Open AccessRating change classication of insurance companies indicators
Author(s) -
Володимир Зубченко,
Ye. Kostiuk,
M. Lukashchuk,
A. Yaroshevskyi
Publication year - 2020
Publication title -
vìsnik. serìâ fìziko-matematičnì nauki/vìsnik kiì̈vsʹkogo nacìonalʹnogo unìversitetu ìmenì tarasa ševčenka. serìâ fìziko-matematičnì nauki
Language(s) - English
Resource type - Journals
eISSN - 2218-2055
pISSN - 1812-5409
DOI - 10.17721/1812-5409.2020/1-2.4
Subject(s) - latent dirichlet allocation , metric (unit) , set (abstract data type) , computer science , actuarial science , order (exchange) , space (punctuation) , naive bayes classifier , econometrics , topic model , data mining , business , artificial intelligence , mathematics , finance , marketing , support vector machine , programming language , operating system
In this paper we investigate the relationship between financial indicators of insurance companies and news space. The news space is considered as a set of topics. The goal of the paper is to fit the model in order to forecast company's rating change for given indicators — whether rating will go up or down regarding the current value. As the data set we use news articles of the relevant insurance topics for the specified time period. The approach we use includes search for the most influential topics for the given indicator. To retrieve topics, we used Latent Dirichlet Allocation (LDA) algorithm and Naive Bayes model. For the validation the Leave-One-Out approach was used with accuracy metric.