College of Business and Law - Masters by Research Theses

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    Core international crimes committed in Ukraine since the Russian full-scale invasion: complementarity, cooperation and Russian distortion
    (University College Cork, 2026-01-31) Sheehan, Rosalyn (Lynn); Cubie, Douglas; Zeffert, Henrietta
    The thesis examines how complementarity between Ukrainian and international initiatives operates in terms of ensuring accountability for the Russian full-scale invasion of Ukraine which began on 24 February 2022. It investigates the capacity of Ukraine’s domestic criminal justice system to process the significant number of core international crimes generated by the Russian invasion and assesses the support provided by the international community. The thesis concludes that Ukraine possesses a functioning system capable of processing many cases with its EU accession process serving as an important driver. The thesis also analyses the range of international mechanisms operating alongside Ukraine’s domestic system. A central focus is the Special Tribunal for the Crime of Aggression, spearheaded by the Council of Europe in 2025, but also examines the role of the International Criminal Court, the Joint Investigation Team, the UN Commission of Inquiry, the Council of Europe Register of Damages and the role of the application of the Principle of Universal Jurisdiction. International support remains essential but is fundamentally constrained by Russia’s capacity to obstruct the international legal order. The situation has been further complicated by the changing political landscape since January 2025, including shifts in US policy towards Ukraine. Despite these geopolitical challenges, practical solutions within both the Ukrainian criminal justice system and International Criminal Law structures remain possible but international peace and security remains under threat.
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    Investigating the future of digital assets in financial services
    (University College Cork, 2025) Stanley, Christopher; McAvoy, John; Feller, Joseph; State Street
    The growth of digital assets and the underlying technology that underpins them – distributed ledger technology – has been highlighted by their popularity amongst retail consumers and general popular culture throughout their emergence in the last decade. These novel forms of assets and their associated technology have significantly altered perspectives of many retail investors on how currency is managed and distributed in the modern technological era. However, there is still a lack of clarity on if and how large institutions across the financial services could leverage these assets and technology to their benefits. This lack of clarity is a key contributing factor to the overarching research objective of this paper. That being to understand digital assets on a more fundamental level, with a view to how they may be adopted and leveraged by organisations across the financial services industry. Existing literature on the area facilitates a more fundamental understanding of digital assets and associated processes across chapters 2 and 3, whilst chapter 4 delves deeper into their use by stakeholders across the financial services industry. Chapter 2 proposes a framework which can be used to classify digital assets and delves into some of the key detailed information around digital assets in the process. This framework adds to the existing body of information around digital assets and given the lack of an existing framework for classifying digital assets according to their structural properties, provides a useful means of doing so. Following this, chapter 3 further explores digital assets, but with a focus on their associated process of conversion called Tokenisation. Tokenisation is examined by comparing it to the process of digitalisation and a conceptual framework is derived which can be used to evaluate the suitability of traditional financial assets to be converted to digital assets through tokenisation. Once the fundamental concepts around digital assets are thoroughly understood, chapter 4 examines how companies within financial services could leverage these digital assets within their respective organisations. This chapter uses foresight research techniques to garner a more future oriented perspective on the application of digital assets within financial services. Results from chapter 2 show that the digital asset classification framework could be utilised for classification, ownership and regulatory purposes, and identifies a core issue for regulation in the area as being a lack of a consistent framework based on the structure of the assets themselves. Chapter 3 gives context to the landscape of tokenisation and demonstrates the importance of organisations considering the suitability of an asset to the process ahead of tokenising it. Chapter 4 finds that trust is the key factor to be considered by organisations operating in the financial services industry if they are to leverage the perceived value that digital assets offer, detailing ways that organisations could address this in the process.
