Darya Lapitskaya will defend her doctoral thesis „Online media analysis and financial markets“

On 10 March at 9:00 Darya Lapitskaya will defend her doctoral thesis „Online media analysis and financial markets“ for obtaining the degree of Doctor of Philosophy (in Economics)

Supervisors:
Lecturer Mustafa Hakan Eratalay, University of Tartu
Visiting Professor Rajesh Sharma, University of Tartu

Opponents:
Prof. Dr. Darius Plikynas, Vilnius University, Lithuania
Associate Professor Dmitrij Celov, Vilnius University, Lithuania

Summary
Online media plays a crucial role in the spread of information in the modern world, and its significance in the global economy and the financial market cannot be overlooked. We can observe that the spread and popularity of online media platforms have drastically changed the way people communicate and share information. For example, buyers of various goods constantly check online reviews, trends, and opinions of online media influencers (individuals who have gained their popularity and reputation through their online presence and activity) before completing the purchase. Moreover, professional stock and financial assets traders also regularly check online media platforms for the latest news and market trends, and we can see examples where a single viral post or comment seems to affect a stock or asset price within hours after appearing online. Therefore, it is important to understand how to analyse this correlation and which tools can be used for accurate analysis.

This doctoral thesis is dedicated to investigating how information spread through online sources affects companies and the everyday purchasing behaviour of regular buyers. It explores various methods, including econometric and machine learning approaches, to determine the most effective way to analyse the stock and cryptocurrency prices.

The studies included in this doctoral thesis analyse the usage of online media sentiment for financial predictions using a combination of traditional econometric models and modern machine learning techniques. In this thesis, various qualitative and quantitative methods are used: machine learning regressions, econometric models, sentiment analysis, and surveys. The thesis investigates different methods for price analysis, highlighting the correlation between online media sentiments and stock returns, and discusses the most accurate methodologies for market analysis. This research also provides a detailed overview of how online media impacts regular users and the financial market and opens up opportunities for future research. The results of the study demonstrate the effect online media tends to have on financial markets and recommend the most suitable techniques for various types of analysis.

The defence will be held online

Thesis: https://hdl.handle.net/10062/119232