by Brian DeChesare Comments (48)

All About Automated Trading: What It Is and What It Isn’t

Automated Trading[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.

Lexicon Confusion?

Part of the problem stems from all the terms used to describe “computer-assisted trading”:

  • “Algos”
  • “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).

Algorithmic Trading

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.

Limited Liquidity

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.

Trade Origination

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.

There’s a huge variety in the strategies these systems use – just think about all the hedge funds and prop trading firms out there and all the different trading strategies they use.

To give a concrete example of how these systems work, though, we’ll focus on just one for now: non-statistical arbitrage.

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.

Still Confused?

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.

M&I - Brian

About the Author

Brian DeChesare is the Founder of Mergers & Inquisitions and Breaking Into Wall Street. In his spare time, he enjoys memorizing obscure Excel functions, editing resumes, obsessing over TV shows, traveling like a drug dealer, and defeating Sauron.

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by Brian DeChesare Comments (157)

It’s Not Rocket Science: Why You Should Stop Learning Partial Differential Equations If You Want to Break Into Investment Banking

It's Not Rocket Science: Why You Should Stop Learning Partial Differential Equations If You Want to Break Into Investment Banking

“Hi, I was wondering which class I should take to break into investment banking: Advanced Partial Differential Equations or Quantum Field Theory. Do you think it will ruin my future if I only learn up through Multivariable Calculus?”

No, I don’t make this stuff up: I get emails like this all the time.

Sometimes they’re from undergraduates, sometimes they’re from MBA students, and sometimes they’re from the occasional MD or PhD candidate.

But my answer is always the same:

It doesn’t matter.

You don’t do “real” math in investment banking, so stop worrying about it and spend your time more wisely.

Got Obsession?

So why is there such an obsession with learning advanced math / winning the Nobel Prize before you start working as an investment banking analyst or associate?

You’re Good at Math

If you’re interested in finance to begin with, there’s a good chance that you’re already good at math and have taken a lot of math classes. You’ve either:

  1. Been interested in finance for a long time and have taken a lot of finance/math classes; or
  2. You were an engineer or science-type who got bored of that and wanted to move into business.

Yes, there are bankers with liberal arts backgrounds as well but bankers in categories #1 and #2 outnumber them.

We Like to Blame Other People

It’s the same reason we believe so strongly in the myth of the career path.

If you can get into finance simply by calling hundreds of people and being very aggressive with networking, failure to break in would reflect poorly on you.

But if you couldn’t break in because you didn’t have that class on quantum physics, then you have the perfect alibi.

We Like to Stay In Our Comfort Zone

Getting out there, talking to people, and meeting them in-person is uncomfortable. It’s way easier just to sit at home watching TV…

…or to sit at home completing your math homework.

Going through dozens of advanced math classes also gives us the illusion of progress without actually requiring us to make any progress. It’s part of the 80% you should be eliminating.

The Truth About Math

There are 3 points you need to know about math in investment banking:

  1. You don’t use it that much.
  2. The math you do use is very simple. As in, arithmetic.
  3. Therefore, you don’t have to be a math genius – but you do have to be good with numbers.

Say What?

You don’t use math that much because you don’t do that much modeling work, even in “technical” groups like M&A.

Think “administrative work,” emailing people and updating lists of information – just look at a few days in the life of an investment banker if you don’t believe me.

And when you do use math, 90% of the time you’re working with existing templates or simple models rather than creating everything from scratch.

Yes, it’s cool to be able to say you can create a hyper-advanced LBO model from a blank spreadsheet, but in the real world no one has time for that – so you use templates.

But What About Modeling?!!

Even when you are working with financial models, none of the math is complex.

There’s addition, subtraction, multiplication, and division… and occasionally built-in Excel functions like IRR, Mean, and Median.

You never use calculus or differential equations or even geometry / trigonometry. Just arithmetic and sometimes algebra.

Think about all the basic formulas in accounting: Revenue – Expenses = Profit. Revenue – Cost of Goods Sold = Gross Profit… and so on.

