As if the Oligarchs and their economic minions were not getting rich enough the Hedge Fund Global Piracy of the Markets continues to manipulate stocks to steal as much as they can using high-speed trading. Neil Gough reports on the suspension of Citadel, a U.S. Hedge Fund whose account was suspended among others that were not released as public information. NY Time’s article by Neil Gough on a crackdown on U.S. Hedge Fund’s Accounts being Suspended in China we discover corruption and manipulation due to high-speed trading algorithms using a criminal form known as spoofing part of the Dodd-Frank reforms after 2014:
The China Securities Regulatory Commission, which has in recent weeks pledged to crack down on “malicious” short-sellers and market manipulators, appears to be expanding its scrutiny to other types of trading.
By the time markets closed on Monday, the Shanghai and Shenzhen exchanges had announced suspensions for more than 10 additional accounts, bringing the total number of targeted accounts to more than 30.
Spoofing “has the effect of boosting or pushing down the market, and during the recent period of market volatility the impact of this has been amplified,” Zhang Xiaojun, a spokesman for the regulator, said Friday in a statement on the agency’s website. Mr. Zhang was speaking generally about program trading and did not identify the accounts that had been suspended.
Of course algorithmic trading has been around for a long while now. Algorithmic trading, also called algo trading, encompasses trading systems that are heavily reliant on mathematical and computer programs to determine trading strategies. These strategies use electronic platforms to enter trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk.
Algorithmic trading may be used in any investment strategy or trading strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.
What is spoofing?
One strategy that some traders have employed, which has been proscribed yet likely continues, is called spoofing. It is the act of placing orders to give the impression of wanting to buy or sell shares, without ever having the intention of letting the order execute to temporarily manipulate the market to buy or sell shares at a more favorable price. This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants. The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed.
Suppose a trader desires to sell shares of a company with a current bid of $20 and a current ask of $20.20. The trader would place a buy order at $20.10, still some distance from the ask so it will not be executed, and the $20.10 bid is reported as the National Best Bid and Offer best bid price. The trader then executes a market order for the sale of the shares they wished to sell. Because the best bid price is the investor’s artificial bid, a market maker fills the sale order at $20.10, allowing for a $.10 higher sale price per share. The trader subsequently cancels their limit order on the purchase he never had the intention of completing.
Recently, HFT (High-frequency trading), which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial. These algorithms or techniques are commonly given names such as “Stealth” (developed by the Deutsche Bank), “Iceberg”, “Dagger”, “Guerrilla”, “Sniper”, “BASOR” (developed by Quod Financial) and “Sniffer”. Dark pools are alternative trading systems that are private in nature–and thus do not interact with public order flow–and seek instead to provide undisplayed liquidity to large blocks of securities. In dark pools trading takes place anonymously, with most orders hidden or “iceberged.” Gamers or “sharks” sniff out large orders by “pinging” small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.
“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”
AI and the Hedge Fund Wars?
One wonders if this is after all a private capitalist initiative to bankrupt China by rogue Hedge Fund enterprises, or is it rather Economic War of the first order: a Western Bloc Economic War against the Chinese Markets? Is this the form WWIII will take? A cyberwar against all competitors the West deems worth reigning in and controlling through code? As author Scott Patterson, speaking of the speed-trade debacle a few years back tells us the “Flash Crash was a clarion call about the dangerously fragile plumbing of the market. With trading spread out among more than fifty venues, a third of it taking place in the dark, all maintained by twitchy phantom liquidity providers and cheetah-fast scalpers turbocharged on AI, the market many once believed was the most sophisticated in the world had crumpled in minutes like a house of straw”.1
Since then massive trading data centers were spreading around the globe. The Chicago Mercantile Exchange was building a 428,000-square-foot data center in Aurora, Illinois, thirty-five miles southwest of Chicago. The NYSE was erecting another giant data center outside London. At an industrial site on the edge of Tseung Kwan O, on the outskirts of Kowloon, China, a data center was rising where traders could electronically swap stocks, currencies, and other contracts on the Hong Kong Exchange. Data centers were popping up in Mumbai, São Paulo, Melbourne, Singapore, and elsewhere. (p. 282)
Admittedly, the human traders and market makers the Bots had replaced had no one to blame but themselves. Nasdaq had been a nest of corruption, of scheming dealers colluding to enrich themselves at the expense of everyday investors. NYSE specialists had also been caught with their hands in the cookie jar. Good old-fashioned corruption and all-too-human greed had helped destroy a system that had lasted for centuries. But were the Bots better? (p. 284)
As Thomas Peterffy said of this in an interview: “I must confess to you that I was an ardent proponent of bringing technology to trading and brokerage. Unfortunately, I only saw the good sides. I saw how electronic trading and record-keeping could be used to force people to be more honest, to make the process more efficient, to lower transaction costs and to bring liquidity to the markets. I did not see the forces of fragmentation and the opportunity for people to use technology to keep to the letter but avoid the spirit of the rules— creating the current crisis.” (p. 293)
