Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Stock markets play an important role in shaping an economic portfolio in many countries and are often used as critical ways to measure economic health and financial status in numerous studies. Financial markets are often volatile and can be influenced by a wide range of direct and indirect variables. The current Covid-19 Pandemic has severely impaired the economic markets in many parts of the world and has negatively affected millions of investors. While some financial markets or stocks are expected to recover or partially recover from this crisis, others may not. With recent crises, such as the 2008 economic crash or the economic impact of the 9/11 event, researchers are looking for innovative ways to analyze the behavior of financial markets under crisis. Can we apply traditional analytical approaches to study the behavior of financial markets under crises, or a different new approach is required to conduct such a study? This paper proposes a new network model and employs a population analysis approach to address such an important research question. We present the basic steps that illustrate how to construct correlation networks of financial stocks and how to utilize graph-theoretic properties of the constructed networks to analyze the behavior of stocks over a given period of time. The proposed population analysis approach allows us to compare the behavior of various groups of companies and their relevant economic sectors in the stock market. We apply the correlation network analysis on different financial data and study the financial implications of two major events, the 2008 economic crash, and Covid-19. In particular, we use the networks to compare the behavior of different economic sectors and uncover the similarities and differences between sectors and their reactions or behavior during these two events. We were able to see certain patterns and extract useful information from the correlation networks. For example, we observed that companies in finance sectors behave in a similar way under the effect of both events and identify some similarities between the behavior of the energy sector during the current pandemic and the utility sector during the 2008 crash.
A Novel Population Analysis Approach for Analyzing Financial Markets under Crises – 2008 Economic crash and Covid-19 pandemic
Online
Stock markets play an important role in shaping an economic portfolio in many countries and are often used as critical ways to measure economic health and financial status in numerous studies. Financial markets are often volatile and can be influenced by a wide range of direct and indirect variables. The current Covid-19 Pandemic has severely impaired the economic markets in many parts of the world and has negatively affected millions of investors. While some financial markets or stocks are expected to recover or partially recover from this crisis, others may not. With recent crises, such as the 2008 economic crash or the economic impact of the 9/11 event, researchers are looking for innovative ways to analyze the behavior of financial markets under crisis. Can we apply traditional analytical approaches to study the behavior of financial markets under crises, or a different new approach is required to conduct such a study? This paper proposes a new network model and employs a population analysis approach to address such an important research question. We present the basic steps that illustrate how to construct correlation networks of financial stocks and how to utilize graph-theoretic properties of the constructed networks to analyze the behavior of stocks over a given period of time. The proposed population analysis approach allows us to compare the behavior of various groups of companies and their relevant economic sectors in the stock market. We apply the correlation network analysis on different financial data and study the financial implications of two major events, the 2008 economic crash, and Covid-19. In particular, we use the networks to compare the behavior of different economic sectors and uncover the similarities and differences between sectors and their reactions or behavior during these two events. We were able to see certain patterns and extract useful information from the correlation networks. For example, we observed that companies in finance sectors behave in a similar way under the effect of both events and identify some similarities between the behavior of the energy sector during the current pandemic and the utility sector during the 2008 crash.
https://aisel.aisnet.org/hicss-55/da/big_data_and_analytics/2