It’s crucial to set clear and very concrete career goals early on. Rather than passively waiting for guidance or instructions, actively figure out what you aspire to achieve, network strategically and proactively to gather information. People who take more risks and more initiatives are more successful over the years - maybe still so after survivorship bias been adjusted.
If you are an aspiring quantitative researcher, cultivate an active puzzle-solving and game-solving culture. My advice for preparing is to stay curious, approach brain teasers as problem-solving exercises rather than stressful tests, and perhaps find (or start one!) a university club with like-minded peers to practice and share ideas. Do math early and often. Learn how to code early!
Sell-side Quant Interviews
Sell-side quant interviews test fluid intelligence, the ability to use logic and intuition to crack quant puzzles. The bar for these interviews is certainly high, there is almost zero tolerance for mistakes. Most interviews consist of four stages:
- General brain-teasers and puzzles
- Math puzzles
- Programming/Algorithmic thinking skills (C++ language features, STL, Python)
- Deal Economics
Quant Forums
- QuantNet run by Andy Nguyen
- quant.stackexchange.com
- Wilmott Forums
- r/quant on reddit
- Mathematics and Finance server on Discord.
QuantNet is an extremely useful forum for aspirants looking to apply to a top school or break into the field. The collective knowledge and network connections on this website go a long way in reducing the information assymetry. QN also publishes university rankings each year, offering detailed insights into placement and admission stats.
Books
I suggest a critical path for learning basic financial mathematics and topics useful for front-office desk-quant roles at banks.
Warmup
- The Primer series of books authored by Dan Stefanica sold by FEPress.
- The Concepts and Practice of Mathematical Finance by Mark Joshi.
- Stochastic Calculus for Finance, Volume I by Steven Shreve.
Fundamentals
- Understanding Analysis(UA) by Stephen Abbott
- Linear Algebra Done Right(LADR), by Sheldon Axler
- Introduction to Probability, by Joe Blitzstein
- Vector calculus, S.J. Colley
- Linear Analysis, by Kreider, Kuller, Ostberg and Perkins(KKOP)
Here are my unofficial solutions to Stephen Abbott’s Understanding Analysis.
KKOP was suggested to me by Daniel Duffy, it remains, to date, a favorite introductory text on differential equations.
Intermediate
- An introduction to Partial Differential Equations, Strauss
- Numerical methods for Computational finance, Daniel Duffy
- Probability Foundations, lectures taught by Dr. Krishna Jagannathan
Stochastic Calculus
- A first course in Stochastic Calculus by Louis Pierre Arguin
- Arbitrage Theory in Continuous Time by Bjork.
I really like LP’s book, for it, interleaves rigor and intuition, written by a quant and trader.
Bjork reads really well. As an economist, he explains important results such as Fundamental Theorems of Asset Pricing (FTAP), Girsanov theorem, change of measure with such clarity.