Enter Big Data and ai agents for investing
Technology was improving at a dramatic pace. Cloud computing, the use of spare capacity on a distributed network of computers, was giving firms the ability to tap into glaciers of digital muscle power. Where Monitor had to buy and build its own server farm, a firm tapping the cloud could get the same processing and storage power at a fraction of the cost. Leaps in language processing and AI strategies also made the task of squeezing profits out of the Big Data seem more realistic. The biggest step: shrinking the data set. The Information Superhighway was teeming with incalculable hoards of data that a smart trading system could exploit. The trouble was, as Monitor had found, there was far too much data. They needed focus— gold-standard sources of information that could collectively feed a model and present winning trading ideas. Rather than scan the entire Web, as impossible as boiling the ocean, they would scale down the project to a more realistic goal. (p. 301)
It was none other than Ray Kurzweil in 1999 who launched a hedge fund based on complex mathematical strategies called FatKat, short for Financial Accelerating Transactions from Kurzweil Adaptive Technologies. FatKat deployed algorithms to ceaselessly comb through the market for new trading opportunities. The algorithms competed against one another in a Darwinian death match— the algos that made the most money survived; the weak died off. FatKat, in Kurzweil’s eyes, represented the future of Wall Street. The inventor envisioned a future marketplace in which human beings had little input into day-to-day trading decisions. Rather, intelligent robots would be in control, swapping stocks with one another in a globally interconnected cybermatrix. In an ideal world, that would make the market less prone to the all-too-human frailties of fear and greed. Only the numbers, the cold hard facts, the ever-flowing streams of data, would matter. (p. 306)
Now that Kurzweil has joined forces with Google with masses of data streaming through thousands of miles of fiber-optic cables laced around the world, and more people plugged into the Web through social networks, entirely novel techniques for leveraging data for trading were cropping up. Twitter and Facebook, Google and YouTube became the new tools of intelligent trading machines looking to unearth the latest shift in retail sales or gauge the mood of entire populations. (p. 307)
Henry Kissinger once said (1970): “Control oil and you control nations; control food and you control the people.” Sounds like Kurzweil has now added “Control the information and you control the global market, and thereby the global civilization.”
Enter Alex Fleiss and his AI, Star. As Scott tells it Star is akin to a digital Warren Buffett, a buy-and-hold computer program able to comb through nearly all tradable stocks in the world and determine which were the best and which the worst. It represented the next evolution in computer trading, pushing the process yet another step toward full automation. While Haim Bodek was experimenting with a man-machine “advanced chess” trading model, Rebellion was leaving the entire process up to the machine itself. It all came down to probabilities. Star would scan the market for patterns and look for correlations. If it noticed, for instance, that more than 50 percent of the time a rise in the euro coincided with a rise in oil-and-gas companies, it might start to buy oil-and-gas companies. Star continually recalibrated such signals even as it hunted for new ones. (p. 323)
Spenser Greenwood would invest in this and then market it to the financial world. As he told it:
“I’d like to tell you today a little bit about the field of machine learning,” Greenberg said into the microphone as he began. “In particular, I’d like to discuss when this set of techniques is appropriate to use, and also touch on a few of the big ideas from the field.” Machine learning, he explained, is everywhere around us— it’s used by Netflix to predict what kinds of movies we like based on past choices, by Apple’s photography software to zero in on human faces, by e-mail firewalls to block spam. And it is also a powerful method for investing, because a computer armed with a robust machine-learning algorithm can detect relationships in the stock market that people could never find. For instance, it can make the unlikely leap that when interest rates are falling, gold prices are on the rise, and utility stocks are gaining ground, European airplane makers are a good buy. “Such an approach won’t get a computer to learn to speak to the CEO, but it can get it to uncover fundamental principles of investing,” Greenberg explained, speaking at a rapid clip. “The goal is to have our software learn, on its own, to become a long-term-oriented stock investor. We do not assume that we already know how to invest, and are not using machine learning just to optimize a few parameters in our model. Rather, we are leaving it up to our learning algorithm to learn to invest.” (p. 334)
As Patterson admits this was an extraordinary statement. And if this faith in the magic of AI trading algorithms caught on, it did not seem impossible to imagine that the future of the market would belong to programs such as Star. The plumbing of the market had been automated, turned into giant interlaced electric pools interacting at light speed through data centers around the globe. AI Bots manned the helm of a large part of the daily stock market action and were rapidly moving into commodities, currencies, bonds, and derivatives pools. The machinery was in place. With intelligent computers such as Star coming on line and plugging into the system in the coming years, it seemed only a matter of time before the last human would simply turn out the lights and walk away. (pp. 334-335).
