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The Value of Priori Information in Data Mining Procedures

Student: Aleksey Smirnov

Supervisor: Petr Koldanov

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

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 9

Year of Graduation: 2020

The subject of this research is to study the influence of a priori information on the quality of statistical tests and the properties of an optimal portfolio of assets. The study is divided into two parts. In the first part, the Student’s test and the Z-test were compared in terms of power. Those tests test whether the mean of a given distribution has a value specified, using unknown and known variance, respectively. In this part, the threshold value of the error in the a priori variance was found at which the Z-test remains more powerful than the Student’s t-test. In the second part, errors in the covariances of market assets were considered as errors in a priori information. The result of this part is the value of error at which an optimal portfolio built based on a priori information is not much worse than the portfolio built on estimated parameters in terms of its properties. To sum up, the obtained value of the error in a priori information is equal to 10% of the value of the original parameter. Thus, if it is possible to get a priori information to that level of error, its usage is justified.

Full text (added May 24, 2020)

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