Master in Financial Engineering | metrics table at end of article
When Mikhail Chernov took over as the academic director of UCLA’s financial engineering programme in July 2015, he immediately started firing people.
“I replaced the adjuncts with regular faculty,” he says matter-of-factly. “And that streamlines the curriculum. If adjunct faculty are teaching core classes, it’s very hard to co-ordinate the material. Now, when a professor comes in the spring quarter, they know exactly what was covered in the winter quarter.”
UCLA’s master of financial engineering runs for 15 months. Before applicants even set foot on campus, they complete up to 60 hours of introductory material. The programme itself consists of 10 core classes and four electives, all of which are offered from inside the department. Students don’t often take additional courses outside of the seven optional modules on offer.
“Theoretically, they can take other classes. But their plate is full. You’d have to be a super hero. They’re busy all day long,” says Chernov.
The degree course, first established in 2009, is “front-loaded”, as Chernov describes. Initial workloads are deliberately intensive so that students are prepared for interviews for the summer internships.
“You interview in the late fall for summer internships,” says Chernov. “And students have just arrived on campus and just started getting used to things. Now, they dive into the more serious material right away. The classes are still of an introductory nature, but it’s a step up in terms of what they learn.”
As well as course material and an internship, the programme includes a seven-month “applied finance” research project, which starts immediately before the summer break. Students undertake the project in teams of four, and each team is attached to a financial company.
According to Chernov, some companies assign UCLA teams on a rolling basis to long-term, ongoing problems. “That’s what we’re doing with Citibank, for example,” he says. “It gets going with one team and the next year it’ll be another. Our students get to see what real quant programmes these institutions are dealing with. They get an academic perspective on the problem as well as the real-life perspective.”
For Chernov, who arrived in Los Angeles in 2013 after a stint at LSE, striking an appropriate balance between academic theory and the real-life application of complex techniques is a priority. In the early years of the programme, risk management classes would focus on historical hedging failures, such as the losses suffered by German conglomerate Metallgesellschaft, or the derivatives-related bankruptcy of Orange County – work that Chernov describes as “boring cases from 20 years ago”.
The programme aims to prepare students for a financial industry within which the employment of neural networks, natural language processing and unstructured data scraping technology is as common or rote as performing basic value-at-risk calculations.
“Our programme is nimble and we’ve been able to adapt quickly,” says Elisa Dunn, executive director of the master’s course. “We have an advisory board comprising industry professionals from consulting, investment banks, insurance and asset management and we get together several times a year to get a sense of the shifts they’re seeing in their industries.”
Despite tech companies, as Chernov puts it, “gobbling up a lot of graduates”, Anderson students remain interested in traditional employment avenues in finance.
“There’s basically unlimited demand on the risk management side and on the asset management side,” he explains. “So we have a dedicated course on asset management, on risk management, as well as the ‘big data’, data analytics and machine learning classes. We’re preparing students for what the industry needs.”
Technologies that are at the fringes of finance will increasingly move into the central ground. Blockchain, for example, will standardise and streamline instruments such as mortgage-backed securities and complex derivatives, Chernov believes.
But automation will not put today’s grads out of a job. “I think robots will probably replace low-level jobs, but you always need high-functioning humans to push things forward.”