A few words of advice for anyone interested in this field as a career:
1. I would caution anyone on speculatively learning a new programming language in the hopes of getting a job. Your boss will tell you what to learn when you get hired. Don't ask strangers on the Internet for how to allocate your time.
2. "Quantitative Finance" is a broad field. High-frequency trading and exotic options trading have no overlap. So putting together a list of what-to-learn-to-be-a-quant is really foolish. It's like creating a master knowledge base for all of programming without narrowing the field to web, embedded, gaming, etc.
3. The best way to learn is to get a job in the field. I'm surprised at how often I have to repeat this.
Someone I know of was head hunted for a quant firm after he did his PhD in fluid dynamics. I don't think he knew anything about finance.
His supervisor was very disappointed.
But to get one, you need to have worked through a lot of books already, right? Job descriptions of this field look to me like: PhD in math finance and senior programming skills.
The friends I have who have a degree in mathematical finance say that the finance portion of their degree was quite useless and only very weakly correlated with how 'finance' worked day to day at their job. Basically they learnt everything they had to know about finance on the job. The optimization and statistics they studied on the other hand is stuff they use every day, and if they could go back and do it again they would study less finance and more math at university.
I'll have to check out Guide to Mortgage-Backed Securities.
I never look at Fabozzi's book these days, but I still break out the Guide to MBS for its appendix on mortgage amortization formulas. That's a pretty weak recommendation.
For good reason!
The 30+ generic C++ books in the posted list says a lot, and many of the more finance-oriented books on the list are very low on signal-to-noise.
This is all a bit depressing cause I feel like the conclusion a lot of society had reached in 2008 was that finance should be boring, simple, and shouldn't attract too much brain power from other fields.
But let's face facts. I'd love to think everybody's really here to "change the world" and "create value." But the biggest stories in tech entrepreneurship focus on silly, time-wasting apps that are sold for millions. Does that really make any more sense than giving a guy a million dollar bonus for designing a high frequency trading algorithm?
If we don't expect more from our community, we can't really criticize theirs.
Bullshit. The difference is that in finance (trading, at least), good technology is seen as a competitive advantage that makes a meaningful impact on P&L. When financial companies do well they tend to share the love in the form of cash bonuses. This is a strategy other industries could adopt but choose not to.
Also one other nit, there are only a few companies (the megabanks) that are big enough to have a government backstop if they blow up. There is a whole world of smaller outfits doing business without any such guarantee.
You may be right, but I just don't think there are a lot of tech companies that could offer a mean salary of $360,000 and remain profitable. Especially small to medium size companies. Compare that to hedge funds, which were basically printing money and could pay their small staffs extremely large salaries (and even larger bonuses).
past participle, past tense of dec·i·mate (Verb)
1) Kill, destroy, or remove a large percentage of.
2) Drastically reduce the strength or effectiveness of (something): "plant viruses that can decimate yields".
Drastically reduce the strength or effectiveness of (something): "plant viruses that can decimate yields".
decimated Intel's offer.
Drastically reduce the effectiveness of Intel's offer.
It fits exactly. Not only that, but you could also say:
Destroyed Intel's offer. (Def 1)
Decimate is the right word choice and it conveyed the intensity of how badly Finance Co. beat Intel's offer. Your definition of decimate may be used by heavy quants but it doesn't fit everyday usage.
Hedge fund managers make orders of magnitude more money than their employees. Investment banks work much like law firms, if you are a partner or partner track you can make tons, but frequently that is built on the back of average paid employees.
What would tech company compensation look like if we included Brin/Cook/Zuckerberg?
There are lots and lots of small shops out there that have good but not idiotic compensation packages. The tech companies could (should?) match them if it weren't an ingrained part of the culture not to.
As I have said before, really huge compensation levels in finance exist for VP-level people. Please keep in mind that "VP" in finance means something like "pretty senior", he might no be a manager at all. These guys have around 5 years experience and if all went well for them they can be making around 300k. These would be hard to match en masse while avoiding a situation when of two engineers doing the same job one is paid X and another 2X.
This is repeated a lot, but the skills of financial wizards are orthogonal to what most tech companies need.
It's also not the case that the tech industry can't afford to pay people high salaries.
There was never a real exodus of talent. The really talented people weathered the storm, and it seemed as if a whole bunch of B-people switched over and are now considering switching back.
I should clarify that I don't think the bubble is over yet, but the euphoria stage has passed
You will likely need a PhD to get a job at all. Maybe a top MFE, but most MFE graduates are perceived to have very shallow knowledge, which has a grain of truth to it.
1) High-frequency trading. Very programming oriented as trade latency is everything. You'll need to be really good at making C/C++ code really fast. Math is somewhat less important than other fields.
