Understanding the industry and it's many moving parts.

[GoNoles]

Currently becoming familiarized with the differences between (for example) a quant working at a derivatives desk for a bank and a "quant dev" working on strategies for asset management and HFs. However I still have a lot to cover. How many different roles are their exactly and what are the main differences? Watched this video recently

Describing six different roles, is this accurate?

 

[KillingField]

"Quant" is a term used for everything that uses mathematics in finance, which is pretty much every function in any financial institution, so no, making a list of 6 types of quants doesn't really reflect reality nor is the document written in 2008 the video is based off of very relevant to today. I mean, sure, the types of jobs described in the video still exist (though pure "research quants" at investment banks less so - though this can also get confusing because research people, as in sell side equity research, sometimes like to call themselves quant researchers), but because the discourse online has moved and terms have evolved, at best though well intentioned, these old subdivisions overemphasizing sell side roles just add to the confusion.

Quant finance online nowadays outside academic circles almost always means different aspects of algorithmic or systematic trading - which has nothing to do with the more traditional meaning of the term of derivatives pricing and adjacent roles. So when you hear "quant research" mentioned online, it doesn't have anything to do with someone trying to come up with a faster PDE solving method.

Now with that all said, and having been rather negative wrt the video, what you're trying to understand, the different types of roles available, is certainly a worthwhile pursuit, and your example of splitting the types of quants between sell/buy side is a bit more informative (though especially larger buy side institutions will often also have a very sell side-y central quant team, e.g. Coremont).

 

[Qui-Gon]

Am I the only one that listens to Dimitri and thinks “this guy is clueless”? Not a good resource for quant finance IMO

 

[SD_]

Am I the only one that listens to Dimitri and thinks “this guy is clueless”? Not a good resource for quant finance IMO

Same here, when it comes to talking about the industry and careers, everything somehow ends up at model validation (CECL, PPNR, etc. - based on the recent videos) and time series. However, I think some of his videos on how to decide a quant master program, what factors to consider, etc., are very good.

Is there any other active content creator for quants, there are many for SDEs in California and NYC, who end up going full-time creating content. Maybe there should be a youtube channel for QuantNet too, haha.

 

[GoNoles]

"Quant"

So in essence, the term itself is convoluted. On the sell side, everyone that can be remotely labelled "quant" belongs to the Sales and Trading teams correct? and those who can be labelled quantitative researchers are performing research that results in new and improved trading algorithms whether it be forex, futures, bonds, equity?

 

[GoNoles]

However, I think some of his videos on how to decide a quant master program, what factors to consider, etc., are very good.

Yeah I came across a video of his describing them. Im in undergrad so i'm just now coming to the understanding that (some) of these grad programs are just another venue in which universities make money, not explicitly helping students initiate career paths. My focus as of now has been on the correct steps that need to be taken in order to become a Quantitative Developer on the buy-side. I have realized that some grad programs, although marketed as helpful for this, may not be in line with what I need. Therefore, searching for a program that emphasizes C++ will be of best interest.

 

[SD_]

Yeah I came across a video of his describing them. Im in undergrad so i'm just now coming to the understanding that (some) of these grad programs are just another venue in which universities make money, not explicitly helping students initiate career paths. My focus as of now has been on the correct steps that need to be taken in order to become a Quantitative Developer on the buy-side. I have realized that some grad programs, although marketed as helpful for this, may not be in line with what I need. Therefore, searching for a program that emphasizes C++ will be of best interest.

All the top US MFEs are heavy on C++ : Baruch, UCB, CMU, etc..
Given that you know you want to work as a quantitative developer, if I were you, I'll avoid CMU (it's a one-size fits all program).

Moreover, I think, what CMU teaches in Mini 3 (Financial Computing II - Master of Science in Computational Finance - Carnegie Mellon University), Baruch basically expects you to know before starting the MFE [check the 4th and 5th pre-program -> https://mfe.baruch.cuny.edu/pre-mfe-program/]. Baruch probably wants their students to be experts in C++ programming before they even step foot on the campus. Even Berkeley has a C++ pre-program course which is compulsory for all students.

