On vacation in Turkey, I am picked up at the airport by a minibus. It’s past midnight, pitch-black, the driver is speeding around corners. Only one headlight is working. And I have my doubts about the brakes. In my head I’m planning the letter of complaint to the tour company. And then the driver’s cell phone rings, he picks it up and answers it, he has only one hand on the steering wheel. Now I’m mentally compiling the list of songs to be played at my funeral.
That’s rather how I feel when people talk about the latest fashion among investment banks and hedge funds: high-frequency algorithmic trading. On top of an already dangerously influential and morally suspect financial minefield is now being added the unthinking power of the machine.
The idea is straightforward: Computers take information – primarily "real-time" share prices – and try to predict the next twitch in the stock market. Using an algorithmic formula, the computers can buy and sell stocks within fractions of seconds, with the bank or fund making a tiny profit on the blip of price change of each share.
There’s nothing new in using all publicly available information to help you trade; what’s novel is the quantity of data available, the lightning speed at which it is analyzed and the short time that positions are held.
You will hear people talking about "latency," which means the delay between a trading signal being given and the trade being made. Low latency – high speed – is what banks and funds are looking for. Yes, we really are talking about shaving off the milliseconds that it takes light to travel along an optical cable.
So, is trading faster than any human can react truly worrisome? The answers that come back from high-frequency proponents, also rather too quickly, are "No, we are adding liquidity to the market" or "It’s perfectly safe and it speeds up price discovery." In other words, the traders say, the practice makes it easier for stocks to be bought and sold quickly across exchanges, and it more efficiently sets the value of shares.
Those responses disturb me. Whenever the reply to a complex question is a stock and unconsidered one, it makes me worry all the more. Leaving aside the question of whether or not liquidity is necessarily a great idea (perhaps not being able to get out of a trade might make people think twice before entering it), or whether there is such a thing as a price that must be discovered (just watch the price of unpopular goods fall in your local supermarket – that’s plenty fast enough for me), l want to address the question of whether high-frequency algorithm trading will distort the underlying markets and perhaps the economy.
It has been said that the October 1987 stock market crash was caused in part by something called dynamic portfolio insurance, another approach based on algorithms. Dynamic portfolio insurance is a way of protecting your portfolio of shares so that if the market falls you can limit your losses to an amount you stipulate in advance. As the market falls, you sell some shares. By the time the market falls by a certain amount, you will have closed all your positions so that you can lose no more money.
It’s a nice idea, and to do it properly requires some knowledge of option theory as developed by the economists Fischer Black of Goldman Sachs, Myron S. Scholes of Stanford and Robert C. Merton of Harvard. You type into some formula the current stock price, and this tells you how many shares to hold. The market falls and you type the new price into the formula, which tells you how many to sell.
By 1987, however, the problem was the sheer number of people following the strategy and the market share that they collectively controlled. If a fall in the market leads to people selling according to some formula, and if there are enough of these people following the same algorithm, then it will lead to a further fall in the market, and a further wave of selling, and so on – until the Standard & Poor’s 500 index loses over 20 percent of its value in single day: Oct. 19, Black Monday. Dynamic portfolio insurance caused the very thing it was designed to protect against.
This is the sort of feedback that occurs between a popular strategy and the underlying market, with a long-lasting effect on the broader economy. A rise in price begets a rise. (Think bubbles.) And a fall begets a fall. (Think crashes.) Volatility rises and the market is destabilized. All that’s needed is for a large number of people to be following the same type of strategy. And if we’ve learned only one lesson from the recent financial crisis it is that people do like to copy each other when they see a profitable idea.
Such feedback is not necessarily dangerous. Take for example what happens with convertible bonds – bonds that can be converted into stocks at the option of the holder. Here a hedge fund buys the bond and then hedges some market risk by selling the stock itself short. As the price of the stock rises, the relevant formula tells the fund to sell. When the stock falls the formula tells it to buy – the exact opposite of what happens with portfolio insurance. To the outside world – if not necessarily to the hedge fund with the convertible bonds – this mix is usually seen as a good thing.
Thus the problem with the sudden popularity of high-frequency trading is that it may increasingly destabilize the market. Hedge funds won’t necessarily care whether the increased volatility causes stocks to rise or fall, as long as they can get in and out quickly with a profit. But the rest of the economy will care.
