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备受争议的高频算法交易让股市雪上加霜

备受争议的高频算法交易让股市雪上加霜

ADRIAN CROFT 2020年03月13日
高频交易给市场带来了流动性,但这种做法存在争议,因为人们认为它造成了一系列原因不明的市场崩盘。

股市经历了有史以来最动荡的一个月,标普500、道琼斯和纳斯达克指数均在2月中旬飙至新高,但在本周一突然暴跌,至本周四,三大股市已完全跌入熊市,短期内两度触发熔断机制。市场观察人士再次质疑,高频算法交易加剧了股市的跌势。

算法交易是指计算机根据预设指令自动执行交易,这种方式已经使用了很长时间,在如今股市每天的涨跌中扮演重要角色。那些打电话给经纪人,让他帮你进行交易的日子几乎已经一去不复返了。

人类做出判断可能会受到情绪或本能的影响,但计算机可以迅速冷静地做出“买进”或“卖出”的决定。

算法交易是“危险的催化剂”

但是,当市场急剧下跌时,人们就会指责算法交易放大了市场暴跌的程度,加剧了投资者恐慌。例如,如果止损限价被整体触发,算法交易可能会导致滚雪球式的抛售,导致市场螺旋下滑。

一些市场观察人士指责算法交易为“危险的波动催化剂”。

然而,值得注意的是,当股市一片大好时,很少会听到对算法交易的批评。

木星资产管理公司的基金经理盖伊·德·布罗内向美国全国广播公司财经频道表示,2018年,美国股市每天80%的交易量都是由机器完成的。摩根大通量化和衍生品研究全球主管马尔科·克兰诺维奇2017年说,“自主交易者”只占股市交易量的10%,相比之下,使用算法做出被动和量化投资决策的占60%。

机构投资者和对冲基金常常使用算法交易,它的优势包括执行速度快、交易成本低、前后策略保持一致。

算法可以被设定为动量策略,即买进上涨的股票,或者卖出下跌的股票,或者,也可以设定为买入或卖出高于或低于近期交易区间的股票。

因此,当市场大幅下跌时,软件将迅速执行大额卖出的指令。美国股市有所谓的熔断机制,当股价暴跌7%时暂停交易,之后在下跌13%和20%时再次发生熔断。这正是近期发生的情景。

算法交易也可能是市场减震器?

算法交易能使投资者通过识别资产价格的微小差异,以进行利润丰厚的套利交易,或许还能从汇率波动中获利。

计算机也可以被设定为某个固定模式,对实时发布的经济数据,如就业率或美联储的利率变动等立刻做出反应,这也会放大这些数据对市场的影响。

交易软件还可以用来避免在交易日中临时产生的问题。例如,养老基金等大型机构投资者在进行大规模股票购买时,可能会把订单拆解为大量小订单,他们就会使用自动交易软件操作,以避免股价突然被推高。

闪电崩盘或因高频交易所致

现在,人们越来越多地把算法交易和机器学习结合,创造出越来越复杂的自动投资方式。

投资银行摩根大通去年表示,该行正在使用机器学习来提供有竞争力的定价,他们有一个名为“演算执行深度神经网”的机器学习平台,利用其来优化每天在外汇市场上6.6万亿美元的交易。

与此同时,投资者越来越多地依赖机器人顾问,后者会利用算法,根据个人目标量身定制出客户的在线投资策略。

算法交易的一个分支是高频交易,指的是投资者在一秒不到的时间内买卖股票,以期从股价的微小波动中获利。高频交易给市场带来了流动性,但这种做法存在争议,因为人们认为它造成了一系列原因不明的市场崩盘。

2010年5月6日,美国股市蒸发了约1万亿美元,道琼斯工业股票平均价格指数在一次奇怪的“闪电崩盘”中暴跌近1000点,随后又收复大部分失地。

2010年的一份官方报告称,此次崩盘发生在市场非常紧张的一天,当时一家共同基金通过自动执行算法,在20分钟内卖出了41亿美元的迷你标普期货合约,引发了迷你期货市场的流动性危机。

这之后,外汇市场上多次闪电崩盘均被认为是因算法交易而被扩大的。

美国财经作家迈克尔•刘易斯在他2014年出版的《快闪小子》一书中称,高频交易商利用超高速通信带来的瞬间优势,以牺牲市场上其他参与者的利益为代价,赚取了数十亿美元。

