POON, Ling

BSc in Risk Management Science
CASH Algo

Working at CASH Algo offered me a valuable opportunity to learn about trading, and particularly about algo trading. My supervisor, Dr Alfred Ma, provided the interns with an appropriate environment within which to absorb knowledge and grow. I was given tasks from the different stages of production so I could see the entire picture of algo trading. I am very thankful for Dr Ma’s guidance.

The first task I was given was to review hypothesis-testing methods. Monitoring strategic performance is an essential and major part of the work of algo trading. The profitability of a strategy may decay due to a fundamental change in the environment or increasing competition. My job was to search for appropriate methods, test for differences in backtesting and real trade returns and then implement them in Python. The experience of coding in Python and seeing examples of real data helped me quickly learn the data-handling procedures in Python.

Although I do not normally read the papers before internships, I learned that a lot of academic knowledge and trading ideas could be gained from doing so, and I learned how to search for the relevant papers.

Moreover, I was able to work with high-calibre colleagues who were very knowledgeable about trading and academia. I had many interactions and discussions with them and learned a lot of trading concepts. Although these results had no physical output, they were equally important to my development.

Overall, the internship was a fruitful experience that allowed me to gain practical knowledge in algo trading, improve my attitude towards work and establish networks within the industry.

CHENG, Hei Ming

BSc in Risk Management Science
Lund University, Sweden

During the first half of 2017, I experienced a very fruitful semester in Lund University, Sweden. The mathematical statistics courses in Lund University suited my academic needs unexpectedly well. During the time of being an exchange student, I left some of the specific investigation in my major study like investment strategy for a period, and went back to the inevitable fundamentals, statistics and mathematics.

During the training in Lund University, I was introduced into non-parametric inference. I found that the investigation in statistical convergence is very crucial when it comes to different applications like simulation techniques, confidence intervals of parameter estimates and statistical model fitting. Also the study in empirical distribution contributes a lot in survival analysis, actuarial science and non-parametric bootstrap. Only through understanding the mathematics behind can we ensure whether we are using the statistical tools in the correct way and right situation. By learning statistical inference, I believe I am able to take several steps further when I return back to my specific major study.

Besides, I took some courses highly related to my major study, which are Monte-Carlo Simulation and Extreme Value Theory. The simulation course exactly covered the materials which are not taught in the course RMSC4001, such as sequential importance sampling, markov chain monte-carlo and bootstrap techniques. Although it introduced the random number generation and variance reduction techniques in the beginning, most of the contents were still fresh to me. The extreme value course is mostly about modeling the extreme values in 3 different approach, which are block maxima, threshold model and point process, and the relations between their respective statistical distributions.

Last but not least, I took a Swedish introductory course for international students in the very beginning of the semester. I met friends from different countries there and we all agreed that Swedish is a vague language to learn.