Hello all,

I am aiming to apply for a Master of Quantitative Finance later in the year. I'm curious if anyone on this forum would be able to provide some insight into what to expect from entry positions, salaries, etc. There is some info online but nothing that goes into too much detail. Any input would be appreciated. Thanks!

I don't think people are gonna write a book for you just in hopes of covering the information you're looking for. Ask more specific questions if you want good answers.

It's pretty much stabilized between 100k and 150k for entry level quant positions.

Thanks for the reply gritty. Is that in USD? do you have any idea of how the salary progresses over time? Cheers!

i wanted to start a thread analogous to this one.

i want to design my own quant analyst self-study program... i know alot about applied quant. but i would like to improve my math skills and especially my computer skills.

as per math, my interest is more econometrics and analysis type work as opposed to derivatives and/or hedging which i know is the rage for most people.

i think i know what i have to learn in math......... wish i'd taken much more math in college as it's SO MUCH EASIER to learn it then.

as per computers though i don't really now.......

what would people recommend as per deal ing with large amounts of data in a flexible manner i.e. beyond microsoft excel although i'd like to learn more about automating that... also, different elements of trading automation.

so what would people suggest? C+.. R? MathLab? Python?.... not sure what else..... i'm thinking C+ to a certain degree is the holy grail for this type of stuff.

thx in advance for any responses.... sorry that my questions are a bit disjointed.

Not an expert, but I would guess Java or Matlab would work well

You're handed a Letter size paper and a pencil.

You have 3 hours to prove your worth on this piece of paper.

What would you produce?

I definitely do believe that there is alot of value in self studying. From what I've heard it's very difficult to get anywhere without numbers on your transcript unfortunately. Hence why I'm returning to school to take several undergrad statistics courses (before applying to the master's program) to improve my fundamentals, as I believe that is by far the most important objective for me before learning more about the nuances of finance. I would imagine coding would help and im pretty proficient with matlab. (My engineering program required this alot). I would like to add something like C+ or R to my skillset though.

With regards to the question asked about proving my worth in 3 hours, I honestly have no idea what I would write. I don't have the deep knowledge of math nor the finance to write anything meaningful at this point in my life. I could write about how I turned a 20$ deposit into 45KUS over the past few years playing poker while obtaining a degree in chemical engineering, but other than that, that's pretty much all I got right now.

Maverick, thanks for the response.........i just want to self-improve. i already have the numbers on the resume and/or transcripts.

i do realize that self-study and nowhere near the value in learning of a two-year program. but i'll save two years of time, two years of incredible hassle and stress, and $200k of debt.

also, can study exactly what i want........ my interests are a bit different than FE program as......

i'm thinking there's a good business idea for someone to bridge some of the gap between what i'm doing and a hard core FE program. defintely needs to be tests, projects, and grades. this may exist in FE already. definitely exists for tons of computer programs - java, indesign etc.

i see that you can take cal-berkeley's pre-requisite FE courses online... other places - MIT being most prominent - you can take non-graded, non-guided FE type courses for free, although it really boils down to not that much different than self-study i.e. reading a text book

IMO it's helpful to think of a hierarchy of tools:
Excel -> MATLAB -> Python

Use Excel for your simpler models and toying/experimenting. Use MATLAB when Excel macros don't give you enough computing power, and use Python when MATLAB won't cut it.

It's understandable that you might just want to do everything in a single language that can cover all cases (Python), but the value in MATLAB is that it's easier (imo) to code and visualize stuff than with a full-on third-generation language like Python (but of course not as easy to code and visualize as Excel). If you're new to finance you probably won't NEED to touch Python for a while.

In that hierarchy you can substitute Mathematica for MATLAB and/or C++ for Python, but I chose MATLAB+Python as I think they're more used in the industry for research purposes (compared to say, development/application purposes), which seems to be your interest per your want to self-study.

