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Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network
The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366270/ https://www.ncbi.nlm.nih.gov/pubmed/35965780 http://dx.doi.org/10.1155/2022/7097044 |
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author | Rouf, Nusrat Bashir Malik, Majid Sharma, Sparsh Ra, In-Ho Singh, Saurabh Meena, Abhishek |
author_facet | Rouf, Nusrat Bashir Malik, Majid Sharma, Sparsh Ra, In-Ho Singh, Saurabh Meena, Abhishek |
author_sort | Rouf, Nusrat |
collection | PubMed |
description | The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic. |
format | Online Article Text |
id | pubmed-9366270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93662702022-08-12 Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network Rouf, Nusrat Bashir Malik, Majid Sharma, Sparsh Ra, In-Ho Singh, Saurabh Meena, Abhishek Comput Intell Neurosci Research Article The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic. Hindawi 2022-08-11 /pmc/articles/PMC9366270/ /pubmed/35965780 http://dx.doi.org/10.1155/2022/7097044 Text en Copyright © 2022 Nusrat Rouf et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Rouf, Nusrat Bashir Malik, Majid Sharma, Sparsh Ra, In-Ho Singh, Saurabh Meena, Abhishek Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title | Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title_full | Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title_fullStr | Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title_full_unstemmed | Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title_short | Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network |
title_sort | impact of healthcare on stock market volatility and its predictive solution using improved neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366270/ https://www.ncbi.nlm.nih.gov/pubmed/35965780 http://dx.doi.org/10.1155/2022/7097044 |
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