Applying Machine Learning To Stock Market Trading : An Interview with RBC’s Head Quant
Applying Machine Learning To Stock Market Trading : After nineteen years of experience in the finance industry, Vasily Strela would say his biggest weakness is that he still enjoys it! After working at Morgan Stanley and JP Morgan Chase & Co., Vasily enjoys his role of Head of FICC Quants at RBC Capital Marketsin New York. Before this, he had the priceless experience of studying at MIT.
Vasily moved to MIT in 1992, after finishing his Masters in Applied Mathematics at the Moscow Institute of Physics and Technology (right at the collapse of the Soviet Union). There he had the invaluable experience of studying under Gilbert Strang, a math professor known for major contributions in many areas of academia. Strela had Strang as his thesis advisor, and when asked about his experiences, said, “We still keep in touch. Working with him was always a pleasure and very important for me personally…”
In his work at RBC Capital Markets today, Strela often finds himself in a similar analytical mindset to the one he used in university. He loves the challenges he faces, which change every day. Strela stands by the importance of good communication, saying that it’s proven critical in both his academic and industry life. This skill he says sometimes is overlooked among students of quantitative majors.
Vasily finds that students need to work more on the ability to communicate and looks for strong communication skills while interviewing for RBC Capital Markets. In his case, he finds that when managing a team, it’s crucial to understand where each member is in a project, and how well they’re getting along. And communication is not so different from teaching, in which someone must understand where their student is, and what they must realize to move forward.
Vasily has maintained ties to MIT. He helps show students how to apply math in finance (“Topics in Mathematics with Applications in Finance,” with materials available in OpenCourseWare). When describing the purpose of the course, Vasily said, “I find it that quite often, MIT students get a great education, but they’re not sure how they could use it.” The course explains how certain mathematical and statistical concepts, that students otherwise see in formal, isolated, mathematical circumstances, can be used to evaluate profit and risk in financial markets.
In a way, this course is a capstone of all Vasily’s experiences. It reviews math topics at first, but after that it delves deep into market analysis. The class shows that finance is a fantastic field for these mathematical tools and models, because it encourages problem-solving and quantitative thinking. Perhaps this is why when asked what he liked to see most in new RBC employees, and what the top students had, Strela responded, “Problem-solving skills. Education can help you with that, even though it does require a broader mindset.”
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