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Master reading list for Quants, MFE (Financial Engineering) students

Hi!
Which book should I read to know the basics of blackscholes,monte carlo etc?
 
What specialisation should I consider for getting into algotrading?

NLP,deep learning,time series,network analysis,biostatistics,economics or social science?
 
My ques:
I've completed 2 chapters from Problem Solving with C++. I've solved all the practice exercises of these 2 chapters(answers are already given at the back;when I get stuck,I turn over the page).

I've not touched the PROGRAMMING PROJECTS as solving them with my present level of knowledge seemed like a daunting task.

I've understood(from my present level of knowledge by going through the C++ list in the masters list and also checking on AmazonDOTcom)that until and unless I'll not solve the two volumes of the book "THINKING IN C++",I'll not be able to solve the PROGRAMMING PROJECTS from Problem Solving with C++ book and be ready to solve REAL WORLD projects as well(afterwards;before joining any MFE program OR maybe after joining an office)!
I've an engg degree but my concepts were not clear and I didn't develop any, let's say, Analytical thinking regarding a programming language.

Is my thinking correct?!

I want to learn everything again in detail. I'm mature now(to have developed an analytical brain rather than a brain that crams everything in the book and pas,which I did in B.E) at the age of 29 and have left my job in a service based industry as I want to pursue higher studies.

Kindly advice!
 
The QN C++ course is a bit of an investment in $ (but very reasonable) and time but it is time and money well spent. Disclaimer: I'm its originator>
 
Hi,

The book 'Financial options:Theory to Practice' has been listed 2 times in the 'Good books to read before the MFE program' section.

It's listed at position no. 2 as well as 9.

Which one is the actual order?
 
Andy Nguyen said:
There are now courses in that track offered by various programs. You may as well look into Data Science Master program since MFE is a short program so you won't learn much by taking a course or two.
Click to expand...

Can I move into the financial domain after completing MS Data Science?

Is there any site like quantnet for DS?

Since data is very vast,I was told by some to go for MFE instead of MS DS because of job safety net.

What should I do?

If after completing my MS DS,I get a job in a Fintech company and if the job requires me to do the work of a quant,then can't I learn about it on the job itself?
I'm flummoxed. :)

If you'd want me to go for MS DS,then please tell me whether Roosevelt University is any good for job prospects thereafter?

Also,as far as I know if I'll pursue STEM related degree,then I'll get 3 years OPT visa.
After I pursue the MS or MFE(whichever one it is that I finally decide),I can easily go to some other foreign country which has demand for data scientist jobs considering the fact that most US universities have global recognition.
 
I have yet to see a book (not written for MBA students, or stylized/popular) that actually tries to describe the institutional side for MFE students in a rigorous/reasonably comprehensive way. Why become expert in C++ in a vacuum without also developing perspective on why you're studying it (model validation, regulatory regimes, what are the considerations depending on what you're doing and where, etc.).

Nobody's going to write a book on this, though.

Edit: I see the point, that the technical stuff is supposed to get you a job, from which you'll get a sense of these institutional factors over a period of years. Just playing devil's advocate.
 
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Just wanted to drop a recommendation for a specific course/topic. For stochastic calculus, there are two books by Steven Shreve. Any edition is pretty good. First volume is discrete-time processes. Second is continuous-time processes.

Stochastic Calculus for Finance I - The Binomial Asset Pricing Model
Stochastic Calculus for Finance II - Continuous-Time Models
 
AppliedMath said:
Just wanted to drop a recommendation for a specific course/topic. For stochastic calculus, there are two books by Steven Shreve. Any edition is pretty good. First volume is discrete-time processes. Second is continuous-time processes.

Stochastic Calculus for Finance I - The Binomial Asset Pricing Model
Stochastic Calculus for Finance II - Continuous-Time Models
Click to expand...
Yeah, I agree with you
 
The Elements of Statistical Learning. Seems like finance and machine learning don't always mix but still good to have a rigorous knowledge of the basics.
 
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Can anyone recommend any good algorithm book to prepare for quantitative analyst interview?

Currently I am reading Introduction to Algorithms by CLRS.

Introduction to Algorithms, 3rd Edition (The MIT Press): Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L., Stein, Clifford: 8601419521876: Amazon.com: Books

Introduction to Algorithms, 3rd Edition (The MIT Press) [Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L., Stein, Clifford] on Amazon.com. *FREE* shipping on qualifying offers. Introduction to Algorithms, 3rd Edition (The MIT Press)
www.amazon.com
 
Ng Hong Wai said:
Can anyone recommend any good algorithm book to prepare for quantitative analyst interview?

Currently I am reading Introduction to Algorithms by CLRS.

Introduction to Algorithms, 3rd Edition (The MIT Press): Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L., Stein, Clifford: 8601419521876: Amazon.com: Books

Introduction to Algorithms, 3rd Edition (The MIT Press) [Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L., Stein, Clifford] on Amazon.com. *FREE* shipping on qualifying offers. Introduction to Algorithms, 3rd Edition (The MIT Press)
www.amazon.com
Click to expand...

I'd recommend ditching that and grab a book more focused on financial models like Clewlow & Strickland. CLRS would be more relevant for something like software engineering.
 
Thanks for your recommendation.
Just to confirm, are you referring to implementing derivative models by Clewlow and Strickland?
Do you think the book contains relevant interview questions for algorithm parts?
Do you also have other recommendation?

Also, as you mentioned that CLRS is more relevant for software engineering, wouldn't this mean that it covers more ground and has a better chance to answer interview questions at quantitative analyst?
 
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