Spreading power of the ‘black box'
In the uncertain equity markets this year, there has been at least one clear trend the growth of computer-driven trading.
In often flat overall market conditions, many FTSE companies have found themselves subject to huge,sometimes record, daily volumes of their sharesbeing traded.
J Sainsbury, Invensys, EMI, Barclays, Reckitt Benckiser, EasyJet and Man Group have all succumbed to bouts of intense trading where the market appears to descend, pack-like, for a couple of days before it moves on again.
It is a sign of the increasing role of what issometimes known as “black-box” trading computer-driven trading based onalgorithms or mathematical models.
Although algorithmic or quantitative trading has been around for a number of years, it has become much more widespread in 2004, particularly trading onmarket trends.
It takes a buy or sell order of a defined quantity and places it into the model that has specific goals decided by the trader. The model generates the timing and the size of the order based on the specific goals of the algorithm. The goals tend to be based on aspecific benchmark, price or time.
For example, the software used by Credit Suisse First Boston uses large amounts of historical data to help predict trading patterns and real-time data to assesscurrent conditions.
It can also control price thresholds and limits and control participation rates and time.
TowerGroup, the US research group, estimates that algorithmic trading volumes will double to about 27 per cent of US equityactivity by the end of2006.
The UK is believed to be witnessing similar growth and the impact is being magnified by a rush to focus on any clear stock trend that emerges in an otherwise choppy market.
Traditionally upsurges in trading volumes in a stock indicated corporate news was about to break, suchas a takeover or a profitwarning.
But often now they can be triggered by even modestinitial stock movement driven by vague rumours or news.
Philip Green's move for Marks and Spencer wasone example where newsfollowed an increase in trading volumes, but often the rumours amount to nothing.
Many hedge funds and proprietary trading desks at leading investment banks run sophisticated software applications, sometimes known as “sniffers”, which screen equities looking for momentum, effectively “sniffing” out trading patterns. “The software doesn't care what the stock is,” a trader at a leading broker said of his company's product.
“It doesn't look at anything else, such as price/earnings ratios, it can just look at momentum in stocks.”
There is little room for technical analysis of historical charts, beloved of many.
“Typically, a programme could look at the return on prices at a particular level, going back through years of data,” said a software programmer at a hedge fund. “Technical analysis is seen as black magic compared to this.”
When a hedge fund or prop trader then submits their interest in a particular stock, it appears on electronic order books, and the sheer volume of the order is soon noticed.
“Effectively it works as a massive disclosure of information, from which people trade off,” said another dealer.
The effect can sometimes be seen in sometimes sharp share price rises and falls on the stock market.
“People soon start thinking there's more to it than meets the eye,” said one dealer, explaining why the interest moves so rapidly. “It works on the semi-credible stories best.”
The success of this type of trading this year has been driven essentially by flat equity markets and the low volatility that are the bane of a trader's life. All parties therefore need to squeeze the most out of minor price changes.
For the investment banks, algorithmic trading also offers the opportunity to lower trading costs.
Among the brokers, the market in algorithmic trading in equities is led by Credit Suisse First Boston, but the other bulge-bracket firms, such as MorganStanley, Goldman Sachs and Lehman Brothers, all have significant operations and are trying to capture an increasing part of thebusiness.
Hedge funds are also highly active. This provides more revenue streams for the investment banks as the hedge funds seek intermediaries to deal.
However, the upsurge in algorithmic trading will have repercussions for traditional traders.
“The long-term is not about lay-offs, but rather a fundamental altering of the trader's role,” said Gavin Little-Gill, an analyst at the investment management research service at TowerGroup.
He forecast that there would be an increasing need for traders with mathematical and programming skills.
“These types of individuals will be able to interface most effectively with an increasingly electronic and quantitative environment. We're going to see more and more PhDs on the trading desk.”
