The rise of the machine learning :
The quantitative investment world plays down the prospect of machines supplanting human fund managers, pointing out that the prospect of full artificial intelligence is still distant, and arguing that human ingenuity still plays a vital role. But the confident swagger of the money management nerds is unmistakable. Already there are quasi-AI trading strategies working their magic in financial markets, and the future belongs to them, they predict.
→ Financial Times
But it brings back bad memories of the stock market crash of 1987 when some Nasdaq dealers simply wouldn’t pick up their phones. They knew the investors on the other end were looking to sell their stocks and as market makers, these dealers would be obliged to buy—and they didn’t want to buy! In the aftermath of that incident, enraged investors demanded that the SEC prevent it from happening again. The Commission responded by forcing changes on Nasdaq, including mandating that market makers respond to messages on the fully electronic Small Order Execution System.
Today, the fully-automated Nasdaq market, with its market makers often using HFT techniques, is the very model of an efficient market that has dramatically lowered costs for investors.
Contrast that with the findings of the joint government staff report on the US Treasury “flash rally” which found high frequency traders “as a group continued to provide the majority of order book depth and a tight spread between bid and ask prices throughout the day, even during the event window.” In short, HFT answered the call.
→ Traders Magazine
Of course, honest traders change their minds all the time and cancel orders as economic conditions change. That’s not illegal. To demonstrate spoofing, prosecutors or regulators must show the trader entered orders he never intended to execute. That’s a high burden of proof in any market. One helpful fact is if most of a trader’s (canceled) orders were on one side (say to buy) when he was mostly actually trading on the other (selling). For instance Sarao allegedly put in huge orders to sell, so that he could buy a few contracts: All his trading was on one side, but most of his orders were on the other. Then he’d switch a little while later. That seems like a bad sign.
→ Traders Magazine
Hallelujah, high-frequency traders didn’t short the market that much :
Our analysis of trading on August 24, 2015, shows that while there were bursts of aggressive HFT activity during the sell-off, it was the institutional activity, not the HFT activity that led and dominated the sell-off. Specifically, the events have appeared to unfold as follows: institutions would sell particular securities, creating acute selling pressure in the markets. Aggressive HFTs would then step in and sell the market further, but only for a relatively short period of time.
→ Traders Magazine
On computer-driven, automatic trading strategies :
Cobras are revered in Indian culture, but the British Raj took a dimmer view of the poisonous snake. Officials promised a lucrative reward for every dead serpent — a scheme that, according to economic lore, backfired horribly.
Enterprising Indians began breeding cobras to collect the bounty, which forced the colonial government to abandon the plan. The frustrated breeders then released the worthless cobras, worsening the infestation. The story has never been fully confirmed by historians, but was seized on by German economist Horst Siebert, who in 2001 published The Cobra Effect on perverse incentives and unintended consequences. The book turned the anecdote into a potent example of how solutions to a problem can make it worse.
→ The Financial Times
In the summer of 2014, Puzz had another puzzle to solve. From March to July, the frequency with which an IEX customer could have gotten a better price less than 10 milliseconds after a trade posted rose from about 3 percent to as much as 10 percent. This wasn’t meant to happen. IEX was supposed to protect investors from what’s known as stale quote arbitrage; that’s when a high-frequency trader takes advantage of milliseconds-long delays in how markets update prices to reflect movements on other exchanges. These tiny delays allow high-speed traders to see a price fluctuation on one exchange and then quickly send an order to another market—often a dark pool—that it knows updates its prices more slowly, hoping to pick off the orders resting there at stale prices. It’s a bit like betting on yesterday’s horse race against someone who doesn’t know the result.
IEX prevents stale quote arbitrage with its “magic shoe box,” a metal container in its data center in Weehawken, New Jersey. Crammed into it are 38 miles (61 kilometers) of coiled fiber-optic wire, creating IEX’s speed bump of 350 microseconds (about one one-thousandth of the time it takes to blink). The idea of countering super-fast traders by creating a slower market might seem like a paradox. It’s not. IEX uses the same high-speed data feeds as HFT firms do to monitor other exchanges for price changes. But because IEX didn’t want to be in a technological arms race with the high-frequency traders to process this information faster than they do, it uses the speed bump to slow down all new orders—just enough to ensure IEX has time to update its prices to reflect any movements on public exchanges. This prevents orders on IEX from being traded against at stale prices.
So how, Aisen wondered, could HFT firms be picking off IEX orders despite the magic shoe box? It didn’t take Puzz long to solve the riddle. He discovered that some HFT algorithms could predict price changes—like surfers sitting out past the break, scanning the swell for their next ride—and target orders before the magic shoe box’s speed bump could protect them.
Of course, honest traders change their minds all the time and cancel orders as economic conditions change. That’s not illegal. To demonstrate spoofing, prosecutors or regulators must show the trader entered orders he never intended to execute. That’s a high burden of proof in any market.