Money, Hours, Models, Bottles: Investment Banking in New York, California, and Everywhere In Between
“Are you guys even in the office past 8 PM? Whenever I call no one’s there.”
“New York is hella lame, people are so much better out here.”
“If you say ‘hella’ again I’m going to make you pay for the bottles next time – and maybe the models too.”
“Fine, I’ll do some research and see what I can send over. NY is still overhyped, though.”
No, it’s not a short story or a new TV show about bankers – it’s a banker from NYC and one from San Francisco talking to each other.
And you read that headline correctly: today you’ll learn how banking differs in different regions of the US rather than going off on adventures to distant lands.
As one reader pointed out a while back, “Hearing about all these different countries is great, but what about how banking is different on the east coast vs. west coast of the US and everywhere in between?”
The Most Common – and Wrong – Arguments
Many people claim that the pay and hours differ significantly and that New York is more “hardcore” than other regions.
That makes sense intuitively: New York is the biggest financial center and the biggest deals tend to happen there.
But in practice, these differences are greatly exaggerated – pay is standardized at the junior levels in finance and bonuses depend more on your bank and group rather than the city you’re in.
At the senior levels, geographic differences become more important because certain offices have better deal flow and clients, and senior bankers’ bonuses depend 100% on performance.
New York bankers like to argue that they work way more than people in other regions, but there are no scientifically controlled surveys to support these claims.
Yes, maybe the hours are somewhat worse since more deals happen there – but we’re talking a difference of 85 hours per week vs. 90 hours per week: you still won’t have a life.
So the more substantial differences have nothing to do with pay or hours, but rather the industries covered, the cost of living, and the exit opportunities.
And yes, I’ll address the ever-popular models/bottles, networking, and a few other points as well.
This is the main difference – banks in the top 5 cities for finance in the US focus on a different industry:
- NYC: Diversified
- Chicago: Industrials
- Houston: Oil & Gas
- San Francisco: Technology / Healthcare
- Los Angeles: Gaming & Lodging / Media
There is no “best” because it depends on what you want to do in the future and how certain you are of your career.
Some of these fields are more specialized than others; something like oil & gas requires more specific knowledge than tech or healthcare since energy companies play by different rules and require different valuation methodologies.
So if you’re already interested in a specific industry, it may be a good idea to start out in the region that focuses on that industry – but if you have no idea yet, New York is the safest bet.
Just as actors get typecast, you will get more and more pigeonholed as you move up the ladder, so you need to consider these options carefully.
One friend worked on a telecom deal at a small VC firm, then got placed into the telecom group at a boutique bank, and was then placed into the telecom group at a bulge bracket bank.
Effectively, he became “the telecom guy” all because of one small deal he worked on ages ago.
And it’s even worse once you move beyond banking: good luck interviewing for that hedge fund that wants people with European telecom merger arbitrage experience if you don’t have any.
But What About Deal Flow?
“But,” you rightly point out, “There’s a difference between deal flow, hours, and industries covered – even if you’re working a lot, you might just be building pitch books all day. And what if your industry isn’t ‘hot’ at the moment?”
I don’t disagree with you there, but it’s almost impossible to determine deal flow of specific offices without talking to real people.
So if you’re such an overachiever that you’re going to pick your bank and group based on deal flow and exit opportunities, go talk to people at the different offices you’re considering and see what they say – but keep a critical eye open because they’re likely to oversell you on everything.
And no, I’m not going to rank cities and groups by deal flow here since that changes quite frequently and since you’re likely an obsessive-compulsive person already if you’re reading this.
Cost of Living
In ancient times, New York was the most expensive city in terms of real estate, taxes, food, and so on.
Now, however, San Francisco is actually more expensive, or at least as expensive, due to the tech boom and the number of high-paid startup employees there (as of 2015).
So you are not likely to save much money during the year in either place; it’s also a bad idea to live in New Jersey or another location outside the main city to save money, since you might go insane in what little free time you have.
The “cost of living” ranking looks something like this:
- NYC ~= SF > LA > Chicago > Houston
You will save the most money working in Houston because Texas has no state income tax, rent is ridiculously cheap, bottles are less pricey, and even the models are less demanding and will give your wallet less of a workout.
