ArticlePloS one2019
Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Causality-driven multivariate stock movement forecasting.PloS one · 2024Article
- Article
- Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.PloS one · 2023Article
- Structural break-aware pairs trading strategy using deep reinforcement learning.The Journal of supercomputing · 2022Article
- A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.PloS one · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
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.
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