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.
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 backrest 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.”
“每个人都想要得到‘圣杯’，某种能够投资并且实现1%恒定月回报率的东西，”位于剑桥(Cambridge)的量化对冲基金Cantab Capital的负责人尤安?柯克(Ewan Kirk)表示，“我不想表现得悲观，但我很怀疑。”
全球最大量化对冲基金之一温顿资本(Winton Capital)负责人戴维?哈丁(David Harding)也怀疑，AI并不能给投资业带来重大飞跃。“我不是卢德分子(Luddite)，我们总是对赚钱的新方式感兴趣。因为总有世界级的人物向我展示实际上并没有效果的灵丹妙药，我不得不对此深表怀疑，”他说。
“机器学习和人工智能将在量化资产管理中起到极大作用，但传统资产管理公司也会在这个领域大举扩张，”高盛(Goldman Sachs)资产管理部门的基金经理奥斯曼?阿里(Osman Ali)表示。
“很多人使用了数十年的一些技术，都可以被称为机器学习技术，”Neuron Capital负责人罗伯特?希尔曼(Robert Hillman)表示，“图片识别和把AI运用到市场之中存在巨大差异。这是否将带来投资的范式转变？我不这么认为……这不是根本性的变化，这是一种效率的提升。”