Abstract
Automated trading is an approach to investing whereby market predictions are combined with algorithmic decision-making strategies for the purpose of generating high returns while minimizing downsides and risk. Recent advancements in Machine and Deep learning algorithms has led to new and sophisticated models to improve this functionality. In this paper, a comparative analysis is conducted concerning eight studies which focus on the American and the European stock markets. The simple method of Golden Cross trading strategy is being utilized for the assessment of models in real-world trading scenarios. Backtesting was performed in two indices, the S&P 500 and the EUROSTOXX 50, resulting in relative good performance, aside from the significant downfall in global markets due to COVID-19 outbreak, which appeared to affect all models.
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References
Abe, M., Nakayama, H.: Deep learning for forecasting stock returns in the cross-section. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10937, pp. 273–284. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93034-3_22
Bailey, D.H., Borwein, J., Lopez de Prado, M., Zhu, Q.J.: Pseudo-mathematics and financial charlatanism: the effects of backtest overfitting on out-of-sample performance. Not. Am. Math. Soc. 61(5), 458–471 (2014)
Borovkova, S., Dijkstra, M.: Deep learning prediction of the eurostoxx 50 with news sentiment. Available at SSRN 3253043 (2018)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, December 2014 (2014)
Dezsi, E., Nistor, I.A.: Can deep machine learning outsmart the market? a comparison between econometric modelling and long- short term memory. Rom. Econ. Bus. Rev. 11(4.1), 54–73 (2016)
Dunis, C.L., Laws, J., Karathanassopoulos, A.: Modelling and trading the Greek stock market with mixed neural network models. Appl. Financ. Econ. 21(23), 1793–1808 (2011)
GarcÃa, F., Guijarro, F., Oliver, J., TamoÅ¡iÅ«nienÄ—, R.: Hybrid fuzzy neural network to predict price direction in the German dax-30 index. Technol. Econ. Dev. Econ. 24(6), 2161–2178 (2018)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. In: 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), vol. 2, pp. 850–855 vol. 2 (1999)
Hanias, M.P., Curtis, P.G., Thalassinos, E.: Time series prediction with neural networks for the athens stock exchange indicator (2012)
Hansson, M.: On stock return prediction with LSTM networks (2017)
Janeski, M., Kalajdziski, S.: Neural network model for forecasting Balkan stock exchanges. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2011. LNCS, vol. 6838, pp. 17–24. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24728-6_3
Ketsetsis, A.P., et al.: Deep learning techniques for stock market prediction in the European union: a systematic review. In: 2020 International Conference on Computational Science and Computational Intelligence, to appear (2021)
Kraus, M., Feuerriegel, S.: Decision support from financial disclosures with deep neural networks and transfer learning. Decis. Support Syst. 104, 38–48 (2017)
Kyoung-Sook, M., Hongjoong, K.: Performance of deep learning in prediction of stock market volatility. Econ. Comput. Econ. Cybern. Studies Res. 53(2), 77–92 (2019)
Metghalchi, M., Marcucci, J., Chang, Y.H.: Are moving average trading rules profitable? evidence from the European stock markets. Appl. Econ. 44(12), 1539–1559 (2012)
Mourelatos, M., Alexakos, C., Amorgianiotis, T., Likothanassis, S.: Financial indices modelling and trading utilizing deep learning techniques: The Athens se ftse/ase large cap use case. In: 2018 Innovations in Intelligent Systems and Applications (INISTA), pp. 1–7 (2018)
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., Mosavi, A.: Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access 8, 150199–150212 (2020)
Shen, G., Tan, Q., Zhang, H., Zeng, P., Xu, J.: Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Comput. Sci. 131, 895–903 (2018)
Shen, S., Jiang, H., Zhang, T.: Stock market forecasting using machine learning algorithms. Department of Electrical Engineering, Stanford University, Stanford, CA, pp. 1–5. (2012)
Stoean, C., Paja, W., Stoean, R., Sandita, A.: Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLOS ONE 14(10), 1–19 (2019)
Zhang, Z., Khushi, M.: Ga-MSSR: Genetic algorithm maximizing sharpe and sterling ratio method for robotrading. In: 2020 International Joint Conference on Neural Networks, pp. 1–8. IEEE (2020)
Acknowledgments
The authors would like to thank Mr. Christos Kourounis for his support on the implementation of the research works [6, 11, 16]. This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code:T2EDK-03743).
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Ketsetsis, A.P. et al. (2021). A Comparative Study of Deep Learning Techniques for Financial Indices Prediction. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_24
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