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    Machine Learning and its application in the Portfolio Management industry
    (University College Cork, 2024) Murphy, Conor C; McAvoy, John; Kiely, Gaye Louise
    Machine Learning (ML) is a subdivision of Artificial Intelligence (AI). AI is a term used for technology that “enables machines to mimic human thoughts and behaviour” (Xu, 2021). Recently, there has been a significant increase in the use of ML techniques by finance professionals, primarily by the Portfolio Management industry (Perrin, 2019). This thesis reflects on ML’s application in the Portfolio Management industry. To further understand this relationship a literature review is carried out in Chapter 2 to identify the intersection of ML and Portfolio Management and to highlight key criteria required for ML’s application in the Portfolio Management industry. A key finding from Chapter 2 found that the lack of quality data is a critical barrier to ML’s application in the Portfolio Management industry. Chapter 2 examines the use of sentiment from unstructured data, in the main tweets, for stock market prediction in the Portfolio Management industry as alternative data source for ML. There are a variety of ML algorithms that can be applied for Twitter sentiment stock market prediction, Chapter 3 employs the Multilayer-Perceptron (MLP). MLP has been employed successfully in other studies for Twitter sentiment stock market prediction (Livingston, 2019; Turchenko, 2011). Before running the MLP prediction algorithm, EmoLex was employed to identify the underlying sentiment that may be apparent in the tweets. Subsequently, a prediction algorithm MLP Classifier was run to ascertain daily sentiment stock price predictions for the data. For the prediction analysis section the Vader Sentiment Analyser from the Natural Language Tool Kit (NLTK) in python was employed to split the Twitter sentiment into three categories (positive/neutral/negative), while the MLP classifier was run through SickIt-learn package in python. Interestingly, the performance of the MLP in Chapter 3 is not as accurate as other studies including, Kolasani (2020), Usmani (2016) & Khan (2020). Following the same guidelines as Pagolu (2016) and Mittal (2012) incorporating a new algorithm (the Random Forest algorithm) using the same methodology outlined in Chapter 3 may outperform the MLP. Studies such as Bollen (2011) indicate that positive Twitter sentiment will be reflected in the stock market by a positive increase in stock price and negative Twitter sentiment will result in a fall in stock price. The use of the Random Forest regressor in this study found that in the case of Black Swan events, Twitter sentiment has the same predictive power as a chance model. These results are integral to the Portfolio Management industry as it clearly indicates that Twitter sentiment cannot be used to gauge the severity of Black Swan events nor can it be used as a method to predict it. Only one of the Tweets in the database collected referenced that China allowed one of its banks to go into liquidation, indicating that the speed of stock price drop was ahead of the Twitter sentiment.
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    Soccer and CO2: air travel at international football tournaments from 1990 to 2024
    (University College Cork, 2024) McCarthy, Conor J.; Butler, Robert; Butler, David
    This research explores the impact of football air travel on the environment for all men’s World Cup and UEFA European Football Championships hosted between 1990-2024. These tournaments required qualifying teams to travel to a host country and attract tens of thousands of supporters to stadiums throughout the host country. The supporter of teams and players travel across the host nation during their stay in the competition. Depending on the success of the team this can range from about 10 days up to 5 weeks.
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    A study on the correlation between future economic conditions and stock returns under China's A-share market
    (University College Cork, 2023) Wang, Zitong; Gao, Jun; Sherman, Meadhbh
    Our study aims to investigate the explanatory power of future economic conditions on the returns of individual stocks in the Chinese A-share market. We use an integrated research methodology to study this topic scientifically and systematically. All A-share stocks in the Shanghai Stock Exchange and Shenzhen Stock Exchange are selected as the primary research objects, and data representing China's real economic activity are widely collected. With these data, we analyze a new trading strategy based on a reasonable prediction of future real activities using the capital asset pricing model and the Fama-French three-factor model. In addition, we have specifically examined the performance of this trading strategy on two different types of stocks (pro-cyclical and counter-cyclical stocks). Particularly noteworthy is that, unlike previous studies, we innovatively adopt the producer price index (PPI) as a measure of China's real economic activity based on the largest possible selection of sample intervals. This innovative approach provides a new perspective for us to deeply understand the operating mechanism of China's A-share market and the impact of economic conditions on the performance of individual stocks. The study results show no significant relationship between future economic conditions and individual stock returns in China's A-share market. Besides, those investors who want to get excess returns can short the pro-cyclical stocks and/or long the counter-cyclical stocks if production growth in the following month is anticipated to be above the steady state and vice versa. Our new trading strategy demonstrates potential advantages and gives investors a unique decision-making perspective. This finding not only provides investors with a more reliable basis for decision-making but also helps us to gain a deeper understanding of the operating rules of the Chinese stock market. This new perspective sheds light on the potential impact mechanism of economic activities on the Chinese stock market, which sheds important light on the optimization of investment strategies and risk management. Meanwhile, our trading strategy research provides an innovative way for investors to seek better returns among different classes of stocks. However, some things could be improved in this study. Future research can be further deepened and expanded to understand the relationship between China's A-share market and economic conditions more comprehensively and continuously optimize trading strategies' practicality and robustness. Overall, the findings of this thesis fill a research gap in the academic field and provide new perspectives and methods for investment decision-making and risk management. These findings will provide valuable references for relevant scholars and practitioners and contribute to a deeper understanding of China's macroeconomic conditions and their relationship with the A-share market.