算法投资靠谱吗 AI progress fails to convince all investors

2024-06-25 03:17 阅读次数:

本文摘要: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 So


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. 艾萨克牛顿(Isaac Newton)有可能是有史以来最聪明的人,然而事实证明他是个差劲的投资者。“我能计算出来出有天体的运动,却无法计算出来出有人的可怕,”他在南海股票泡沫中损失了一大笔钱以后感叹道。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. 然而,更加多通晓技术的投资者指出他们可以利用数学和计算机尖端科技,来预测金融市场的起起伏伏。一些最先进设备的资产管理公司现在于是以无可奈何人工智能(AI)技术,其中还包括需要自动自学、适应环境和搜寻大量数据组以研究出可交易的模式的投资算法。

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. 但有些“分析”金融家(quant,即分析分析师)猜测这类工具有可能不过是一种高明一点的陷阱。他们指出“机器学习”这类领域被过度抹黑,AI则是一种营销噱头。“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.” “每个人都想获得‘圣杯’,某种需要投资并且构建1%恒定月回报率的东西,”坐落于剑桥(Cambridge)的分析对冲基金Cantab Capital的负责人尤安柯克(Ewan Kirk)回应,“我想展现出得乐观,但我很猜测。” 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. 全球仅次于分析对冲基金之一温顿资本(Winton Capital)负责人戴维哈丁(David Harding)也猜测,AI并无法给投资业带给根本性进步。

“我不是卢德分子(Luddite),我们总是对赚的新方式感兴趣。因为总有世界级的人物向我展出实质上并没效果的灵丹妙药,我被迫回应深表猜测,”他说道。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. 计算能力的明显提高彻底改变了投资界,依据算法的交易商和投资者在市场上的影响力更加大。

大量资金涌进持续从市场杂音中分析出有风向的计算机驱动对冲基金。这造成许多资金管理公司竞相雇用计算机专家,必要与硅谷技术巨头和热门初创企业争夺战人才。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. AI正处于领域的最前沿。近年来AI领域也经历了几次进步。

最引人注目的是,谷歌(Google)旗下DeepMind的AI部门研发的程序,最近击败了一位知名棋士运动员。棋士是一种古老的中国游戏,因为过分简单,大多数专家此前都指出,计算机最少还必须10年才能击败人类棋士冠军。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. DeepMind的AlphaGo这类算法所运用的技术也许还能获得更加普遍的应用于,这引起了有关投资管理有可能将要步入另一场技术革命的悲观情绪。在规模上,这场革命有可能和上世纪七八十年代的市场电子化革命相若。

“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. “机器学习和人工智能将在分析资产管理中起着很大起到,但传统资产管理公司也不会在这个领域乘机扩展,”高盛(Goldman Sachs)资产管理部门的基金经理奥斯曼阿里(Osman Ali)回应。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. 计算机可以利用机器学习等风行的AI策略自律自学和发展。

比如,一种机器学习算法可以独立国家初学者和掌控如何玩游戏《超级马里奥》(Super Mario)这样的游戏。一开始算法不会随机地玩游戏这款经典街机游戏,但迅速算法就能摸清如何操作者和通关。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. 因此,权利的机器学习算法在海量数据中找寻稍纵即逝的可盈利模式的创造力,引发人们的普遍兴趣。

“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. “我指出算法就相等于享有极大潜力的幼童。你可以教教它们同时读者数百万本书,”美国国家航空航天局(NASA)前计算机科学家、现在供职于贝莱德(BlackRock)坐落于旧金山的“科学主动股票投资”部门的布拉德贝茨(Brad Betts)回应。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. 然而,甚至是在很多分析分析师中,猜测情绪仍然广泛。在他们显然,机器学习和深度自学——后者承托了DeepMind的AlphaGo引人注目的顺利——只不过是对早已投入使用很长时间的技术的拓展或强化。

“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.” “很多人用于了数十年的一些技术,都可以被称作机器学习技术,”Neuron Capital负责人罗伯特希尔曼(Robert Hillman)回应,“图片辨识和把AI运用到市场之中不存在极大差异。这否将带给投资的范式改变?我不这么指出……这不是根本性的变化,这是一种效率的提高。” 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. 柯克认为,最少见的AI策略着重于模式识别,比如区分出有图片中的一只猫和一只狗。但市场上弥漫着杂音和乱流,要寻找模式更加艰难。

“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. “作为一名极客,AlphaGo让我超级激动,但从输掉一个有明晰规则和目标的游戏、到展开投资,中间还有极大的跨度,”他说道。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. 即使是对AI在投资界的应用于前景抱着慎重悲观态度的分析分析师,也警告这个领域不存在许多陷阱。一些看上去有可能很精妙、与历史数据极致与众不同的算法,在面临金融市场的反复无常时却经常出毛病。“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.” “能玩游戏《超级马里奥》不一定能匹敌市场。