Browsing by Author "Lapitskaya, Darya, juhendaja"

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Evaluation of alternative weighting methods for the selection of portfolio optimization model
(Tartu Ülikool, 2023) Rahimov, Nabi; Mammadov, Ismayil; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Market manipulation in cryptocurrencies through social media: the role of influencers
(Tartu Ülikool, 2023) Rahimov, Kamran; Rahimov, Elchin; Lapitskaya, Darya, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Measuring Corporate Reputation through Online Social Media: A case study of the Volkswagen Emission Scandal
(Tartu Ülikool, 2020) Odeyinka, Olubunmi T; Sharma, Rajesh, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Reputation, a priceless but yet essential factor in a company’s success, influences a company’s customers, employees, and even the public relations with the company. Therefore, most companies consider reputation building as a priority in their daily interaction with their staff and the public. Such companies that successfully build a good reputation, which attracted investors, staff, and goodwill of the public sometimes run into issues or events (i.e. self-inflicted or not) that can result in a significant change in their reputation. The series of events that happen after a reputation tarnishing event includes more related news article releases, comments in message boards, and an increase in the number of people talking about the event on social media. In this thesis, we analyzed the company’s online media reputation (i.e. online media sentiment) and compared it with their financial reputation (i.e. stock price and volume) using the Volkswagen (VW) emission scandal as a case study. We did this by computing the monthly correlation, weekly correlation, rolling correlation, partial correlation, trend analysis, and brought out the context of a discussion with a word clouds and we tested our hypothesis of a relationship between online media sentiment and stock values using the Granger causality test. This analysis was done not just on VW online media sentiment and stocks but also on Ford, Toyota, and Audi as control cases for the period under analysis (i.e. VW scandal period). The result shows that during the heat of the scandal the company that is involved (i.e. VW) is the one that presents their sentiment result to be a measure of their reputation only in the media sources that had their context based on the scandal(i.e. news and Twitter) and with a stronger relationship in the weeks of major scandalous news or events. This relationship is not so for other substitute companies’ media dataset like Ford and Toyota whose media context and sentiment were not influenced by the VW scandal events. While Audi, a subsidiary of VW, and our third control case presented sentiment to be a relative measure of the corporate reputation in the news dataset because that is the only dataset of it that had the VW emissions scandal as one of its main focal points of discussion during the scandal timeline.
Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market
(Tartu Ülikool, 2021) Nõu, Anders; Sharma, Rajesh, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Predicting the stock market is a widely researched area of study that is a challenging task. The nature of the problem lies in correctly forecasting the direction and the magnitude of the stock market movement. The severity of the problem exists due to the stock market being impacted by a multitude of factors. There are numerous ways to analyse the stock market and make appropriate investment decisions, but it is challenging to decide the best approach. Here we show which approach is more effective in predicting the returns and volatility of the Baltic stock market: the machine learning or econometric approach. There is a low amount of research on using machine learning or econometric models to predict the Baltic stock market. However, there are no comparative researches that offer a fair comparison between the different approaches for the Baltic stock market. Regarding results, the lowest symmetric mean absolute percentage error for the support vector regression model is 61.90%, and for the autoregressive moving average model, it is 165.43%. The lowest symmetric mean absolute percentage error for GARCH is 51.05%, and for the GARCH-ANN model, it is 61.65%. Overall, the machine learning models outperform the econometric models in most of the evaluated metrics. However, the econometric models’ results are comparable to the machine learning models’ results in most cases.