From Academic Finance to Quantitative Research
Disclaimer: This blog post is highly subjective.
Resources
Quant Funds
Quant funds are hedge funds that use quantitative methods to make trading decisions.
Like most hedge funds, these firms usually raise institutional capital.
Quantitative trading:
Some of the larger firms, such as Bridgewater and Citadel, do a more than just
“quantitative trading”.
Market-making or prop trading:
Online
Reddit:
Forums:
Books
Less formal books:
- Frequently Asked Questions in Quantitative Finance by Paul Wilmott
- A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou
- Paul Wilmott introduces quantitative finance
More formal books that are often recommended:
- Option Volatility and Pricing: Advanced Trading Strategies and Techniques by Sheldon Natenberg
- Advances in Active Portfolio Management by Grinold and Khan
- Paul Wilmott on Quantitative Finance, 3 Volume Set
Coding:
- Cracking the Coding Interview by Gayle Laakmann McDowell
Others:
- Technical Analysis is Mostly Bullshit by Tim Morris
Podcasts
- Flirting with Models
- Chat with Traders
Introduction
This blog post is created today (2024-09-05) as a help for myself to write down
information and advice for the process of switching from an academic career to an
industry career. In the end, if this blog post carries some significant value, I will
publish it on my website.
Why I Feel the Call to Leave Academia
Academia is a Slow
You Work by Yourself
The Pay is Bad
Things I Like in Academia That I Would Like to Keep
Keeping Up to Date with Research
Academia is built upon seminar series, all university departments have at least one, and
it is common to attend and present at other universities seminar series. So although
academia is slow, it is common to present ideas, get feedback, and work in an
interactive environment.
Meeting “Famous” Researchers
In academia great researchers are generally quite accessible. I’ve met and talked to
Nobel laureates and thought leaders in different fields at conferences and seminars.
Different Industry Roles in Finance
Different Types of Quants
In essence, there are two categories of quants: buy-side and sell-side. Buy-side quants,
commonly found in hedge funds, focus on developing profitable strategies through
mathematics, statistics, and coding. Sell-side quants, typically employed by investment
banks, provide quantitative analysis for clients. Financial engineering degrees
generally prepare individuals for sell-side positions, which often involve traditional
mathematical finance techniques such as stochastic calculus and differential equations.
Buy-side:
- Quant Trader
- Quant Researcher
- Quant Developer
Sell-side/LPs:
- Model Validation
- Quant Stategist (Strat)
- Quant Risk Analyst
- Quant Analyst/Financial Engineer/Sell-side Pricing Quant
- Actuarial Analyst
Production Pipline of a Trading Firm
- Data pipelines: Collect, store, index, and adjust data. (Databases etc)
- Feature extraction: Visualization, simpler statistics, and understanding the data. In
the end you will have a catalogue of features ready to be used in models.
- Model development/Investment strategy: Identify economic mechanisms that cause prices
to move, capital to change value, etc.
- One example is that we first create different signals. E.g., text data, tweets,
etc etc. Then with 100s of signals we use more hardcore machine learning to combine
these signals (in non-linear ways) to trades.
- Backtesting: Stress testing, avoid overfitting, etc.
- Deployment: Convert the model prototype into a lower latency version.
- Paper trading: The model is now in production, but it still needs to be tested
and monitored.
-
Real money trading: The portfolio manager decides how much capital that will be used
for the specific trading strategy. Usually, it starts out with a small amount and if
the model is successful it will increase. When the model stops to work, it will be
discontinued.
- Quantitative models are usually over lower trading horizons, since it is hard to
predict, e.g., a year into the future.
Quant Trader
What you do:
- Work with monitoring and the execution of the models.
- Look for events that may affect the model and monitors that situation.
Other:
- Usually also have a very strong quantitative background (but not as rigorous as a
quant researcher).
- Compensation is similar to quant researcher, but the bonuses are a bit smaller.
- Bachelor and master degrees are the most common.
Quant Researcher
What you do:
- Kind of a data scientist, “turn data into trades”, “you can think of it as creating
one portfolio weight per stock per time stamp”.
- Comes up with ideas and test them in (usually) Python.
- Convert raw data to trade signals.
- Production code is written in Java or C++.
What skills you need to have:
- Worked with data in a professional setting.
