Isn’t he just a student? Does BK even have a job?
I am a 5th year graduate student in theoretical physics. I make $0.00 per year, so I think it is more than 3 income brackets…lol. Sorry for taking up your thread atlas_shrugged. I apologize again to everyone. I am signing off for a while.
>Isn’t he just a student? Does BK even have a job? Exactly. Junior analyst wannabe.
No worries. Good luck with your studies.
farley for president…
Anyway… Atlas - The problem is that Ph.D. quant programs just aren’t geared that way. If you think the stuff you are studying now is just not practical, it’s ridiculously more so in a Ph.D. quant field. You have to love it or you will just not make it (because there are 17 other things that you would rather do).
And i thought I would gain some insights to quant vs mba topic… what a waste of my time. I am interested in quant, but how “practical” is it really? My coworker is doubtful of anything quant in finance and refers to the fact that 80% of the funds out there don’t beat the index as his supporting evidence.
Google “Styles of Quantitative Finance.” It can give some perspective on different quant roles in finance. It sounds like the original poster should be considering an MFE program instead of an MBA. Or maybe there is some way to combine the two (not sure about that, but worth investigating). I have been wondering if quant funds are facing serious diminishing returns as more and more quant actors reduce the opportunities for arbitrage by eliminating mispricings faster and faster. To some extent, quant funds can increase leverage to compensate for a reduced opportunity set, but even this can run into limits, and the leverage also increases the risk that the funds are taking on. I think the initial advantage that quant funds had was that computers could do calculations on 1000s of securities extremely rapidly and therefore one could start to take advantage of small statistical edges that fundamentally driven funds could not obtain. However, as more and more funds scan the same universe of securities, the opportunities are diminishing. Another advantage of quant funds and algorithmic trading is the ability to enforce a trading discipline in a relatively unemotional way. The cognitive distortions that are likely to happen as you find yourself losing money may make sensible non-quantitative strategies difficult to stick to. Quant fund managers of course can make emotional mistakes too when deciding to override the quant model, or maybe the decision to override can be a sensible one. So where to go? Some of the options are: 1) Try quant techniques on new asset classes. That’s ok, but there aren’t all that many asset classes to chose from, and the most likely new sources would be exotic derivatives almost always have achilles heels in the assumptions that must be made to develop a pricing model. 2) Search for new paid risk factors. This sounds the most interesting to me, but new risk factors are likely to have smaller risk premia than already-known ones, meaning that they are getting harder to find AND have diminishing returns to finding them. 3) Create faster algorithms so that you can beat all the other quant funds to the trade. That’s fine, but there can really only be one “fastest” fund per asset class, and the fastest algorithm might actually be applicable to many asset classes. If this is your strategy, you need to be very confident that you’re going to be the best, and you’re going to be able to stay the best. 4) More basic research, which might come out of Ph.D. graduates in Finance, Econ, Math, Physics, Engineering, and CS. There might be new theoretical relationships that can be exploited, but that is where a Ph.D. (having developed a dissertation) is different from a Master’s (having passed a bunch of advanced classes). The process of developing and defending a dissertation is what forces people to build new tools and subject them to the kind of scrutiny that’s necessary for developing new theoretical relationships. Masters’ level students might be able to do this too, but if you’re trying this strategy, a Ph.D. is a better bet. Hmmm… maybe this should be a blog post.
Thank you for your post Dr. Chadwick. Very good read on your blog! It’s much appreciated.
Interesting post. So with all the armies of MFE thats coming out of school, how are they able to add value to 1. their work place, and 2. the finance industry? There are only so many things that you can do with MFE; what happens when the market becomes saturated with these people?
Derivatives pricing is still important, and doesn’t depend on quant fund performance. Things like MBS modeling too. Inexpensive indexing methods might still be valued too; BGI’s big push these days is using quant techniques to produce ETFs that mimic various risk factors, so they can be assembled into portfolios by non quants; definitely a good way for them to capture a value added. Risk management is getting more and more important, particularly as derivatives become more complex and therefore have more hidden stingers. So there will be some demand for people with the MFE. I just think that the opportunities for creating funds like Rennaisance or Global Alpha are getting harder and harder to find.