Alumni Profiles Series: Yang Chen
Dr. Yang Chen is managing director of the Risk and Quantitative Analysis Group at BlackRock, an investment company and the world’s largest asset manager. He completed his Ph.D. in Statistical Science at Duke in 1997, advised by Dr. Mike West. After earning his Ph.D., he immediately began his career on Wall Street, and since then has held quantitative analysis and risk management positions at several major financial institutions. He was promoted to his current role at BlackRock in 2012. Along the way, he completed an MBA at New York University’s Stern School of Business in 2005.
Tell me about your current Position.
My group supports the $1 trillion fixed-income portfolio management business here at BlackRock. Risk is central to portfolio management, especially at BlackRock, where a founding principle was to bring state-of-the-art risk analytics and risk management practices from the sell side to the buy side. Our team is responsible for those risk analytics to support decisions in portfolio construction and risk management.
Our day typically starts at 7:30 a.m. with a series of investment meetings to analyze what has happened in the world and in the markets. In these meetings, a big part of the discussion is around the risk exhibited by our portfolio. We then try to anticipate a range of scenarios for future news and events, and how those situations might impact the market and, ultimately, our portfolio. That’s an essential part of any investment discussion.
To what extent does your training in statistics play a role in your work?
At the end of the day, finance and investing is a probability game. If you go into an interview with a finance company, there’s a good chance, regardless of whether you’re a statistics major or not, they’re going to ask you about probability and statistics, because that’s really a big part of what we do on a day-to-day basis. There is at least one true similarity between investing and gambling: having a positive expected value is necessary but not sufficient if you want to make money. You also need to maintain a very high probability that you will survive the worst drawdowns, because you have to be able to stay in the game. So, in that sense, estimation of probabilities is essential to risk analytics.
However, there are also differences between modern risk analytics and statistical modeling approaches typically encountered in academic settings. In real-world financial data, there are all kinds of distributions that you observe. Before 2008, the finance industry became fascinated with a concept called Value at Risk. It was widely adopted because it is simple, easy to communicate, and elegant. But then during the 2008 financial crisis, people realized that simple and elegant summaries like that, which are based on a lot of assumptions, can fail quite badly. So, post-financial crisis, pretty much every participant in the industry has supplemented that type of elegant, model-based risk analytics with more scenario-driven methods which, one might say, are more subjective. We spend a lot of time with experts modeling very extreme scenarios. For example, if Russia were to initiate a nuclear attack, what would happen to the economy, to the markets, and how would that impact our portfolio? Clearly these are subjective exercises, but it’s important to assess our vulnerabilities to truly extreme scenarios, and not just scenarios that would be extreme based on past experience or fitted normal distributions. This is all related to probability, but we approach it in a very practical way.
What made you decide to return to school for an MBA, and how has that experience shaped your career?
The choice to pursue my MBA was very deliberate. When I finished my Ph.D. and got a job on Wall Street, I was excited to enter a new field, but the first couple of years were eye-opening. After four years of Ph.D. training in statistics, I knew a lot about models, but I really didn’t know anything about the world of finance and business. So, as I was sitting in the business meetings, I didn’t know how I would tell my story about my modeling work to the business decision-makers in an intuitive way that they could quickly understand. I realized that this communication element is critical, and it was missing from my training. So, my motivation for going to business school was mostly to develop my ability to communicate in these situations, and it turned out to be a great decision.
In my first semester of business school, I was fascinated by the MBA students. During a Ph.D., we’re trained to make simple things complex, because we want to go deeper. We ask “Why?”, we focus more on the conceptual part of things, and not as much on finishing them, whereas these business students were very much goal-oriented and deadline-focused. When the professor would give us a project, I would think hard on “Why is he asking me this?” or “What is the concept that we want to extract?”, whereas the other MBAs approached it as, “You do this; I’ll do that; let’s get together tomorrow and finish it.” For me that was a fascinating, different way of thinking, and it was very practical.
How did you go about breaking into the finance industry?
I tried everything. At that time [in 1997], there weren’t many Ph.Ds. on Wall Street, so many companies didn’t know how they could use a Ph.D. I went to Duke’s Fuqua School of Business for an undergraduate career fair, because I was collaborating with a Fuqua professor on my dissertation. People didn’t know what to say to me because they had never hired a Ph.D.! Eventually, I started to build my own network in the industry by sending out cold emails to people through GARP—the Global Association of Risk Professionals—and that worked! In the world of finance, and in fact I think in any industry job, networking is very, very important.
What advice would you give to someone just starting out in the industry?
If you want to work in industry, having a balance of different skills is very important, and that balance has to involve analytical skills, leadership skills, and communication skills. The ability to work with people is so critical in a practical setting. These skills are often underemphasized in a Ph.D. setting, so you want to be aware of that and be consciously developing those types of skills.
In your opinion, what is the most important trend in the financial industry today, and how will this affect your work over the next five years?
Finance as a field constantly evolves, and that’s what drew me to it in the first place. Every day is new, and you’re always facing new challenges. Going forward, we’re in a world of disruption—as machine learning continues to advance and new data sources appear, every industry is now focusing on using these tools to help with automation, research, and decision making. The development of these technologies will continue and will impact how we invest. Finance is always at the cutting edge of such changes and adapts quickly, and we’re really in the middle of this next wave of change.
Ph.D. candidate, Statistical Science
David Buch is a fourth-year Ph.D. student in the department of Statistical Science at Duke University. His research involves developing inference methods which are robust to model misspecification.