MOST people agree that a combination of the slowdown in Chinese economic growth and the impending rate hike in the US are to blame for the recent market sell-off, but neither adequately explains its ferocity.
It is no secret that the Chinese economy has been ailing for some time and that the Federal Reserve has been planning a hike, so what caused the panic that saw the Dow shed more than 1,000 points in a morning and Chinese stocks crash 40% from their peak?
Enter accusations of a "quant meltdown", which is not a new idea. The most famous happened in August 2007, when several equity hedge funds experienced record losses from automated "portfolio deleveraging" and a "temporary withdrawal of market-making risk capital" — whatever that means.
Quant is short for quantitative trading — a big word to say that traders are using computers and not just their guts to analyse vast swathes of market data and make trading decisions. Two of the quantitative trading techniques commonly used are high-frequency trading, and algorithmic or black-box trading. In high-frequency trading, automated computer programs are used to place multiple buy and sell orders milliseconds apart, while black-box trading uses complex mathematical formulae and high-speed computer programs to determine and execute trading strategies.
High-frequency trading is contentious to say the least and probably best left as a discussion for another day, but algorithmic trading has also come under scrutiny, not because it has been used intentionally to move markets, but because it unintentionally has.
An example of a very basic algorithm was described in the Wall Street Journal: if volume in a stock hits a minimum threshold and the 50-day moving average of the stock’s price crosses above the 200-day moving average, buy $100 worth of shares. If volumes hit the threshold and the 50-day moving average crosses below the 200-day moving average, sell $100 worth.
It is easy to see the appeal of algorithmic trading. You plug in the volumes and levels to target and the computer does the rest — much faster than you could.
The growing popularity of these super-fast and allegedly super-smart beta strategies has raised concerns about crowded trades. A crowded trade occurs when the positions of many managers overlap. In the days before algorithmic trading, this wasn’t a good thing either, but the fact that trading decisions were made by people and that placing a trade didn’t happen in a millisecond meant markets had time to digest movements.
Algorithmic trading has increased the incidence of crowded trades and decisions are executed so fast that the market doesn’t have time to react — at least the flesh and blood market. As a result, a single market event can trigger a series of rapid automated trades. This is made worse by the fact that the increase in algorithmic trading means that a small number of firms may account for a large proportion of trading volume. When this happens quant-driven hedge funds are their own worst enemies — it’s the market equivalent of the Tragedy of the Commons.
With this in mind, it shouldn’t come as a surprise that before the market meltdown there were concerns about crowded trades and stern warnings that it was just a matter of time before the system went to hell in a hand basket.
Joshua M Brown, a New York stockbroker who calls himself The Reformed Broker, wrote in June how quants were the new systemic risk. "There’s an idea going around that asset management — specifically the metastasising quantitative strategies run via black box — are where the next big scare is due to come out of."
He argued that because volatility had been so low for so long, winning trades had become crowded and leverage was bountiful. As a result, far too many managers were running the same playbook and loading up on the same trade.
It makes sense that if a large number of funds are all programmed to respond in the same way to certain market conditions, it could become self-reinforcing.
Quant guru Andrew Lo, a finance professor at the Massachusetts Institute of Technology’s Sloan School of Management, says that although there is insufficient evidence to blame systematic trading for all of the recent volatility, computerised trading does have a tendency to exaggerate market moves if it is lined up with what the market wants to do.
"If the market is looking to sell because of an impending recession, then we’re going to see a lot of the algorithmic trading going in the same direction," he says.
Although the tendency is for algorithms to reinforce existing market sentiment, Lo says that under different conditions, algorithmic trading can also damp market swings if they are going in the opposite direction to the general trend. "The one thing that is true, though, is algorithmic trading is speeding up the reaction times of participants, so that’s the choppiness of the market," he says.
In other words, algorithmic trading means everyone can move to the left side of the boat then the right side of the boat within minutes as opposed to hours or days.
Not surprisingly, the actions of these multibillion-dollar, computer-driven hedge funds have attracted the attention of legislators and regulators who are eager to understand and preempt any quant-driven market collapses.
In the days after August 24, the China Financial Futures Exchange said it would be suspending 164 investors from trading because of their high daily trading frequency. The decision followed a finding by the China Securities Regulatory Commission that the practices of algorithmic traders tended to amplify market fluctuations.
This again is nothing new. There is mounting evidence that the predominance of quants in the market is increasing systemic risk because an error at a relatively small trading firm may cascade and result in a sizeable effect on the financial markets through direct errors or the reactions of other algorithms to the error.
The question is whether or not banning individual players will solve the problem. Probably not. At the moment, regulators aren’t really able to manage event risk, but as with everything in financial markets, it’s probably just a matter of time before new regulations are passed that will further eliminate illegal activities and legal, but unintentionally harmful, ones, too.
