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经济学人双语:对冲基金与人工智能


笔译、口译、同声传译
Hedge funds and artificial intelligence

对冲基金与人工智能

 

Return on AI

AI回报

 

AI-driven hedge funds need human brains, too

由AI操盘的 对冲基金也离不开人脑

 

 

 

ARTIFICIAL intelligence (AI) has already changed some activities, including parts of finance like fraud prevention, but not yet fund management and stock-picking. That seems odd: machine learning, a subset of AI that excels at finding patterns and making predictions using reams of data, looks like an ideal tool for the business. Yet well-established “quant” hedge funds in London or New York are often sniffy about its potential. In San Francisco, however, where machine learning is so much part of the furniture the term features unexplained on roadside billboards, a cluster of upstart hedge funds has sprung up in order to exploit these techniques.

人工智能(以下简称AI)已经改变了一些活动,其中包 括某些金融工作,如防范欺诈,但它却 尚未用于资金管理和选股。这似乎有些奇怪:作为AI分支的 机器学习技术善于运用庞大数据寻找模式并做出预测,似乎是 这一领域的理想工具。但伦敦或纽约的大牌“量化”对冲基 金往往对该技术的潜力不以为然。然而在旧金山,情况则不同。在这个城市,机器学习司空见惯,路边广 告牌上就直接印着这个名词。一批希 望利用该技术的新兴对冲基金已在这里涌现。

 

These new hedgies are modest enough to concede some of their competitors’ points. Babak Hojdat, co-founder of Sentient Technologies, an AI startup with a hedge-fund arm, says that, left to their own devices, machine-learning techniques are prone to “overfit”, ie, to finding peculiar patterns in the specific data they are trained on that do not hold up in the wider world. This is especially true of financial data, he says, because of their comparative paucity. Share-price time series going back decades still contain far less information than, say, the image data used to train Facebook’s facial-recognition algorithms.

这些新 型对冲基金态度足够谦虚,肯承认 对手的一些观点。设有对冲基金部门的AI创业公司感知科技(Sentient Technologies)的联合创始人巴巴克·霍加特(Babak Hojdat)表示,如果不加干预,机器学 习技术很可能会出现“过拟合”现象,即在用 于训练的特定数据中识别出了特殊模式,却无法 在现实世界广泛应用。他指出,金融数据相对稀缺,因此尤 其容易引发这一问题。累积几 十年的股价时间序列包含的信息远远少于其他数据,例如Facebook用于训 练人脸识别算法的图像数据。

 

The trick, then, is to take a more thoughtful approach to deploying AI. Technical prowess obviously matters; Sentient employs a couple of dozen AI experts and constantly researches new methods. But business models matter enormously, too. Sentient started out as a tiny fund a decade ago, managing only its own founders’ money. In the past three years it has expanded into other applications for AI, such as online shopping and website optimisation. Only earlier this year did it launch a hedge fund open to outside money, on which it hopes to apply the insights gleaned elsewhere in its investment arm.

那么,关键就 在于采取更周密的方式来部署AI。技术实 力显然非常重要:感知科 技聘请了几十位AI专家,不断开发新方法。但商业 模式也极为重要。感知科 技在十年前起步时是一家小型基金公司,只管理创始人的资金。过去三年来,公司已扩展到AI的其他应用领域,如网络 购物和网站优化。直到今年早前,该公司 才推出一只接受外部投资的对冲基金,希望将 从其他地方获得的洞见运用在自己的投资部门中。

 

Another San Francisco hedge fund that draws on an even wider pool of expertise, by virtue of its unusual business model, is Numerai, a firm founded in 2015 that launched its first fund this autumn. It starts by taking financial data and then encrypts them so that they are unrecognisable. Its chief operating officer, Matthew Boyd, says this turns them into a “pure math problem”. The idea is that this avoids biases creeping into models—and appeals to Valley types better than the grubby business of picking securities.

另一家 旧金山对冲基金公司Numerai有着不 同寻常的商业模式,从而利 用了更多的专业人才。这家成立于2015年的公 司在今年秋天推出了首只基金。该公司 先是收集金融数据,然后加密,令其无法被识别。公司的 首席运营官马修·博伊德(Matthew Boyd)表示,这将数据变成了“纯粹的数学问题”。公司认 为这样可以避免模型中掺杂偏见,而且,相比挑 选证券牵涉的肮脏交易,这也更 能吸引硅谷人士。

 

It then runs two-stage competitions for machine-learning algorithms that perform best on the data. Some 1,200 data scientists now take part weekly, competing for virtual prizes (in the fund’s own cryptocurrency) in the first round and cash prizes in the second. That structure seeks to encourage algorithms that do well at picking winners over time. The firm takes the results of the best algorithms, decrypts these results back into financial data, and uses the insights to decide which shares to trade. The fund owes at least as much to crowdsourcing, then, as it does to harnessing AI.

之后,Numerai开展两个阶段的比赛,挑选在 运用上述数据时表现最佳的机器学习算法。现在,每周约有1200名数据 科学家参与比赛,在第一 轮争夺虚拟奖金(该基金 公司自己的加密货币),在第二 轮争夺现金奖励。如此设 计为的是促进开发具备长期优选能力的算法。公司把 最佳算法的结果还原为金融数据,利用这 些发现来决定交易哪些股票。如此看来,众包对 该基金的贡献并不低于AI技术。

 

One hedge fund that does tout its machine-dependent model, despite naming itself after the human brain, is Cerebellum Capital. Founded as an arbitrage fund in 2008, it started work on a fully AI-run American equity fund in 2016, and launched it in April this year. The fund uses machine learning not just to crunch data and come up with strategies. The classification system that gauges the relative merits of these strategies is itself run by machine learning. But humans do the actual trading, following the algorithm’s instructions.

对冲基 金公司小脑资本(Cerebellum Capital)倒是标 榜其高度依赖机器的商业模式,尽管它以人脑命名。在2008年创立之初,该公司 是一家套利基金, 自2016年开始 筹备一只完全采用AI投资美国股票的基金,并于今年4月推出。该基金 不只用机器学习分析数据和出谋划策,就连衡 量这些策略优缺点的分类系统本身也是由机器学习技术运作的。不过,实际交 易操作还是由人工根据算法的指示来完成。

 

However they perform in the long term, therefore, one feature of these new AI funds is already clear. At least in investing, more artificial intelligence does not necessarily mean less of the human kind.

因此,无论这些新型AI基金长期表现如何,它们的 一个特点已经明显呈现。至少在投资领域,更多地 采用人工智能并不一定意味着需要的人类智慧就少了。




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