We are pleased to announce that Paulina Mikuciauskaite, a graduate of the Finance Engineering Department at the VILNIUS TECH Business Management Faculty, has been awarded in the "Best Master's Thesis 2024" competition in the field of social sciences. Paulina's master's thesis, "Securities research using deep learning and portfolio optimization," was recognized as outstanding in the competition organized by the Lithuanian Young Scientists' Union. The thesis was supervised by Associate Professor Dr. Nijole Maknickiene from the Finance Engineering Department.

We spoke with Paulina and her thesis supervisor about the award, the research process, and the formula for success.

Paulina's success brings joy not only to herself but also to her thesis supervisor, Associate Professor Dr. Nijole Maknickiene. According to the supervisor, this award proves that hard work, innovative solutions, and perseverance can lead to exceptional results. She proudly shares her insights on what led to such a high evaluation of the work as well as the success formula.

– Why do you think this thesis was recognized in the competition?

The thesis is very consistent. The foundation is based on a classical idea, but the methods are innovative and logically structured. The MCDM method was used to rank stocks, the LSTM algorithm was applied for price forecasting, and the Black-Litterman optimization algorithm was used to create portfolios that were tested in real-time. This synergy was unique.

– What was the basis for preparing a successful thesis?

It was the synergy between the student, the university, and the supervisor. The university provided access to MATLAB software and research publications. Paulina stood out for her perfectionism and persistence. Although the thesis was nearly completed, she conducted additional research in the final weeks, which helped achieve excellent results.

Paulina Mikuciauskaite's journey to the prestigious award is a story of courage in tackling complex topics, a passion for science, and consistent work. Choosing to explore stock market forecasting and portfolio optimization using deep learning, Paulina not only overcame academic challenges but also developed a practical decision support system for investors. She shares her insights, results, and advice that can inspire other students to aim for the highest goals.

– Why did you choose this topic, and why is it relevant?

I chose this topic because it was one of the most interesting yet challenging subject matters. I am intrigued by the investment field, especially the difficulty of forecasting the stock market. Deep learning is currently a modern tool, and its application to portfolio optimization seemed interesting as a new challenge.

– What were the main results of your research?

I managed to create a system for investors that combines several methods. We defined investment parameters, narrowed down stock choices using MCDM, forecasted prices with the LSTM algorithm, and applied the Markowitz as well as Black-Litterman methods for portfolio optimization. The system helps not only to select but also to evaluate the best strategies.

– What is the recipe for success when writing a master’s thesis?

It is important to balance work and rest, but the thesis must be a priority. I aimed for perfection and persistently searched for solutions even when difficulties arose. It is also crucial to accept insights and constructive criticism from the supervisor and professors.

– What advice would you give to other students?

Paulina's success not only inspires but also demonstrates how modern scientific methods can be applied in practice. We congratulate Paulina and wish her many more professional and academic victories!

We would like to thank the thesis supervisor, Associate Professor Dr. Nijole Maknickiene, and Business Management Faculty alumna Paulina Mikuciauskaite for their excellent results and the warm conversation. We are proud of our community!

The interview was prepared by Dr. Jolanta Nalivaike, Vice Dean for Communication and Community at the VILNIUS TECH Business Management Faculty.