UDC 303.72: 005.7
DOI: 10.36871/2618-9976.2023.03.005
Authors
Ilya M. Tolstobrov,
2nd year student of the Department of Data Analysis and Machine Learning of the Faculty
of Information Technology and Big Data Analysis of the Financial University under the Government
of the Russian Federation, majoring in Applied Mathematics and Informatics
Yulia B. Kamalova,
Senior Lecturer, Department of Data Analysis and Machine Learning, Faculty of Information
Technology and Big Data Analysis, Financial University under the Government of the Russian Federation
Abstract
In the work, the movement of the stock market in different countries
during the crisis year of 2020 was studied, which did not
depend on the decisions made in this country, but obeyed the
general trend. In particular, the behavior of sectoral indices
of different countries during the first year of the coronavirus
was studied and it was concluded that the movement of markets
is subject to the global trend.
For the analysis, sectoral indices of countries affected to varying
degrees by the coronavirus were selected, whose securities
markets received various support measures: the United
States, Russia, China, Japan and Germany. The impact of the
pandemic on industries such as financial services, metallurgy,
chemicals, consumer goods and telecommunications is considered.
Quotes are considered for the period from 01.01.2020/31.12.2020/XNUMX
to XNUMX/XNUMX/XNUMX. Graphs of the values of sectoral indices of the
Russian Federation and the USA in the specified period are presented
and analyzed. Graphs are built in the Jupyter Notebook
environment using the Python language and the plotly data visualization
library. The concept of hypothesis and criterion is defined
and the most important definitions are given.
With a decrease in the probability of making a type XNUMX error,
the probability of making a type XNUMX error increases, so the significance
level is chosen so that the power of the criterion
is maximum.
To test the hypothesis, we used the multivariate KruskalWallis
test and its modified version for mediumsized
samples with
a significance level of α=0,05.
The novelty of the study lies in the application of the KruskalWallis
criterion to financial data. The study was conducted in
Python in the Jupyter Notebook environment using the numpy,
pandas, itertools, scipy and plotly libraries.
Keywords
Kruskal-Wallis test, Stock markets, Python, Numpy, Pandas, Itertools, Scipy and plotly