Buying stocks used to be about long-term value, doing your research and finding the company that you thought had good prospects. Maybe it had a product that you liked the look of, or perhaps a solid management team. Increasingly such real value is becoming irrelevant. The contest is now between the machines – and they’re playing games with real businesses and real people.
That’s rather how I feel when people talk about the latest fashion among investment banks and hedge funds: high-frequency algorithmic trading. On top of an already dangerously influential and morally suspect financial minefield is now being added the unthinking power of the machine.
The idea is straightforward: Computers take information – primarily "real-time" share prices – and try to predict the next twitch in the stock market. Using an algorithmic formula, the computers can buy and sell stocks within fractions of seconds, with the bank or fund making a tiny profit on the blip of price change of each share.
There’s nothing new in using all publicly available information to help you trade; what’s novel is the quantity of data available, the lightning speed at which it is analyzed and the short time that positions are held.
You will hear people talking about "latency," which means the delay between a trading signal being given and the trade being made. Low latency – high speed – is what banks and funds are looking for. Yes, we really are talking about shaving off the milliseconds that it takes light to travel along an optical cable.
So, is trading faster than any human can react truly worrisome? The answers that come back from high-frequency proponents, also rather too quickly, are "No, we are adding liquidity to the market" or "It’s perfectly safe and it speeds up price discovery." In other words, the traders say, the practice makes it easier for stocks to be bought and sold quickly across exchanges, and it more efficiently sets the value of shares.
Those responses disturb me. Whenever the reply to a complex question is a stock and unconsidered one, it makes me worry all the more. Leaving aside the question of whether or not liquidity is necessarily a great idea (perhaps not being able to get out of a trade might make people think twice before entering it), or whether there is such a thing as a price that must be discovered (just watch the price of unpopular goods fall in your local supermarket – that’s plenty fast enough for me), l want to address the question of whether high-frequency algorithm trading will distort the underlying markets and perhaps the economy.
It has been said that the October 1987 stock market crash was caused in part by something called dynamic portfolio insurance, another approach based on algorithms. Dynamic portfolio insurance is a way of protecting your portfolio of shares so that if the market falls you can limit your losses to an amount you stipulate in advance. As the market falls, you sell some shares. By the time the market falls by a certain amount, you will have closed all your positions so that you can lose no more money.
It’s a nice idea, and to do it properly requires some knowledge of option theory as developed by the economists Fischer Black of Goldman Sachs, Myron S. Scholes of Stanford and Robert C. Merton of Harvard. You type into some formula the current stock price, and this tells you how many shares to hold. The market falls and you type the new price into the formula, which tells you how many to sell.
By 1987, however, the problem was the sheer number of people following the strategy and the market share that they collectively controlled. If a fall in the market leads to people selling according to some formula, and if there are enough of these people following the same algorithm, then it will lead to a further fall in the market, and a further wave of selling, and so on – until the Standard & Poor’s 500 index loses over 20 percent of its value in single day: Oct. 19, Black Monday. Dynamic portfolio insurance caused the very thing it was designed to protect against.
This is the sort of feedback that occurs between a popular strategy and the underlying market, with a long-lasting effect on the broader economy. A rise in price begets a rise. (Think bubbles.) And a fall begets a fall. (Think crashes.) Volatility rises and the market is destabilized. All that’s needed is for a large number of people to be following the same type of strategy. And if we’ve learned only one lesson from the recent financial crisis it is that people do like to copy each other when they see a profitable idea.
Such feedback is not necessarily dangerous. Take for example what happens with convertible bonds – bonds that can be converted into stocks at the option of the holder. Here a hedge fund buys the bond and then hedges some market risk by selling the stock itself short. As the price of the stock rises, the relevant formula tells the fund to sell. When the stock falls the formula tells it to buy – the exact opposite of what happens with portfolio insurance. To the outside world – if not necessarily to the hedge fund with the convertible bonds – this mix is usually seen as a good thing.
Thus the problem with the sudden popularity of high-frequency trading is that it may increasingly destabilize the market. Hedge funds won’t necessarily care whether the increased volatility causes stocks to rise or fall, as long as they can get in and out quickly with a profit. But the rest of the economy will care.
Buying stocks used to be about long-term value, doing your research and finding the company that you thought had good prospects. Maybe it had a product that you liked the look of, or perhaps a solid management team. Increasingly such real value is becoming irrelevant. The contest is now between the machines – and they’re playing games with real businesses and real people.
Custom duty on gold re-imposed