Machine Learning's "Overhyped" Potential Is Headed Toward The "Trough Of Disillusionment"

Bloomberg’s series on automation on Wall Street has certainly given the hundreds of thousands of highly educated individuals employed in the US financial services industry a lot to think about, like, for example, ‘will my job be here in ten years when it’s time for my oldest to head to college?’”

However, Bloomberg’s latest installment in the series was apparently meant to provide some measure of relief to the legions of analysts, traders and salespeople worried about losing their jobs to a robot. While advances are being made in the field of artificial intelligence at an increasingly rapid clip, the truth is, efforts to automate an investor’s process have mostly fallen flat.

Bloomberg cites several examples, including a push by Paul Tudor Jones to build an algorithm that would mimic his analytical process, of these types of efforts fizzling – though of course firms like Bridgewater have successfully automated many of their trading strategies. Renaissance Technologies, a quant fund founded by former military code-breaker Jim Simons, has been a pioneer in using machine-learning techniques for decades while building an enviable investing track record.

Back in the 1990s, Paul Tudor Jones assigned a team of coders to a project dubbed “Paul in a Box.” The effort sought to break down the DNA of the hedge fund manager’s trading - how he sizes up markets and generates ideas - to train a computer to do the same.


The code created then was upgraded many times and is still used at his firm, Tudor Investment Corp. But it never took over.


Again and again, programmers had to feed in new types of data to mimic the changing price signals that Jones, famous for predicting the Black Monday market crash 30 years ago, zeroed in on, according to people with knowledge of the project. Even then, the machine couldn’t capture intangibles like his gut instincts and conviction, as well as the market’s uncertainties.


Ultimately, Jones remains the final decision-maker for trades - not the box.

The shortcomings of Jones’s approach show why many jobs at the high end of finance are probably safe – for now, at least. While machine-learning algorithms and other technologies are indeed encroaching on work performed by money managers, traders and analysts, many firms are still working out the kinks, and coders still have a long way to go.

Furthermore, the notion that Wall Street powerhouses use all the latest and greatest technology is misleading. One not-so-well kept secret, according to Bloomberg, is that many companies rely on aging computers and Microsoft excel. Bloomberg reports the average age of software used by financial firms is about 38 years, according to technology tracker CB Insights. And data, the bedrock upon which AI is built, are often fragmented or inaccurate.

Even after coders manage to build these types of systems, their software will probably need frequent fine-tuning. Michael Dubno, the chief architect of Goldman Sachs Group Inc.’s risk-management system known as SecDB, told Bloomberg that’s one reason why salespeople and traders, at least for now, aren’t obsolete.

“They have mental models of the world that are more complex perhaps than most of the computer systems,” he said. In the short term, artificial intelligence isn’t going to move as fast as people expect. “It will go through a number of fits and starts, where it will look like it’s going to solve everything and then solve very little of it - and then it’s going to reset.”

For what it’s worth, Goldman has led the push into automation, just like the investment bank was a pioneering presence in the field of high frequency trading, something that netted the bank billions of dollars in short-term trading profits.

Some hedge funds, like Steve Cohen’s new shop, are also experimenting with automating their investment managers’ decisions.

However, replicating the skills of an equity or credit trader remains an obstacle. Automating trading in credit markets has been especially challenging because so much of a credit traders job hinges on judgment, the idiosyncrasies of each trade and human interaction. Traders use data that aren’t standardized or work with clients to create bespoke contracts, such as for commercial mortgage-backed securities.

But perhaps the one insurmountable obstacle for adoption of AI across trading floors is institutional inertia. Executives might resist automation to try and preserve the status quo because they fear losing their income and status.

Bosses may be reluctant to displace large swaths of their staffs, reducing their authority, or to embrace technology that they themselves don’t understand.

“Top management rarely want change, they want to keep intact a system that has worked for them for decades,” said Mansi Singhal, a former trader at Brevan Howard Asset Management and Bank of America. “And it can also get political when you have lots of executives, silos and budgets, and there are managers who just don’t want to cede control.”

That’s not to say that the current limitations of the technology aren’t also a factor. Machine learning, according to Gartner Inc.’s hype cycle for new technologies, is at the “peak of inflated expectations” and heading to the “trough of disillusionment,” Bloomberg reports.

To that point, while ETFMG claims its new new Watson-powered ETF can replace an army of research analysts, it’s really a gimmick. Those junior analysts will be able to keep their jobs – for now, at least.