Any interest or focus within econometrics? Bayesian stuff? Micro? Macro? Volatility? The future path of interest rates?

pocket, thx for the very excellent response...

my interest in econometrics is two-fold:

1) stuff i do, time series analysis mostly and to a large degree excel is fine for coming up with the type of models i use right now - 10 data series say. maybe monthly returns for the 10 major USA industry sectors......and to some degree i don't have learn that much more for this. and i appreciate that excel is very very flexible and good for tweaking things. that was my thought before asking any of this........... i do alot of interesting work in excel myself. and i've come up with decent semi-automated analytics template with autocorrelation

2) reading other people's work........ here's where it's alot more complex and confusing given that i don't control the work/narrative. for what i've seen things like ARCH, GARCH, regime shifts etc.......... authors i've wanted to understand better are john cochrane and his collaborator monica piazzezi and others like stock/watson and diebold.................. so predicting returns and lead/lag relationships in all kinds of things....

as per #2........ what of course happens is people do all kinds of complex non-econometric math on their models i.e. setting them up, and then test the models with econometrics so that really opens the math up completely to just about anything.

one big battle is just learning the notation............ many things are understandable with the notation and things like interation functions and other ways of estimating things..... i apologize if that doesn't make complete sense.

two more things:

alot of it is just managing a large amount of data...... a large canadian money manager i'm aware of probably formally tracks thousands of indicators and has done very small studies on most of them i.e. you read about comstock indicator so you program it, doing a cursory back-test and make a few comments and then save it.. then anyone can access it, they can call it up at any time. they might even do PCA on it with other similar indicators to try a come up a with a purified indicator for it. ........ so some of my interest is just learning computer skills to set something up like that.

a perfect example would be using yield curve to predict the economy and/or stock market. then the next step of course is which yield curve to use. and then there's problems with short rates having been so low. do you use spread or ratio of long/short rates? both have their shortcomings.

but of course i then come across a 120 page paper on this that looks really good but i don't understand most of it other than it's conclusion that the short rate is all that matters. it's actually better than the yield curve..... but is that short rate nominally? meaning today is very bullish for economy and stock market??. or is that inflation-adjusted or relative to recent short term rates?..... that type of things i don't understand from the paper.

cxoadvisory.com level analysis is pretty similar to what i'd like to learn and the papers they choose to summarize....

so basically backtested investment strategies and lead-lag relationships would probably summarize my interest

BTW, don't judge me on comstock indicator. i'm not a technical analyst type of guy. it's just a uniquely named indictor that comes to mind.

Nice. Good stuff. I was actually going to suggest Diebold's blog as something to read depending on your response. When I was unemployed I spent a good chunk of time simply trying to reproduce the results of papers he'd link to - wrote a fair amount of MATLAB code in the process.

You might also be interested in checking out papers from Rob Stambaugh (also at Wharton) - he also does a fair amount of time series analysis and volatility modeling, usually from a Bayesian perspective. I also think he happens to be one of the better academic writers out there.

pocket, thank you again for your help... chatting with you has helped me define what i want to learn on both math and computer sides.

my interests would be:

1) work of people like campbell harvey, john cochrane, robert shiller. might even put meb febane here but his stuff is pretty trivial. i do much much more sophisticated work than what i've seen of his. obviously he was 1st on alot of stuff and is a great communicator.... forgot antii ilmanen. that's another good name. alot of simple stuff but i know in his case it's backed up by impressive analytical ability... fama/french... i think there's a commonality amongst these people.

2) AQR Cliff Asness type stuff........ but much of the math is semi-trivial - not all of it. it's often just doing incredible number of calculations. that's where heavy duty computer skills probably come in handy.

3) all sort of lead/lag relationships....... stock/watson, diebold, james hamilton etc...

4) i mentioned the data repository and sharing info amongst people. probably just figuring out which computer programming languages to learn and doing it... and of course any employer is already using something for this.

