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

Stoean, Cătălin; Paja, Wiesław; Stoean, Ruxandra; Săndiță, Adrian

doi:10.1371/journal.pone.0223593

Cited by 53 publications

(23 citation statements)

References 15 publications

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“…In addition, there is much evidence in the literature that hybrid LSTM methods outperform the other single DL methods [37]. In the application of the stock market, LSTM has been combined with different methods to develop a hybrid model.…”

Section: Lstmmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

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Section: Lstmmentioning

confidence: 99%

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

et al. 2020

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“…While LSTM have been constructed to possess a long-short term memory in order to handle temporal data [31], 1D CNN have recently also proved to be able to model time series within real-world applications either alone [32] or in combination with the former [33]. Therefore, the architecture used herein will be another combination of the two deep networks with 1D CNN being in charge of extracting the features and the LSTM of modeling the time dependencies.…”

Section: Deep Learning For the Classification Of Regrouped Saccadesmentioning

confidence: 99%

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Stoean

Becerra-García

et al. 2020

PLoS ONE

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Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the 'cleaned' samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important.

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“…Feature engineering by CNN Figure 6 shows the accuracy of twenty (20) iterations of different randomly selected features by our CNN model. We observed that 21 features gave an accuracy of 82.52%, while 52 features recorded an accuracy of 81.06%, as shown in Fig.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

“…The proposed techniques perform favourably compared with traditional methods in terms of prediction accuracy. In the same way, Stoean et al [20] applied an LSTM based predictive model to predict the closing-price of twenty-five (25) firms enlisted on the Bucharest Stock Exchange, using historical stock price. Notwithstanding the achievement recorded by authors, they acknowledged in their conclusion that the fusion of multiple stock price indicators can improve prediction accuracy.…”

Section: Studies Based On Quantitative Datasetmentioning

confidence: 99%

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

2021

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The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more. Several Petabytes volumes of data are generated every second from different sources, which affect the stock market. A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market. However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text). In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM. Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources. Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters. Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy. Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE). The results show a good prediction accuracy of 98.31%, specificity (0.9975), sensitivity (0.8939%) and F-score (0.9672) of the amalgamated dataset compared with the distinct dataset. Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources.

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

Cited by 53 publications

References 15 publications

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

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