About a month ago, Citi announced they'll be launching a multi-disciplinary unit of bankers and data scientists that can improve their deal making. They call it the Strategic Advisory Solutions
https://www.barrons.com/articles/citigroup-furthe…
https://finadium.com/fn-citi-combines-investment-…
https://www.businessinsider.com/citi-forms-dealma…
The purpose is two-fold.
1) To deliver better and faster analytics to clients.
2) To automate certain tasks for bankers (ie. Mindlessly building financial models on Excel).
As a financial data scientist, this is the kind of change I love seeing. My prediction is that in the future, lots of modeling and valuation done through manual Excel work will be automated through large-scale precision statistics that is data science. That way, bankers will have get to focus more on sourcing deals, managing relationships, and providing expert financial opinions.
What do you bankers actually think? Will this trend decrease the number entry level banking jobs? How will this effect your hours? Will your pay be impacted? How much will your day to day schedule change?
So they won't need a quarter million $ MBA associate like me to splash around in excel?
...shiiiiiit...
I remember seeing a thread a couple years ago asking what the future of banking will look like in 5-10 years and safe to say...(albeit Ive only been in banking 2-3 years including internship) not much has changed and don’t see anything changing within the next 6+ months.
In that sense while the role will definitely evolve it will take an extremely long time for things to look starkly different for the junior experience.
In the end of the day, banking is a business of trust. Clients would rather want their models to be "hand-made" from qualified analysts than being made off a computer with god knows what type of algorithm. Automation is way more likely to be seen in the markets division (or literally any other field of finance) anytime soon than banking.
Also, I'll add that deals are not made because a certain analyst/associate's model shows the merger as accretive, but off of strategy that can't be quantified by numbers.
Both of your comments highlight why the junior level -- specifically analyst is going to get cut dramatically. If not by automation then by cheaper off-shore labor. Lots of firms already do it for some of the most time consuming and menial tasks.
Clients want models hand made --> DE shaw / 2 sig / ren tech actually manage people's money. LP's there don't mind and want the science to drive decisions. Experienced MD's are actually just extremely good process managers and negotiatiors who have been at mulitple tables of complex transactions before and can guide C suite through a process.
You're talking about trusting a firm with managing money vs trusting a firm to handle the sale your life's work. Completely different things, there are a million other jobs in finance that would be cut before IB due to automation. Why pay big 4 accountants millions to trudge through the complexities of MBS tax regulations to get a single correct line number when a computer can do that much easier and faster?
Deals are made because of the people, not the numbers. Banking is a client facing role unlike the quant firms you've described. The business models are inherently different and while analysts are dealt with more menial tasks, saying that they'll be replace by computers leaving MDs working with a bunch of programmers is science fiction.
Feel free to disagree with me that's just my two cents having been in the industry for a few years. Sounds like you're a prospect so if you're convinced juniors are going to be gone in a few years, why bother recruiting or spending time on WSO?
Just an account i made a few years ago. I've finished my analyst stint in IB and left the industry.
Fair points. I think analysts / assoicates will be neccesary as pipelines for in-house grown MD's down the line. I just think that headcounts will be going in opposite direction rather than trend upwards. I generally think that juniors in IB overstate and overthink their importance to the overall firm -- I know I did, especially with the herd mentality towards banking.
Examples you gave are theoretical “models” developed to measure risk / return.
It definitely is one of the work streams but most of the work in an IPO process is centred around
(1) due diligence (2) roadshows (3) analyst education (4) legal docs (5) structuring the process (e.g. should we offer to retail? Should we incentivise investors? What type of investors do we want to onboard? Etc) (6) modelling (7) prepping the sales teams
Even then i would say that the bigger part of the modelling work stream is around the DD to create a business plan, which can be sold to potential investors
Then of course you’d look at WACC but it’s just one of the many work streams...
So where would your regressions fit?
If it’s the business plan -> i don’t quite see institutional investors writing big cheques just because somebody built a DCF in Python or fed lots of past data into some type of Python-based model, which gave a price, without (!) speaking to the management (!).
No amount of past events however analysed can predict what the management want to do with the money. They may want to open a new plant if we are talking manufacturing.
But then another question is -> most businesses going IPO route have pretty similar business models to what’s out there so if your brand new way of regression analysts won’t give out a WACC that is drastically different than what I can calculate looking at comparable companies , then why bother spending the time and the money?
Otherwise if the difference is substantial, how would you sell your a WACC to investors? If there is a bunch of good comps, you would need to put forward a pretty convincing argument for why they should write a heavy cheque based on your regression or whatever than just looking at the comps.
So I still don’t get what is smart IPO valuation, why is it smart and who and how would convince investors to write cheques?