Notice how there are no integrals anywhere in those equations.

So Why Do You Still Need to Be Good With Numbers?

If the math is so simple, why do you need to be good with numbers at all?

Although the individual mathematical operations are simple, you can end up working with huge spreadsheets where a lot of calculations are linked together.

1 + 1 = 2 is simple, but now let’s say you have 100 similar calculations, and the input of each one is linked to the output of another calculation.

That’s exactly what you run into in investment banking, and it gets tricky to trace everything – especially when it’s someone else’s model.

Exceptions & Other Fields

In other fields of finance the math can get more advanced.

The main example is trading, where some funds may use advanced algorithms and higher-level math to make trading decisions – so if you go into one of those, advanced math classes might actually be helpful.

For hedge funds, it depends on what strategy the fund uses: long-term fundamental investing has less math than algorithmic trading.

Also in trading, mental math (17 * 35) is more important because you need to make quick decisions.

Outside of those, the math in other industries like private wealth management is as simple as it is banking.

So What Should You Do About It?

Stop taking advanced math classes – especially if they hurt your GPA.

Bankers look at the overall difficulty of your major but they don’t go in and analyze every single class – a 3.8 GPA with easier classes is much better than a 3.3 GPA with “tough” classes.

Plus, taking such advanced classes takes away from time you could be spending on internships, school-year internships, networking, and activities.

When reading your resume, bankers pay attention to the school you attended, your internships, and your GPA – not individual classes.

Beyond Undergraduate

Despite rumors to the contrary, sometimes you have to do work to get through business school.

At this level, taking “more advanced” classes is an even worse use of time because:

  1. At the MBA-level networking is even more important.
  2. Hardly any “math-intensive” finance positions hire directly from business schools – you don’t need an MBA to be a top trader. You just need to make a lot of money.

So if you’re at this stage and you’re serious about breaking into investment banking, forget about advanced statistics / financial math classes and do the bare minimum.

Summer School?

I also get a lot of questions on whether “finance summer school” or taking classes during the summer instead of an internship is worth it, and the answer is always “No, unless you have no better options.”

Bankers don’t like taking risks, and they always prefer to hire someone with investment banking internship experience over a newbie.

What About Your PhD / MD?

Bankers tend to look down on advanced degree holders.

They want people who can burn the midnight oil and who are aggressive enough to find ways to make or save money – and they don’t think that advanced degree holders fall into this category.

Getting these degrees is far more difficult than anything you do in banking, but most bankers don’t like to acknowledge this.

So if you’re already deep into one of these programs, cut your losses and get out early or take the path of least resistance if you’re too far in to drop out now.

Improve Your Communication Skills

If you really want to improve your skills before you start working, forget about math and focus on your writing and speaking skills.

There are tons of analysts who are good at math, but few can describe what they did and how it helped their bank make money in plain English.

And if you want to move up, you need to interact with senior bankers a lot – so getting to the point without rambling or stuttering is essential.

And If You Really Want to Improve Your Math Skills…

If you’re still set on improving your skills, forget about classes and have a friend in the industry send you a complex model with many different tabs.

Then, try to “reverse engineer” it and figure out what the key drivers are and how they affect the output.

Creating a model yourself is relatively easy because you control everything – the real challenge is looking at someone else’s model and figuring out how it works in the first place and how to modify it.

So spend some time playing around with complex models and get used to the process of tracing individual formulas and outputs.

And please, no more partial differential equations.

M&I - Brian

About the Author

Brian DeChesare is the Founder of Mergers & Inquisitions and Breaking Into Wall Street. In his spare time, he enjoys memorizing obscure Excel functions, editing resumes, obsessing over TV shows, traveling like a drug dealer, and defeating Sauron.

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by Jerry Chi Comments (155)

What You Actually Do In Sales & Trading, Part 2: Equity Trading

What You Actually Do In Sales & Trading, Part 2: Equity Trading
Man Analyzing Financial Data and Charts on Computer Screen

While there are plenty of day-in-the-life and week-in-the-life stories detailing what you do in investment banking, there’s surprisingly little information on sales & trading.

