Applications of deep learning in stock market prediction: recent progress

@article{Jiang2020ApplicationsOD,
  title={Applications of deep learning in stock market prediction: recent progress},
  author={Weiwei Jiang},
  journal={Expert Syst. Appl.},
  year={2020},
  volume={184},
  pages={115537}
}
  • Weiwei Jiang
  • Published 29 February 2020
  • Computer Science
  • Expert Syst. Appl.
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170 Citations
Highly Influential Citations
7
Background Citations
56
Methods Citations
24
Results Citations
2

170 Citations

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References

SHOWING 1-10 OF 213 REFERENCES

Hybrid Deep Learning Model for Stock Price Prediction

This hybrid model is a combination of two well-known networks, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) and provides less error by considering this random nature (change) for a large scale of data.

Stock Market Prediction Based on Generative Adversarial Network

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

The task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein, and a trading strategy, whose decision to buy or sell depends on two different thresholds, is proposed.

Evaluation of bidirectional LSTM for short-and long-term stock market prediction

Evaluating and comparing LSTM deep learning architectures for short- and long-term prediction of financial time series and benchmarking them with shallow neural networks and simple forms of L STM networks is conducted.

Deep learning-based feature engineering for stock price movement prediction

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

A generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network for adversarial training to forecast high-frequency stock market can effectively improve stock price direction prediction accuracy and reduce forecast error.

Novel Deep Learning Model with CNN and Bi-Directional LSTM for Improved Stock Market Index Prediction

A novel deep learning model that combines multiple pipelines of convolutional neural network and bi-directional long short term memory units is proposed that improves prediction performance by 9% upon single pipeline deepLearning model and by over a factor of six upon support vector machine regressor model on S&P 500 grand challenge dataset.

Deep learning for stock market prediction from financial news articles

Results has shown that CNN can be better than RNN on catching semantic from texts and RNN is better on catching the context information and modeling complex temporal characteristics for stock market forecasting.

Time series prediction of stock price using deep belief networks with intrinsic plasticity

The results show that the application of IP learning can remarkably improve the prediction performance of the stock market prediction, and may have important implications on the modeling of neural network for complex time series prediction.

An innovative neural network approach for stock market prediction

An innovative neural network approach to achieve better stock market predictions by using the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network.
...