数学驰骋金融城
在今年变幻莫测的股市中，至少有一个明显的趋势，那就是计算机交易的增长。
在往往平淡无奇的整体市场环境中，许多富时指数(FTSE)成分股公司发现，其股票的日交易量非常巨大，有时甚至刷新纪录。
每当市场似乎要走低时，桑斯博里(J Sainsbury)、英维思(Invensys)、百代唱片(EMI)、巴克莱银行(Barclays)、利洁时(Reckitt Benckiser)、易航(EasyJet)和曼恩集团(Man Group)等企业的股票都会成群出现持续一、二天的数轮密集交易，然后才再次稳定下来。
这表明基于算法或数学模型的计算机交易正日益发挥作用。这种交易有时被称为“黑箱”交易。
算法或定量交易虽已存在多年，但此等交易的应用力度在2004年有大幅提高，尤其是根据市场走势进行的交易。
这种交易把一个指定交易量的买入或卖出指令放入模型，该模型包含交易员确定的某些目标。根据这些特殊的算法目标，该模型会产生执行指令的时机和交易额。而这些目标往往基于某个基准、价格或时间。
例如，瑞士信贷第一波士顿(Credit Suisse First Boston)使用一种软件，该软件利用大量历史数据来帮助预测交易模式和实时数据，从而评估当前的市况。
该软件还能控制价格区间，并控制参与率和参与时间。
美国研究集团TowerGroup预测，到2006年末，算法交易量将增长一倍，达到美国股票交易量的约27%。
据信算法交易量在英国也会有类似的增长。在这个变化无常的股市中，对于任何清晰的股票行情趋势，人们都会急于关注，这也在扩大算法交易的影响。
传统上，如果某只股票的交易量飙升，就表明该公司即将发布收购或盈利预警等消息。
但现在，即使是模糊的谣传或消息形成微弱的股票动向，都有可能触发巨大的交易量。
菲利浦?格林(Philip Green)对玛莎百货(Marks and Spencer)采取的行动就是一个例子，即新闻在交易量上升之后发布，但谣言往往并没有什么事实根据。
很多对冲基金和主要投行的交易部门都使用复杂的交易软件，这些软件有时被称作“嗅探器”(sniffers)，能对股票进行筛选，寻找动量，有效地“嗅出”交易模式。“软件不管选出来的是什么股票，”一家主要经纪商的交易员在谈到他公司的产品时说。
“软件并不关注市盈率之类的其它任何参数，它只关注股票交易的动量。”
很多人喜欢对历史图表进行技术分析，但在这里，这种技术分析几乎不用。
“典型情况下，一个程序能够回溯多年的数据，关注特定水平下的股价回报率，”一家对冲基金的软件编程人员说，“相比之下，技术分析被看成巫术。”
当某个对冲基金或交易员提交买卖某只股票的指令时，该笔交易就会出现在电子指令记录中，而庞大的交易额很快就会引起关注。
“它其实相当于披露大量信息，人们则根据这些信息进行交易，”另一位交易员说。
有时候，当股市中出现剧烈的股价波动时，就可以看到这种效果。
“人们马上就会想，事情不像表面看到的那么简单，”一位交易商在解释市场的兴趣变化为何如此迅速时说，“对于让人将信将疑的消息，这种交易最管用。”
这种交易今年之所以获得成功，主要是因为股票市场平淡无奇，而且缺乏波动性。对交易员来说，这两种状况都是灾难。因此，所有市场参与者都需要从微小的价格变化中挤出最多的利润。
算法交易也为投行提供了降低交易成本的机会。
在各家券商中，瑞士信贷第一波士顿是算法股票交易市场的领先者。但其它一些华尔街大投资银行，如摩根士丹利(MorganStanley)、高盛(Goldman Sachs)和雷曼兄弟(Lehman Brothers)在这方面的业务也都很庞大，而且正试图获得更高的市场占有率。
对冲基金也高度活跃。这为投资银行提供了更多的收入来源，因为对冲基金需要通过中介来进行交易。
但算法交易的急剧增长将会对传统的交易员产生影响。
“长期来看，这种影响并不意味着交易员下岗，而是交易员的角色将发生根本性改变，”TowerGroup投资管理研究服务部门的加文?利特尔-吉尔(Gavin Little-Gil)表示。
他预测说，具备数学和编程技能的交易员将面临越来越大的需求。
“这类人最有能力在日趋电子化和量化的环境中游刃有余。我们将看到越来越多的博士从事交易这一行。”
