Rating change classication of insurance companies indicators

Authors

  • V. P. Zubchenko Taras Shevchenko National University of Kyiv
  • Ye. O. Kostiuk Taras Shevchenko National University of Kyiv
  • M. O. Lukashchuk Taras Shevchenko National University of Kyiv; Instituto Politecnico Nacional, Centrode Investigacionen Computacion, Mexico City, Av. Juan de Dios Batiz S/N, Nueva Industrial Vallejo, Gustavo A. Madero, 07738 Ciudad de Mexico, Mexico
  • A. M. Yaroshevskyi Taras Shevchenko National University of Kyiv

DOI:

https://doi.org/10.17721/1812-5409.2020/1-2.4

Abstract

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.

Key words: LDA, news space analysis, topic modelling, Naive Bayes, financial indicators of insurance company.

Pages of the article in the issue: 31 - 35

Language of the article: Ukrainian

References

Blei, David M. and Ng, Andrew Y. and Jordan, Michael I. (2016), "Latent Dirichlet Allocation", JMLR.org, v. 3, pp. 993-1022.

Rish, Irina (2001), An Empirical Study of the Naive Bayes Classifier", Empir. methods Artif. Intell. Work. IJCAI 2001, v. 22230, pp. 41-46.

Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas (2017), Enriching Word Vectors with Subword Information", Transactions of the Association for Computational Linguistics, v. 5, pp. 135-146.

Stone, M. (1974), Cross-Validatory Choice and Assessment of Statistical Predictions", Journal of the Royal Statistical Society: Series B (Methodological), v. 36 , pp. 111-147.

Aletras, Nikolaos and Stevenson, Mark (2013), Evaluating Topic Coherence Using Distributional Semantics", Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) — Long Papers, pp. 13-22.

Gagniuc, Paul A. (2017), From Theory to Implementation and Experimentation, John Wiley & Sons, pp. 1-235.

Shunrong Shen and Haomiao Jiang and Tongda Zhang (2012), Stock Market Forecasting Using Machine Learning Algorithms, Department of Electrical Engineering, Stanford University, Stanford, CA.

Hegazy, Osman and Soliman, Omar S. and Abdul Salam, Mustafa (2013), A Machine Learning Model for Stock Market Prediction", International Journal of Computer Science and Telecommunications, v. 4, pp. 17-23.

Coupelon, Olivier (2007), Neural network modeling for stock movement prediction: A state of the art", https://cutt.ly/Js9lhiM

Tetlock, Paul (2007), Giving Content to Investor Sentiment: The Role of Media in the Stock Market", Journal of Finance, v. 62, pp. 1139-1168.

Engelberg, Joseph and Parsons, Christopher (2011), The Causal Impact of Media in Financial Markets", The Journal of Finance, v. 66, pp. 67-97.

Atkins, Adam and Niranjan, Mahesan and Gerding, Enrico (2018), Financial News Predicts Stock Market Volatility Better Than Close Price", The Journal of Finance and Data Science, v. 4.

Khan, Wasiat and Ghazanfar, Mustansar ali and Azam, Muhammad Awais and Karami, Amin and Alyoubi, Khaled and Alfakeeh, Ahmed (2020), Stock market prediction using machine learning classifiers and social media, news", Journal of Ambient Intelligence and Humanized Computing.

Floreddu, P. and Cabiddu, Francesca (2014), Managing Online Reputation: The Role of Social Media in Insurance Industry", Academy of Management Proceedings, v. 1.

Cerqueira, Vitor and Torgo, Luis and Mozetic, Igor (2019), Evaluating time series forecasting models: An empirical study on performance estimation methods.

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How to Cite

Zubchenko, V. P., Kostiuk, Y. O., Lukashchuk, M. O., & Yaroshevskyi, A. M. (2020). Rating change classication of insurance companies indicators. Bulletin of Taras Shevchenko National University of Kyiv. Physical and Mathematical Sciences, (1-2), 31–35. https://doi.org/10.17721/1812-5409.2020/1-2.4

Issue

Section

Algebra, Geometry and Probability Theory