This is for anyone that comes from a mixed background like myself and is looking to break into AM. This is also me asking for any insight from anyone who was in my shoes that has been successful in their transition into AM.
Up until this year, I've been a Nat Gas FP&A Analyst, a R programmer/Developer and a Data Scientist at a major insurance company.
I got my Undergrad in Economics with a minor in Math (mainly stats) from a small catholic school and completed my MS in Analytics from a semi-target, 3.0 and 3.5 GPAs respectively. I am the ripe age of 27.
I start at a very large AM this week as a 1st Year Associate (PM) on the cash desk. The road here was different, but something I'd like to say was very educational.
The reality of the matter is that aside from my Econ degree, my exposure to the markets as been pretty much 3rd person. So I wondered what it was that got me here.
Data Science. Maybe one of the sexiest fields right now and yes, maybe even more lucrative depending on where you go with it. So for anyone that sitting there coming out of undergrad from a non-target wondering how to make themselves look DIFFERENT enough to be brought in, here is where you can start.
Take programming classes. Learn Python/R (with an emphasis on Python) - this is the meat and potatoes. You need to be able to provide insight on whatever asset they tell you to look at, so dig up those old Econometrics notes. In some cases you'll need to prototype something in Python to be sent off to a developer who will rewrite in C++. You'll also need to automate a bunch of nonsense. Converting old Excel Macros into Python is something I hear about all the time, so maybe even brush up on your VBA (that is the language in which macros are written). Learn SQL - more and more, companies are expecting you to access data on your own. Lastly, learn a data visualization software like Tableau or Qlikview. Year sure, MatPlotLib and ggPlot2 are good, but for easy interactive dashboards, I would recommend Tableau. I use Udemy.com for most of the stuff I need to brush up on and its like $30 a course.
With the current fee wars, becoming more efficient internally is the approach most firms seem to be taking to increase alpha:
Of the 300 firms in our global survey, roughly 20 belong to this select group of asset managers creating digital alpha (see sidebar, “Methodology”). They share three characteristics. First, they have erased the traditional boundaries between their operations and technology groups, combining their budgets and development strategies. Second, they have focused both on reducing costs in legacy areas while simultaneously investing new data, digital capabilities, and talent. Third, they have made their operations and technology capabilities central to their competitive strategies, describing their digital strategies as creating, and not only enabling, value.
- McKinsey
"By automating repeatable tasks like generating insights based on the variety of sources mentioned on the job description, it frees up staff members so that they can work on tasks that require human intelligence"
- BlackRock
Providing information more readily to the Senior PMs will naturally free up their time to make decisions will boost performance, but more importantly, create a pipeline to their thoughts and strategies.
This is awesome, because you get to take their thoughts, deconstruct and understand them to building meaning programs to benefit the team.
Coming from a place where automation was MA critical to being efficient in conducting research - it seems that my data science background is what attracted them the most. However its also how you sell your willingness and ability to learn. With the way programming is, you need to learn new languages and how to apply them as tools. Demonstrating this is paramount to success.
A lot of this may seem obvious, and it should. Surprisingly people wont do it.
What I want to know; is if anyone here has also come from this background, and if there is anything you can add to this. Or even if you work with someone who comes from this role and has helped boost the performance around them.
Hey OP, thanks for sharing! I'm making a similar move to a Quant Strategy team at a megafund (I'm not a data scientist in my current role however; it's more Corporate Strategy). What are some concerns you had (like work-life balance etc.) while evaluating the opportunity and what made you eventually decide to take the offer?
Yea, so my biggest concerns were work-life balance, technical requirements, and desk culture.
My last role was certainly research heavy and also very interesting. Plus the projects had very generous deadlines, so I had an excellent work life balance (9a-4:30p) This new role is very macro oriented - and as an Econ major this is what I find the most interesting, but now I'm looking at 7a-5:30p. I think as long as the work itself is something you can dig into, hours fly by and you don't care as much for the extra hours demanded of you. I think it all depends on what time you get home to be honest and I'm only adding an hour on the back half of the day, so no biggie there. (edit: This doesn't factor in work related events after hours, which I know is foolish to ignore, but also isn't an everyday thing)
The technical concerns I had mainly were switching my programming language focus. Old job used R a lot and the new gig is Python. However, I wanted a reason to become more advanced in Python - so this is great. The math doesn't change, just new tools.
