Evaluation of Machine Learning Techniques for Stock Market Movement Prediction
- Computer Science2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID)
It is found that the fully convolutional neural network has no obvious advantages over other machine learning models, and the 10-day movement is easier to predict, which would be useful for designing a long-term investment strategy.
Stock Market Prediction via Deep Learning Techniques: A Survey
- Education, Political Science
This poster presents a probabilistic procedure to identify the neurons in the brain that secrete During the Yangtze River blackout, a process known as “red-plating”.
The Application of Deep Learning in Stock Prediction
- Computer ScienceHighlights in Science, Engineering and Technology
In recent years, deep learning and neural network become the mainstream in research and the technique is successful, but it is not perfect.
Price movement prediction using deep learning: a case study of the China futures market
- Computer ScienceConference on Computer Science and Communication Technology
An improved deep learning model combining the local feature extraction ability of Convolutional Neural Network with the sequential feature extraction able of Long Short-Term Memory is proposed and evaluated on RB dominant contracts in the China futures market and it is concluded that the model’s performance on prediction is better than that of single CNN and LSTM models.
Stock Market Prediction Using Deep Learning Based on Modiﬁed Long Short-Term Memory
- Computer Science
A framework based on convolutional neural network, support vector machine, and other techniques to predict stocks’ tendency, which is the main part of quantitative investment, is proposed.
Deep learning in the stock market—a systematic survey of practice, backtesting, and applications
- Computer ScienceArtificial Intelligence Review
This study demonstrates that, although there have been some improvements in reproducibility, substantial work remains to be done regarding model explainability and suggests several future directions, such as improving trust by creating reproducible, explainable, and accountable models and emphasizing prediction of longer-term horizons.
Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series
- Computer Science
Experimental results show that the latest deep learning models such as N-BEATS, ES-LSTM and TCN produced better results for all stock market related datasets by obtaining around 50% less Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) scores for each prediction horizon as compared to other models.
An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges
- Computer ScienceApplied Sciences
The utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement learning in Quantitative Finance and the Stock Market is explained and potential future study paths are outlined based on the overview that was presented before.
ND-SMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction
- Computer Science2020 IEEE 23rd International Conference on Information Fusion (FUSION)
The proposed ND-SMPF predictive framework uses information fusion to combine twitter data with extended horizon market historical prices to boost the accuracy of the stock movement prediction task.
Stocks of year 2020: prediction of high variations in stock prices using LSTM
- Computer ScienceMultimedia Tools and Applications
Whether deep learning can predict so high variations in stock prices in the Year 2020 and build proposed neural network model is investigated and mean Absolute Percentage Error (MAPE) values are better than traditional data analytics techniques.
SHOWING 1-10 OF 213 REFERENCES
Hybrid Deep Learning Model for Stock Price Prediction
- Computer Science2018 IEEE Symposium Series on Computational Intelligence (SSCI)
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
- Computer ScienceIIKI
Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations
- Computer SciencePloS one
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
- Computer Science2018 9th International Conference on Information and Communication Systems (ICICS)
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
- Computer ScienceKnowl. Based Syst.
Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets
- Computer Science
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
- Computer Science2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
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
- Computer Science2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)
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
- Computer Science2017 29th Chinese Control And Decision Conference (CCDC)
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
- Computer ScienceThe Journal of Supercomputing
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.