We started to fix that problem by looking at what you do in fixed income – besides bankrupting your firm and causing the economy to spiral into a bottomless abyss.

This article will continue that thread by telling you all about equities, the second major area within trading.

Once again, Jerry’s the expert and the author here, so direct all questions to him.

Quick Recap

Trading is divided into groups depending on whether or not you have clients or you’re making your own decisions – agency trading and prop trading, and then the gray area of flow trading in between.

And then it’s also divided according to what you’re trading – stocks, bonds, currencies, or derivatives of those.

Originally Fixed Income was supposed to mean only securities that involved debt, but these days FX and commodities traders are often put under the umbrella of “Fixed Income” as well inside investment banks.

Similarly, “equities” originally just meant trading stocks of companies, but these days it has expanded to cover a couple different areas.

So let’s delve in and see what you do in equity trading besides gambling, eating junk food, and abusing interns.

Agency Trading

This is the most basic type of equity trading: you work at a sell-side financial institution, like a large investment bank, and you execute orders for clients throughout the day.

You don’t make decisions on what or how much to buy – you just take what the client wants and you make it happen.

Why do they need to go through an investment bank just to buy or sell stocks? Couldn’t they just use E*TRADE?

No – because of the size of the transactions.

Let’s say that a mutual fund wants to buy 1 million shares of Microsoft – if that order were placed immediately, it would be too big for the market to absorb and it would disrupt the share price by quite a bit if they attempted to buy all 1 million shares at once.

So it’s your job to divide this into smaller pieces and buy a portion at a certain interval throughout the day, or through another period of time – usually you follow a fixed schedule for buying the stock (e.g. every 20 minutes).

The only thing you control is how to vary the order timing and order routing – if you work in the US you have more control over these variables because of the sheer number of ECNs (electronic communication networks) and dark pools. In other markets you don’t have as many options.

Pros: If you’re interested in trading but you’re not quite ready for more advanced jobs, this could be a good fit to get your foot in the door.

Also, the work hours are much shorter than most other jobs in finance: you might arrive right before the market opens and leave right after it closes; there’s not much analysis to do since you’re not making your own decisions on what to trade.

Cons: The job itself can be boring at times, and you don’t have many exit opportunities. Also, more and more agency traders are being replaced by automated trading algorithms over time.

Proprietary Trading – Plain Vanilla Equity

NOTE: In theory, the Volcker Rule passed after the 2008-2009 ended proprietary trading for banks. In practice, however, banks can still use it for certain hedging activities, so it has not gone away completely, though it is greatly diminished.

Plain ol’ stocks. As cool as “synthetic collateralized mezzanine bespoke hybrid exotic derivatives structuring / trading” might sound as a job title, there are still plenty of benefits to being a normal stock trader.

This is the opposite of the agency trading discussed above, because you’re making your own trading decisions and there are no clients.

You don’t have to deal with too many logistical or IT issues that come with trading over-the-counter instruments, and the market liquidity is excellent in developed markets – so you don’t have to worry about not being able to exit your positions.

Bid/ask spreads are small, trading fees are low, and if you make a mistake you can even undo your trade immediately without much damage.

The only problem is that there are so many market participants that it’s very difficult to find great trading opportunities that everyone else has missed.

Usually you focus on a specific sector and you hold positions for a few days to several weeks; you normally do both micro- and macro-analysis, though there are some traders who ignore the fundamentals and just focus on technical analysis.

You don’t need to be a rocket scientist to do the job: you just need to be good with numbers and a quick decision-maker.

Pros: More interesting work than agency trading; better exit opportunities within trading; potential to make more money than agency traders.

Cons: Hours tend to be longer, especially if you need to do a lot of analysis; you could argue it’s still less “interesting” than trading more exotic securities.

Proprietary Trading – Equity Derivatives

There’s a smorgasbord of derivatives based on equities, but here I’ll just focus on the most basic one: options.

