摩根大通(JPMorgan)不久將利用一款首創(chuàng)的機器人在其全球的股票算法業(yè)務(wù)部門執(zhí)行交易,此前該行在歐洲對其新型人工智能(AI)程序的試驗表明,它的效率比傳統(tǒng)的買賣方法高得多。
The AI — known internally as LOXM — has been used in the bank’s European equities algorithms business since the first quarter and will be launched across Asia and the US in the fourth quarter, Daniel Ciment, JPMorgan’s head of global equities electronic trading, told the Financial Times.
摩根大通全球股票電子交易業(yè)務(wù)負責人丹尼爾•西蒙(Daniel Ciment)告訴英國《金融時報》,在內(nèi)部被稱為LOXM的這款人工智能自第一季度以來被用于該行的歐洲股票算法業(yè)務(wù),并將在第四季度在亞洲和美國啟用。
LOXM’s job is to execute client orders with maximum speed at the best price, by using lessons it has learnt from billions of past trades — both real and simulated — to tackle problems such as how best to offload big equity stakes without moving market prices.
LOXM的職責是以最佳價格和最高速度執(zhí)行客戶交易指令——運用它從數(shù)十億筆過往交易(既有真實交易,也有模擬交易)中汲取的經(jīng)驗教訓(xùn)來解決各種問題,比如怎樣拋出大筆股份而不影響市場價格。
“Such customisation was previously implemented by humans, but now the AI machine is able to do it on a much larger and more efficient scale,” said David Fellah, of JPMorgan’s European Equity Quant Research team. Mr Ciment said that, so far, the European trials showed that the pricing achieved by LOXM was “significantly better” than its benchmark.
“這種定制操作以前是由人實施的,但現(xiàn)在AI機器能夠以大得多的規(guī)模和高得多的效率來做,”摩根大通歐洲股票量化研究團隊的戴維•費拉(David Fellah)表示。據(jù)西蒙介紹,到目前為止,歐洲的試驗顯示,LOXM達成的定價“顯著好于”基準水平。
Investment banks have been trying to use AI, automation and robotics to help cut costs and eliminate time-consuming routine work. For example, UBS’s recent deployment of AI to deal with client post-trade allocation requests, which saves as much as 45 minutes of human labour per task. UBS has also brought in AI to help clients trade volatility.
各投資銀行一直在嘗試使用AI、自動化和機器人技術(shù)來幫助降低成本,消除耗時的日常工作。例如,瑞銀(UBS)最近部署了AI來處理客戶的交易后配置請求,為每個任務(wù)節(jié)省了多達45分鐘的人力勞動。瑞銀還已采用AI來幫助客戶利用市場波動進行交易。
JPMorgan, which is the world’s biggest investment bank by revenue, believes it is the first on Wall Street to use AI with trade execution and said it would take rivals 18 to 24 months and an investment of “multiple millions” to come up with similar technology.
按營收計算為世界最大投行的摩根大通相信,它是首家使用AI執(zhí)行交易的華爾街投行,并稱,競爭對手將需要18至24個月和“數(shù)百萬”美元的投資才能開發(fā)出類似技術(shù)。
“Best execution is becoming more and more important to clients,” said Mr Ciment of JPMorgan’s decision to invest in the pioneering technology, adding that it could become part of the marketing pitch the bank makes to clients.
“最佳執(zhí)行對客戶來說越來越重要,”西蒙在談到摩根大通投資于這種開創(chuàng)性技術(shù)的決定時表示。他補充說,該項技術(shù)可能成為該行對客戶營銷宣傳內(nèi)容的一部分。
The AI was developed using “Deep Reinforcement Learning” methods, which are able to learn from millions of historic scenarios. Mr Fellah said DRL had “many other potential uses in banking, such as in automatic hedging and market making”.
這款A(yù)I是利用“深度強化學習”(DRL)方法開發(fā)的,這類方法能夠從數(shù)百萬種歷史情形中學習。費拉表示,深度強化學習在銀行業(yè)有“其他很多潛在用途,比如自動對沖和做市”。
One possible evolution of LOXM is teaching the machine how to get to know individual clients, so that it could consider their behaviour and reaction as it decides how to trade. “Any customisation would only be if the client agrees to that,” Mr Ciment added.
一個可能的發(fā)展是,向LOXM機器傳授如何了解個人客戶,以便它在決定如何交易的時候考慮他們的行為和反應(yīng)。“任何定制將在客戶同意的情況下進行,”西蒙補充說。
Unlike the robo advisers offered by some private banks, JPMorgan’s AI has no decision-making capabilities around what is bought and sold, its role is solely to decide how things are bought and sold.
與一些私人銀行提供的機器人顧問不同,摩根大通的AI對于買賣什么是沒有決策能力的,其作用僅僅是決定買賣的方式。
The bank has had no risk management issues with the technology. “The machine is restricted in its trading behaviour, as it learns under, and operates within, our general electronic trading risk framework, which is overseen by internal control groups and validated by regulators,” Mr Fellah said.
該行認為這種技術(shù)沒有風險管理問題。“機器的交易行為受到限制,因為它在我行的通用電子交易風險框架下學習和運行,這個框架受到內(nèi)控小組監(jiān)督,并由監(jiān)管機構(gòu)驗證,”費拉表示。