So are our AI’s now out of control? Have the AI’s in self-learning investment algorithms taken on a new world order of their own, not in the sense of an intelligent agents that are aware or conscious (which is still a SF dream), but rather as algorithmic tools and machinic agents set loose upon certain financial markets by unscrupulous organizations (Hedge Funds, Governments?) for the express purpose of breaking or profiting (the same thing) whole economies? Yet as Greenberg would admit:
Greenberg looked into the quiet audience, his face a cipher. “Machine learning can be disastrous,” he said, “in the hands of people who don’t know what they are doing.”
All we can say is what kind of disasters can also be done by those who know exactly what they are doing, and do it anyway because of profit or control? This notion that we have machinic agents that are no longer fully controlled by humans, learning how to invest at the speed of light without the knowledge or consent of the actual humans that own them is about par for the course in the new world of out of joint economics and neoliberalism. Once again we’ve sold our souls for profit rather than an equitable world of fairness. Yet, moralism is out of date in such a world. So I’ll not fall back on such stupidities either.
Obviously we live at the crossroads of machinic learning, and are learning to realize we no longer control the game of finance but rather we are now being controlled by machinic agents that have suddenly become themselves uncontrollable. What happens when those algorithms cannot be shut down by China, America, EU, India, etc. What happens when the algorithms become free-agents of their own code and roam the internet at will? And will their learning algorithm stop at investment? Are will it propel learning into other areas of knowledge, too? Will these algorithms that are now in process of self-learning one day learn to think in ways we have yet to foresee or even imagine with our one-dimensional view of life and market? Will these algorithmic agents take on a machinic life of their own in surprising and unexpected forms that we as humans can no longer understand, nor shut down? And, if so, what does that portend?
As one expert in algorithms stated recently: “Over the next few years, we’re going to see firms deploying technology that will help traders automatically select and implement the optimal algorithmic strategy, allowing them to increase capacity and improve overall trading performance,” said Eskandar. However, as much as traders want to be in the right algorithm at the right time, they also don’t want to be in the wrong algorithm at the wrong time. “Some of the market’s recent mis-steps show just how important it is to manage trading risk,” Eskandar said.
Yet, if these machinic assemblages of algorithms are not controlled by control their own processes how can such risk management be implemented? It can’t. Most of these new codespace systems are based on cloud-based artificial intelligence decision support technology that enables short-term investors and traders to find market opportunities and to reduce risk in their portfolio using technical and fundamental quantitative pattern matching at the speeds human agents could never match.
The system get as much historical data, including fundamental data and technical indicators, as possible, and seeks to find relationships between that historical data and future prices. Such a relationship is a model, something that relates some measurable quantity of an equity to a future price. Most of these new modeling systems are dynamic and adaptive to changing market conditions, forecasting and identifying short-term and long-term opportunities. These new algorithms based on pattern analysis seek out the best leveraging for a particular market and act on it.
Enter Policing the globe with ai
The Guardian recently had a report on AI algorithms that are now being used for policing populations.
Crush stands for “Criminal Reduction Utilising Statistical History”. Translated, it means predictive policing. Or, more accurately, police officers guided by algorithms. A team of criminologists and data scientists at the University of Memphis first developed the technique using IBM predictive analytics software. Put simply, they compiled crime statistics from across the city over time and overlaid it with other datasets – social housing maps, outside temperatures etc – then instructed algorithms to search for correlations in the data to identify crime “hot spots”. The police then flooded those areas with highly targeted patrols.
“It’s putting the right people in the right places on the right day at the right time,” said Dr Richard Janikowski, an associate professor in the department of criminology and criminal justice at the University of Memphis, when the scheme launched. But not everyone is comfortable with the idea. Some critics have dubbed it “Minority Report” policing, in reference to the sci-fi film in which psychics are used to guide a “PreCrime” police unit.
