer International Publishing. doi:10.1007/978-3-319-13572-4_31.
   Google Scholar
• Rundo. 2019. Deep LSTM with reinforcement learning layer for financial trend prediction in fx high frequency trading systems. Applied Sciences 9 (20):4460. doi:10.3390/app9204460.
   Google Scholar
• Saâdaoui, F., and O. B. Messaoud. 2020.Multiscaled neural autoregressive distributed lag: A new empirical mode decomposition model for nonlinear time series forecasting. International Journal of Neural Systems 30: 2050039.doi: 10.1142/S0129065720500392
   PubMed Web of Science ®Google Scholar
• Selvin, S., R. Vinayakumar, E. Gopalakrishnan, V. K. Menon, and K. Soman (2017). Stock price prediction using lstm, rnn and cnn-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), USA, pp. 1643–47. IEEE.
   Google Scholar
• Sharang, A., and C. Rao 2015. Using machine learning for medium frequency derivative portfolio trading. Papers. doi: 10.48550/arXiv.1512.06228.
   Google Scholar
• Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–58. doi: 10.1016/j.neucom.2015.09.116.
   Google Scholar
• Taylor, G. W., G. E. Hinton, and S. T. Roweis. 2006. Modeling human motion using binary latent variables. Advances in Neural Information Processing Systems 19, 1345–52.
   Google Scholar
• Thawornwong, S., and D. Enke. 2004. The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56:205–32. doi:10.1016/j.neucom.2003.05.001.
   Web of Science ®Google Scholar
• Ticknor, J. L. 2013. A bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications 40 (14):5501–06. doi:10.1016/j.eswa.2013.04.013.
   Web of Science ®Google Scholar
• Tsai, C.-F., and Y.-C. Hsiao. 2010. Combining multiple feature selection methods for stock prediction:Union, intersection, and multi-intersection approaches. Decision Support Systems 50 (1):258–69. doi:10.1016/j.dss.2010.08.028.
   Web of Science ®Google Scholar
• Tumminello, M., F. Lillo, and R. N. Mantegna. 2010. Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior & Organization 75 (1):40–58. doi:10.1016/j.jebo.2010.01.004.
   Web of Science ®Google Scholar
• Wang, J.-Z., J.-J. Wang, Z.-G. Zhang, and S.-P. Guo. 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications 38 (11):14346–55. doi:10.1016/j.eswa.2011.04.222.
   Web of Science ®Google Scholar
• Xiong, R., E. P. Nichols, and Y. Shen. 2015. Deep learning stock volatility with google domestic trends. arXiv preprint arXiv :151204916 2015 .
   Google Scholar
• Yeh, C.-Y., C.-W. Huang, and S.-J. Lee. 2011. A multiple-kernel support vector regression approach for stock market price forecasting. Expert Systems with Applications 38 (3):2177–86. doi:10.1016/j.eswa.2010.08.004.
   Web of Science ®Google Scholar
• Yoshihara, A., K. Fujikawa, K. Seki, and K. Uehara 2014. Predicting stock market trends by recurrent deep neural networks. In Pacific rim international conference on artificial intelligence. 759–69. doi: 10.1007/978-3-319-13560-1_60.
   Google Scholar
• Zhang, X., G. Naijie, C. Jie, and Y. Hong. 2021. Predicting stock price movement using a dbn-rnn. Applied Artificial Intelligence 35 (12):876–92. doi:10.1080/08839514.2021.1942520.
   Web of Science ®Google Scholar
• Zhong, X., and D. Enke. 2017. Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications 67:126–39. doi:10.1016/j.eswa.2016.09.027.
   Web of Science ®Google Scholar
• Zhu, X., H. Wang, L. Xu, and H. Li. 2008. Predicting stock index increments by neural networks: The role of trading volume under different horizons. Expert Systems with Applications 34 (4):3043–54. doi:10.1016/j.eswa.2007.06.023.
   Web of Science ®Google Scholar