They’re generally riskier than plain vanilla stocks and require more skill to trade: the bid-ask spreads are higher, there’s less liquidity than with stocks, and any one stock could have a few dozen different options with different strike prices and maturities.

There are also more inputs in valuing the options: just as one example, you need to decide what measure of volatility to use, which makes the task considerably more complicated than just valuing the underlying stock.

The coolest part about options is that you can make a LOT of different types of trades.

One common example is a straddle – buying a call option and put option at the same strike price, which gives you a positive payout if the stock moves up or down past a certain amount by maturity.

Even the names themselves sound pretty creative: besides the straddle, you’ve also got the butterfly, the iron condor, and the strangle.

You can also come up with all sorts of crazy payout profiles of your own by combining options and plain vanilla equities.

Leverage is also built into options, so you can easily quadruple the money you put into one stock option: the risk management department would never let you put most of your money into a single option, but even trading smaller amounts of money gives you an adrenaline rush with options.

Even though more math is required, you still don’t need to be the next Isaac Newton to trade derivatives: being able to derive the Black-Scholes equation from Ito’s Lemma isn’t necessary.

However, you do need to understand the Greeks – how an option’s price responds to changes in time, interest rates, volatility, the underlying stock price, and more. You also have to be good with Excel because you need to make more calculations than plain vanilla equity traders.

Pros: More “interesting” and quantitative than plain vanilla equity trading; you can be more creative in devising your own trading strategies.

Cons: Again, hours are longer than agency trading; more quantitative ability is required; as you move up and become more specialized your exit opportunities also narrow.

Algorithmic Trading

No, I’m not talking about the trading robot here (please don’t fall for that scam).

Also known as algo-trading or program trading, in algorithmic trading you don’t actually trade anything yourself: you just program a computer to trade for you based on technical analysis, market volume, or even parsing news headlines.

To do this, stock analysts need to look at historical market patterns, programmers need to implement the system, and everyone else needs to use, monitor, and configure the system.

Sometimes algo-trading is 100% automatic, but it can also be combined with human trading.

Some hedge funds do 100% algo-trading and have performed well – but ever since the financial crisis and the onset of apocalypse in the markets, many previous market patterns stopped holding true.

The result: trading algorithms that had made a lot of money in the past started losing money and thousands of hedge funds and trading firms collapsed.

That’s not to say this is a “bad” field to go into: it’s just that the financial crisis made it necessary for trading algorithms to change their assumptions, and that may mean more challenges for those designing the algorithms.

When people say algo-trading, you would normally think of proprietary algo-trading – but there’s actually agency algo-trading as well, which focuses on how best to execute a client’s trades.

If you’re coming from a highly analytical or IT background, you like working with data, and you want the thrill of huge trading profits without having to stare at charts every second, this could be a good option for you.

Pros: What could be better than machines making money for you when you’re not even there? Also, this is a good option if you’re from a more technical background and you want to move into finance. Since there’s analysis and programming, you can also exit to non-finance jobs more easily.

Cons: Financial crisis will present new challenges for trading algorithms; also, a lot of the work in actually creating the algorithms and sifting through data can be rather tedious.

Exit Opportunities

Not too much is different here vs. fixed income: you either stay in trading at an investment bank, or you move to a hedge fund or prop trading firm.

Or if trading is not for you, you move to a different industry.

If you haven’t been in the field too long then it’s possible to move over to investment banking or others in your bank – but if you start out a hedge fund or prop trading firm, your mobility is more limited.

All of the above is true for the same reason it’s true of fixed income: your skill set is more specialized than, say, an investment banker’s, and on paper most of what you do doesn’t seem relevant to other fields.

Coming Up Next

Sales & Trading vs. Investment Banking and some day-in-the-life stories.

About the Author

Jerry Chi graduated from Stanford, worked in equity research and trading in Japan, and then started and sold his own prop trading firm in China. He earned his MBA from Wharton, and then worked at Google and Supercell in Japan.

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