It's a very wide category, but all those things could apply. I used to be a partner in a couple of funds, and at the time the major distinction was between people who priced complex derivatives and people who thought about how to use data to guess what the market would do.

You'd be more likely to find deriv quants at a bank selling such things to people, whereas the strategy quants would be what you'd find in the funds.

But even within that fund side I'd imagine the job looks very different depending on what firm you're at and what they ask you to do. Not everything is glamourous blue-sky "what should I buy or sell" stuff. A fair bit of it is things like cleaning the data. And there's going to be a lot of tooling. Since nobody says what they are doing, everyone has their own version of a research pipeline, and that needs to be kept clean. Likewise on the execution side, there's a load of code to be looked at, and a lot of data coming out relating to trading costs and such.

> It's a very wide category

The term is historical, not functional.

Before option-pricing theory, securities pricing was an art. Yes, there were capital asset pricing theories for fundamental analysts. But at banks, a phone and hustle were the tools of the trade.

With Black-Scholes (and put-call parity) came the ability to (a) manufacture options out of other securities and (b) print objectively-wrong quotes. The former gave banks an incentive to build the business. The latter gave them the incentive to automate. The people they hired to do that were quants.

The first generations of quants automated option-pricing models. Through the 80s, they found themselves involved in more products, e.g. securitised loans and mortgages. By the 90s, they were launching funds. Today, almost everyone on a modern trading desk is a quant to some degree.

I thought now with securities with negative interest there were a lot of securities that now had to take that into account and black-scholes is just no help whatsoever there.

Is there any answer to it yet ?

> with negative interest there were a lot of securities that now had to take that into account and black-scholes is just no help whatsoever there

Not really. Black-Scholes (and its variants) tend to assume log-normal rates distributions. But that’s just a default, and one chosen with the explicit assumption of positive rates.

Most practitioners have had custom curves for decades; using one that pierces zero is a trivial modification.

That's part of it. There's also classical modelling, some natural language processing probably more techniques.

In short it's trying to find the signal in a huge amount of noise/data.

I'm not a quant but I support the Quants at my firm. Some of our datasets are hundreds of millions of rows with up to 450 factors/features per row.

They also run NLP on terabytes of SEC filings (like quarterly and annual reports). Not sure how they analyze the results of that.

Quant is a pretty broad term. Some would say it’s often working on nonlinear desks to implement/calibrate volatility surfaces and things like that or working more on the risk management side. There’s also the whole HFT world (Jane St, Virtu, Jump etc) many would call ’quant’ but really is a different game than the HF space.

On the machine learning side, in my experience it’s often simple, linear models that work best in the messy world of financial data. I’m sure there are shops out there breaking out the GPU clusters and training NNs with 6 trillion parameters but in no way will your super deep NN guarantee alpha whatsoever.

Yes. Exactly this. Building models that assess risks and potential gains. A quant is a catch-all term though, so one person may be working on models that predict some sector of the market and another person could be looking at online allocation algorithms for maximizing risk-adjusted returns.

Furthermore, what is a good way to become a quant?

The type of quants talked about in this article probably has a PhD in math, stats, CS (with a focus on machine learning), or something similar.

This particular article is about MFE students from one school, but hedge funds love PhDs too.

and especially physics, since financial modeling is based on physical modeling.

Well not directly, it's just that they both make use of stochastic calculus.

Any good MOOCs, etc for the topic?

One way to get interviews at least is by doing well in math contests.

wth is a master quant? and why would anyone refer to themselves as such?

Is the job repetitive ? What % of work is solving Math and coding and how much PowerPoint ?

How much do you get paid?

...they just did!

What's your education background?

Math, advance geometry, CS

Isn't math, advanced geometry redundant?

No: topology, analysis, number theory, etc.

advanced geometry? what about advanced arithmetic? I hear thats a much needed discipline.

Can you get me one of those million dollar jobs referred to in the article?

I've only got a bachelor's degree in software engineering, but I've been doing data science(ish) at my job since I graduated.