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.
the strategy of getting a PhD can be quite hit and miss - sometimes trading firms want very specific knowledge, e.g. specialist in solving certain kinds of PDEs, or DSP specialists, or bayesian/uncertainty statisticians. or sometimes they just want somebody who knows how to look at ambiguous problems and ambiguous term sheets and contract specifications and put it into solvable forms so that the already existing megabrains in the team can try to crack it. it really depends on the outfit. if one already has a phd and wants to get out of academia, then finance might be a good option. getting a phd purely to get into quant finance doesn't seem like a positive NPV investment (not just opp cost but also the soul crushing life of grad studies). as it turns out, quant funds also hire a lot of bachs and masters. not everyone needs to be churning out new models, 99% of the work is keeping all the systems and the analytics running and expanding existing knowledge to other markets.
Agreed - there is plenty of work for non-PhDs in both the buy and sell side. Quant developers are often possess undergraduate degrees, particularly in CompSci. Not much need for a PhD in the vast majority of systems development.
Yes but PhDs tend to be better on average than MFE graduates. If a fund has its choice of PhDs (which is the situation now) there is not much reason to hire someone with only a masters, except if she has some crazy skills.
Everything in this comment rings true from my experience (ex-science-software engineer, current equity derivative financial engineer at an investment bank, pining for the start-up fjords).
>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.
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.
Knowing math and programming is important, knowing finance isn't.
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.
You mainly just need to be a good C++ programmer to get a job. To get a job where you aren't just a C++ programming resource you need the advanced degree, advanced math, previous experience, etc.
I think the best way to get a tech job in finance is to live in a city where finance is a big industry (New York, Boston, Chicago, London). If you punch "C++" into Indeed for Chicago, IL, half the listings that come back are for positions at trading companies.
I apears that Algorithm trading Firms are slowly moving to SF/Silicon Valley. I see some postings for SF-jobs in this domain. May be they want to use the software talent in this area.
#3. Definitely #3. I mostly model mortgage prepayments and defaults, the housing market, and do econometrics-style forecasts. That's not in any of those books.
Not really. Lakhbir Hayre's Guide to Mortgage-Backed Securities isn't bad. Fabozzi's MBS book is okay, but a bit of a cut-and-paste job. I don't know of anything that deals with the post-2007 environment. Otherwise, just a combination of statistics (GLM, GAM, NLS, etc.) books, time series books (e.g. Durbin & Koopman, and econometrics books (Greene, etc.)
Thanks! I've been recommended Fabozzi's book before but with the caveat that it was quite dry (one person said that he felt like he now understood what ADD felt like).
I'll have to check out Guide to Mortgage-Backed Securities.
I don't think either of those books is very good, so try to borrow before you buy. The best of a bad bunch is still bad.
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.
Is it common to have a job in HF trading as C++/infrastructure architect guy, but having no interest in quantitative or any other type of finance? I mean no interest in strategies.
Yes. Most of the people who work in an very HFT firm will be developers and ops people who are good at integrating cutting edge technologies. An understanding of how the exchanges operate is useful but knowledge of finance isn't really required.
Garbage. Many hedge funds only require a BS from a top school. Others, of course, are only looking for PhDs. These exams and certificates, IMHO, are money-making machines (if you need those certs for your job -- like Series exams -- your firm will sponsor it)
It seems like the topic of QF is appearing more frequently these days on HN. I guess it's a sign of the times after an absence after the implosion of 2008. There also seems to be general leveling off of posts on how awesome working for startups are compared to 2008/9 and the exodus of talent from wall street. Sign of the times I guess. Makes me think we're headed back to the 'good old days.'
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.
I've noticed it too. I think many of us believe that the size of the financial sector is extremely disproportionate to the value it adds. It drains talent by offering smart people salaries that other industries can't afford. It gambles with the economy recklessly, but the public pays the debt when their gambles turn sour. And on, and on.
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.
"It drains talent by offering smart people salaries that other industries can't afford."
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.
The mean salary in New York City's finance industry rose from $80,000 in 1981 to $360,000 in 2011, while average New York City salaries rose from $40,000 to $70,000.
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).
Google and Microsoft are in the same ballpark of profitability per employee as say Goldman Sachs or Morgan Stanley. The difference is that financial firms take pride in how much they pay their employees, while tech firms do things like collude to depress employee wages.
This couldn't be more true. I got an offer at Intel after working there for the summer. A finance company asked what they were paying and decimated Intel's offer. Upon telling Intel the recruiter at Intel demanded to know where I was going. Luckily I wasn't going to Apple, Adobe (or one of the other companies they allegedly had illegal no competes with or I would have no job at all.
Even under definition 2, it still means 'reduce'. So if the salary was 'reduced', the second offer was less than the first one. It may have 'decimated the attractiveness' of the first salary, sure.
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.