5) nothing really to do with FE........ but computer skills vis-a-vis presentations. powerpoint, indesign, illustrator etc........... that's probably highly learnable from self-study and fairly well defined as to "what to learn"

#1 and #3 are the most important as per my original post. and i'm thinking #1 and #3 may have more commonalities that i realize... i would say #1 is often working with variants of investment data whereas #3 is working with economic data............

are there big differences in working with time series investment data and economic data?... i would think investment data is often much more short term in nature. economic data much more subject to regimes. i.e. inflation used to be very high. been low now for a long time. will it go back to high in foreseeable future?

i thought i'd add a bit of helpful color to this thread, even though i'm one of the posters that was looking for and got good help.

three analytical books i know of that are available free of charge - and legally - on internet are shumway, diebold and pirrong. i think i'm right about that. 1st 2 are econometrics/time series i think while the last one is structural models (commodities in pirrong's case).... i think i have the names right on the authors.

i see willmott sp? has a new book out about quant's having taken over finance.

scott paterson sp? had a good book on quant investment and its credit crisis meltdown. he also did dark pools, good companion book to lewis' flash boys.

Satisfied python user here. The math, stats, ML packages are solid. Visualization is better now too I think.

i will add john cochrane to the list of professors that seem to have a free edition of thir book online (asset pricing.. and maybe an early version)... also, good slide from classes he teaches and what i think is a non-published book for free too.

anyway, good stuff... the math side of things might come together for me yet.

Thank you very much river for the information you've provided in this thread (even though you were the one initially asking for questions!). I wish I could provide some insight. Hopefully further down the line I'll be able to.

moderators, can we turn this into a general quantitative finance thread? or just start one? sorry if i missed that there is one already.

i think the interest is there and quant type analysis i think is pretty analogous to winning poker .

i would be more than happy to be major contributor and/or one of the unofficial overseers.

thanks in advance

one more name i'd add is andrew ang. he has all kinds of stuff available for free. often, rough drafts of chapters for his asset allocation book.

i ended up spending $100 to buy the book as i don't feel like printing out everything and with the rough drafts nothing is embedded in the text.

it's funny i was asking about analytics and learning them... i was reading GARCH 101 by engel - one of the 2 originators - and eventually he says that unless you are statistics academic to eventually just use commercial software like matlab. just read his work for an understanding of the process.

that's basically the level of understanding i'm trying to get to - basically a real understanding of the material but not to a level where i can use/program it myself.

is there a difference? assuming you're capable of programming in general

I think there are a fair number of folks/analysts who can intelligently experiment with the GARCH toolbox in MATLAB (e.g. feed into it different time series and grab the coefficients that are output) but wouldn't be able to program the underlying GARCH functions (e.g. building the MLE) in, say, Python without spending a lot of time doing so. Perhaps that's the difference the author is referring to.

yes, that's exactly what i was thinking...........

not sure how many people there are on earth who could program GARCH in python who don't have an advanced degree in math or something similar... some of these types of functions have a massive amount of iteration.. the data/computations grow like crazy.

an analogous example is craig pirrong at uhouston with his commodity models... apparently he takes advantage of all UH's or maybe all the UT network computers for his models........ how am i going to program that even if i understand it thoroughly?

i think there's such a gulf in learning things......

i've seen people say that you learn GARCH in CFA studies. but to me that means that you learn GARCH is based on long term volatility, yesterday's garch estimate and then yesterday's surprise given yesterday's forecast... it might be slightly different than that and not sure you learn that in CFA but the idea is the same.

at the other end is grainger and engel (UCSD or affiliated) and their knowledge. but even engel says just try to understand it and then use matlab.... fyi, grainger is dead and engel as far as i know is affiliated with both UCSD and NYU

i want to get 70% of the way to their knowledge... however you interpret that...... edit: maybe more like 50%

things like moving averages and autocorrelation are reasonably trivial to modle in excel....... sometimes it takes some prior thought when you make it very complex.

i should also i want to get to the level where,

1) i can converse intelligently about it. variations. interpretation, usage..

2) i can understand academic articles - again in an intelligent manner i.e. way beyond reading the summary and the conclusion.

3) implement some things in something like matlab.

i certainly don't want to do the underlying programming of GARCH for example.