> recent graduates working at hedge funds made significantly more than their peers working at banks
This has always been true, for the entirety of my career. Buy side pays more than sell side for alpha-generating activities. (Sell side pays more for flow and scaling advantages.)
This article strikes me as a submarine [1] for Baruch’s program.
Agreed. Otherwise the story makes no sense. It's always been the case that hedge funds pay more than banks just like silicon valley pays more than government. This is so well known, I knew it when I was a kid and people outside of the finance industry are aware of it.
Considering that QuantNet ranks Baruch's Financial Eng program #2 (behind Princeton's) and that MIT ranks #10, I'd say you may be jumping to conclusions...
If you read the linked article you'd see that it's a more specific phenomenon to news articles. Maybe you'd consider it a type of trojan horse because it's misleading.
You could call it the news industry's equivalent of tv/movie's sneaky product placement. A paid ad pretending to be a legitimate news story just like a product placement is a paid ad pretending to be part of the plot.
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.
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.
> 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.
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.
Hedge Funds have always paid quants significantly better.
In the past decade or so many banks have built out their engineering teams. Goldman is now 25% engineers. More recently these banks have started to compete with hedge funds for the same quant talent that can better take advantage of their new technical prowess. But the comparatively strict compensation structure still means you can make a lot more on the buy side.
Would you rather that resources be allocated blindly? The fact that some people can live comfortably off their investments does not mean that the very investments themselves do not create wealth. Hedge funds do the latter, it's up to the government and society to decide on the former - i.e. how to distribute this wealth.
I'm not the OP, but it's a switch I'm strongly considering making. I'm looking at doing it by going back to get a Masters in Quantitative Finance, so that's one possible option.
Not going to fight the paywall to read the article, but as a developer consultant (not a quant), hedge fund clients pay better across the board than both commercial and investment banks. They tend to be small shops, so it's not really a fair comparison. I've worked for boutique hedge funds, market makers, wealth management funds, pension funds, and two huge investment banks. The small hedge funds pay a lot more and the work is more interesting and less bureaucratic. Just my anecdotal take.
What percent of ‘ideas’ make it to live trading. How much time is spent on average validating idea. How much of the testing is automated/reuse of code and how much is custom per idea.
This has always been true, for the entirety of my career. Buy side pays more than sell side for alpha-generating activities. (Sell side pays more for flow and scaling advantages.)
This article strikes me as a submarine [1] for Baruch’s program.
[1] http://paulgraham.com/submarine.html