A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift
This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models.
Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift
This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models.
Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift
J. Inst. Eng. India Ser. B
Nayak, Janmenjoy (author) / Dash, Pandit Byomakesha (author) / Naik, Bighnaraj (author) / Mohapatra, Subhashree (author) / Routray, A. R. (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1459-1478
2022-10-01
20 pages
Article (Journal)
Electronic Resource
English
Stock Market Prediction During COVID-19 Pandemic: A Time-Series Big Data Analysis Method
| Springer Verlag | 2023
Stock Market Forecasting Using Computational Intelligence: A Survey
| Springer Verlag | 2021
Stock Market Forecasting Using Computational Intelligence: A Survey
| Online Contents | 2020
FBC steam generators for Indian utility services - future scenario
| British Library Online Contents | 1996