Cost of living shouldn’t be your top concern, but you should be aware of it.
Finance people are notorious for making millions of dollars and then blowing it all on luxury spending – so pay attention if you want to retire on more than $50K in that savings account you forgot about.
One other note: driving will be required in most of these places, especially in a city like LA where there is no public viable transportation.
So if you hate driving and owning a car, your best bet is New York.
NOTE: Ride-sharing services such as Uber and Lyft are actually changing this dynamic.
If you live relatively close to the office, you might be able to take one of those to and from work every day and gain some peace of mind in the process.
The main problem with exit opportunities is that it’s hard to interview when you’re far away.
You need to take time off work by using questionable excuses, hope people don’t notice your repeated absences, and then visit the firm enough times to seal the deal.
Since New York to SF or LA is a 5-6 hour trek, it’s not easy to hop from banking on one coast to the buy-side on the other coast. Pretty much all the analysts I knew in California stayed there, and pretty much all the ones in New York stayed on the east coast.
So you’re more likely to stay in your first region unless you can pull off in-person trips or interview entirely via video conference (unlikely for traditional exit opportunities).
Again, people like to argue that New York has “better” exit opportunities, but plenty of analysts on the west coast and elsewhere get into mega-funds as well; it’s just that they work at local offices rather than in NYC.
One legitimate difference is that there are more exit opportunities in New York just because it’s the biggest financial center.
And you also run into the pigeonholing problem if you start out in another region: go to Houston and you’ll more than likely recruit only for energy-focused PE firms and hedge funds.
But aside from those differences, the actual quality of exit opportunities doesn’t differ as much as you might expect.
Networking opportunities are another more significant difference, and one that people overlook all the time.
Since NYC is much bigger than the other regions, you’ll simply meet more people there and you’ll be better equipped to network your way into other roles.
Just as with other financial centers like Hong Kong and London, sometimes half the people you meet in NYC will be in finance (the other half will be “aspiring” artists or models, which is great for you as a financier).
How much does the quality of networking really matter?
It depends how certain you are of your “career path” – if you’re interested in doing tech banking and then doing venture capital in California, you’re better off starting in SF and networking with tech and VC groups there.
But if you have no industry preference, you’ll gain more options by starting out in New York.
How to Satisfy the Models
Ah, now to the fun part.
The main difference is that the New York models tend to be higher-maintenance, more expensive, and more demanding; LA comes close since everyone is required to get plastic surgery, but you’ll still spend more overall in NYC.
But flashing around wads of cash also doesn’t impress as much in New York because $200K is barely middle class – not enough to satisfy models who are expecting a new bag every day.
In all seriousness, you really will spend a lot more money going out in New York if you actually enjoy it.
LA and SF can also be expensive, while Chicago and Houston are more reasonable. Some also argue that people in the South and Midwest are “friendlier” but I don’t want to get into a debate over that one.
I’m not qualified to comment on the quality of men in each place, other than to say that SF is probably the worst place to find hot guys unless you’re into tech guys with a ton of money from startups.
(Yes, a female friend recently asked if there were a lot of tall, muscular blonde guys in SF and I started laughing.)
“Aha,” you say, “But even if the pay and hours are not much different, surely they must ask completely different interview questions in each region, right?”
Sorry to disappoint, but no, not really.
No one sits down and says, “Well, in Chicago we should ask this specific set of questions but in Houston it will be completely different.”
Once again, the main difference comes down to the industry focus: you don’t need to be an expert on the industry of focus in each city, but you should know something about recent deals and any industry-specific valuation methodologies.
It’s not really “easier” or “harder” to get into finance in different cities – there are fewer spots outside of New York, but there’s also less competition.
Yes, there are banks in places besides NYC, Chicago, Houston, SF, and LA – but the offices tend to be much smaller and they don’t always recruit on-campus.
Other cities with a presence in finance include Boston (similar to SF due to the industry focus), Washington, DC (aerospace/defense), Atlanta (lots of wealth management), Miami (healthcare, Latin America), Dallas (got equities?) and maybe a few others.
I can’t recommend starting out in these places if you have the option to go to one of the 5 major centers listed above.
