Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations

Autoři: Catalin Stoean aff001;  Wiesław Paja aff003;  Ruxandra Stoean aff001;  Adrian Sandita aff004
Působiště autorů: Romanian Institute of Science and Technology, Cluj-Napoca, Romania aff001;  Grupo Ingeniería de Sistemas Integrados (TIC-125), E.T.S.I. Telecomunicación, Universidad de Malaga, Malaga, Spain aff002;  Faculty of Mathematics and Natural Sciences, University of Rzeszów, Rzeszów, Poland aff003;  Faculty of Sciences, University of Craiova, Craiova, Romania aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223593


Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model. Based on their predictions, a trading strategy, whose decision to buy or sell depends on two different thresholds, is proposed. A hill climbing approach selects the optimal values for these parameters. The prediction of the two deep learning representatives used in the subsequent trading strategy leads to distinct facets of gain.

Klíčová slova:

Climbing – Convolution – Deep learning – Machine learning – Neural networks – Simulation and modeling – Stock markets – Romanian people


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