《金融时报》:大宗商品交易开始向数据要利润 - 互联网金融门户 未央网

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《金融时报》:大宗商品交易开始向数据要利润

其他国际资讯

《金融时报》:大宗商品交易开始向数据要利润

随着大宗商品交易商的利润空间遭到竞争对手挤压,他们正寄望数据专家帮助自己获取优势。

多年来,货币、股票和利率投资者一直利用算法、机器学习和人工智能,将数据转化为交易。

现在,大宗商品交易商开始探索如何利用自己的信息,帮助自己获利于价格波动。

EDF Trading的彼得·莱昂尼(Peter Leoni)表示:"这实际上是把'知道要找什么'和'使用合适的数学工具'结合起来。"EDF Trading是法国电力(EDF)设在伦敦的交易部门,该部门最近成立了一个10人团队,拥有数学博士学位的莱昂尼是团队内部两名数据科学家之一。

EDF Trading首席商务官菲利普·比森许特(Philipp Büssenschütt)表示:"我们希望能够提取数据并将其运用到算法中。然后我们计划使用机器学习来改进交易决策,从而提高我们的盈利能力。

为了集中自己的数据,法国电力的这个交易部门正在投资于相关的人才、流程和系统,而这么做的不止它一家。猎头公司Human Capital的达米安·斯图尔特(Damian Stewart)表示:"(大宗商品界的)每个人都意识到了一件事:数字化时代已经来临了。

在大宗商品行业,交易商们掌握着独家信息,从西非油田作业中断,到俄罗斯的农作物情况,他们以此争取比对手抢占先机,而过去20年的信息大众化对他们形成了挑战。

新闻、天气预报和货物跟踪信息的广泛传播,让大宗商品交易商的利润空间受到挤压,因为他们的信息优势--曾经支撑他们作为中间商的盈利--被削弱了。

各大交易公司的股本回报率(ROE)——衡量股东投资所产生利润的指标——均显著下降。

一些石油和金属交易商在2000年代中期的回报率约为50%至60%,现已下降至15%左右。

农产品交易商的回报率向来较低,但也遭遇下跌,著名的"ABCD"--阿彻丹尼尔斯米德兰(Archer Daniels Midland, ADM)、邦吉(Bunge)、嘉吉(Cargill)和路易达孚(Louis Dreyfus Commodities)--在最新财报中均报告个位数回报率。

因此,越来越多的交易商将多年积累的原材料实物交易海量信息馈入计算机程序,试图发现能够孕育交易点子的规律,希望以此提高竞争力。

摩根大通(JPMorgan)前银行家、如今是货物跟踪软件开发商Vortexa董事长的艾蒂安·阿米克(Etienne Amic)表示:"在农业、金属或能源行业,交易商们正寻求大规模收集数据,然后运用机器学习算法,找到将基本面与价格变动联系起来的规律。"

例如,嘉吉从两年前开始建立其全球数字化团队,现在有75人专注于数字创新和孵化,还有一支12人的数据科学团队。

嘉吉农业供应链部门总裁格特-简·范登奥凯尔(Gert-Jan van den Akker)表示,数据和尖端技术的结合已经带来了更好的交易决策。他说:"人类在交易和了解期货市场方面一直发挥着至关重要的作用,但我们不再单纯只靠人脑力量。"

与此同时,计算机模型和算法程序也对期货市场的大宗商品价格产生了更大影响,从而加大了对冲头寸的难度。

EDF Trading的比森许特说:"我们注意到,随着由算法执行的交易量增加,市场上的基本面和价格越来越脱节。"

根据美国商品期货交易委员会(CFTC) 2017年的一项研究,2012年至2016年期间,在芝加哥商品交易所(CME)交易的原油合约有近三分之二是自动执行的,而之前为54%。

在大豆和小麦方面,这一数字从39%上升到近50%,而贵金属则从46%上升到54%。

一些交易商正在加盟算法基金以了解其工作方式。德国农业交易商BayWa已经与德国基金Quantumrock Capital和专业从事计算机"量化交易"的美国基金Molinero Capital Management携手。

尽管有这股热情,但对于一部分交易商,数字化可能不会那么轻松实现。一位咨询顾问表示,与其他金融和工业行业相比,"他们从远远落后的地方起步"。

一个问题是,在将信息集中在一个平台方面,一些规模较大的大宗商品交易商面临内部阻力。波士顿咨询集团(Boston Consulting Group)数字化大宗商品交易部门主管安提o贝尔特(Antti Belt)表示,考虑到在一家交易公司,每个交易部门负责自己的盈亏账户,就连同事之间也会严密保护数据。他补充称:"转向'在彼此间分享我们所有的数据'是一种非常重大的文化变革。

另一个问题是在一些交易公司,员工在多个技术平台上操作,不同部门使用不同的系统。

一些数据科学家和工程师无法专注于分析,而不得不聚焦于协调这些平台,以便从公司的不同部门导入数据。

即便数字基础设施到位,可能也需要一段时间才能让人工智能成为大宗商品交易的重要组成部分。过去3年,维多(Vitol)首席信息官杰拉德o戴尔萨德(Gerard Delsad)把他的团队扩充五分之一,达到100多人,包括几名数据科学家。

他表示:"你仍需要交易员提出一些想法,就应该关注什么给出一些暗示,然后你才能发现一些有趣的东西。"

然而,很多大宗商品高管表示,数字化正越来越多地推动交易,必须被接纳。比森许特补充称:"这是交易员们必须了解的另一件工具。"

Commodity houses are on the hunt for data experts to help them gain an edge after seeing their margins squeezed by rivals.

