Quant Reading List 2019 | Math, Stats, CS, Data Science, Finance, Soft Skills, Economics, Business

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INTRO QUANT BOOKS:
A Primer For The Mathematics Of Financial Engineering
Fundamentals of Futures and Options Markets
Derivatives

SOFT SKILLS:
The Age of Turbulence
Crucial Conversations Tools for Talking When Stakes Are High
The Wealth of Nations

QUANT LIFE:
The Problem with HFT
Business Cycles and Equilibrium
Models Behaving Badly
My Life as a Quant
Fischer Black and the Revolutionary Idea of Finance

QUANT TECHNICAL:
Advanced Financial Risk Management
Intermediate Microeconomics: A Modern Approach
Probability and Statistics for Engineering and the Sciences
Probability and Statistical Inference
Statistical Analysis of Financial Data in R
Introductory Econometrics: A Modern Approach
Stochastic Calculus for Finance 1 and 2
Arbitrage Theory in Continuous Time
Options, Futures, and Other Derivatives
Mathematics for Economics
Time Series Analysis

PROGRAMMING & DATA SCIENCE:
The Art of R Programming
Mastering Python
Pattern Recognition and Machine Learning

FINANCE & BUSINESS:
Introduction to Managerial Accounting
Fundamentals of Corporate Finance
Essentials of Investing
The VaR Modeling Handbook
Financial Modeling
Security Analysis

[DavidFernandez-odfz]

This is exactly what I've been needing, everything is lining up at once, thank you.

[danisugimoto]

I think you just saved my life. THANK YOU SO MUCH for this list!!!

[quantgeekery]

Ruy Tsay was my gateway to Time Series and Jorion was my access into VaR

[markmonfort]

been waiting for this - thanks Dimitri! Will be sharing this with my team

[dAntony]

Just coming back to this video, I've been working through "Introductory Econometrics" so I can lean how to apply my stats modelling skills to a different domain since I'm becoming more and more interested in quantitative finance, specifically the risk stuff. Anyways, I just wanted to say the book is actually pretty fun to work through, but more importantly, there is a small companion book that works through the examples in the text using a computer that I'd also highly recommend if studying that text. 

There are two versions, "Using R for Introductory Econometrics" and "Using Python for Introductory Econometrics", they're the same material wise and follow the textbook exactly, just pick your preference for language. The packages used include all the data sets from the book and works through each example presented using R or Python. It's made reading the textbook even better. First I'll read an entire section and work through some examples with pencil and paper to make sure I still understand how the computations work and then double back to work through the examples/exercises with the companion book.


Also, an FYI for anyone who reads the "Statistical Analysis of Financial Data in R" book, the R packages for that text are way out of date at this point and the datasets used in the examples aren't readily available anymore. I struggled for a few days to get them, but they're impossible to find at this point, it's just a bunch of dead links. I gave up on it even though the information was very good until that point. Not saying the text isn't useful, just know that if you buy it, you likely won't be able to work with the examples throughout the book and will have to just use it for the information to apply to your own datasets.

[javelinjool-pxkr]

well Iam attending your lectures, very fantastic indeed! Financial engineering lectures is very nice course

[leofranklin]

thanks, I keep waiting for your latest videos

[gracia]

Love your Channel ! It is so helpful as a data scientist wanting to break into finance

[artemshiryaev]

Will definitely check out a couple of these books, Thanks so much!

[aparnamishra]

Great job Dimitri! I was really looking for a channel like yours.

[avgb]

Thanks for sharing such a valuable info...Please make a separate video on books that are your personal favorites, which you suggest someone to study chronologically starting from scratch building foundations and going on to become an advanced Quant.

[kidsdontreact]

Hey! Thanks for your guidance here. Becoming very interested in quantum finance and finance in general despite not much knowledge in the area

[arkadipgupta]

Hey @Dimitri. this is amazing aggregation. thanks for putting this up.

[ian]

"Mathematics for Machine Learning" published by Cambridge University is a great choice of learning DS or ML.

[nicorobin]

Many thanks, you did make my path easier

[trwbdr]

Any new additions during these last years that would be worthy? Thanks for the help.

[malvynolizter]

Great Help. That's what I was looking for. Thankyou.

[MrKidmuscle]

Hey Dimitri,
Got a request, please do an in-depth review of WorldQuant University.
1. If their course is good (teaching, depth level etc)
2. If ssk the HR guys you know if they give any value to something like that.
3. In their catalogue they've talked about intellectual property rights in dense legal terms couldn't understand the point where they talk about students employer, talk about it's actual implications.
4. If those topics are available elsewhere for free ?

PS- Do you plan on making tutorials on request of the subscribers ?

[Henry-qp]

Thanks for sharing! I love it. I have a small question, If I finished undergrad level of Econometrics with hypothesis testing, linear and non-linear regression, panel data set, time series analysis... which stats book should I start with? Thank you!

[JoshMadakor]

I'm struggling with the order in which to read these... lol. Anyone?