Maybe if you’re interested in only a very specific industry, like aerospace and defense, then DC makes sense – but you’ll be at a disadvantage in terms of deal flow and exit opportunities.
A lot of boutiques are also based in other regions, so you should jump at the opportunity if you have nothing lined up in a bigger city – but otherwise, stick to the top 5 above.
Outside of IB: Sales & Trading, Hedge Funds, and More
You run into the same differences in other fields like private equity, sales & trading, hedge funds, and asset management: a different industry focus and more geographically limited exit opportunities.
Some cities also tend to be stronger in certain fields.
For example, Chicago is great for prop trading and the SF Bay Area is the spot to be for venture capital.
One downside to any type of markets-based role such as trading or hedge funds is that you have to wake up very early if you’re on the west coast because you work New York market hours.
If you’re fine waking up at 4 AM, getting off work at 5 PM, and sleeping at 9 PM every night, you might be OK; if you’re not a morning person, though, you may want to stay away.
So, Where Should You Work?
If you have absolutely no idea what you want to do and don’t mind spending more money, New York is your best option – there’s more networking, more opportunities, bigger deals, and you don’t even have to drive.
But if you have a more specific goal such as going into VC, joining a tech startup, or working in the oil & gas industry, you could make a good argument for starting out in a different city.
There may be slight differences in pay, hours, and how much you save in your first year (with bigger differences on that last one), but those don’t matter much in the long-term.
To figure out which office has the best deal flow, network with bankers and ask directly – that information changes quickly and you’re always better off going straight to the source.
And whatever else happens, make sure you don’t end up doing equities in Dallas.
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All About Automated Trading: What It Is and What It Isn’t
[This is a guest post from a reader who currently works in an arbitrage development team. He wanted to clear up a few points about what “automated trading” is and isn’t.]
Ah, taking a mid-day nap and waking up with extra money in your trading account… who wouldn’t want to make money while sleeping?
That promise of set-it-and-forget-it money draws lots of traders into the field and attracts computer science and engineering students who suddenly “discover their interest in finance.”
Only one problem: “automated trading” is far from automated cash flow, and you always need human intervention.
To find out why and to learn all about algorithmic trading, arbitrage and other forms of automated trading, read on.
Part of the problem stems from all the terms used to describe “computer-assisted trading”:
- “Trading machines”
- “High-frequency trading”
- “Black-box trading”
People use these interchangeably but are referring to different concepts – so let’s clear that up.
Algorithmic Trading vs. Trade Origination
Here’s the key question you need to ask:
- Is a human making the trading decisions and simply having a computer help with the execution, or is the machine handling the execution and making the trading decisions?
The first category – where the computer only assists with the execution – is called algorithmic trading.
The second category – where the computer actually makes decisions – can be called trade origination, although that is not a canonical name (there isn’t any that I’m aware of).
So now we’ve cleared up the first misconception about algorithmic trading.
The second misconception: that algorithmic trading is about making the most money possible.
It’s actually about losing as little money as possible and reducing your costs.
To illustrate this, let’s walk through an example of how an algorithmic trading system might work:
Let’s say that you’re a pension fund manager and you’ve decided to sell 1 million of Company X’s shares that you currently own.
Notice how you – the human – make the initial decision here based on your analysis.
You want to get the best price available on the exchange that Company X’s stock is traded on, and you know that the highest bid price – the highest price at which someone else is willing to buy the stock – is $20.
So ideally, when you sell those 1 million shares you will get $20 million.
But not quite.
The problem is volume – most likely, that bid order for $20 was for far less than 1 million shares; it might have been your next-door neighbor trading 20 shares in his E*Trade account.
If that’s the case, then you’ll end up selling only 20 shares for $20 – and the remaining 999,980 shares will go for whatever the next best price after $20 is.
If the highest anyone else is willing to pay is $10, then you might only get around $10 million from your 1 million shares rather than $20 million – even though the bid price was $20 according to your trading software.
This problem is called “limited liquidity” in trading circles – you receive less than the “paper value” of your position because there aren’t enough bid orders at the price you thought you were getting.
As a human, you could simply monitor the market all day long and be on the lookout for those $20 bid orders.
That’s extremely time-consuming and labor-intensive, but that was exactly what agency execution traders did in the “old days.”