You can look into MS in CS as well. Maybe consult with someone who is working in Buy-side as a Quant developer (your dream job).

 

[KillingField]

So in essence, the term itself is convoluted. On the sell side, everyone that can be remotely labelled "quant" belongs to the Sales and Trading teams correct? and those who can be labelled quantitative researchers are performing research that results in new and improved trading algorithms whether it be forex, futures, bonds, equity?

I'm going to focus on sell side here as that's what you asked about.

No, not all quants are directly aligned with S&T. Well it depends on what S&T means to you but organizationally e.g. Risk will be separate from S&T. Functionally though the markets facing business of banks is S&T and Risk is a support function. Now, quants are also a support function. Desk quants support a desk. "Capital quants" (though a more common name'd be Market Risk quant) support a Capital/Risk function that supports the trading desks. A research quant as described in the video is a quant supporting other quants. A quant dev is a dev, not a quant, a quant trader is a trader not a quant - this is almost without exception the case in organizational reporting lines as well.

Organizationally quants usually used to report to S&T but were made part of the Risk function around the financial crisis and now have started to be transferred back to S&T - an industry trend but not every single firm has done this. This doesn't mean that quants are really part of S&T; as I said, it's a support function and they do not own PnL like the trading desk does - often the quants won't even know what the number of the day is (or much less as to why) or what might be a good trade to pitch. Nor do they need to - a quant's job is not to follow the market, or really to even understand it, but to build models to help the trading desk manage their books.

Some banks call ALL their quants quantitative researchers (just an organizational name) - the naming far preceeds the now-common internet usage of the word and does not mean that they work on trading algorithms - only very few quants in a bank do (depending on what is meant by a trading algorithm, as some might call implementing a VWAP one, and to someone else it might mean signal generation - and I suppose technically you could call any derivatives pricer a trading algorithm in itself as well, but I don't think this is the type of algorithm you had in mind though it does highlight the crucial role quants, all quants, play). But as I mentioned earlier, naming can be confusing as an even older function than quants, namely research (who write reports on companies, analyst recommendations on stocks to buy/sell, predictions of macroeconomic trends and of central bank hikes), will sometimes prefix their titles with quant to call themselves quant researchers (and so in these cases "quant" is just to be understood as a prefix like in the case of a quant trader or a quant developer).

 

[GoNoles]

All the top US MFEs are heavy on C++ : Baruch, UCB, CMU, etc..
Given that you know you want to work as a quantitative developer, if I were you, I'll avoid CMU (it's a one-size fits all program).

Moreover, I think, what CMU teaches in Mini 3 (Financial Computing II - Master of Science in Computational Finance - Carnegie Mellon University), Baruch basically expects you to know before starting the MFE [check the 4th and 5th pre-program -> https://mfe.baruch.cuny.edu/pre-mfe-program/]. I think Baruch wants their students to be somewhat experts in C++ programming before they even step foot on the campus. Even Berkeley has a C++ pre-program course which is compulsory for all students.

You can look into MS in CS as well. Maybe consult with someone who is working in Buy-side as a Quant developer (your dream job).

Wow. This comes as somewhat of a shock. I'm currently double-majoring in Math & Finance. I will be taking one C++ 15-week course as part of the math major. I'm trying to determine what I can do now to keep my programming skills competitive. Would you advise self-study?

 

[GoNoles]

I'm going to focus on sell side here as that's what you asked about.

No, not all quants are directly aligned with S&T. Well it depends on what S&T means to you but organizationally e.g. Risk will be separate from S&T. Functionally though the markets facing business of banks is S&T and Risk is a support function. Now, quants are also a support function. Desk quants support a desk. "Capital quants" (though a more common name'd be Market Risk quant) support a Capital/Risk function that supports the trading desks. A research quant as described in the video is a quant supporting other quants. A quant dev is a dev, not a quant, a quant trader is a trader not a quant - this is almost without exception the case in organizational reporting lines as well.

