Benefit of more math

[Lukee]

I'm looking for insight on how beneficial taking additional advanced math classes is for MFE applications. By the end of next semester I will have all of the basic prerequisites done, beyond that, my degree requires PDE and numerical analysis. I have enough time and room on my schedule to take some real analysis (and potentially measure theory depending on how that goes).

My main concern is that taking these classes will force me to spend most if not all of my time studying rather than on interview prep and extracurriculars.

Do you think that the benefits of taking more math exceeds the opportunity cost?

 

[benzenephi]

Generally no. Simply stacking up math courses is not helpful beyond a point. But what else are you planning to do with that time. Tell us that.

 

[Daniel Duffy]

Online Courses :: Datasim

www.datasim.nl

 

[Lukee]

Generally no. Simply stacking up math courses is not helpful beyond a point.

At what point does it become stacking up math classes vs. learning useful math and developing mathematical maturity? I’m purposefully avoiding classes that seem completely irrelevant such as abstract algebra.

But what else are you planning to do with that time. Tell us that.

This is what I’m trying to figure out now, any advice is appreciated

 

[benzenephi]

That depends on your profile. You gotta identify in which areas its not so strong and boost them up.
Stuff you could do:

Programming: Quantnet C++certificate. optional: Codeforces, Kaggle.
Work Experience: Internship at a BB/ hedge fund. Anything finance related is okay.
Math courses: Baruch Pre-MFE seminars assuming you didn't do well at preliminary courses like AC, prob, NLA. If you did, further math courses should be done only if you have nothing else to do. Let me know if you need a list of math courses most relevant to quant.
Finance: CFA Level 1.

 

[Lukee]

Programming: Quantnet C++certificate. optional: Codeforces, Kaggle.

I have 2 light programming courses next semester. Might do the qn c++ this summer and plan on taking some classes later on in data structures, algorithms, and machine learning

Work Experience: Internship at a BB/ hedge fund. Anything finance related is okay.

My biggest weak point, I haven’t had anything relevant yet. This is something I want to focus on

Math courses: Baruch Pre-MFE seminars assuming you didn't do well at preliminary courses like AC, prob, NLA. If you did, further math courses should be done only if you have nothing else to do.

I have a 4.0 in all related classes so far

Let me know if you need a list of math courses most relevant to quant.

Let’s hear it, I’m curious now

Finance: CFA Level 1.

Too expensive, I probably won’t be doing any CFA before I graduate

 

[Andy Nguyen]

C++, Python, Options, interview books, mock interviews, etc.
That will get you ahead instead of more math courses beyond what needed for the degree.
We are all here to get ready for interviews on day 1.

 

[benzenephi]

I have 2 light programming courses next semester. Might do the qn c++ this summer and plan on taking some classes later on in data structures, algorithms, and machine learning

My biggest weak point, I haven’t had anything relevant yet. This is something I want to focus on

I have a 4.0 in all related classes so far

Let’s hear it, I’m curious now

Too expensive, I probably won’t be doing any CFA before I graduate

Okay, from where I see it, your work experience is what needs to be improved. Your math background is sufficient if you already got a 4.0 in all the pre-req courses. Don't waste any more time doing additional irrelevant courses. Focus all efforts on getting relevant work-experience. I applied to around 100 places, practically every role in my country that was related to some kind of quant finance, including quant dev, research. After 2 months, out of the blue, I received an interview call and I attended it. It was for a quant dev internship role and I got selected, with zero prior experience and a subpar GPA. Point is you gotta persevere when it comes to interviews. You might have an easier time than me, since you are still in uni and have good grades. I dunno. But thats what you should focus on, imo. Work Experience.

 

[Lukee]

It was for a quant dev internship role and I got selected, with zero prior experience and a subpar GPA.

What did you have on your resume, projects?

I definitely agree that I need to focus on getting experience, thanks for confirming this.

Can I also please see that list of quant related math classes you mentioned earlier?

 

[Daniel Duffy]

Apart from grades, jobs etc. _now_ is the time (in your 20s) to learn deep maths. Later is too late.

 

[benzenephi]

Take with grain of salt, as these are based on my personal research. All these are not pre-reqs and are NOT necessary for MFEs. I wanted to just see the relevance of a typical math curriculum to quant finance.

"""MATH 490 - Mathematics of Machine Learning Rating: 90/100. It dives into error, loss, risk, and empirical risk minimization, which are crucial for building robust quant models. Things like linear regression, logistic regression, and support vector machines .