As for desk culture, I'm actually super excited to work with these folks. I managed to finesse a Happy Hour which turned into a wild evening, fully immersed in the culture. I developed a lot of good relationships that night and had great candid conversation with them. So i was happy to see nobody was a hardo, but also very down to Earth.
And bonus concern, the competitiveness. I'm a competitive person. Always have been, but my coworkers to date haven't. They just treated their jobs as such and never looked to be better. So I am interested to see what it'll be like working with people with higher expectations of themselves and teammates. Again, they are all great people - so it doesn't worry me all too much.
What are your best sources online to learn Python, VBA, SQL, Tableau, Power BI and Econometrics? Or any books you have to recommend?
Time flies during COVID.
So, first, I hope you were able to find sources on the above languages and that you found success through them.
Second, for the sake of closure, I would use the following.
UDEMY is typically my goto recommendation for people. They’re cheap and relatively thorough and provide applicable projects to tie it all together.
tableau is tricky however since you need a license to develop/use the full extent of the program. So if your current company allows you to use the software - just grab a data set/connect to the server and play around using online tutorials (UDEMY)
The **important thing** is to continue using the languages and to gradually build those skills so they become almost instinctual when you begin to develop or design your analysis.
I hope you don't mind me asking where did you get your MS Analytics degree?
Currently enrolled in a MS Analytics program and was just curious
Nova.
you?
Can you talk more about your day-to-day now that you've been in the role for 7 months? Are you still refactoring macros to Python? Are you developing your own predictive models? Curious to know how you're applying your software skills on the job.
I use Python and SQL to make my work life somewhat automated, but I'm sure curious to see how you're applying them. That said, I don't work with large data sets, so perhaps there's no common thread except for the languages.
I can do you one better, now that I’ve been doing this for almost 3 years.
It’s not an everyday use, but more so a toolset for me and my team. However, I can break my usage down over my time here.
Months 1-8:
Rebuilding old macros, converting to Python/R, and assisting in research/presentations for driving investment strategy
Months 9-18:
Being asked to run with analysis on market events, highlight significant factors - provide market color to broader teams through automating curated data points. Brought on to larger initiatives as a SME (subject matter expert) for the trading desk to better communicate with developers
months 19-present:
It’s more so a tool now for me to do my own research to drive decision making. I continue to do all the same things from months prior, but now that I have a proper role on the desk - I do it to enhance my performance and elevate my team when I can. I also serve as a “educator”, if you will, for interns.
So, with being involved in the market as my core focus - coding has certainly taken a back seat. It [coding] is certainly something I use often, but just not day to day. Sometimes I write something just to back-check a view/expression, sometimes it’s an AdHoc request to provide senior mgmt. it’s opened up many opportunities for generating alpha and getting exposure/networking.
long story short:
it’s better to have and not need, than it is to need and not have.
I have some thoughts to add but I want to clarify because I'm unfamiliar with the cash desk. What are you making decisions on? Investments in very short-term and liquid assets? Is this fundamental research or quantitative research?
Exactly the above. It’s all quantitative.
So it’s mostly macro analysis and then event driven studies. The fact it’s ultra-short, a lot of this really goes into our positioning on the curve.
This is a great discussion! I joined a FI Research team out of undergrad a month ago and have a pretty extensive background in R/Econometrics.
However, I’ve been struggling to find opportunities to integrate this into my work. For example, I can run an IRF to assess how a oil price shock or 50 BPS ir hike next quarter might impact the economy, but I’m struggling to justify allocating time to build a model that pales in comparison to what a economist with a true PhD macro background could do.
What types of situations do you use Python/R and are there any general examples you could possibly share?
Is coding skill basically required now in FI research, macro fund?
I don’t think it is but it’s a great talking point to have during interviews.
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