Yuanming Shan

Emerson Corporate Fellow

McKelvey School of Engineering, Olin Business School: Electrical and Systems Engineering | MA and Finance | MS


Cohort 2006


Graduated 2010

Partner University:

Fudan University


Yuanming Shan graduated from Washington University in St. Louis in May 2010. Yuanming came to WUSTL from Fudan University and entered the Academy and the Department of Electrical and Systems Engineering (ESE) in 2006. In addition to earning a masters degree in ESE, he earned a Master of Science in Finance from the Olin Business School in 2008. In January 2010 he accepted a position at the Houston green tech firm Horizon Wind Energy.

Shan made a gift his first month’s salary to the McDonnell International Scholars Academy.

In looking back at his time in the Academy, Yuanming spoke of “the great opportunities provided by the Academy to develop ourselves” and concluded that “hard work alone does not guarantee achievement.” Instead, “leadership and networking are also about caring for others’ success,” a point that led him to say he hopes that “when we gain more ability and accumulate more resources, we can help create more opportunity for new scholars.”

Jim Wertsch, director of the Academy, said, “The Academy is deeply appreciative of the generous gift Yuanming has made. It is the first gift of its kind to the McDonnell Academy. In addition to the financial support, we value his reflections on the Academy’s role in shaping his future and the future of those who will follow in years to come.”

Scholar Highlights

More or Less Qualification?

Nowadays there is a trend for investigators in more and more areas of inquiry to harness complex quantitative measurement techniques in their research. While fields such as physics, chemistry, and engineering have been very successful in using quantitative methods, efforts in other areas have become a bit “over quantified” in my judgment. I have doubts that many real world problems in areas such as finance are best understood by adopting such methods.

Take the 2008 buyout of Bear Stearns by JP Morgan Chase, for example. Do you know how JP Morgan Chase computed the bidding prices, first at $2 and then at $10 per share? Honestly, I do not know. But I do know that the quantitative models of finance taught in business schools would be very unlikely to arrive at $2, $10, or any other whole number for that matter. The valuation of a company’s stocks in such cases is too complex and involves far too many factors to arrive at such round numbers.

Instead, it is often more reasonable to arrive at a general figure based on the operational conditions and financial status of a company. Indeed it would not be very productive in such cases to model something like stockholders’ psychological states and client relationships. My guess is that the $2 and $10 bidding prices were based on general experience and elements of compromise. Using basic estimation techniques would probably provide little justification for the prices.

As another warning against putting too much stock in quantitative models, don’t forget the crash of the Long-Term Capital Market hedge fund, which operated under the leadership of two Nobel Prize winners. No matter how sophisticated the models, small random factors can destroy the whole enterprise.

Qualitative and semi-quantitative methods are helpful for research in sociology, finance and psychology, but investigators in these fields tend to overuse advanced mathematical tools to describe the past and predict the future.

These are practices that I see as having doubtful justification. The reason why so many people keep developing these eye-catching models is presumably the illusion of precision and objectivity that numbers bring with them. We tend to think numbers are objective, hard to manipulate, and precise, and these qualities are viewed as providing more power when justifying a line of reasoning or action.

It is important to remember that according to Mark Twain statistics is one of the three kinds of lies. If you really delve into those models, you often come up with a common finding: in many cases the models neither surmount nor solve difficult issues. Instead, they frequently just transfer the failure to understand something from one place to another.

This brings to mind a joke: A buyer asks a vendor peddling flea pesticide if it is effective. The vendor says, “Sure, if you catch a flea, smear the pesticide on its mouth, it will die immediately.” Are the facts caught by vendors of mathematical models any more trustworthy than this? In my experience, it is hard to identify the real benefits of practices such as estimating key parameters or translating verbal descriptions into concrete numbers – despite the fact that such practices are usually quite expensive and time consuming.

On balance, then, the fact that quantitative modeling and measurement are quite successful in natural science inquiry should not blind us to the limitations they have when applied to human affairs. As a rule, we should be very cautious about quantifying everything. When deal.ing with problems concerning human relationship, standard experience and judgment often make more sense than complex mathematic models.

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