"Money Machines An Interview with an Anonymous Algorithmic Trader"

From Logic Magazine:

In recent years, we’ve been hearing more about the role of algorithms in our lives. Algorithms are helping decide whether people get a job or a loan, what news (fake or otherwise) they consume, even the length of their prison sentence.
But algorithms are also rewiring the world of finance, with immensely important consequences. In an era of financialized capitalism, finance plays a powerful role in the global organization of wealth and work—which means that everyone will feel the effects of the industry’s transformation.
We sat down with an algorithmic trader to learn more about how algorithms are remaking the industry, and why it matters. We talked about what algorithmic finance actually looks like, who the winners and losers are likely to be in the new big data gold rush, and why we may be entering an era of irrational cyborg exuberance. 

Let’s start by talking about your background. How did you get involved in finance?
I was always interested in economics and had a quantitative background. Anyone who succeeds academically where I grew up ends up being very quantitatively oriented. After school, as I was trying to find a profession that would be financially rewarding but would also allow me to use what I studied, I started looking at the financial industry. I ended up taking a job on a trading floor in an investment bank.

Most large banks have at least one, typically several trading floors. It’s an actual floor, about the size of a football field, filled with traders who do business with large investors looking to trade stocks, bonds, or futures, or to borrow money. The bank makes money by taking a commission, or by “market-making”—intermediating between buyers and sellers, taking some risk with its own money while it waits for the two sides to match up.

When I think of a trading floor, I think of a bunch of guys screaming into the phone, Wolf of Wall Street-style.
It’s not so much people yelling into the phones anymore. The trading floor has evolved quite a bit over time. It used to be more about being alive to the transactional flow of global markets. It’s increasingly about the operations that enable that flow, and the intellectual property that allows people to make money off that flow.

I liked it. The trading floor is still where a lot of the actual design and transactions of global markets take place. And it’s stimulating. If you want to use your intellectual muscles, you can do so pretty quickly. You’re not just sitting at a desk somewhere out of the way, or trying to pitch corporate titans with some arbitrary analysis to back you up —which can be more of a salesmanship game and less of an intellectual exercise.

Anyway, over time I migrated to the investment strategy part of the financial world. I started helping large asset owners—entities like pension funds and sovereign wealth funds—allocate their money to systematic investment programs. That’s where I migrated to because that’s where most of the financial world was migrating to after the 2008 financial crisis, as everyone realized that the old ways of investing were not really doing what they wanted them to do.

Portfolios had been too exposed to the same underlying risks. Technology was now enabling investors to understand their risks better, and to take more direct control over their investments. Part of the shift involved removing human decision-making when it wasn’t perceived as adding any value.

What do those new ways of investing look like? What are “systematic investment programs,” and how do they fit into the field of algorithmic finance as a whole?
There are many ways that algorithms are actually used in finance, so the term algorithmic finance gets used more loosely than it should.

There are at least two major domains in which algorithms dominate. The first is what’s frequently called algorithmic trading, which focuses on market microstructures. It programs computers to make split-second automated decisions on how stocks are bought and sold. Should you buy a whole bunch of shares at once? Or should you split up your purchases over time? How do you more intelligently execute trades? Algorithmic trading uses algorithms to help answer these questions—and it’s an enormous industry. There are a lot of hedge funds and traditional investment banks that try to make money there.

The other domain, which is the one that I’m more focused on, is sometimes called systematic investing and sometimes called quantitative investing. It’s also very much algorithmic investing. It involves using algorithms to allocate money systematically based on data.

An early version of quantitative investing—starting roughly in the 1950s, with the birth of Modern Portfolio Theory—was designed to create rules to save for retirement. These rules were supposed to help people decide how much of their money to put into stocks and how much of it to put into bonds. Once you’ve made that decision, you have a rule that lets you allocate money across stocks and bonds at some defined frequency automatically, without a human being going in and having to make any qualitative decision. This basic framework was rapidly adopted across investment portfolios at every scale, from mutual funds for individual investors to asset allocation decisions by the largest funds in the world.

Then people took that framework and applied it to an increasing number of underlying assets, with a much finer degree of granularity. So now you’re not just making rules that determine the overall mix of stocks and bonds in a portfolio but which stocks, which bonds, which commodities, which corn futures, and so on. And the rules used to distribute assets become far more complex.

When you say “making rules,” what exactly are we talking about here?
The simplest rules can be run on a basic spreadsheet. For instance, classic pension portfolios used to allocate 60% of the portfolio to large-cap stocks and 40% to bonds. Then Modern Portfolio Theory started to allocate assets accounting for mathematically measured risk and return. You run a giant optimization that promotes diversification—across stocks within your stock portfolio, across asset classes—to maximize the return you make per unit of risk.

These rules didn’t need any further human intervention, in the sense that they completely defined a portfolio. But in practice, investment wasn’t completely rule-based: investors used the model outputs as a baseline, and then tweaked it with their own decisions. Human insight could further improve the asset mix, in a variety of ways. Investors might want to buy cheaper stocks, for instance, or “time” the market by getting in or out at the right time. Or they might want to find new assets like commodities and mortgage securities, or improve the measurement of risk.

More recently, however, advances in computing power and financial engineering have vastly expanded the universe of analytical tools that can be applied to investing. The latest “rules” involve developing machine learning models that train on large amounts of data. It could be data from the financial statements of publicly traded companies. It could be macroeconomic data. It could be the price history of certain financial instruments. It could also be more esoteric data like satellite imagery.

