Money Machines: An Interview with an Anonymous Algorithmic Trader (2)

Q: 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.
Q: 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.
In the financial industry, investors want firms that use big data and machine learning and artificial intelligence—but do those new tools actually generate better results?
That’s a good question. The best way to explore it might be to talk about the role of data. There’s a lot of excitement in the financial industry about the amount of new data that’s being made available. Think about what kind of data might be useful for predicting the price of an oil future. It might be a piece of political news, public announcements from regulators, satellite images of oil refineries to calculate oil reserves. There are tons of different kinds of data out there—pretty much anything you can think of.
Along with new forms of data, there are also new forms of data analysis. The early versions of complex data analysis included looking at the financial statements of publicly traded companies. But now you can parse through the data in those statements in more interesting ways. Back in the day, you might care about how much debt the company has or what its earnings are relative to its price, and you might compare those figures to the broader market. But you were ultimately limited by your capacity to source and process this data.
Now you can analyze more variables more systematically across thousands of stocks. You can also do more exotic things like use natural language processing techniques to figure out what the company is saying in its statement that isn’t reflected in its numbers. How did the commentary change from previous earnings reports? What is the tone of the words they use to describe the underlying business? How does this tone compare to words used by its competitors? Even though it’s the same data you had access to before, you have more processing power and better techniques to understand that data.
The challenge is that not all of these sources of data and ways to analyze them will be useful for predicting the prices of financial instruments. Many of the new data sets, like satellite imagery, tend to be quite expensive. And they may not add any information more useful than what is already available to market participants from the vast streams of data on prices, companies, employees, and so on. We’re still in the phase where we’re trying to figure out what to do with all the data that’s coming in. And one of the answers might be that most of it is simply not that valuable.

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