Getting into a top hedge fund as quantitative analyst or quantitative researcher?
Would graduating from New York University's financial engineering program be one of the best routes to achieve this or are there better routes or schools?
Trending Content
| +29 | Advice ahead of a pair trade presentation - HF interview | 7 | 5d | |
| +28 | Background check issues | 6 | 11s | |
| +27 | College search for getting into HF | 23 | 1d | |
| +26 | Why are so many people here interested in Biotech? | 32 | 2d | |
| +23 | VP HF Lateral To PE | 1 | 4d | |
| +20 | % hit rate on quarters | 14 | 11h | |
| +19 | How to approach Consumer L/S ideas | 6 | 1h | |
| +19 | Drunk Thought: Shouldnt PM's at MM inverse their Portfolio's? | 6 | 4d | |
| +17 | How to learn more about Cyclicals? | 6 | 1d | |
| +17 | Is fundamental L/S equity a dying strategy? | 15 | 3d |
Career Resources
No
Go Harvard/Yale/Princeton or any other ivy, Stanford, MIT etc., and do CS/Stat/Math/Physics/Engineering.
MFE is only good for sellside quants. The knowledge of MFE program focus on derivative pricing and is heavily outdated compared to modern buyside quant strategies. Machine learning and statistics are more sought-after skills than financial math. Top quant funds recruit heavily from FAANG ML interns
So what graduate program area would be best suited for buyside quant?
Imo, physics >= statistics >= CS with AI/ML concentration > data science > general mathematics > CS with non-ML concentration >>>> MFE for buyside recruiting. Phd >>>> master. Also, quant funds do not expect candidates to have finance background, so any program with “finance” in its name isn’t gonna help get quant roles.
In what universe is physics better than stats for a qr type role?
Literally the entire job is applying statistics to equity datasets, and intuitions about expected values, covariance (risk) and priors (bayes) are extremely important
I hear what you're saying and it sounds like it should be logical; but in my experience at several top-tier quant funds, for true researchers, physics PhDs seem to be by far the most common career path. I don't think statistics is even in the top three. Of course every fund has a slightly different definition of "quant", "researcher", "research engineer", "trader" so it might be different at other shops. It's different for other roles (EG, for non-PhD traders/engineers/PMs there are a lot more stats people than physics people). But for the researchers, I see far more physics PhD researchers on my desk that statistics PhDs.
Just a guess, but I think it's because the math and statistics PhDs who don't want to do academia have lots of other career paths available, but if you're a physics PhD who doesn't like academia, then quant finance is clearly your best (only?) option.
Stats is just one of the skillset needed for quant. Physics is about finding truth using quantitative techniques ( the exact definition of quant ). Many of the AI / machine learning breakthrough originates from physic knowledge. Stats major is better for tweaking existing strategies ( most of the quant traders do this ). Physics is better for researching new strategies. But both majors are good. The more important thing that distinguish you is personal talent and aptitude.One thing that I disagree with the above: physics does not have less optionality than stats / math in non-finance industry. Many top AI researchers are physicist. The risk and bayesian stuff you said sounds like middle office risk quant. Alpha quant is different ballgame. They require expertise in deep learning, data mining, and advanced linear algebra .
I would ignore the above advice. NYU Courant Math Fin is a very good program. Approach the quant career from a long-run perspective. From a number's perspective, you likely will not end up at a hedge fund out of school (also depends on asset class), but you take a job that will train you in the relevant skills and you'll be marketable to hedge funds a year or two out. ML is much more relevant in certain asset classes (equities is pure data science), and you should pick a program that will give you flexibility in classes to learn that material. If you're doing relevant internships/jobs you'll apply those skills as well and build on them. I'm not sure how NYU's curriculum is but look for that.
Coming out of a good program with relevant experience will convince funds to atleast interview you, and from there it’s about demonstrating you can do the work by talking about projects and doing some technical questions (more of these if you’re less experienced). Generally, these opportunities are much more meritocratic imo, you just need to cross a certain bar of education/experience.
In sed sint eius est molestias. Illum iure sint laboriosam exercitationem repudiandae cum accusamus consequatur. Assumenda sunt accusantium iure et magni. Placeat recusandae sit dignissimos laudantium.
Est quia vero omnis consequatur sint temporibus amet. Et praesentium voluptas aut alias est itaque at voluptatem. Consequatur accusantium tempore quia aperiam debitis ut. Sapiente qui quod voluptatem maiores ab molestiae quasi.
Excepturi consequatur in porro repudiandae magnam nihil illum. Inventore et dolore vitae molestiae nisi. Nam earum exercitationem reiciendis odit qui. Quidem non officia ipsam qui nisi blanditiis modi in. Et aperiam eos et eaque et. Dolor rerum explicabo beatae saepe ut assumenda fuga asperiores.
See All Comments - 100% Free
WSO depends on everyone being able to pitch in when they know something. Unlock with your email and get bonus: 6 financial modeling lessons free ($199 value)
or Unlock with your social account...