Organizationally quants usually used to report to S&T but were made part of the Risk function around the financial crisis and now have started to be transferred back to S&T - an industry trend but not every single firm has done this. This doesn't mean that quants are really part of S&T; as I said, it's a support function and they do not own PnL like the trading desk does - often the quants won't even know what the number of the day is (or much less as to why) or what might be a good trade to pitch. Nor do they need to - a quant's job is not to follow the market, or really to even understand it, but to build models to help the trading desk manage their books.

Some banks call ALL their quants quantitative researchers (just an organizational name) - the naming far preceeds the now-common internet usage of the word and does not mean that they work on trading algorithms - only very few quants in a bank do (depending on what is meant by a trading algorithm, as some might call implementing a VWAP one, and to someone else it might mean signal generation - and I suppose technically you could call any derivatives pricer a trading algorithm in itself as well, but I don't think this is the type of algorithm you had in mind though it does highlight the crucial role quants, all quants, play). But as I mentioned earlier, naming can be confusing as an even older function than quants, namely research (who write reports on companies, analyst recommendations on stocks to buy/sell, predictions of macroeconomic trends and of central bank hikes), will sometimes prefix their titles with quant to call themselves quant researchers (and so in these cases "quant" is just to be understood as a prefix like in the case of a quant trader or a quant developer).

As of now I am unfamiliar with the risk departments of banks. Very interesting write-up however. Obviously I have a lot to learn. It seems as though S&T fundamentally changed when the Volcker rule was implemented.

 

[SD_]

Wow. This comes as somewhat of a shock. I'm currently double-majoring in Math & Finance. I will be taking one C++ 15-week course as part of the math major. I'm trying to determine what I can do now to keep my programming skills competitive. Would you advise self-study?

I think @Daniel Duffy will be able to guide you much better. You can try solving problems regularly on LeetCode to keep the concepts fresh.

 

[GoNoles]

Okay, thank you for that info.

 

[KillingField]

As of now I am unfamiliar with the risk departments of banks. Very interesting write-up however. Obviously I have a lot to learn. It seems as though S&T fundamentally changed when the Volcker rule was implemented.

I mentioned Risk more as an example of looking at org charts, as I think understanding them can give some insight into several things: e.g. a quant trader is better thought of as a trader than a quant because (among other things) they will, in virtually all instances I've seen in banks, report to the trading part of the org chart and not the quant org. Why does this matter? For many reasons, but let's take one that's maybe most relevant for you: Say you wanted to become a quant trader, the typical entry would then likely be through trader entry routes rather than quant hiring pipelines - these are entirely different (large very organized analyst classes vs small sometimes off-cycle internship conversions into full time associates).

As for Volcker rule, this sounds more like parroting something you've read online. Sure prop trading is much more limited, but that's not really a core sell-side operation anyway (who are the clients and what are they being sold?) whereas e.g. market making or risk managing a structured portfolio are.

 

[Daniel Duffy]

Okay, thank you for that info.

The discussion here might be useful.

Valid Sudoku - LeetCode

Hi , I want to implement the JAVA solution to the valid sudoku problem on LeetCode in C++. It uses hashset form . I understand std::unordered_set would be the ideal DS but I am struggling to default initialize it . You can see the snippet of the JAVA solution below , public boolean...

quantnet.com

 

[alovya]

Quant finance online nowadays outside academic circles almost always means different aspects of algorithmic or systematic trading - which has nothing to do with the more traditional meaning of the term of derivatives pricing and adjacent roles. So when you hear "quant research" mentioned online, it doesn't have anything to do with someone trying to come up with a faster PDE solving method.

Hi, thanks for taking the time to reply to this thread. I had a couple of questions for you, off the back of your replies, if you don't mind.

Are PhD graduates with skills in PDE/high performance computing well-placed to get roles in algorithmic/systematic trading (my case, if you wish to read), and if not, could you give the profile of someone would be (my profile)?