2) Derivatives pricing. Usually working for banks/hedge funds, and use various methods (Monte Carlo, PDE, transform) to price options. Will need to know C/C++ and/or probably Matlab, C# (Excel is king here, so MSFT languages is a big deal), and Python is on the up and up. Will need to have a serious math background. This is more or less what I do.
3) Algo trading. Uses mathematical techniques to find patterns and execute trades. Probably works for a hedge fund. Programming languages vary by fund and include Matlab, Python, C/C++, Java (one uses Ocaml). Less theoretical, lots of playing around with data.
4) Quant risk. Usually works for a bank or hedge fund in the risk department. Responsible for things like building VaR models. Usually requires light programming (VBA, although serious languages help), and some lightweight math.
5) Quantitative portfolio management. This usually involves managing a portfolio of stocks, either long-only or long-short. People usually come into this from either a heavy math/tech background or from doing academic finance research. Skills include portfolio optimization using linear/quadratic/etc. programming, machine learning, simulation and backtesting. Some companies that do this include:
- Acadian Asset Management
- AQR Capital
- Goldman Sachs Asset Management
- Janus InTech
- MDT Advisers (discl: I work there)
- Renaissance Technologies Institutional Equities Fund
That said, you probably could build bespoke machines out of modern processors that perform better (and someone might be doing this), but it would be very expensive. FPGA's offer a middle ground between that and software.
I work in python. I'm staggered by the range and quality of the libraries. Numpy/Scipy + Pandas + ipython = instant productivity.
One I would recommend is:
This will go into some level of detail into all areas.
Once you are comfortable with this material, you can branch into more deeper study in each area.
A lot of trading systems attempt to exploit arbitrage opportunities (however time limited) in the market place. A book like the above will give you a deeper understanding of the stock market and the different financial instruments that are used in the market.
It will also provide real insight into how companies manage their capital budgeting (I have $100 million dollars, how do I decide what to spend this money on this year). Not every company will follow the same approach, but the fundamental concept of net present value is very common.
Once you have a good understanding of the basics then you can delve further into derivatives. It would be hard to get into derivatives without a solid background in what a MBA/graduate level corporate finance book gives you.
As an aside, anyone in IT that wants to expand their business know-how would gain quite a bit by reading a corporate finance book. If you want to know what the executive team spends their time on, the knowledge around capital budgeting and understanding how projects are assessed (ie. using WACC and NPV) is very valuable.
E.g. there's no need to know all about leasing or taxes or mergers in order to price derivatives. In fact I doubt that most people working in the area know much about corporate finance, whether they are math and physics PhDs working as quant researchers or regular programmers implementing the models.
But if one has no clue about how stock markets work, how risk is quantified, the intricacies of call and put options, the various types of bonds, etc. than I would suggest a corporate finance book is a good place to start.
Why? It starts slow and builds the concepts on top of each other. These books take a long time to put together and they are usually pedagogically well put together. Just for this along I would recommend such a book. The effort that has been put together to make sure that students have the best opportunity to easily pick up the knowledge is really high.
I especially like the above book as it builds the concept of the law of one price and arbitrage very well.
I have made the assumption that kevincrane is at this starting point.
The reality of all this is that this stuff is hard.
It's quite amazing much the study of finance is so complex. A corporate finance book let's you gaze into the window without actually stepping inside.
As I suggested, its a starting point.
In my opinion, if one can't get their head around the knowledge in a corporate finance book the rest of the material in the more technical focused books on that book list will not be very accessible. There is a reason why people in the field have Masters and PhD's in strong heavy math focused specializations.
"Principals of Corporate Finance" (Brealey & Myers) is the definitive newbie handbook for Corporate Finance, which is not the same subject as Quant Finance.
If you'd like more of a live view into what is going on in the markets (strategy-wise), check out nuclearphynance.com.
As for technical books, I prefer Maureen O'Hara's structural approach over continuous time finance. Beyond providing more of a "trader's view", she is also a lovely person. I don't much care for continuous time finance.
If you actually qualify for a quant job in today's crappy markets (solid technical PhD is a start; keep in mind your bonus potential is about half of what it was 5 years ago for HFT), please do yourself a favor and pick an industry where you actually get to produce something. You're a decade too late.
an industry where you could actually earn something would be much better!
As for salary, it varies a lot. For a large hedge fund, you might look at $200K base, for smaller shops maybe $150K. Of course, most people are more interested in the bonus. Also, it varies a lot based on experience.
It overlaps somewhat with the OP's list, but I have a tiny bit of commentary on each book.
This is simply the evolution of trading, another tool to help traders. And someone will always make a ton of money, while someone else will always lose. That's how trading works.