QLLSCOIL TEICNEOLAIOCHTA BHAILE ATHA CLIATH DUBLIN: Funded PhD Position in FinTech

With the rapid evolution of finance, technological advancements have paved the way for innovative solutions that address complex market challenges. Among these advancements is the burgeoning field of Financial Technology (FinTech), which integrates cutting-edge technology with financial services. As part of this trend, Technological University Dublin (TU Dublin) is excited to announce a fully-funded PhD position focused on the intersection of FinTech and machine learning, specifically targeting American option pricing.  As a result, We have provided a detailed overview of the PhD opportunity, the project's significance, student requirements, funding details, and the application process.

The Project: Machine Learning for Option Pricing

American options, which provide the holder the right to buy or sell an underlying asset at a predetermined price before or on a specified expiration date, play a critical role in modern financial markets. With over 1,900 options available for major stocks like Apple, the complexity and dynamic nature of option pricing necessitate advanced methodologies to ensure accurate and timely valuations. As stock prices fluctuate, so too must the metrics associated with these options, presenting a significant challenge for traders and investors alike.

The Role of Machine Learning

For over five decades, the financial industry has grappled with the complexities of real-time American option pricing. Traditional models often fall short in capturing the volatile nature of the markets, leading to inefficiencies and suboptimal decision-making. The introduction of machine learning (ML) offers a promising avenue to address these challenges. By leveraging ML algorithms, the project aims to develop sophisticated models that can accurately predict option prices in real time.

Machine learning techniques can transform the limited number of option parameters into input-output pairs suitable for predictive algorithms. The swift processing capabilities of ML can significantly enhance the efficiency of repetitive pricing tasks, enabling traders to make informed decisions based on real-time data. This project represents a significant step forward in the FinTech landscape, particularly for trading platforms and investment banks that require accurate pricing for effective risk management and speculative strategies.

Internship Opportunity

An exciting component of this PhD program is the inclusion of a three-month paid internship with Allied Irish Banks (AIB) Financial Risk Team during the second year of study. This placement provides the successful candidate with invaluable industry experience, allowing them to apply their academic knowledge in a practical setting. The internship will include participation in relevant work projects, training programs, and workshops within the Group Risk division, further enhancing the student's professional development and research capabilities.

Student Requirements

To be eligible for this prestigious PhD position, candidates must meet specific academic and technical criteria. Applicants should possess a minimum of a 2.1 Bachelor’s or Master’s degree in a quantitative discipline, such as Quantitative Finance, Computer Science, Econometrics, or Mathematics. Additionally, a strong foundation in programming languages such as Python, R, VBA, or C/C++ is essential. Familiarity with derivative pricing, machine learning techniques, and previous experience in academic publication will be advantageous for prospective candidates.

For international applicants whose first language is not English, evidence of English proficiency is required to ensure effective communication within the academic and professional environments.

Funding Details

This PhD position is supported by TU Dublin and funded through the Technological University Research and Innovation (R&I) Supporting Enterprise scheme. This initiative is co-financed by the Government of Ireland and the European Union through the ERDF Regional Programme 2021-2027. The funding package is comprehensive, covering all essential aspects of the PhD journey:

• Stipend: €25,000 per annum for four years
• Registration Fees: €5,500 per annum
• Project Costs: £2,000 per annum (with an additional €1,500 in the first year for laptop costs)
• Travel Expenses: £1,000 per annum

This generous funding ensures that the successful candidate can focus entirely on their research without financial constraints.

Application Process

Interested candidates are invited to apply for this remarkable opportunity by submitting a curriculum vitae (CV) and a cover letter to Dr. Qianru (Jennifer) Shang at the Faculty of Business, Technological University Dublin. The application deadline is August 26, 2024, with the project set to commence in September 2024.For more information on the application process and entry requirements, including details on English language proficiency for international students, please visit the TU Dublin website.

TU Dublin Official Website

Conclusion

The PhD position in FinTech at Technological University Dublin represents a unique opportunity for aspiring researchers to contribute to the evolving field of financial technology through innovative machine learning approaches. By addressing the critical challenge of American option pricing, the successful candidate will not only enhance their academic credentials but also gain practical experience through a valuable internship with Allied Irish Banks.

As the financial landscape continues to transform, the need for skilled professionals who can bridge the gap between technology and finance has never been more pronounced. This PhD opportunity at TU Dublin is a stepping stone for those interested in making a significant impact in the FinTech sector. We encourage all eligible candidates to apply and embark on this exciting academic journey.

For further details and to submit your application, please contact Dr. Qianru (Jennifer) Shang at gianru.shang@tudublin.ie. Your future in FinTech awaits!