Could you also give your definition of "algorithmic/systematic trading"?

your example of splitting the types of quants between sell/buy side is a bit more informative (though especially larger buy side institutions will often also have a very sell side-y central quant team, e.g. Coremont).

Could you please expand on what you think is the difference in skill sets demanded from "quants" on the buy side versus quants on the sell side? Thanks a lot!

 

[Andy Nguyen]

There are many books, free PDF guides that many of us here have used over the year. Some old members here started with them back in 2006.

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

QuantNet International Guide to Financial Engineering Programs DOWNLOAD GUIDE FREE QUANT CAREER GUIDES What do quant do ? A guide by Mark Joshi. (see attachment) Paul & Dominic's Guide to Quant Careers (see attachment) Paul & Dominic's Guide to Getting a Quant Job (see attachment) Career in...

quantnet.com

 

[alovya]

Currently becoming familiarized with the differences between (for example) a quant working at a derivatives desk for a bank and a "quant dev" working on strategies for asset management and HFs. However I still have a lot to cover. How many different roles are their exactly and what are the main differences? Watched this video recently

Describing six different roles, is this accurate?

TL;DR: There are two types of quants: “buy side” quant and “sell side” quant. A buy side quant might work in a hedge fund coming up with directly applicable money making strategies (using maths/stats/coding). A sell side quant would work in an investment bank crunching numbers for clients. Financial engineering degrees typically prep you to be a sell side quant, not a buy side quant.

Square brackets in the text below refer to sources (links to sources are at the very bottom of this post).

What is a quant?

When someone says quant online, they are probably referring to a fancy maths/stats/coding-heavy role in algorithmic/systematic trading, not someone doing pricing [QN1]. As we will see, this is basically a “buy side” quant. There are really only two types of “quants”: buy side quants working in hedge funds, and sell side quants working in investment banks [QN5].

Sell side quants

Before the 2008 financial crisis, sell side quants reported to Sales and Trading (S&T) and owned a share of profit/loss (PnL) [QN2, Q2] but after the crisis their roles changed. To understand what changed, we need to understand that a sell side firm makes money by being a liquidity provider and/or taking a cut in the form of commissions: they create, price, hedge and arrange the sale/purchase of tailor made financial products for their clients, or they buy/sell products held by the firm and make money from the bid/ask spread [B, C, D].

Sell side firms traditionally employed quants in large swathes for pricing and portfolio modelling to price and hedge the positions they took when servicing clients [WSO1]. The modelling/pricing task for a sell side quant is predetermined by the products a client is buying/selling [WSO1]. As such, a front office sell side quant is what comes closest to a buy side quant.

Nowadays though, sell side quants are in the Risk department of a firm (doing pricing, risk management, hedging, etc); they only support the S&T department indirectly and do not own PnL [Q1]. “Sell side quants more often use traditional mathematical finance - stochastic calculus, differential equations, etc.” [R], so this is where an MFE degree would be helpful. Traditional mathematical finance topics are [B, C, QN5]:

• Derivatives pricing
• Partial differential equations (e.g. Black-Scholes equation)
• Numerical methods
• Ito (stochastic) calculus
• Others (risk management, model validation, CVA, XVA, HPC, etc)

There has been a trend of people trying to switch over from the sell side to the buy side for many reasons: fewer working hours, more interesting work, salary ceiling is higher, etc. However, switching over is not easy because skills such as derivatives pricing may be irrelevant to the buy side firm [Q4]. If a sell side quant with pricing knowledge is trying to move to the buy side, the value of their knowledge depends on the function within the firm they have applied to [QN4].

Lastly, a year or two of being in a sell side quant role is going to give everything that could ever come in useful in a buy side quant role, technically speaking, and one should try to move roles soon thereafter [WSO2].

Buy side quants

Buy side firms are not as concerned about pricing because they are not making structured products [WSO1], and the traditional definition of quant (pricing and structuring of derivatives) is mostly outdated on the buy side [Q1].

