The Hour Between Dog and Wolf: Risk-taking, Gut Feelings and the Biology of Boom and Bust. John Coates

Чтение книги онлайн.

СКАЧАТЬ the positions for only a few minutes, sometimes mere seconds. High-frequency traders do not try to predict where the market is going in the next day or two, let alone the next year, as do asset managers who invest for the long term; they try to predict the small moves in the market, a few cents up or down. As a general rule, the shorter the holding period for a style of trading, the greater the need for its traders to have fast reactions.

      Having said all this, there are good reasons for expecting the physical aspect of trading to decline in importance in the financial world. More and more activities are now carried out electronically. The first and most dramatic sign of such a change was the closing down of physical stock exchanges, such as the London Stock Exchange. In their place mainframe computers took over the task of matching buyers and sellers of securities. Today only a few physical exchanges, with tumultuous floors and face-to-face execution of trades, remain, the New York Stock Exchange and the Chicago Board of Trade being the most famous.

      The same evolution has begun in bond and currency trading at banks. Many banks began to post the prices of the most liquid securities, beginning with Treasuries and mortgage-backed bonds, on computer screens, and then allowed their clients access to the screens. That way they could execute trades themselves, without the need of going through a salesperson like Esmee. Normally traders like Martin post prices on these screens for a limited size, say $25 to $50 million, and these will be executed electronically by clients; but for bigger trades, like DuPont’s, clients still prefer to call their salesperson. Nonetheless, many people within the banks think the flow traders are dinosaurs, and will eventually go extinct.

      Perhaps the greatest threat facing the human trader, though, comes from computerised trading algorithms known as black boxes. Life for many traders has always been nasty, brutish and short, given the vicious competition between them. Survival has depended on their relative endowment of intelligence, information, capital and speed. But the advent and insidious spread of the black boxes has begun squeezing humans out of their ecological niche in the financial world. These computers, backed by teams of mathematicians, engineers and physicists (‘quants’, they are called) and billions in capital, operate on a time scale that even an elite athlete could not comprehend. A black box can take in a wide array of price data, analyse it for anomalies or statistical patterns, and select and execute a trade, all in under 10 milliseconds. Some boxes have shaved this time down to two or three milliseconds, and the next generation will operate on the order of microseconds, millionths of a second. The speeds now dealt with in the markets are so fast that the physical location of a computer affects its success in executing a trade. A hedge fund in London, for example, trading the Chicago Board of Trade, lags at least 40 milliseconds behind the market, because that is the time it takes for a signal, travelling at close to the speed of light, to travel back and forth between the two cities while a price is communicated and a trade executed, and the delays added by routers along the way mean the actual time is considerably longer. Most companies running boxes therefore co-locate their servers to the exchange they trade, to minimise the travel time for an electronic signal.

      Many of these boxes are what are called ‘execution-only’ boxes. This type of box does not look for trades, it merely mechanises their execution. At this task, boxes excel. They can take a large block of equities, for example, and sell it in pieces here and there, minimising the effect on prices. They test the waters, looking for deep pools of liquidity, a practice known as pinging, just like a sonar searching the depths. When they find large bids hidden just below the surface of existing prices they execute a block of the trade. In this way they can move enormous blocks of stock without rippling the market. At this trading exercise, boxes are more efficient than humans, faster and nimbler. They do what Martin did when he pieced out of the DuPont trade, only they do it better. Many managers have started to ask why traders spend so much time and effort executing client trades when a box could do it just as well, and never argue over its bonus.

      Other boxes do more than execution: they think for themselves. Employing cutting-edge mathematical tools such as genetic algorithms, boxes can now learn. Funds running them regularly employ the best programmers, code-breakers, even linguists, so the boxes can parse news stories, download economic releases, interpret them and trade on them, all before a human can finish reading even a single line of text. Their success has led to an exponential growth in the capital backing them, and boxes already make up the majority of trading by volume on many of the largest stock exchanges; and they are now spreading into the currency and bond markets. Their growing dominance in the markets is one of the most significant changes ever to take place in the markets. I, like many others, find the markets increasingly inhuman, and when I trade now I often have difficulty catching the scent of the market’s trail.

      Human traders such as Martin are therefore in a fight for their lives. Unbeknownst to outsiders, every day a battle rages up and down Wall Street between man and machine. Some informed observers believe human traders have had their day, and will meet the same fate as John Henry, the legendary nineteenth-century railway worker who challenged a steam drill to a competition and ended up rupturing his heart.

      Others, however, note with optimism that human traders are more flexible than a black box, are better at learning, especially at forming long-term views on the market, and thus in many circumstances remain faster. Evidence of their greater flexibility is found when market volatility picks up after some catastrophic event, like a credit crisis. Then managers at the banks and hedge funds are forced to unplug many of their boxes, especially those engaged in medium- and long-term price prediction, as the algorithms fail to comprehend the new data and begin to lose ever increasing amounts of money. Humans quickly step into the breach.

      Something much like this occurred during the credit crisis of 2007–08. Anecdotal evidence and published fund performance statistics give us something like the following scorecard: in high-frequency trading, humans and machines fought to a draw, both making historic amounts of money; in medium-term price prediction, in other words seconds to minutes, humans pulled slightly ahead of the boxes, as flow traders made record amounts of money; but in medium- to long-term price prediction, minutes to hours or days – the boxes engaged in these time horizons are known as statistical arbitrage and quantitative equity – humans outperformed the boxes, because only they understood the implications of the political decisions being made by central bankers and Treasury officials. Thus, in what may have been the first major test of human versus machine trading, humans won, but only just. And so it is that this futuristic battle ebbs and flows.

      Конец ознакомительного фрагмента.

      Текст предоставлен ООО «ЛитРес».

      Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.

      Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.

/9j/4AAQSkZJRgABAgAAZABkAAD/7AARRHVja3kAAQAEAAAAPAAA/+IMWElDQ19QUk9GSUxFAAEB AAAMSExpbm8CEAAAbW50clJHQiBYWVogB84AAgAJAAYAMQAAYWNzcE1TRlQAAAAASUVDIHNSR0IA AAAAAAAAAAAAAAEAAPbWAAEAAAAA0y1IUCAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAARY3BydAAAAVAAAAAzZGVzYwAAAYQAAABsd3RwdAAAAfAAAAAUYmtw dAAAAgQAAAAUclhZWgAAAhgAAAAUZ1hZWgAAAiwAAAAUYlhZWgAAAkAAAAAUZG1uZAAAAlQAAABw ZG1kZAAAAsQAAACIdnVlZAAAA0wAAACGdmlldwAAA9QAAAAkbHVtaQAAA/gAAAAUbWVhcwAABAwA AAAkdGVjaAAABDAAAAAMclRSQwAABDwAAAgMZ1RSQwAABDwAAAgMYlRSQwAABDwAAAgMdGV4dAAA A СКАЧАТЬ