Currencies, equities and interest-rates investors have for years used algorithms, machine learning and artificial intelligence to turn data into successful trades.

Now, commodity traders are seeking ways of exploiting their information to help them profit from price swings.

"It is really a combination of knowing what to look for and using the right mathematical tools for it," said Peter Leoni, who holds a mathematics PhD and is one of the two data scientists within a newly created team of 10 at EDF Trading, the London-based trading arm of the French utility group.

"We want to be able to extract data and put it into algorithms," added Philipp Büssenschütt, EDFT chief commercial officer. "We then plan to move on to machine learning in order to improve decision-making in trading and, as a result, our profitability."

"The French trading arm is investing in people, processes and systems to centralise its data - and it is not alone."Everybody [in the commodity world] is waking up to the fact that the age of digitisation is upon us," said Damian Stewart at headhunters Human Capital.

In an industry where traders with proprietary knowledge, from outages at west African oilfields to crop conditions in Russia, vied to gain an upper hand over rivals, the democratisation of information over the past two decades has been a challenge.

Through the broad dissemination of news, weather reports and cargo-tracking, commodity traders have found their margins under pressure as their information edge, that once buttressed their profits as middlemen, has been blunted.

Return on equity for leading trading houses, a measure of the profit generated from the money shareholders have invested, has dropped significantly.

Some of the oil and metals traders enjoyed returns of about of 50-60 per cent in the mid-2000s, but this has declined to levels in the mid-teens.

Agricultural traders' returns have historically been lower, but they have also dropped, with the commodity companies known as the ABCDs - Archer Daniels Midland, Bunge, Cargill and Louis Dreyfus Company - all recording single-digit ROE in their latest results.

As a consequence, an increasing number of traders are hoping to increase their competitiveness by feeding computer programs with mountains of information they have accumulated from years of trading physical raw materials to try and detect patterns that could form the basis for trading ideas.

"In agriculture, metals or energy, the traders are looking to gather data on a large scale and run machine-learning algorithms to find patterns linking fundamentals with price movements," said Etienne Amic, a former JPMorgan banker and chairman of Vortexa, a company making cargo-tracking software.

Cargill, for example, started to build its global digital team two years ago and now has 75 people focused on digital innovation and incubation, with a data science team of 12.

According to Gert-Jan van den Akker, president of Cargill's agricultural supply chain division, the combination of data and cutting-edge technology is already leading to better trading decisions.Humans have always played a vital role in trading and understanding futures markets, but we're no longer relying on human brain power alone," he said.

"At the same time, computer models and algorithmic programs are also exerting greater influence on the prices of commodities in the futures markets, making it more difficult to hedge positions.

"We notice that the fundamentals and prices increasingly get out of balance in the market with the increased volumes executed by algos," said Mr Büssenschütt of EDFT.

Between 2012 and 2016, almost two-thirds of crude oil contracts traded on CME's futures exchange were automated, up from 54 per cent, according to a 2017 study by the US Commodity Futures Trading Commission.

In soyabeans and wheat, the figure rose from 39 per cent to almost half, while in precious metals it has climbed to 54 per cent from 46 per cent.

Some traders are joining forces with algorithmic funds to learn how they work. BayWa, a German midsized agricultural trader, has linked up with Molinero Capital Management, a US fund specialising in computer-backed "quantitative" trading, and German fund Quantumrock Capital.

Despite this new enthusiasm, the road to electronification may not come easily for some traders. Compared to other financial and industrial sectors, "they are coming from way behind," said one consultant.

"One issue is that some of the larger commodities traders face internal resistance in centralising information on one platform. With each desk in a trading house in charge of its profit-and-loss account, data are closely guarded even from colleagues, said Antti Belt, head of digital commodity trading at Boston Consulting Group. "The move to 'share all our data with each other' is a very, very big cultural shift," he added.

Another problem is that in some trading houses, staff operate on multiple technology platforms, with different units using separate systems.

Rather than focusing on analytics, some data scientists and engineers are having to focus on harmonising the platforms before bringing on the data from different parts of the company.

Even where the digital infrastructure is in place, it may take some time before AI becomes a large part of commodities trading. Vitol's chief information officer Gerard Delsad has increased his team by a fifth to more than 100 in the past three years, including several data scientists.

He said a computer still struggles to find patterns in the data and come up with trading ideas on its own. "You still the need the trader to give some ideas, some hints [of] where to look, and then you can find some interesting stuff."

Nevertheless, digitisation is increasingly driving trading and needs to be embraced, say many commodities executives.Mr Büssenschütt said: "It's another tool that traders have to understand."


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