In contrast to the sell side, who are liquidity providers, buy side firms can be thought of as liquidity seekers [D] who want to buy/sell various products (e.g. trading in open exchanges or over-the-counter) and grow retail, institutional and/or house accounts by taking calculated gambles on the markets [2, 3].

The value in the buy side lies in coming up with money making strategies that work [WSO1]. Buy side quants develop, refine and manage these money making strategies [Q1] to generate alpha (returns above market growth, [D]) using whatever means available to them, usually maths/stats/coding. These means can be almost anything: stats, time series and machine learning, etc [R, 2, QN5].

Note that while topics like numerical methods and C++ are often said to be core skills for quants, this only true for those working on pricing libraries (who are generally sell side quants), and buy side quants would code in Python, not C++ [QN3].

Sources:

• R: Difference between buyside and sellside quants
• A: 'P' Versus 'Q': Differences and Commonalities between the Two Areas of Quantitative Finance
• B: Buy-side vs. Sell-side Quants: All their differences! – Quantitative Methods Research
• C: https://apply.mscf.cmu.edu/article/steve-shreve-industry-brief.pdf
• D: What are the Career Paths in Systematic Trading? | QuantStart
• Q1: What's the difference in job scope between buyside and sellside quants?
• Q2: What's the difference in job scope between buyside and sellside quants?
• Q3: What's the difference in job scope between buyside and sellside quants?
• Q4: How hard is it to move from a bulge bracket (GS, JP, MS, etc.) to a quantitative hedge fund (Citadel, Two Sigma, etc.)?
• QN1: Understanding the industry and it's many moving parts.
• QN2: advice on how to find internship opportunities?
• QN3: From Academy to Quant research
• QN4: Frustrated with derivatives pricing. Where else can I use my pricing skills?
• QN5: Mathematical and programming skills
• WSO1: Sellside quant trying to understand more about how the quant buyside works | Wall Street Oasis
• WSO2: Sellside quant trying to understand more about how the quant buyside works | Wall Street Oasis

 

[SamDQuant]

Same here, when it comes to talking about the industry and careers, everything somehow ends up at model validation (CECL, PPNR, etc. - based on the recent videos) and time series. However, I think some of his videos on how to decide a quant master program, what factors to consider, etc., are very good.

Is there any other active content creator for quants, there are many for SDEs in California and NYC, who end up going full-time creating content. Maybe there should be a youtube channel for QuantNet too, haha.

Unfortunately, I have not seen any quant content creator on Youtube outside Dimitri Bianco. He is doing an excellent job of giving his audience some perspective on quantitative finance, and It would be interesting to have more quant creators on Youtube. It's always beneficial to harness the power of visual media.

 

[SD_]

Unfortunately, I have not seen any quant content creator on Youtube outside Dimitri Bianco. He is doing an excellent job of giving his audience some perspective on quantitative finance. It would be interesting to have more quant creators on Youtube. It's always beneficial to harness the power of visual media.

Absolutely, the Quant Finance industry is like a hidden gem and the following should definitely be on Youtube:
- Day in the life of a Quant researcher/developer/portfolio manager @ a bank/hedge fund
- Day in the life of an MFE student @ XYZ university (if they could find some time to shoot some videos, lol)
- A group discussion of MFE alumni from various programs across continents, etc.

Maybe someone from this forum will soon start a Youtube channel as Dimitri did. I guess up until now only people on the risk side had the time to make videos though

 

[Michelle]

Absolutely, the Quant Finance industry is like a hidden gem and the following should definitely be on Youtube:
- Day in the life of a Quant researcher/developer/portfolio manager @ a bank/hedge fund
- Day in the life of an MFE student @ XYZ university (if they could find some time to shoot some videos, lol)
- A group discussion of MFE alumni from various programs across continents, etc.

Maybe someone from this forum will soon start a Youtube channel as Dimitri did. I guess up until now only people on the risk side had the time to make videos though

There are plenty of A Day in the Life of ... threads here from years past.
There are plenty of Youtubers, influencers, podcasters who pump out repetitive contents to try to gain views and make a buck. Most of their contents are very shallow.