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

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 cause...

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Detalles Bibliográficos
Autores principales: Li, Yang, Pan, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8446482/
https://www.ncbi.nlm.nih.gov/pubmed/34549080
http://dx.doi.org/10.1007/s41060-021-00279-9
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author Li, Yang
Pan, Yi
author_facet Li, Yang
Pan, Yi
author_sort Li, Yang
collection PubMed
description 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 that can affect the share price like news releases on earnings and profits, future estimated earnings, the announcement of dividends, introduction of a new product or a product recall, secure a new large contract, employee layoffs, a major change of management, anticipated takeover or merger, and accounting errors or scandals. Furthermore, these factors are only company factors, and other factors affect the future trend of stocks, such as industry performance, investor sentiment, and economic factors. This paper proposes a novel deep learning approach to predict future stock movement. The model employs a blending ensemble learning method to combine two recurrent neural networks, followed by a fully connected neural network. In our research, we use the S&P 500 Index as our test case. Our experiments show that our blending ensemble deep learning model outperforms the best existing prediction model substantially using the same dataset, reducing the mean-squared error from 438.94 to 186.32, a 57.55% reduction, increasing precision rate by 40%, recall by 50%, F1-score by 44.78%, and movement direction accuracy by 33.34%, respectively. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.
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spelling pubmed-84464822021-09-17 A novel ensemble deep learning model for stock prediction based on stock prices and news Li, Yang Pan, Yi Int J Data Sci Anal Regular Paper 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 that can affect the share price like news releases on earnings and profits, future estimated earnings, the announcement of dividends, introduction of a new product or a product recall, secure a new large contract, employee layoffs, a major change of management, anticipated takeover or merger, and accounting errors or scandals. Furthermore, these factors are only company factors, and other factors affect the future trend of stocks, such as industry performance, investor sentiment, and economic factors. This paper proposes a novel deep learning approach to predict future stock movement. The model employs a blending ensemble learning method to combine two recurrent neural networks, followed by a fully connected neural network. In our research, we use the S&P 500 Index as our test case. Our experiments show that our blending ensemble deep learning model outperforms the best existing prediction model substantially using the same dataset, reducing the mean-squared error from 438.94 to 186.32, a 57.55% reduction, increasing precision rate by 40%, recall by 50%, F1-score by 44.78%, and movement direction accuracy by 33.34%, respectively. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods. Springer International Publishing 2021-09-17 2022 /pmc/articles/PMC8446482/ /pubmed/34549080 http://dx.doi.org/10.1007/s41060-021-00279-9 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Regular Paper
Li, Yang
Pan, Yi
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
title_full A novel ensemble deep learning model for stock prediction based on stock prices and news
title_fullStr A novel ensemble deep learning model for stock prediction based on stock prices and news
title_full_unstemmed A novel ensemble deep learning model for stock prediction based on stock prices and news
title_short A novel ensemble deep learning model for stock prediction based on stock prices and news
title_sort novel ensemble deep learning model for stock prediction based on stock prices and news
topic Regular Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8446482/
https://www.ncbi.nlm.nih.gov/pubmed/34549080
http://dx.doi.org/10.1007/s41060-021-00279-9
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