Qimeng Liu

Meet Qimeng Liu, who studied Financial Risk Management MSc at UCL Computer Science. Learn more about the skillset Qimeng developed during her Master's and how it prepared her for a career in finance.

What's your background? 

I was born and raised in China, and my academic background is strongly rooted in finance and quantitative analysis. I completed my undergraduate studies in BSc Statistics, Economics, and Finance at UCL, which gave me a solid foundation in data analysis, economic theory, and financial principles. 

Building on this, I pursued a Master's degree in Financial Risk Management at UCL, where I gained deeper insights into market risk, financial engineering, and machine learning applications in finance. 

This combination of statistical knowledge and financial risk management equipped me with the technical and analytical skills to navigate complex financial data and make data-driven decisions.

Why did you choose to study Computer Science at UCL?

I chose to study Financial Risk Management in the Computer Science department at UCL because I wanted to gain a unique blend of skills that integrate finance, risk management, and cutting-edge computational techniques. 

In today’s financial industry, the ability to analyse large datasets, implement machine learning algorithms, and design quantitative models is becoming increasingly crucial.

UCL’s Computer Science department offers an innovative approach to financial risk management, emphasising data-driven decision-making and the use of advanced technologies like algorithmic trading and machine learning. All of this would help me to grow professionally with tangible outcomes.

What were the highlights of Financial Risk Management MSc?

One of the highlights of my MSc programme was the module on Machine Learning with Applications in Finance, where I learned so many technical analysis skills and applied machine learning algorithms to predict financial market trends. 

This experience allowed me to explore the practical applications of advanced data science techniques in finance, which I found particularly rewarding.

Fabio Caccioli taught the Machine Learning and Networks and Systemic Risk modules. He was an excellent teacher—patient and thorough, always ensuring we fully understood complex topics

The industry project allowed me to consolidate everything I had learned throughout the year. I applied advanced quantitative methods to solve a real-world financial problem, which was an incredibly fulfilling experience.

What were the highlights of your time at UCL?

The highlights of my time at UCL were both academic and personal. Living and studying in London provided a unique opportunity to network with people from around the world, which helped broaden my perspective. 

 In addition, I also appreciated UCL’s extensive resources and facilities, especially the access to cutting-edge research and technology in the financial and computing fields. I also had friendships that I really cherished, and living in London will bring me many fond memories.

What industry and career opportunities did you access on your programme?

Throughout my MSc, I had access to numerous career-building opportunities. UCL hosted several career fairs, bringing in companies from the finance and tech sectors. There were also many finance-related clubs and societies. 

I also attended guest lectures by industry experts, which provided a practical understanding of how financial risk management and AI are applied in real-world scenarios. These opportunities helped clarify my career goals and expanded my professional network.

What did you write your dissertation on? 

For my dissertation, I focused on using machine learning to predict significant corporate events in financial markets. By analysing data from over 500 Chinese A-share listed companies, I applied models like Random Forests, SVM, Multi-layer perceptrons to forecast positive corporate events such as stock issue and dividend announcements.

I chose this topic because of my interest in how market data can signal corporate events and create arbitrage opportunities. The project enhanced my skills in data analysis and machine learning and deepened my understanding of financial market behavior.

How did you manage your workload, and what advice would you give to prospective students?

I managed my workload by staying organised and maintaining a balance between coursework, self-study, and extracurricular activities. I found that setting clear priorities and creating a detailed study schedule helped me stay on track with assignments and exam preparation. 

My advice to prospective students is to never wait until the last minute, it is important to have everything done earlier as it would help you avoid many unnecessary problems. 

 Another suggestion to prospective students would be to try to actively engage with the resources and opportunities available, whether it's through career fairs, student societies, or networking events. 

How did you find studying in the UK?

Initially, I felt both excited and a bit worried about moving to London. However, UCL provided a lot of support, including orientation activities and opportunities to connect with fellow students through societies and the Student Union.

At the beginning of the course, you will get to know your classmates and start building lasting friendships. I quickly grew fond of the city's charm-particularly its stunning sunsets and the beautiful evening glow. I loved going out with friends after class, exploring everything in London.

Overall, London offered a perfect blend of cultural and social experiences, making my time at UCL truly unforgettable.

Where are you working now, and what are your career goals? How did your programme help you achieve this?

Currently, I am pursuing opportunities in finance sector and find full-time job. My time at UCL, especially the focus on data-driven finance and machine learning, helped me develop critical skills that are directly applicable to my career in finance and risk management. 

The industry networking events and guest lectures were instrumental in connecting with professionals and gaining insights into potential career paths. 

Why would you recommend this course?

I would highly recommend this course because it offers a strong blend of technical expertise and real-world applications. The staff at UCL are renowned in their fields, and their support is invaluable. 

The programme’s interdisciplinary approach which combines finance, statistics, and computer science, provides a robust foundation for anyone looking to enter the world of financial risk management.

Additionally, UCL’s location in London means you're right in the heart of one of the world’s leading financial hubs.