- “We hire a lot of people from academia”. At Citadel, probably 75% of the quant
researchers have a PhD. Wide range of subjects, what they have in common is that they
are good at turning unstructured data into insights.
- Coding.
- Strong intuition of linear regressions and constructing models with noisy input.
- Maths and statistics.
- Machine learning.
Other:
- PhD degrees are common.
- Starting compensation is around 200k. If you come up with good algos you can get huge
bonuses.
Quant Developers
What you do:
- “Software developer in finance”.
- Integrate models from quant researchers.
- Focus on speed and performance.
What skills you need to have:
- C++ and be able to make things run fast.
Deciding which role is right for you
Before starting to prepare for a new job and interview process, you need to decide which
is your target role and target company. Once you know this, you can figure out how their
interview process works, and how you can best prepare. Understand the requirements for
the role as the same title can mean different things for different firms.
You can go to LinkedIn and look at people that work in the roles that you want to work
at and see what background they have.
In the end you should create a structured preparation roadmap and allocate time each day
or week to this preparation.
Interviewing
- You need to be passionate about the topic and the job. Be ready to answer any
question regarding why you want to work there.
- What is your favorite investor (and don’t say Warren Buffett), investor book? Be
thoughtful of your answers!!
- Be ready to give them an invested algo pitch that you have worked on.
Do you need to be a top researcher from an Ivy League?
Competition for top quantitative trading researcher roles is extremely tough, especially
in the UK, where many positions are filled through headhunting rather than public
interviews. Candidates with deep expertise in areas like market microstructure,
high-frequency trading, stochastic calculus, or machine learning are often recruited
from elite universities (e.g., Cambridge, Oxford, or Ivy League schools) and are
expected to have achievements such as Mathematical Olympiad wins or extensive
publications. However, not all roles demand these credentials; many well-paying and
prestigious positions are available at smaller funds, which actively seek talented PhDs
without such elite backgrounds.
What makes someone a successful quant?
The following is taken from Citadel’s quant research webpage.
Mindset:
- Ownership/initiative.
- Attention to detail.
- Creativity.
- Ambition and resilience, and drive to seek out problems/solutions.
Skills:
- BS/MS/PhD in mathematics, statistics, physics, computer science or another highly
quantitative field.
- Math, probability, and statistics: you spend a lot of time modelling and forecasting.
- Engineering and coding.
- Working with data.
- Applied research: deep understanding of conducting and applying research is critical.
Experience working on long-term research project is extremely beneficial.
From the Mergers & Inquisitions blog post:
- “Some of these firms, such as Jane Street and Two Sigma, like to hire undergrads, while
others prefer Ph.D.’s and other advanced degree holders”.
- “There are plenty of Ph.D. holders in the field, and some funds do prefer to hire
Ph.D.’s, but the degree is not necessary to win offers at all funds. The reason for this
is simple: most of the math used at quant funds is not that advanced. It’s closer to
what undergrads in technical fields need to know (multivariable calculus and
differential equations).”
- “If you’re interviewing for Trader or Developer roles, your coding skills matter more
than your knowledge of advanced math.”
- “There are plenty of academics who are “good at math,” but unless they have a serious
interest in the markets, trading, and investing, they tend to perform poorly at quant
funds.”
- “If you interview for Quant Developer roles, you’ll have to complete coding exercises
and whiteboard problems, similar to tech interviews.”
The Recruiting Process
From the Mergers & Inquisitions blog post:
- “Firms that use on-campus recruiting follow the standard process: they’ll give you an
online test, move to HireVue or phone interviews, and then do back-to-back interviews
with several full-timers.”
- “Many quant funds don’t necessarily want to hire anyone, but they always want to get
new strategies that have worked for others. So, if you keep going through interviews
where they ask increasingly detailed questions about your strategies but don’t give you
a decision time frame, you may want to reconsider the fund.”
- “In the interview process you can expect a wide range of math, probability, and
statistics questions, so you should prepare accordingly.”
The following are two quotes from this blog post.
- How should I prep for interviews? - Leetcode, brainteasers, mental math. Leetcode has
been beaten to death on how to do it, just google how to study. For brainteasers, look
at the green book and heard on the street. Those cover just about everything and
whatever you get will likely be some subtle change to one of those - focus more on
understanding the thought process behind an answer vs. knowing the answer, as this lets
you generalize when you get a curve ball. For mental math, look up Arthur Benjamins book
and drill that.
