Cargando…

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

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 ac...

Descripción completa

Detalles Bibliográficos
Autores principales: Rekha, KS, Sabu, MK
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748829/
https://www.ncbi.nlm.nih.gov/pubmed/36532805
http://dx.doi.org/10.7717/peerj-cs.1158
_version_ 1784849911348461568
author Rekha, KS
Sabu, MK
author_facet Rekha, KS
Sabu, MK
author_sort Rekha, KS
collection PubMed
description 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 LSTM/GRU layers for prediction. The autoencoder is used to denoise the historical stock data, and the denoised data is transferred into the deep learning model along with news sentiments. The stock data is concatenated with the sentiment score and is fed to the LSTM/GRU model for output prediction. The model’s performance is evaluated using the standard measures used in the literature. The results show that the combined model using deep autoencoder with news sentiments performs better than the standalone LSTM/GRU models. The performance of our model also compares favorably with state-of-the-art models in the literature.
format Online
Article
Text
id pubmed-9748829
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-97488292022-12-15 A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis Rekha, KS Sabu, MK PeerJ Comput Sci Data Mining and Machine Learning 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 LSTM/GRU layers for prediction. The autoencoder is used to denoise the historical stock data, and the denoised data is transferred into the deep learning model along with news sentiments. The stock data is concatenated with the sentiment score and is fed to the LSTM/GRU model for output prediction. The model’s performance is evaluated using the standard measures used in the literature. The results show that the combined model using deep autoencoder with news sentiments performs better than the standalone LSTM/GRU models. The performance of our model also compares favorably with state-of-the-art models in the literature. PeerJ Inc. 2022-11-30 /pmc/articles/PMC9748829/ /pubmed/36532805 http://dx.doi.org/10.7717/peerj-cs.1158 Text en ©2022 S and K et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Rekha, KS
Sabu, MK
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title_full A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title_fullStr A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title_full_unstemmed A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title_short A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
title_sort cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748829/
https://www.ncbi.nlm.nih.gov/pubmed/36532805
http://dx.doi.org/10.7717/peerj-cs.1158
work_keys_str_mv AT rekhaks acooperativedeeplearningmodelforstockmarketpredictionusingdeepautoencoderandsentimentanalysis
AT sabumk acooperativedeeplearningmodelforstockmarketpredictionusingdeepautoencoderandsentimentanalysis
AT rekhaks cooperativedeeplearningmodelforstockmarketpredictionusingdeepautoencoderandsentimentanalysis
AT sabumk cooperativedeeplearningmodelforstockmarketpredictionusingdeepautoencoderandsentimentanalysis