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A Comparative Study of Deep Learning Techniques for Financial Indices Prediction

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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https://hal.inria.fr/hal-03287677
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Submitted on : Thursday, July 15, 2021 - 6:10:37 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
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Argyrios P. Ketsetsis, Konstantinos M. Giannoutakis, Georgios Spanos, Nikolaos Samaras, Dimitrios Hristu-Varsakelis, et al.. A Comparative Study of Deep Learning Techniques for Financial Indices Prediction. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.297-308, ⟨10.1007/978-3-030-79150-6_24⟩. ⟨hal-03287677⟩

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