An investor takes notes as he watches a board showing stock prices at a brokerage office in Beijing, China, on Monday. Picture: REUTERS
MOST people agree that a combination of the slowdown in Chinese economic growth and the impending rate hike in the US are to blame for the recent market sell-off, but neither adequately explains its ferocity.
It is no secret that the Chinese economy has been ailing for some time and that the Federal Reserve has been planning a hike, so what caused the panic that saw the Dow shed more than 1,000 points in a morning and Chinese stocks crash 40% from their peak?
Enter accusations of a "quant meltdown", which is not a new idea. The most famous happened in August 2007, when several equity hedge funds experienced record losses from automated "portfolio deleveraging" and a "temporary withdrawal of market-making risk capital" — whatever that means.
Quant is short for quantitative trading — a big word to say that traders are using computers and not just their guts to analyse vast swathes of market data and make trading decisions. Two of the quantitative trading techniques commonly used are high-frequency trading, and algorithmic or black-box trading. In high-frequency trading, automated computer programs are used to place multiple buy and sell orders milliseconds apart, while black-box trading uses complex mathematical formulae and high-speed computer programs to determine and execute trading strategies.
High-frequency trading is contentious to say the least and probably best left as a discussion for another day, but algorithmic trading has also come under scrutiny, not because it has been used intentionally to move markets, but because it unintentionally has.
An example of a very basic algorithm was described in the Wall Street Journal: if volume in a stock hits a minimum threshold and the 50-day moving average of the stock’s price crosses above the 200-day moving average, buy $100 worth of shares. If volumes hit the threshold and the 50-day moving average crosses below the 200-day moving average, sell $100 worth.
It is easy to see the appeal of algorithmic trading. You plug in the volumes and levels to target and the computer does the rest — much faster than you could.
The growing popularity of these super-fast and allegedly super-smart beta strategies has raised concerns about crowded trades. A crowded trade occurs when the positions of many managers overlap. In the days before algorithmic trading, this wasn’t a good thing either, but the fact that trading decisions were made by people and that placing a trade didn’t happen in a millisecond meant markets had time to digest movements.
Algorithmic trading has increased the incidence of crowded trades and decisions are executed so fast that the market doesn’t have time to react — at least the flesh and blood market. As a result, a single market event can trigger a series of rapid automated trades. This is made worse by the fact that the increase in algorithmic trading means that a small number of firms may account for a large proportion of trading volume. When this happens quant-driven hedge funds are their own worst enemies — it’s the market equivalent of the Tragedy of the Commons.
With this in mind, it shouldn’t come as a surprise that before the market meltdown there were concerns about crowded trades and stern warnings that it was just a matter of time before the system went to hell in a hand basket.
Joshua M Brown, a New York stockbroker who calls himself The Reformed Broker, wrote in June how quants were the new systemic risk. "There’s an idea going around that asset management — specifically the metastasising quantitative strategies run via black box — are where the next big scare is due to come out of."
He argued that because volatility had been so low for so long, winning trades had become crowded and leverage was bountiful. As a result, far too many managers were running the same playbook and loading up on the same trade.
It makes sense that if a large number of funds are all programmed to respond in the same way to certain market conditions, it could become self-reinforcing.
Quant guru Andrew Lo, a finance professor at the Massachusetts Institute of Technology’s Sloan School of Management, says that although there is insufficient evidence to blame systematic trading for all of the recent volatility, computerised trading does have a tendency to exaggerate market moves if it is lined up with what the market wants to do.
"If the market is looking to sell because of an impending recession, then we’re going to see a lot of the algorithmic trading going in the same direction," he says.
Although the tendency is for algorithms to reinforce existing market sentiment, Lo says that under different conditions, algorithmic trading can also damp market swings if they are going in the opposite direction to the general trend. "The one thing that is true, though, is algorithmic trading is speeding up the reaction times of participants, so that’s the choppiness of the market," he says.
In other words, algorithmic trading means everyone can move to the left side of the boat then the right side of the boat within minutes as opposed to hours or days.
Not surprisingly, the actions of these multibillion-dollar, computer-driven hedge funds have attracted the attention of legislators and regulators who are eager to understand and preempt any quant-driven market collapses.
In the days after August 24, the China Financial Futures Exchange said it would be suspending 164 investors from trading because of their high daily trading frequency. The decision followed a finding by the China Securities Regulatory Commission that the practices of algorithmic traders tended to amplify market fluctuations.
This again is nothing new. There is mounting evidence that the predominance of quants in the market is increasing systemic risk because an error at a relatively small trading firm may cascade and result in a sizeable effect on the financial markets through direct errors or the reactions of other algorithms to the error.
The question is whether or not banning individual players will solve the problem. Probably not. At the moment, regulators aren’t really able to manage event risk, but as with everything in financial markets, it’s probably just a matter of time before new regulations are passed that will further eliminate illegal activities and legal, but unintentionally harmful, ones, too.
