Introduction
Greetings, I’m Yue Yao, originally from Nanjing, China and currently live in Chicago, Illinois. My interest in quantitative finance began in my freshman year. At that time, I took a course called 'Introduction to Computational Finance.' This course introduced how computer programs can be used to predict stock prices and how fast algorithms can be employed to identify mispricing in the market. This was very different from my understanding of investing. I had initially thought that investing required fundamental analysis, and I didn’t know that computers could help us find the alpha in noisy data. At the end of the course, the professor introduced us to several quantitative finance companies. Through my learning and efforts, I was fortunate enough to secure an internship at one of these companies as my first internship
During my first internship, I made learning the fundamentals of quantitative finance my highest priority. I learned how to use linear regression for short-term stock price prediction and how to use natural language processing to enable computer programs to understand company financial reports. Due to the recognition, I received from my manager for my approach to learning while working during my first internship, I gained a substantial amount of knowledge while also earning an exceptionally good final rating. This success helped me secure an internship opportunity at a more prestigious quantitative finance company
My second internship was at a world-leading hedge fund. The colleagues here were incredibly smart and had a cutting-edge knowledge of quantitative finance. We worked together on alpha research, then conducted risk allocation, optimized different strategies in the portfolio, and finally designed a fast and accurate execution algorithm.
This experience convinced me that this was the field I wanted to continue developing in, so I joined the company full-time. Now, every morning, I start by reviewing financial news and analyzing its impact on our strategies. I also spend some time reading papers, trying to implement unique alpha ideas, and incorporating them into our trading strategies to see if they add marginal value. This job is a mix of opportunities and challenges.
Quantitative Finance Fellow
career options
Quantitative finance positions include many roles, range from front office as a trader, mid office as a quantitative researcher, and back office as a quantitative research engineer. All these roles closely cooperate with each other and work on projects including raw data collection, alpha generation, portfolio construction and order execution.
Quantitative Finance Fellow
skills
What are the main hard skills you use on a daily basis in your current job?
Coding in different languages is a must-have skill in quantitative finance industry. For example, C++ is good at handling low latency trades; Python is good at back testing system building; R is good at data processing and visualization. I learnt Python during my freshman year, C++ during junior year and R during my internship.
Statistics is a crucial part of quantitative finance. Through data mining, natural language processing, and linear regression, people can find the alpha in noisy data. I have continuously studied these statistical methods during my time in university, internships, and full-time work.
An alpha signal is complex and can be composed of thousands of different datasets. Therefore, when discussing your ideas with colleagues or senior management, it's essential to use tools like Matplotlib and RStudio to create clear and concise visualizations of the complex data. This directly determines whether your idea can capture the attention of the portfolio manager. I learnt these skills during my college years.
What are the main soft skills you use on a daily basis in your current job?
Communication is a very important soft skill. In the highly competitive field of quantitative finance, teamwork is especially crucial, and good communication is the foundation of collaboration. During my internships and full-time work, many of my ideas were generated through discussions with team members. Clearly expressing your thoughts and data, and receiving clear feedback from colleagues on how to improve your ideas, helps us create better quantitative strategies.
How you prioritize projects determines whether you can sustain a long-term career in quantitative finance. Most companies in this industry are composed of vast amounts of data and very lean teams, which means everyone needs to be very clear about which ideas are worth spending more time on. In my full-time work, I have a weekly catch-up with my manager to decide which idea to focus on for the week. If the idea doesn't yield good results, we quickly switch prioritization to try another project.
The finance industry often means that your performance is directly tied to the profit you generate. However, we all know that the stock market is highly volatile, and despite doing a lot of research, there are times when you might make incorrect price predictions. The work pressure in the quantitative finance industry is immense. I try to set a weekly maximum for my working hours, engage in fitness activities during my free time to maintain energy, and ensure I get enough sleep to relieve work-related stress.
Yue
’s personal path
Tell us about your personal journey in
Quantitative Finance Fellow
:
My career in quantitative finance began with an internship at Bank of America during the summer of my sophomore year. I approached the internship search process in three stages: first, I researched the company by reviewing their recent projects and the roles they typically offer to interns. Next, I identified the technical skills and qualifications they value in their interns. Finally, I tailored my resume and cover letter to align with what the company was seeking. After navigating multiple rounds of interviews, which included both technical assessments and discussions about my career goals with various companies, I received an offer from Bank of America. The summer internship flew by quickly and smoothly and I got a return offer after 12 weeks of hard work. I gained valuable insights into collaborating with others and understanding how large companies operate.
I interned at Citadel during the summer of my junior year. Citadel’s internship interviews include both phone and onsite interviews, which require substantial preparation including many rounds of mock interviews with friends. The four back-to-back onsite interviews focused on assessing fundamental quantitative finance knowledge, system design, and algorithms. My three-month internship at Citadel was both challenging and fulfilling. My coding skills improved, and my perspective on finance became sharper. The experience solidified my determination to stay in the financial industry
After graduation, I accepted a return offer from Citadel and began my full-time position there. I have worked at Citadel for four years now. I started as a software engineer and transitioned to a quantitative researcher after two years. Despite the heavy workload, the visa situation became an even greater challenge. I applied for OPT after graduation and maintained that status for three years. After four unsuccessful H-1B lottery attempts, I had to move to Canada for a year. Working remotely for a hedge fund proved difficult, and it took some time to find an effective working mode. Additionally, I had to fly back to the U.S. almost every month. I managed to push through, and now I am working towards a management path within the company.
What would you tell your younger you regarding building your current career?
If I had the chance, I would tell my younger self two things:
First, don't be afraid to speak up your ideas. I remember when I started my second internship, I had an idea to speed up the existing market data API. However, at the time, I lacked confidence and felt that, as an intern, I should focus more on following the manager's planned schedule, so I didn't present my idea. When I returned to the same company as a full-time employee, I expressed the idea and received unanimous praise from everyone. I always regretted not bringing it up during my internship.
Second, work hard and play hard. Quantitative finance is a very challenging but also highly rewarding industry. It's important to work hard while also paying attention to your physical and mental health. Only those who achieve a balanced development can have a longer career in quantitative finance.
Final thoughts & tips
In conclusion, although the term 'quantitative finance' may seem very sophisticated, it is actually a science made up of many fundamental principles of computer science and data science. Exploring quantitative finance is like exploring the world, helping you better understand how different factors such as company news, international relations, and public life influence the economy. In this industry, countless brilliant ideas will emerge—hopefully, one of yours will be among them.
Resources to dig in more
Python learning and API installation
The homepage for all python related resources
Quantitative finance intro course
A free quantitative finance introduction course
Data visualization package
A python package called Matplotlib that can make data visualization pretty easy to manage
