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Dependence Measure of Several Random Variables and its Application on Network Analysis.

Student: Aleksandr Somov

Supervisor: Alexander P. Koldanov

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

Since the advent of business, businessmen have been looking for activities to invest their money. Nowadays, the most attractive investment is stocks from list of blue chips. However, there is a lot of investors who like to risk and choose unknown companies to investing. Both of them need metrics that will help them to make decisions. One of that metrics is measure of dependency between stocks. If investor will know how stocks depends on each other, it will be much easier to make decision – buy or sell. Obviously, it can be applied not only for stocks, but at anything that can be interpreted as random variables. This work focuses on the investigation the measures of dependency between stocks. The aim of this work is to understand if the strong pairwise dependency means strong dependency in general. To achieve the aim, it will be applied two different approaches: theoretical and practical. After that that, approaches will be compared.

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