What’s a concrete example of an investment decision driven by a machine learning model?
You could purchase a “sentiment” score developed by a firm that trawls Twitter on a continuous basis to understand changes in the mood of the market, or around a specific company. You could use that data to train your model, which could then determine whether to buy or sell certain shares. Usually signals like a sentiment score decay pretty quickly though, so you would want to be able to make that trade fast.

How automated would that process be? Are we talking about software making recommendations to human traders, or actually executing trades itself?
The level of human oversight varies. Among sophisticated quantitative investors, the process is fairly automatic. The models are being researched and refined almost constantly, but you would rarely intervene in the trading decisions of a live model. A number of hedge funds, mutual funds, and exchange-traded funds (ETFs) run on auto-pilot.
By contrast, most traditional investors use models to provide guidance rather than to generate automated trading decisions, since its unlikely that they could operationalize a complex trading strategy.

One of the challenges with machine learning is explainability. As the model becomes more complex, it can become harder, even impossible, to explain the results that it generates. This has become a source of concern as public scrutiny of the tech industry has increased, because you have algorithms making decisions that affect people’s lives in all sorts of ways while the reasoning for those decisions remains completely opaque.

When the financial industry plugs a bunch of data into a model in order to make an investment decision, how important is the explainability of the result?
I think the result should be very explainable. But that’s not a universal view. In fact, there’s a fairly big split between people who have concluded that explainability is holding back the advancement of the use of these techniques, and the people who hold on to the rather quaint notion that explainability is important.

But to some extent, explainability was already an issue well before we started using machine learning, because even traditional models of investing were hampered by some of these same issues. Finance is not like physics. You have a lot of feedback loop mechanisms impacting how participants interact with financial markets.

To give you a simple example, you might look at the price data of a stock and conclude that because that stock went up last month, it’s a good idea to buy that stock today. And if you do that systematically, you might expect to make some money. But if everybody else comes to the same conclusion, then the stock could get overbought today based on the movement of the stock over the past month. And if it’s overbought, you might actually expect to lose money on it over the next month.

Looking at historical data to figure out where your investment is going to go is useless if you haven’t thought about the mechanism by which it’s going to do that. In the example I gave, if you didn’t have an explanation for why the stock was moving the way it was moving, you might have missed the fact that the underlying mechanism didn’t really exist, or that it wasn’t robust enough to weather a whole lot of market participants looking to take advantage of that phenomenon.

So explainability has been an issue for a while. Everyone is always looking for a story for why they’re doing what they’re doing. And many of those stories aren’t that robust.

But isn’t there a strong financial incentive to try to understand why you’re doing what you’re doing, whether it’s an algorithm or a human executing the trades? Otherwise it seems very easy to lose a lot of money.
Sure. But the market structure of investing dilutes that incentive.
The people who are developing the most sophisticated quantitative techniques work for hedge funds and investment banks. For them, there are two ways to make money. You make money by charging fees on the assets you manage, and you make money on the performance of the fund. That split will give you a sense of why there’s a dilution of the incentive. Because even if your assets don’t perform well, you can still make money on the fees that you’re charging to manage those assets.

The rewards from those fees are so large that if you can sustain a story for why your technique is superior, you can manage assets for a long time and make a ton of money without having to perform well. And, to be fair, sometimes it takes a number of years before you know whether the quantitative technique you tried actually works or not. So even if you aren’t making money in the short term, you could have a reasonable story for why you aren’t.

At the end of the day, for the manager, it’s as important to gather a lot of assets as it is to run a successful strategy. And gathering assets can be largely a marketing game.

And you play that marketing game by talking about your algorithms and machine learning models and artificial intelligence techniques and so on.
That’s right. Let’s look at hedge funds in particular. Hedge funds are a very expensive form of investment management. So they need to justify why they’re getting paid as much as they’re getting paid.

There’s a large amount of data that suggests that the average hedge fund, after you’ve paid all the fees that they charge, is not doing much for you as an investor. The last several years in particular have not been very kind to the hedge fund industry in terms of the returns they’ve produced. So hedge funds have a strong incentive for differentiation in their marketing story. The first marketing question for a hedge fund is always, “Why are you not the average hedge fund?”

Investors want to know how a hedge fund is going to make money, given the poor performance of the hedge fund industry as a whole. These days, investors are excited by an orientation towards technology and big data and machine learning and artificial intelligence. These tools offer the promise of untapped returns, unlike older strategies that may have competed away the returns they were chasing. Regardless of whether you’re actually good at technology as a hedge fund, you want to have a story for why you might be.

Some of the most prominent hedge fund managers of the last few decades—Steve Cohen, Paul Tudor Jones—are going against type and launching technology-driven quantitative investment funds. They employ physicists and computer scientists to write algorithms to invest money, because that’s what investors want. You’re seeing a massive arms race across hedge funds to rebrand themselves in that direction.

It reminds me a bit of startup founders marketing themselves to Silicon Valley venture capitalists by peppering their pitch decks with buzzwords related to artificial intelligence or some other hot field. The startups might get funded, but the technology might not really work—or it might not even exist. What the startup is calling artificial intelligence could be a bunch of workers in the Philippines doing manual data entry....

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