Nine tenths as much to be correct. From wiki: The word decimation is derived from Latin meaning "removal of a tenth". A cohort selected for punishment by decimation was divided into groups of ten; each group drew lots (Sortition), and the soldier on whom the lot fell was executed by his nine comrades.
Ah yes, my favorite pet peeve - descriptivism vs prescriptivism in linguistics. Lives have been wasted arguing about this exact interpretation of decimate, my position is that 'decimate' in a 2013, Western context means one of the definitions posted above - i.e., significantly reduce in number, not by 10%.
You are not wrong about the amount of money to be made in the finance industry, but when talking about those salaries you have to be very careful.
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.
Well, total compensation, say at Google, can be around 200k for senior guys. A lot of people in finance are making less than that and so people do leave for software jobs.
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.
I think most of the easy money in startups has been made (and from here on out the dumb ideas that raised millions of dollars won't see as much money), so people are jumping back to finance.
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 get a feeling that people somehow do not want to believe that quant finance is in very bad shape right now. It is very hard to get a job, especially without experience. Salaries are not growing, might be even decreasing. Starting base salaries are around 100k. You might get a bonus, but it all depends on firm performance. Before VP level the pay is not much greater than in tech.
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.
Saying "I want to learn Quant Finance" is like saying "I want to learn programming," in that it really all just depends. There's an impossible number of fields and sub-fields, and many people know one field intimately but may not know another at all (high-frequency trading and derivative pricing, for instance, have little overlap). Also, some of these jobs (i.e. high-frequency trading) are about 90% programming, whereas other jobs tend to be significantly more mathematical. If you can't narrow your interests beyond just the exceptionally broad category of quant finance, then learn enough to figure out what subcategory you want to jump into, and then let me know and I'll give you a reading list, if I know anything about 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
I'd add that if you are truly interested in HFT the time when you could make C/C++ software fast enough is either gone or very short lived. Much more valuable would be FPGA programming or other similar skills.
For a layman, Can you please explain why FPGA skills are good to have? Some years, I could understand that processors were not fast enough so FPGA parallelism was useful...I think modern processors have caught up.
FPGA cards are the easiest/cheapest way to get programs into the chip level. You can bypass OS level operations, memory/cache operations etc.
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 like the topic, but I have no idea where to start on these books. Can anyone recommend one or two sources that would be good entry points for learning this kind of stuff?
A good corporate finance book is going to give you the foundation of all this. If you read a corporate finance book and then start going through the book list, it will make a lot more sense. A lot of the books in his book list assume a foundation in graduate level understanding of finance.
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.
Hmm, I think that's backwards. If you're going to work in corporate finance, then you need to know about capital markets, derivatives, etc. But if you're primarily interested in quantitative trading then you don't need to know all the stuff in that book.
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.
I agree that one doesn't need to know all that stuff in that book for this area. You are right that leasing, taxes, mergers, or corporate governance don't apply. Those chapters in any corporate finance book should be considered the sandwich for the meat. If you don't like those chapters, be a picky eater and just go for the meat and leave the sandwich alone.
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.
I used the book I referenced for a course so that's what I am basing my recommendation on. Your suggestion also seems like a good choice but I don't have any direct experience with it.
I always recommend "Trading and Exchanges" by Larry Harris. If you want a slower mathematical introduction, I recommend the Stochastic Calculus for Finance books by Steven Shreve.
I couldn't tell from your comments, strongvigilance, but do you code as well? I'm interested finding who on HN code but also show a deeper understanding of finance beyond just passing interest.
Another endorsement of the Harris Book here. The technology is all out of date (hello SuperDot), but otherwise it's a great introduction to modern financial markets.
"Options, Futures, and Other Derivatives" (John Hull) is the definitive newbie handbook in quant finance. If you are interested in Quantitative Finance, you should understand this book, cover-to-cover.
"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.
Don't forget the Coursera courses on finance, econometrics and financial engineering. They might not cover everything, but they should be enough for getting you interested or bored. I'm not in a Finance field, but I like these courses a lot.
I know the "pull of the markets" is strong and my words will likely fall on deaf ears, but here it is:
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.
While I see your point, I think mentality required for this field is...be a go getter and do whatever it takes to beat even the best guys...I believe general software engineering and other fields are a little more forgiving than that.
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.
Around 100k base for people without experience. Around 150k base for VP level (~5 years of experience). Bonus is highly variable (20-100% of base is typical).
Don't suppose anybody knows of any service that provides historical market pricing data available to download en masse? The only services I can find just allow you to view data for a specific company, whereas I'd like to just dump the data for as many companies as possible.
Many people say its too late to enter HFT..Is it true for developed world or is it true for ALL countries? (India/China/etc). For example, I am not sure if Indian stock markets have so much of HFT.
Absolutely it does. Just about every hedge fund, trader, proprietary trading firm, etc..., uses it. But it doesn't guarantee success. Your algorithms, programs and analysis have to be better than the next guy, otherwise you'll just lose money very quickly.
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.
https://www.quantnet.com/threads/master-reading-list-for-qua...
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.