AI Innovations Fail To Convince Skeptical Investors. So Far.

Those who doubt that tech innovation will eventually overtake their industry or profession and take their jobs are usually proven wrong.

Financiers have always considered themselves smarter and more ruthless than those in almost every other business. But that doesn't mean they're always right. JL

Robin Wigglesworth reports in the Financial Times:

Technology-savvy investors think they can harness mathematics and bleeding edge computer science to predict the ebb and flow of financial markets. Some asset managers are turning to artificial intelligence techniques, with investment algorithms that can autonomously learn, adapt and scour vast data sets for tradable patterns. But some quants are sceptical and argue that areas such as “machine learning” are overhyped and AI used as a marketing gimmick.

Isaac Newton may have been one of the finest minds of all time, but he turned out to be a miserable investor. “I can calculate the motions of the heavenly bodies, but not the madness of people,” he lamented after losing a fortune in the South Sea bubble.

Increasingly, however, technology-savvy investors think they can harness mathematics and bleeding edge computer science to predict the ebb and flow of financial markets. Some of the most advanced asset managers are turning to artificial intelligence techniques, with investment algorithms that can autonomously learn, adapt and scour vast data sets for tradable patterns.

But some “quantitative” financiers (quants) are sceptical that these tools are any more than a somewhat better mousetrap, and argue that areas such as “machine learning” are overhyped and AI used as a marketing gimmick.

“Everyone wants the Holy Grail, something they can invest in and it will make 1 per cent a month forever,” says Ewan Kirk, head of Cantab Capital, a Cambridge-based quantitative hedge fund. “I don’t want to be cynical, but I am sceptical.”

David Harding, head of Winton Capital, one of the biggest quantitative hedge funds in the world, is also doubtful that AI represents a quantum leap for the investment industry. “I’m not a Luddite, we’re always interested in new ways to make money. But I have to be very sceptical because I constantly have world-class people showing me miracle cures that don’t actually work,” he says.Dramatic improvements in computing power have revolutionised the investment world, with algorithmic traders and investors increasingly influential across markets. Money is pouring into computer-driven hedge funds that have consistently managed to parse signals amid market noise. As a result many money managers are scrambling to hire computer scientists, often pitting them in direct competition for talent with Silicon Valley’s tech giants and hot start-ups.

AI is at the forefront of this. The field has also enjoyed several leaps forward in recent years. Most notably, Google’s DeepMind AI arm has created a programme that recently thrashed a legendary player of Go, an ancient Chinese game that is so complex that most experts previously reckoned it would take at least a decade before a computer could beat a human champion.

The potentially wider applications of techniques used by the likes of DeepMind’s AlphaGo algorithm has fuelled optimism that investment management could be on the cusp of another technological revolution, possibly similar in scale to the electronification of markets in the 1970s and 1980s.
“Machine learning and artificial intelligence is going to play a very large role in quant managers, but also with traditional asset managers that are aggressively expanding in this space,” says Osman Ali, a fund manager at Goldman Sachs Asset Management.

AlphaGo marks stark difference between AI and human intelligence

Google’s robot relied on processing power and data storage, writes Daniel Susskind

Popular AI approaches such as machine learning can be used by computers to learn and develop autonomously. For example, a machine learning algorithm can learn to play and master a computer game such as Super Mario independently, at first playing the arcade classic randomly but quickly figuring out how the controls work and how to get to the end of the level.

There is therefore widespread enthusiasm over the potential of unleashing machine learning algos to find fleeting but profitable patterns in the vast sea of data.
“I think of algos as little children that can scale tremendously. And you can teach them to read millions of books at the same time,” says Brad Betts, a former Nasa computer scientist working in BlackRock’s San Francisco-based Scientific Active Equity arm.
Yet scepticism, even among many quants, is still pervasive. They see areas such as machine learning and deep learning — the latter underpinned DeepMind’s Go exploits — merely as extensions or enhancements of techniques that have for long been in use.
“Lots of people use techniques that could be called machine learning for decades,” argues Robert Hillman, head of Neuron Capital. “There’s a huge difference between image recognition and using AI in markets. Will this be a paradigm change for investing? I don’t think so . . . It’s not a fundamental change, it’s an efficiency improvement.”
Mr Kirk points out that most common AI approaches are focused on pattern recognition, such as telling the difference between a cat and a dog in an image. But markets are dominated by noise and chaos, the patterns are harder to find.
“As a geek I’m super-excited about AlphaGo, but it’s a big leap from beating a game with clearly defined rules and objectives and investing,” he says.
Even quants that are cautiously optimistic on the future of AI in investing warn of many pitfalls. Algorithms that may look ingenious and backtest superbly against historical data have a nasty habit of unravelling when confronted with unforgivingly fickle financial markets.
“Playing Super Mario might not necessarily work for markets. If you hit the button you always know what will happen, but you don’t in markets,” says another quant at a large hedge fund. “It can take time for it to find the good trades and to optimise them. It can go through a lot of bad trades.”