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Aspiring Quant - the math way or the econ/finance way?

Good Morning Quant Network,

My name is Donny, a math major from a USNews top 10 college. Through some reading, talking to people, and classes, I have set a path to be a quant when I come out of college. I realize that my love for math and my love for gambling (I find Texas Hold 'em a beautiful game) is a good combination for a quant.

Reading up more on this field, I get even more intrigued in the workings of these highly quantitative firms such as Renaissance Technologies, DE Shaw, Jane Street, and Winton capital. I am so interested to know what is this 'secret' algorithms they use to take advantage of short term inefficiencies in the market. I also respect the idea that only PhD holders in Math and Physics have the necessary knowledge to access these ideas, a level I seek to attain, no matter how remotely, once I graduate.

So given my goal, a careful selection of my classes in the coming fall will define the skills I acquire and set the path for my learning. Thus, I have a dilemma which I am sure many of you can give me some input. It is a simple 4 choose 3 subjects between ... (assume all necessary background)

1. Intermediate Finance - a class in the econ department which teaches asset pricing and how to reduce risk.
2. Numerical Hyperbolic PDE - math grad class. Highly useful I think because it is a combination of numerical analysis, pde and C++.
3. Artificial Intelligence - a grad level A.I. class. Standard syllabus I guess.
4. Adaptive Filtering - a class in electrical engineering department. Certainly not a finance class but I'm wondering could this be the 'secret' methods of finance I read about.

The source of the dilemma is to decide whether should I even take a finance class in the econ department. Professors in the econ department says that such a class will provide a good baseline for whatever area of finance I wish to go into. Little do they know that I want to inch myself into the 'highly quantitative advance modeling' scene. As for 2, 3, 4, I just find them to be the concepts that when learnt properly allow me to forge these novel methods in finance.

Oh and just a quick query to the mathematicians, for 2. which would you highly recommend, Numerical PDE or Graduate level PDE, you know the one dealing with operators in Sobolev spaces. Numerical PDE seems highly more applicable. Really, does a advance PDE class give you the intuition to deal and model situations better?

Much thanks to the community.
Donny
P.S.: Andy, I think you are doing everyone a great service with your comments.
 
Time classes and course load classes. Feedback on choosing 3 out of the 4 will also be me a good indicator on how really applicable are these highly scientific and mathematical in the area on quantitative finance.

For example, can I come out of a 'Adaptive Filters' class and tell myself all right, let's use this concept to create a highly adaptive trading system that executes profitable trades.
 

alain

Older and Wiser
For example, can I come out of a 'Adaptive Filters' class and tell myself all right, let's use this concept to create a highly adaptive trading system that executes profitable trades.

I don't know. However, I would expect you to come out of that class and being able to understand and implement adaptive filters for signal processing. How you apply those to finance is another ball game.

Maybe you want to assume the behavior of a return time series is similar to the behavior of a signal that you study in class and take it from there.... but those classes are no alchemy. i.e. take this class, make money next.
 

Joy Pathak

Swaptionz
I will go with 2 3 and 4. A basic finance class is good to have to your arsenal in an application, but is not a high priority. It would be ideal to have at least 1 finance class by the end of your undergrad.

Regarding the electrical engineering class, it would be nice to have the knowledge, but implementation will depend on you.
 

CGiuliano

Lowly Undergrad
My name is Donny, a math major from a USNews top 10 college. Through some reading, talking to people, and classes, I have set a path to be a quant when I come out of college. I realize that my love for math and my love for gambling (I find Texas Hold 'em a beautiful game) is a good combination for a quant.

This is exactly how my attention was drawn, also.

Any online hold 'em stars on quantnet? :D
 
Hey guys,

Thanks for the reply. So looks like it's gonna be Numerical PDE, Adaptive Filtering and Artificial Intelligence for me. Yup, most of the points you guys mention was exactly what I'm thinking, that it is easier to read up finance on your own than it is to go through programming exercise in signal processing and numerical methods without a higher intellect guiding you.

On that note though, I am hoping it is perfectly reasonable that I approach the finance field with these specialized non-finance methods. I know myself and I'm not the type to make trades based on a broad knowledge of the market. Instead, I hope to equip myself with the knowledge that gives me a technological edge above the rest, finding statistical patterns in the market and capitalizing on them in that short time frame.

And that's the reason why I refrain from an econ major. I just hope that me avoiding being a generalist will mark me as someone 'who doesn't know enough'.
 
Here is an outline of what I'll be learning in the class.

CPS170

At first I thought, AI/Machine Learning in trading? Not much of a clear connection. Then I thought to myself. If I were to really build an automated trading system that made decisions based on the information received and not me be present 24/7 to monitor the algorithms, a course on A.I. would serve me well. Again, I seek to be that specialized person who attempts to develop novel methods to challenge the systems, methods which revolve around A.I., Machine Learning, and Neural Networks.

In addition, from the articles I read,

Automated Trading and Artificial Intelligence The Online Investing AI Blog

A.I. does seem to have its uses. People study A.I. because it is part of a computer science major. However, my intent is clear - one of which is to build automated trading systems. Having said that, does a class in A.I. really help me achieve my goal.
 