据《华尔街日报》最近报道,自那以后,越来越多的交易所设置了“减速带”,在执行交易时设置微小延迟,以削弱高频交易者的优势。(财富中文网)

译者:Agatha

责编:雨晨

股市经历了有史以来最动荡的一个月,标普500、道琼斯和纳斯达克指数均在2月中旬飙至新高,但在本周一突然暴跌,至本周四,三大股市已完全跌入熊市,短期内两度触发熔断机制。市场观察人士再次质疑,高频算法交易加剧了股市的跌势。

算法交易是指计算机根据预设指令自动执行交易,这种方式已经使用了很长时间,在如今股市每天的涨跌中扮演重要角色。那些打电话给经纪人,让他帮你进行交易的日子几乎已经一去不复返了。

人类做出判断可能会受到情绪或本能的影响,但计算机可以迅速冷静地做出“买进”或“卖出”的决定。

算法交易是“危险的催化剂”

但是,当市场急剧下跌时,人们就会指责算法交易放大了市场暴跌的程度,加剧了投资者恐慌。例如,如果止损限价被整体触发,算法交易可能会导致滚雪球式的抛售,导致市场螺旋下滑。

一些市场观察人士指责算法交易为“危险的波动催化剂”。

然而,值得注意的是,当股市一片大好时,很少会听到对算法交易的批评。

木星资产管理公司的基金经理盖伊·德·布罗内向美国全国广播公司财经频道表示,2018年,美国股市每天80%的交易量都是由机器完成的。摩根大通量化和衍生品研究全球主管马尔科·克兰诺维奇2017年说,“自主交易者”只占股市交易量的10%,相比之下,使用算法做出被动和量化投资决策的占60%。

机构投资者和对冲基金常常使用算法交易,它的优势包括执行速度快、交易成本低、前后策略保持一致。

算法可以被设定为动量策略,即买进上涨的股票,或者卖出下跌的股票,或者,也可以设定为买入或卖出高于或低于近期交易区间的股票。

因此,当市场大幅下跌时,软件将迅速执行大额卖出的指令。美国股市有所谓的熔断机制,当股价暴跌7%时暂停交易,之后在下跌13%和20%时再次发生熔断。这正是近期发生的情景。

算法交易也可能是市场减震器?

算法交易能使投资者通过识别资产价格的微小差异,以进行利润丰厚的套利交易,或许还能从汇率波动中获利。

计算机也可以被设定为某个固定模式,对实时发布的经济数据,如就业率或美联储的利率变动等立刻做出反应,这也会放大这些数据对市场的影响。

交易软件还可以用来避免在交易日中临时产生的问题。例如,养老基金等大型机构投资者在进行大规模股票购买时,可能会把订单拆解为大量小订单,他们就会使用自动交易软件操作,以避免股价突然被推高。

闪电崩盘或因高频交易所致

现在,人们越来越多地把算法交易和机器学习结合,创造出越来越复杂的自动投资方式。

投资银行摩根大通去年表示,该行正在使用机器学习来提供有竞争力的定价,他们有一个名为“演算执行深度神经网”的机器学习平台,利用其来优化每天在外汇市场上6.6万亿美元的交易。

与此同时,投资者越来越多地依赖机器人顾问,后者会利用算法,根据个人目标量身定制出客户的在线投资策略。

算法交易的一个分支是高频交易,指的是投资者在一秒不到的时间内买卖股票,以期从股价的微小波动中获利。高频交易给市场带来了流动性,但这种做法存在争议,因为人们认为它造成了一系列原因不明的市场崩盘。

2010年5月6日,美国股市蒸发了约1万亿美元,道琼斯工业股票平均价格指数在一次奇怪的“闪电崩盘”中暴跌近1000点,随后又收复大部分失地。

2010年的一份官方报告称,此次崩盘发生在市场非常紧张的一天,当时一家共同基金通过自动执行算法,在20分钟内卖出了41亿美元的迷你标普期货合约,引发了迷你期货市场的流动性危机。

这之后,外汇市场上多次闪电崩盘均被认为是因算法交易而被扩大的。

美国财经作家迈克尔•刘易斯在他2014年出版的《快闪小子》一书中称,高频交易商利用超高速通信带来的瞬间优势,以牺牲市场上其他参与者的利益为代价,赚取了数十亿美元。

据《华尔街日报》最近报道,自那以后,越来越多的交易所设置了“减速带”,在执行交易时设置微小延迟,以削弱高频交易者的优势。(财富中文网)

译者:Agatha

责编:雨晨

The stock market has had one of its most tumultuous months on record, with the S&P 500, Dow and Nasdaq all soaring to new highs in mid-February only to crash to within a whisker of bear territory on Monday. Market watchers once again are casting a suspicious eye on the role of high-frequency algorithmic trading in exacerbating the slide.