- How can I make sure I get a first round? - I had a 100% hit rate for getting the
screener by emailing recruiters at the firm / traders asking to talk to them. Applying
IB recruiting techniques to non-IB industries often gives absolutely killer results.
Interview Process for a Quant Research role at Wincent
Interview Process:
-
Chat with a recruiter. Consider it an introductory call; there’s no need to prepare
anything apart from your questions regarding the company, team, culture, and any
work-related inquiries.
-
Online Math test + Coding Challenge. Low-to-medium complexity problems focused on
probability, logic, and mathematical skills + algorithmic coding task.
-
Quant Interview. General discussion about your background and passions, followed by
medium-hard complexity problems to assess your intuition and problem-solving approach.
-
On-site Interview. Engage in a set of betting games to evaluate your quantitative
reasoning and decision-making skills.
Interview Process for a Trading role at Byte Trading
Interview Process
- Initial chat to discuss the role, learn about each other and explore whether it’s a
good fit
- 1 hour interview focused on Stats/Math/Trading
- Take home research challenge
- Final interview - review of take home and a chance to have a general discussion, meet
the rest of the team and make sure we’re the perfect fit for each other. You’ll have
learnt more about us by this stage, and this is a chance to have any remaining questions
answered!
Prepare for Standardized Tests
-
The first one or two online “automatic” stages, usually on platforms like HireVue,
could seem very easy. Just like a pre-screening test. However, they are extremely
important and only a small percentage of candidates will proceed further to other
stages, with “human” interviewers.
-
“They actually ask a lot of questions on stochastic calculus, finance (the
Black–Scholes model plus some technical knowledge in trading options), programming, as
well as probability and some maths. A pure mathematician can easily outstand in maths
and probability, but the rest is not something common in our background. If you really
want the internship, please take a whole month to seriously prepare all these subjects.”
Prepare for Coding Tests
In
this
Reddit post, a senior quant stated that “There is less of an emphasis on brainteasers
and coding assessments have replaced math tests.”
In the initial stages, coding skills are typically assessed through LeetCode-style
online tests. In later stages, candidates may receive a take-home assignment. If you’re
aiming for larger firms with structured recruiting processes, practicing Leetcode is
unavoidable. However, if you’re targeting places without a formal recruitment pipeline,
showcasing projects that demonstrate your ability to efficiently manipulate data (and
reproducing these techniques on demand) will generally suffice.
When starting with LeetCode you should have an understanding of general data structures
such as arrays, string, binary trees, linked lists, stacks, etc, as well as basic
algorithms such as sorting, searching, and recursion. Is is also beneficial to learn Big
O notation to calculate the time complexity of algorithms. Willian Fiset has a good
youtube channel covering a lot
of these topics, including a good playlist about data structures.
Also, when working with LeetCode start solving problems by topic, don’t randomly try
problems as there is no point trying to solve complex problems without knowing the
basics.
Spend maximum 30 to 60 minutes on a problem, then look at the hints and answer, and
go through the solutions thoroughly to understand it.
Prepare for Behavioral Interviews
- Dress appropriate even if it is an online interview.
- The STAR method (Situation, Task, Action, Result) is a structured approach to
answering behavioral questions. These questions often focus on how you handled past work
situations, your problem-solving skills, or how you interact with others. Preparing
answers using this method helps provide clear, concise, and focused responses.
focus on code clarity, appropriate abstractions, extensibility, and the use of suitable
data structures and algorithms. Both rounds are usually completed in a language chosen
by the candidate from a predefined list.
Networking
The following is a quote from this blog post.
Is networking important? - Similar to banking, it is one of the most important parts.
This is likely a bit of a hot take, so I’ll elaborate. Quant finance is a very small
world, much smaller than the fundamental L/S one where it already seems like everyone
knows each other. While the first gig may come from one of those big pipelines that is
impersonal and regimented, every good opportunity comes from knowing a guy who knows a
guy. You know how everyone says in the HF world that all the best opps come from
referrals / never get posted (Tiger Cubs, etc)? It’s the same shit in quant. Similarly,
there is a HUGE world of opps for new grads outside of the big bureaucratic firms who’s
names you recognize,, and you only learn about those by digging around and emailing.
List of Quant Funds
The following list comes from this Github repo.