Corpus ID: 236134155

Stock price prediction using BERT and GAN

@article{Sonkiya2021StockPP,
  title={Stock price prediction using BERT and GAN},
  author={Priyank Sonkiya and Vikas Bajpai and Anukriti Bansal},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.09055}
}
The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the… Expand

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References

SHOWING 1-10 OF 51 REFERENCES
Prediction of stock values changes using sentiment analysis of stock news headlines
TLDR
A significant difference is discovered between the different models in terms of the effect of emotional values on the change in the value of the stock market by the correlation matrices. Expand
BERT for Stock Market Sentiment Analysis
TLDR
This work proposes the use of bidirectional encoder representations from transformers BERT to perform sentiment analysis of news articles and provide relevant information for decision making in the stock market. Expand
A local and global event sentiment based efficient stock exchange forecasting using deep learning
TLDR
This study considers four countries- US, Hong Kong, Turkey, and Pakistan from developed, emerging and underdeveloped economies’ list and explores the effect of different major events occurred during 2012–2016 on stock markets. Expand
Deep learning for Stock Market Prediction
TLDR
Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability, and for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost. Expand
Applying BERT to analyze investor sentiment in stock market
TLDR
The experiments show that the Bert model used in this paper can achieve an accuracy of 97.35% for the analysis of investor sentiment, which is better than both LSTM and SVM methods. Expand
Stock Market Prediction with Historical Time Series Data and Sentimental Analysis of Social Media Data
In the Indian stock market, stock costs are viewed as exceptionally fluctuating due to various factors such as political decision results, bits of gossip, budgetary news, public safety events and soExpand
Systematic analysis and review of stock market prediction techniques
TLDR
This work presents the detailed review of 50 research papers suggesting the methodologies, like Bayesian model, Fuzzy classifier, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifiers, Neural Network (NN), Machine Learning Methods and so on, based on stock market prediction. Expand
Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty
Abstract From the last twenty years, the application of Internet based technologies had brought a significant impact on the Indian stock market. Use of the Internet has eliminated the barriers ofExpand
Stock Market Prediction Based on Generative Adversarial Network
TLDR
Experimental results show that the novel GAN can get a promising performance in the closing price prediction on the real data compared with other models in machine learning and deep learning. Expand
Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets
TLDR
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. Expand
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