Algorithmic trading, where a computer automatically executes trades based on pre-programmed instructions, has been around for a long time and is now a big factor in the daily ups and downs of the stock market. The days when you would call your broker to instruct a human to place the trade are mostly gone.

A computer makes “buy” or “sell” decisions quickly and dispassionately, unburdened by emotion or instinct that might cloud human judgement.

"Dangerous accelerant"...

But, when the markets fall dramatically, as they did on Monday, algorithmic trading is often accused of magnifying the market slump, and fueling investor panic. When stop-loss limits are triggered en masse, for example, it can lead to a snowball selling effect, sending a market into a downward spiral.

In the midst of Monday's historic sell-off, some markets observers were pointing a finger at algorithmic trading as a possible cause, calling it a "dangerous accelerant of volatility."

It's notable, however, that you rarely hear criticism of computerized trading when stocks are booming.

Guy De Blonay, a fund manager at Jupiter Asset Management, told CNBC in 2018 that 80% of daily moves in U.S. stocks were machine-led, while Marko Kolanovic, global head of quantitative and derivatives research at J.P. Morgan, said in 2017 that “fundamental discretionary traders” accounted for only about 10% of trading volume in stocks, compared with 60% for passive and quantitative investing, which uses algorithms to make investment decisions.

The advantages of algorithmic trading, typically used by institutional investors and hedge funds, are speed of execution, lower trading costs and sticking to a consistent strategy.

An algorithm might be designed to momentum strategy—that is buy stocks that are rising, or sell shares that are falling. Or, the software is programmed to buy or sell shares that have broken above or below their recent trading range.

And so when the markets lurch significantly lower, the software will quickly execute big sell orders. The U.S. markets have so-called staged circuit breakers to halt trading when shares surge down 7%—and later by 13% and 20%. That's precisely what happened a few minutes into the trading session on Monday.

...or markets shock-absorber?

Algorithmic trading also enables investors to make lucrative arbitrage trades by identifying tiny differences in the price of assets, perhaps profiting from exchange rates fluctuations.

Computers can also be programmed to react instantaneously, and in a set way, to timed releases of economic data—think jobs numbers or Fed interest rates moves—which can magnify their impact on markets.

The software can also be used to avoid big hiccups during the trading day. For example, big institutional investors, such as pension funds, making a large stock purchase, may break up their order into a lot of smaller orders, using automated trading software to avoid driving up the price of the shares.

Flash crash

Algorithmic trading is increasingly being coupled with machine learning to create ever more sophisticated automated investing.

Investment bank J.P. Morgan said last year it was applying machine learning to provide competitive pricing, and optimize execution in the $6.6 trillion-a-day foreign exchange market with its Deep Neural Network for Algo Execution (DNA).

Meanwhile, investors are increasingly turning to robo-advisors, which use algorithms to create and manage online investment strategies tailored to an individual's goals.

A sub-set of algorithmic trading is high-frequency trading, where investors buy or sell shares in a fraction of a second, seeking to profit from tiny fluctuations in prices. High-frequency trading brings liquidity to markets but the practice is controversial as it's blamed for contributing to a number of unexplained market crashes.

On May 6, 2010, around $1 trillion was wiped off U.S. stocks as the Dow Jones Industrial Average plunged by nearly 1,000 points in a bizarre “flash crash” before recovering most of the losses.

An official 2010 report said the crash happened on a nervous day in the markets when a mutual fund sold $4.1 billion of EMini S&P 500 futures contracts via an automated execution algorithm in 20 minutes, precipitating a liquidity crisis in the EMini market.

Since then, algorithmic trades are thought to have magnified a number of flash crashes in the foreign exchange market.

Michael Lewis, in his 2014 book “Flash Boys”, alleged that high-frequency traders used split-second advantages provided by ultra-high-speed communications to make billions at the expense of other market players.

Since then, a growing number of exchanges have created “speed bumps”, tiny delays in executing trades, to blunt the advantage of the high-frequency traders, as The Wall Street Journal recently reported.

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