A novel ensemble deep learning model for stock prediction based on stock prices and news

@article{Li2022ANE,
  title={A novel ensemble deep learning model for stock prediction based on stock prices and news},
  author={Yang Li and Yi Pan},
  journal={International Journal of Data Science and Analytics},
  year={2022},
  volume={13},
  pages={139 - 149}
}
  • Yang Li, Yi Pan
  • Published 23 July 2020
  • Computer Science
  • International Journal of Data Science and Analytics
In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. One of the most popular and complex deep learning in finance topics is future stock prediction. The difficulty that causes the future stock forecast is that there are too many different factors that affect the amplitude and frequency of the rise and fall of stocks at the same time. Some of the company-specific factors… 
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10 Citations
Background Citations
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Methods Citations
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10 Citations

Deep learning based non-linear regression for Stock Prediction
TLDR
This paper proposes a deep learning based non-linear regression method to predict the stock price and reveals that the proposed method performed better than existing machine learning based approaches.
Trading Stocks Based on Financial News Using Attention Mechanism
TLDR
This experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%).
Multiclass Classifier for Stock Price Prediction
TLDR
The individual stock-wise evaluation of the Multiclass (One V/s All) classifier is found to achieve the highest accuracy among all other classifiers which is outperforming all the recent proposals.
A Comprehensive Review on Summarizing Financial News Using Deep Learning
TLDR
This work aims to evaluate these models’ performance to choose the robust model in identifying the significant factors influencing the prediction in sentiment analysis of news headlines, the sole purpose of this study.
Stock Movement Prediction using Technical and Data
TLDR
This work utilizes Tesla data with sentiment analysis performed on news titles pertaining to the company as well as technical data on its stock over a three year period in order to predict closing price movement.
A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin
TLDR
A novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision is proposed.
Pretraining Financial Text Encoder Enhanced by Lifelong Learning
TLDR
This work presents a pretraining financial text encoder, named F-BERT, a domain-specific language model pretrained on large-scale financial corpora, which achieves strong results on several financial text mining tasks.
Intelligent Traffic Management in Next-Generation Networks
TLDR
A comprehensive background beginning from conventional ML algorithms and DL is presented and follow this with a focus on different dimensionality reduction techniques, and the study of ML/DL applications in sofwarized environments is presented.
Incorporating causality in energy consumption forecasting using deep neural networks
Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting…
Predicting Stock Price Movement with Multiple Data Sources and Machine Learning Models

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