Deep learning in exchange markets

Gonçalves, Rui; Ribeiro, Vitor Miguel; Pereira, Fernando Lobo; Rocha, Ana Paula

doi:10.1016/j.infoecopol.2019.05.002

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…Dingli and Fournier [64] applied CNN to predict the future movement of stock prices and found that the predictive accuracy of their model was 65% in predicting the following month's price and was 60% for the following week's price. Gonçalves et al [47] compared the results of the prediction of CNN, LSTM, and deep neural network classifier (DNNC) for finding the best model to predict the price trends in the exchange markets. Their findings reveal that CNN, on average, has the best predictive power.…”

Section: Cnnmentioning

confidence: 99%

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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: Cnnmentioning

confidence: 99%

“…Similar to LSTM, the CNN algorithm is applied mainly for financial time series data to stock price prediction [47,52,54,64] and cryptocurrencies price prediction [90]. CNN algorithm is also used for analyzing social media data for sentiment analysis [58].…”

Section: Cryptocurrencymentioning

confidence: 99%

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

et al. 2020

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“…The reader should notice that fuzzy learning is commonly used to reduce uncertainty in the data [42], so such solutions can be useful for financial forecasting. Several other interesting studies have been carried out in the literature, such as a comparison of deep learning technologies to price prediction [43], the use of deep learning and statistical approaches to forecast crisis in the stock market [44], and the use of reward-based classifiers such as Deep Reinforcement Learning [45], among others.…”

Section: Background and Related Workmentioning

confidence: 99%

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

et al. 2019

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Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.

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“…DNN is a neural network having input layer, multiple hidden layers and output layer. In DNN layers are ordered and each unit inside the layer is connected to unit inside the previous layer [12]. The illustration of DNN architecture is shown in the figure 4.…”

Section: Ba Deep Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

2019

IJEAT

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Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.

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Deep learning in exchange markets

Cited by 21 publications

References 13 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 Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

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