A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis.

ABSTRACT: Stock market prediction is a challenging and complex problem that has received the attention of researchers due to the high returns resulting from an improved prediction. Even though machine learning models are popular in this domain dynamic and the volatile nature of the stock markets limits the accuracy of stock prediction. Studies show that incorporating news sentiment in stock market predictions enhances performance compared to models using stock features alone. There is a need to develop an architecture that facilitates noise removal from stock data, captures market sentiments, and ensures prediction to a reasonable degree of accuracy. The proposed cooperative deep-learning architecture comprises a deep autoencoder, lexicon-based software for sentiment analysis of news headlines, and

SUBMITTER: Rekha KS 

PROVIDER: S-EPMC9748829 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature