The BxMx Programme offers students, pursuing BS in Mathematical and Computational Sciences, the opportunity to seamlessly transition into MS in Quantitative Finance. This unique career trajectory is designed for those with a foundational understanding of the principles governing various phenomena, as taught in undergraduate Mathematical and Computational Sciences programmes, and wishing to integrate this knowledge with the context-specific understanding of financial and economic systems required for a successful career in quantitative finance.

The Programme allows students to develop cross disciplinary skills encompassing analytical thinking and problem-solving which are crucial in mathematical and computational sciences and quantitative finance. By integrating these fields, students can acquire a diverse skill set in mathematical modelling, data analysis, and critical thinking, equipping them for quantitative roles in finance, economics, or related fields.

The Programme recognises the value of mathematical and computational sciences in understanding complex systems, a skill that can be applied to financial and economic models. Concepts from mathematical and computational sciences, such as statistical analysis, network theory, and computational modelling, can aid in analysing and modelling the behaviour of financial markets and economic systems.

Moreover, the  Programme acknowledges the strong foundation in mathematical modelling that mathematical and computational sciences provide which can be extended to develop sophisticated models for financial markets. By integrating the two, students can gain a deeper understanding of the underlying principles of financial models, risk management, and derivative pricing.

The interdisciplinary nature of the Programme also enables students to contribute to cutting-edge research in areas such as market microstructure, econophysics, behavioural economics, and quantitative risk analysis. Machine Learning and Artificial Intelligence have become indispensable tools in Finance and are being increasingly applied in empirical methods. Many pressing challenges in finance and economics require interdisciplinary approaches, and the integration of mathematical and computational sciences with these fields fosters collaboration among experts from different disciplines and can lead to innovative solutions.

Lastly, graduates of the Programme can explore a wide range of career opportunities. With expertise in mathematical and computational sciences and quantitative finance, they can pursue roles in quantitative trading, risk analysis, financial engineering, economic consulting, data analysis, and research. The integration of these fields equips graduates to adapt to various sectors, including finance, technology, energy, and government.

To strike a balance between each discipline, the Programme offers a strong foundation in mathematical and computational sciences and quantitative finance. It also incorporates interdisciplinary coursework and research opportunities. Additionally, collaborations with industry and research institutions bridge theory and practice.

Programme Outcomes

• Knowledge and Comprehension
  • Demonstrate a comprehensive understanding of fundamental principles of mathematical and computational sciences, as well as quantitative finance.
  • Recall and effectively articulate key concepts, methodologies, and models employed in the fields of mathematical and computational sciences and quantitative finance.
  • Summarise and interpret relevant research findings and scholarly literature in mathematical and computational sciences and quantitative finance 
• Application and Analysis
  • Apply mathematical and computational sciences techniques, algorithms, and programming languages to proficiently solve financial problems and examine financial data.
  • Critically analyse and evaluate financial models, econometric/quantitative methods, and statistical techniques to interpret economic data and draw meaningful conclusions or for effective decision-making and risk assessment.
  • Assess and interpret the implications of computational solutions and economic/financial models in practical real-world scenarios.
• Synthesis and Evaluation
  • Integrate mathematical and computational sciences principles and quantitative finance concepts to develop innovative approaches to financial problem-solving.
  • Design and implement computational tools and software applications tailored for addressing economic research questions and facilitating data analysis or for financial analysis, modelling, and decision-making.
  • Critically evaluate and compare different economic/financial models, strategies, policies, computational methods, and technologies, considering their strengths, limitations, and ethical implications.
• Application of Technology and Tools
  • Utilise statistical/financial software, programming languages, and computational tools to analyse and visualise economic/financial data effectively.
  • Employ advanced computational techniques, such as machine learning algorithms or simulation models, to enhance economic/financial modelling, risk assessment, and trading strategies.
  • Apply data analytics and visualisation techniques to gain insights from economic data and support evidence-based data-driven decision-making in the economics/finance domain.
• Communication and Collaboration
  • Effectively communicate complex ideas, quantitative analyses, and computational solutions to diverse audiences using appropriate methods and tools.
  • Collaborate with professionals from different backgrounds such as economists, policymakers, and computer scientists, to solve interdisciplinary problems and engage in team-based projects.
  • Participate in academic and professional discussions, present research findings, and contribute to scholarly conversations in mathematical and computational sciences and quantitative finance.
• Research and Innovation
  • Conduct independent research projects, applying research methodologies, and quantitative techniques to explore new areas in the domain.
  • Innovate and propose novel approaches, algorithms, or technologies to advance economic analysis, modelling, and decision-making, or to address existing challenges or improve financial processes.
  • Evaluate and synthesise existing research, identifying gaps, and proposing areas for future investigation at the intersection of the two.