Math 442 (Introduction to Partial Differential Equations): Rating: 70/100. Partial Differential Equations (PDEs) have their place in the quant finance realm, especially when modeling derivative pricing and other financial instruments. However, this course dives into a lot of mathematical theory which might not offer a direct route to practical quant finance skills. It’s useful, but not a knockout punch.

Math 441 (Differential Equations): Rating: 60/100. Differential equations are a part of the quant finance toolkit, but this course doesn't seem to tailor its content towards financial applications. It's more of a general course on differential equations which might not offer the laser-focused training you need.

Math 446 (Applied Complex Variables): Rating: 45/100. Now this one’s got a bit more meat for the quant world, especially when delving into contour integrals and series convergence which can be applied in quantitative finance. However, it still veers off into a lot of complex variables theory that won’t pay direct dividends .

Math 448 (Complex Variables): Rating: 40/100. There are some tidbits like Cauchy’s theorem, and understanding complex plane could have applications in quant models. However, a big chunk of it delves into complex analysis which might not provide the direct practical advantage you need.

Math 481 (Vector and Tensor Analysis) Rating: 35/100. While understanding manifolds and tensor fields could lend a hand in high-level quant modeling, this course dives too deep into the mathematical jungle. It's too theoretical and won't directly fuel your quant fund rocket.

Math 447 (Real Variables): Rating: 30/100. Diving into real variables, metric spaces, and integration is useful, but this course might get too bogged down in theory rather than tackling real-world quant finance problems. Some concepts here are foundational, but it's not a direct route to mastering quant finance.

MATH 417 (Introduction to Abstract Algebra): Rating: 30/100. Abstract algebra has its beauty but it's not the heavy artillery you need. Some concepts like modular arithmetic and cryptography could be relevant in certain quant finance scenarios, like security and encryption in financial transactions. However, the bulk of this course veers off into pure mathematical theory.

Math 423 (Differential Geometry): Rating: 20/100. This one’s a deep dive into geometric theory which doesn't directly translate to the quant finance battlefield. The theory of curves and exterior calculus are elegant, but they won’t provide the ammo needed for financial modeling and algorithmic trading.

MATH 444 - Elem Real Analysis: Rating: 20/100. This course is all about the basics. It’s like learning how to punch in boxing. Important, but not the killer blow you need. It covers fundamental concepts that you should already have a handle on. It’s not going to give you the advanced ammo you need."""

 

[Daniel Duffy]

Math 442 (Introduction to Partial Differential Equations): Rating: 70/100. Partial Differential Equations (PDEs) have their place in the quant finance realm, especially when modeling derivative pricing and other financial instruments. However, this course dives into a lot of mathematical theory which might not offer a direct route to practical quant finance skills. It’s useful, but not a knockout punch.

Math 441 (Differential Equations): Rating: 60/100. Differential equations are a part of the quant finance toolkit, but this course doesn't seem to tailor its content towards financial applications. It's more of a general course on differential equations which might not offer the laser-focused training you need.

yes, few do finance DE. And not relevant as such.
446 ... not on critical path.
?? no numerical analysis/methods??

These ODE/PDE are geared to quants. There is no other like it. UCB students etc. do it. State of the art PDE/FDM.

Online Courses :: Datasim

www.datasim.nl

Numerical Methods in Computational Finance: A Partial Differential Equation (PDE/FDM) Approach

This book is a detailed and step-by-step introduction to the mathematical foundations of ordinary and partial differential equations, their approximation by the finite difference method and applications to computational finance. The book is structured so that it can be read by beginners, novices...

www.wiley.com

 

[alovya]

Apart from grades, jobs etc. _now_ is the time (in your 20s) to learn deep maths. Later is too late.

is 27 too late?

 

[Daniel Duffy]

is 27 too late?

Start now I'm 71and still doing lots of maths.

 

[Paul Lopez]

Start now I'm 71and still doing lots of maths.

Never too late!!!

 

[Daniel Duffy]

Never too late!!!

yeah, Euler did his best maths when he turned 60!

 

[alovya]

Start now I'm 71and still doing lots of maths.

Hmmm that's a bit misleading I'd say! You already did maths at uni and then continued to work on theory/numerics in computational finance, so doing maths at 71 is reasonable. I've never done real maths, so starting at 27 might be reaching a bit...

 

[Daniel Duffy]

Hmmm that's a bit misleading I'd say! You already did maths at uni and then continued to work on theory/numerics in computational finance, so doing maths at 71 is reasonable. I've never done real maths, so starting at 27 might be reaching a bit...

The options are:

1. Start now
2. Don't start now

It's your choice in life.