One thing of note: You said you went to a USNews Top 10 college and then you created a link to what school you go to.

Not saying you did anything wrong, many people here are open about what school they go to, but as a point of continuum, if you're trying to keep something under wraps, don't accidentally give it away. Again, it's not big, and it may sound like I'm nitpicking, but if you're operating without telling us something for a purpose, don't accidentally give it away.

P.S. Go Terps.
 

alain

Older and Wiser
At first I thought, AI/Machine Learning in trading? Not much of a clear connection. Then I thought to myself. If I were to really build an automated trading system that made decisions based on the information received and not me be present 24/7 to monitor the algorithms, a course on A.I. would serve me well. Again, I seek to be that specialized person who attempts to develop novel methods to challenge the systems, methods which revolve around A.I., Machine Learning, and Neural Networks.

Machine learning is widely use but this class doesn't cover it. I don't know how much of an introduction you are going to get.
 

atreides

Graduate Student
Donny, for machine learning maybe you can leverage this site CS 229: Machine Learning for some self learning. It has all the materials, handouts, past projects (which are quite interesting), etc...and you can also get all the class videos (about 40 of them or so) from itunes U.


Machine learning is widely use but this class doesn't cover it. I don't know how much of an introduction you are going to get.
 

alain

Older and Wiser
Correct me if I'm wrong, but most likely to be able to implement AI/machine learning on the market would require a PhD in CS?

Sorry but that is nonsense.

---------- Post added at 11:22 AM ---------- Previous post was at 11:17 AM ----------

I can tell you more. You can buy this book

http://www.amazon.com/Elements-Stat...=sr_1_1?ie=UTF8&s=books&qid=1272554398&sr=1-1

go to the book website

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

And get all datasets, functions and sample code in R using the package ElemStatLearn:

http://cran.r-project.org/web/packages/ElemStatLearn/index.html

And if you are really into R, you can set up the whole Machine Learning Task View.

http://cran.r-project.org/web/views/MachineLearning.html
 

Daniel Duffy

C++ author, trainer
Oh and just a quick query to the mathematicians, for 2. which would you highly recommend, Numerical PDE or Graduate level PDE, you know the one dealing with operators in Sobolev spaces. Numerical PDE seems highly more applicable. Really, does a advance PDE class give you the intuition to deal and model situations better?

Numericl PDE with finite difference; make sure you know matrix methods, nonlinear solvers, interpolation the level of Dahlquist.

And you can program this stuff in C++.

Graduate PDE, nice to have, later. Sobolev spaces are not used much in finance.

hth
 
Thanks so much for the reply guys. I find this quant network to be highly supportive in the community.

So looks like I've made up my mind. I'll go with numerical PDE and adaptive filters. Since the A.I. course isn't taylor to Machine Learning, there's actually a separate Machine Learning course but it isn't offered in the fall, I'll go with Alain's advise on learning it, either in the summer and during the fall.

Maybe I can use the other slot to take Graduate level PDE or Functional Analysis. I know I know, totally abstract classes in math and not in finance. It is for my second plan to head straight to PhD should I have that option or should I need to proceed further in Quant finance.

I was thinking that I may be able to go to quant finance after I graduate. But at the same time, there must obviously be a reason why certain quant firms have PhD for 1/4 of their force.

Makes me wonder again ... would someone with a dissertation of say algebraic topology have any use in the quant world. The reason I believe is that PhD gives you problem solving skills and that is what you need to have, along with the ability to quickly understand math literature, to make it further.
 

alain

Older and Wiser
If you go the PhD route, concentrate on knowing the topic/field of your choosing really well. Don't worry about the money or the job in finance afterwards. That part will sort itself out.
 
Makes me wonder again ... would someone with a dissertation of say algebraic topology have any use in the quant world. The reason I believe is that PhD gives you problem solving skills and that is what you need to have, along with the ability to quickly understand math literature, to make it further.

This seems to be a common misapprehension and one which I don't share. If your doctorate is in algebraic topology or (say) number theory, you will stay have to slowly and methodically learn PDEs and numerical analysis and your expertise in one will not necessarily have any impact on how quickly pick up the other. Indeed, the patterns of thought and inherent biases of the first may interfere with learning the second. If you are aiming for finance, do a PhD in a related area -- such as the numerical analysis of PDEs.
 

Joy Pathak

Swaptionz
There aren't too many options to do PhD Finance in "quant finance" types of topics. Most of that work is done out of the OR or Math departments of universities. PhD finance is mostly focused on empirical work nowadays. Atleast 2/3rd of the research published in some form of empirical work.

It's hard to find too many quant finance professors in business departments. Obviously there are quite a few, but usually one applies to multiple universities for PhD Finance, and it is hard to find that many profs in the business school doing that kind of research. I doubt many at the top 5 schools have ever taken a class in some of the topics mentioned in this thread.

At-least this is something I ran across not too long ago while researching PhD Finance programs. And if you think, a PhD Math student will have the opportunity to do research with a professor in the finance department...that is a really far stretch. It almost never happens, not even as a co-advisor. There are instances though, but it is not easy.
 
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