And that’s why algorithmic trading was invented – to manage the trading process over an extended period of time and get as close to the “paper value” as possible.
A trading algorithm for this scenario might divide the order up into many smaller pieces – 1,000-share blocks rather than 1 million all at once – and execute them over the course of a day or longer.
This is one of the key reasons why algorithmic trading has become so popular: there’s a high upfront investment, but a single machine can replace tens of pure agency execution traders – so you start seeing huge cost savings once you’ve been up and running for awhile.
Algorithmic trading saves traders a lot of time and money, but there’s a small problem: you still have to make your own decisions.
To make the process truly automated (in theory), various systems to originate trades were created.
To give a concrete example of how these systems work, though, we’ll focus on just one for now: non-statistical arbitrage.
Arbitrage refers to buying and selling multiple securities at the same time in the hope of making a profit.
The simplest type is non-statistical, or deterministic, arbitrage, where you find and exploit price discrepancies between 2 or more securities whose prices should be related. Ideally (ignoring technical issues), this kind of arbitrage is risk-free.
(Statistical arbitrage, by contrast, deals with expected values of securities over the long-term. There’s no guarantee that the future will behave like the past and so this is not risk-free in any form.)
Here’s how you might apply non-statistical arbitrage, and then how a computer could make it more effective:
The S&P 500 index has a futures contract associated with it – that just promises to deliver the stocks at a certain point in the future and is traded on an exchange.
The underlying stocks of the S&P 500 trade on exchange as well, so you can take their prices, figure out how much it would cost to hold the stocks until the future expires, and based on that decide whether the future is a bargain or rip-off at the current price.
So let’s say you think the future is too expensive – it’s $1,000 but the underlying stocks are worth only $990 and it will cost you $5 to hold them until the future’s expiration.
You can then sell the future and buy the underlying stocks – you deliver the stocks when the future expires and then make a profit based on the difference between what you thought the future was worth and the higher price you sold it for.
Does That Actually Work?
This is a very simple example, and it would never work in real-life because everyone else is looking for the same price discrepancies.
And even if you’re Rain Man, it would still take at least a few seconds to spot this type of price discrepancy…
…which is where machines come in. They can spot arbitrage opportunities like this in milliseconds rather than seconds or minutes, and make trading decisions far more quickly than any human.
If you were creating an algorithm like this, you might program in the specific securities or trends to look for in the market and then give exact instructions on what to buy and what to sell when certain conditions are met.
These days algorithms have become far more advanced and go well beyond just looking at prices – some actually try to scan news stories to determine “sentiment” for or against a company and make trading decisions based on that.
So going back to the terms at the top of this article, what does each one actually mean?
- “Algos” – Short for “algorithms” – Could be either algorithmic trading or trade origination.
- “Trading machines” – Generally trade origination.
- “High-frequency trading” – A type of trade origination system where securities are held for milliseconds (or less) rather than hours or days.
- “Black-box trading” – Might refer to either algorithmic trading or trade origination.
Many of these terms could refer to either variety of “computer-assisted trading,” so you need to dig in and ask what’s really going on when you see them.
Time to Retire to the Beach?
So you have a trade origination system set up and you’re making a lot of money with no intervention or decision-making on your part… time to retire?
Not so fast.
All types of automated trading systems must be supervised, checked, and updated constantly.
Even if the software itself is correct and has no bugs, market conditions themselves can be a “bug.”
We saw this back in 2008 during the start of the financial crisis when hedge funds started blowing up – supposedly “once-in-a-lifetime” events started happening every day and breaking all the old algorithms.
So no matter how great your algorithm is, it will only be effective until the next crisis, the next “unusual market condition,” or until everyone else starts copying you.
The top banks have spent a small fortune developing trading algorithms, and the tens of millions of dollars (or more) you need for such technology puts it well out-of-reach of anyone small.
And then there’s the small matter that no software is ever bug-free – especially when the algorithm is new, you need a human to monitor it all the time.
So even if your new automated trading program is making bank, you might want to hold off on buying that beach bungalow.
For Further Learning
This was just intended as a brief overview of automated trading – if you want to find out more, get a copy of Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson.
Good luck with your trading, and let us know if you ever do make it to the beach for early retirement.
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