PhDs -- investment banking
Contributer: haris aziz
I recently received an email from a ‘banking science’ organisation that had arranged a workshop where practitioners from the financial services industry in London would give an insight into their job. The workshop was specially geared towards PhDs. Since quite a few friends from mathematics and science have ended up in this sector, I thought it was worth checking what the excitement was about. A (Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets) which I had read recently also interested me towards this event.
The whole event was well managed and it was surprising to find over two hundred students there who were doing postgraduate or doctoral studies. I was sitting next to a German man doing a PhD in economics and a Spanish man doing a PhD in Biology. Most of the presenters were from the top investment banks (Deutsche Bank, Morgan Stanley, Barclays Capital, Goldman Sachs, HSBC, JP Morgan etc.). They had done PhDs in scientific subjects and then switched to banking. A chemistry researcher raised concerns that it seemed that investment banks seemed inclined towards mathematics and physics PhDs. Although the speaker gave examples of how varied backgrounds were welcome, it did seem evident that mathematical PhDs were desirable because of the complex modelling and quantitative analysis involved in the job.
Although most talks were about the London financial sector, there was one presentation about the numerous opportunities in Qatar and other centres that are part of Gulf Cooperation Council. There was an interesting comment by Dr. Jessica James from Citi was that in the banking sector, mathematical PhDs are almost like the classics degrees of the pervious era, a sign of an ‘intelligent’ and ‘useful’ person. She gave a relatively technical presentation on how to make better predictions.
One common piece of advice was for PhDs to take part in an internship program so that they can see if the financial sector is the career for them before taking a plunge. I was particularly impressed by the presentation of a Polish physicist who switched to Wall Street and has a major role in HSBC. I will try to convey some useful advice he gave in my next entry.
Piotr Karasinski is the global head of quantitative development at HSBC. He was a successful academic before he shifted to Wall Street. Therefore if anyone is able to give some insight into investment banking it would be him. Piotr Karasinski explained that the role of quantitative analysts is to “implement derivates pricing models, develop tools for calibrating model parameters, analyse model performance and provide trading desk support.”
He felt that applicants for banking jobs need to demonstrate interest in finance either by personal reading or specialized workshops. Among quantitative tools he particularly emphasized partial differential equations, probability & statistics, stochastic and programming skills. He also emphasized basic knowledge about stocks, bonds, call/put options, interest rates and inflation. He commented that some knowledge of Capital Asset Pricing Model, Black-Scholes model and Gaussian Mean-Reverting Short-Rate model is helpful.
Compared to other speakers, Dr. Karasinski gave tips (Principles of Money, Banking and Financial Markets (<Addison-Wesley Series in Economics>; <My Life as a Quant: Reflections on Physics and Finance>; <Options, Futures and Other Derivatives>; <Frequently Asked Questions in Quantitative Finance (Wiley Series in Financial Engineering)>) certain books to read. Among magazines, he recommended (http://www.risk.net/),(http://www.cfapubs.org/loi/faj) and (http://www.wilmott.com/). He commented that additional dimensions of knowledge of psychology, economic history and business are crucial for any mathematical PhDs applying to be a quantitative analyst.
There were a couple of more focussed presentations on algorithmic trading which is of particular interest to me. There is great development in this area and it is something that has also attracted considerable interest (http://www.marketbasedcontrol.com/blog/) in the computer science academic community. On the banking side, algorithmic trading involves complex modeling of historic movement to predict the future, and analysis for hedge strategy. There is also a scope of exotics which includes pricing of complex financial instruments and risk management techniques. One speaker emphasized proprietary trading as one of the key areas of focus. Overall, the presentations were insightful enough to provide a flavour of what life in an investment bank is like.
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