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Author: search.filters.author.Majhi, R.Subject: search.filters.subject.COVID-19

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    Impact of COVID-19 on the Indian seaport transportation and maritime supply chain
    (Elsevier Ltd, 2021) Narasimha, P.T.; Jena, P.R.; Majhi, R.
    Impacts of COVID-19 in maritime transportation and its related policy measures have been investigated by more and more organizations and researchers across the world. This paper aims to examine the impacts of COVID-19 on seaport transportation and the maritime supply chain field and its related issues in India. Secondary data are used to analyze the performance indicators of major seaports in India before and during the COVID-19 crisis. We further explore and discuss the expert's views about the impact, preparedness, response, and recovery aspects for the maritime-related sector in India. The results on the quantitative performance of Indian major seaports during the COVID-19 indicate a negative growth in the cargo traffic and a decrease in the number of vessel traffic compared to pre-COVID-19. The expert survey results suggest a lack of preparedness for COVID-19 and the need for developing future strategies by maritime organizations. The overall findings of the study shall assist in formulating maritime strategies by enhancing supply chain resilience and sustainable business recovery process while preparing for a post-COVID-19 crisis. The study also notes that the Covid-19 crisis is still an ongoing concern, as the government, maritime organizations, and stakeholders face towards providing vaccine and remedial treatment to infected people. Further, this study can be expanded to the global maritime supply chain business context and to conduct interdisciplinary research in marine technical fields and maritime environment to measure the impact of COVID-19. © 2021 Elsevier Ltd
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    Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach
    (Elsevier B.V., 2023) Jena, P.R.; Majhi, R.
    The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences. © 2022 The Author(s)