Prediction of Stock Market Volatility Using Social Media Data

Student: Shaidullin Ansel

Supervisor: Mariam Mamedli

Educational Programme: Applied Economics (Master)

Year of Graduation: 2021

The relevance of the chosen topic lies in the fact that in the modern world the role of using various forecast methods is growing. The work materials have practical value for banking organizations and investors who can apply the specified model to predict risks and threats. The work analyzed the stock market volatility of several Russian companies that belong to different sectors of the economy: from oil and gas industry (Lukoil, Rosneft, Gazprom, Novatek) to companies engaged in the service sector - the banking sector (Sberbank), IT (Yandex), telecommunications (MTS). As it turned out, different companies (with different focus) react differently to sentiment and attention factors based on social networks. The paper also offers practical recommendations for choosing the scope of application of the results and models obtained. Key words: news tone, realized volatility, stock market. UDC: 004.852; 004.043; 336.76.066; 336.761.532; 336.763.215; 336.763.218 JEL: G17, G32

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