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

  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},
  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… 
Deep learning based non-linear regression for Stock Prediction
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
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
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
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
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
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
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
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


Deep learning for stock prediction using numerical and textual information
A novel application of deep learning models, Paragraph Vector, and Long Short-Term Memory, to financial time series forecasting and models the temporal effects of past events on opening prices about multiple companies with LSTM is proposed.
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading
This work explores recurrent neural networks with character-level language model pre-training for both intraday and interday stock market forecasting, and shows that this technique is competitive with other state-of-the-art approaches.
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
A novel deep neural network DP-LSTM is proposed for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
A Hybrid Attention Networks (HAN) is designed to predict the stock trend based on the sequence of recent related news, and the self-paced learning mechanism is applied to imitate the third principle.
Stock Price Forecasting via Sentiment Analysis on Twitter
The ultimate goal of this project is to forecast how the market will behave in the future via sentiment analysis on a set of tweets over the past few days, as well as to examine if the theory of contrarian investing is applicable.
Stock market's price movement prediction with LSTM neural networks
This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators, and the results are promising.
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation
This work proposes to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts, and outperforms bags-of-words-based baselines and previous systems trained on S&P 500 stock historical data.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
This work applies LSTM recurrent neural networks (RNN) in predicting the stock price correlation coefficient of two individual stocks using the ARIMA-LSTM hybrid model, which turned out superior to all other financial models by a significant scale.
Ensembles of Recurrent Neural Networks for Robust Time Series Forecasting
An ensemble architecture is proposed that combines forecasts of a number of differently parameterized LSTMs to a robust final estimate which, on average, performs better than the majority of the individual LSTM base learners, and provides stable results across different datasets.
Factors Affecting the Stock Price Movement: A Case Study on Dhaka Stock Exchange
Stock market is the mirror of the economy of any country. The strength of the economy reflects in the stock market numeric value. After crisis and fall down in different times over years, Bangladesh