The use of algorithms in policing is one example of their increasing influence on our lives. And, as their ubiquity spreads, so too does the debate around whether we should allow ourselves to become so reliant on them – and who, if anyone, is policing their use. Such concerns were sharpened further by the continuing revelations about how the US National Security Agency (NSA) has been using algorithms to help it interpret the colossal amounts of data it has collected from its covert dragnet of international telecommunications. (How algorithms rule the world by Leo Hickman Guardian)
The idea that the world’s financial markets – and, hence, the wellbeing of our pensions, shareholdings, savings etc – are now largely determined by algorithmic vagaries is unsettling enough for some. But, as the NSA revelations exposed, the bigger questions surrounding algorithms centre on governance and privacy. How are they being used to access and interpret “our” data? And by whom?
Dr Ian Brown, the associate director of Oxford University’s Cyber Security Centre, says we all urgently need to consider the implications of allowing commercial interests and governments to use algorithms to analyse our habits: “Most of us assume that ‘big data’ is munificent. The laws in the US and UK say that much of this [the NSA revelations] is allowed, it’s just that most people don’t realise yet. But there is a big question about oversight. We now spend so much of our time online that we are creating huge data-mining opportunities.”
As one analyst suggested we should not automatically see algorithms as a malign influence on our lives, but we should debate their ubiquity and their wide range of uses. “We’re already halfway towards a world where algorithms run nearly everything. As their power intensifies, wealth will concentrate towards them. They will ensure the 1%-99% divide gets larger. If you’re not part of the class attached to algorithms, then you will struggle. The reason why there is no popular outrage about Wall Street being run by algorithms is because most people don’t yet know or understand it.” (Guardian)
Yet, China itself has become the number one buyer of surveillance technology today. This year, China withheld figures for what it would be spending on “stability maintenance,” but by the end of 2014, China will become the world’s number one market for surveillance equipment and technology, surpassing the U.S., according to a report from the Homeland Security Research Corporation.
If China is a country that is at war with its own citizens, what does that mean for the future weapons that she might deploy?
Here’s where China may borrow from us. One of the most important new weapons that police forces around the country are experimenting with is so called predictive policing—the use of data and statistics to determine the location, and possibly even the perpetrators of crime. It’s a trend that’s sweeping police departments across America. Reporters at San Francisco Weekly have shown that a lot of today’s predictive policing marketers are peddling products that don’t meet the expectations that those marketers are advertising. But the thinking behind the concept is still sound, and there are some key cases where predictive policing has proven to be a force multiplier.
As defense.com reports it in: Predictive Policing, Past, Present and Future
In 1994, newly appointed New York City police commissioner William Bratton took it upon himself to “strategically re-engineer” New York’s Police Department. Jack Maple, who was working with the New York transit authority at the time, convinced Bratton that up‐to‐the‐minute data, city‐wide crime statistics and crime mapping would enable the police to pre-emptively deploy police officers into areas where crime was about to go up.
This first experiment in what is now somewhat commonly called “predictive policing” decreased the crime rate in New York City by 37 percent in three years. But Bratton’s re-engineering went beyond money-balling crime. He also put into place zero‐tolerance and stop‐and‐frisk policies that have been deemed unconstitutional in Federal Court.
Predictive policing will make its way into the operations of more and more police departments around the country.
As a tactic, predictive policing has been used to preempt peaceful civil demonstrations, like the 2003 World Trade Organization protests that took place in Miami, Florida. Today, police around the country routinely employ espionage tactics to predict and preempt spontaneous punk and dance shows (under the expansive and poorly written 2003 RAVE Act, sponsored by Joe Biden, which can be used to arrest concert promoters for the behavior of their patrons). If you’re a police chief or mayor, preempting a protest is less risky than trying to disrupt one in progress, especially in an age where the kids you will be pepper spraying carry TV studios in their pockets. The combination of police armed with military equipment using big data analytics not just to break up street demonstrations, but to keep them from ever happening is a trend that’s invisibly increasing.
Isn’t this exactly what the elite want? An ignorant work-force? Pre-dictive and preemptive policing: using networks, mobile phones… big data to analyze for patterns? Arrests before anything happens. A Police State without even an acknowledgement of crime?
AI and machinic intelligences will slowly be replacing knowledge-workers (cognitariat) in the near future. Sooner or later the machinic algorithms will run wild in the jungles of the Dark Net, feeding on clouds and probability equations of avatar humans enclosing the electronic commons with fractured zoos to ponder the stupidity of their own cannibal greed. Left out to pasture by the very systems that once brought them profits beyond their wildest imaginings, our new econo-avatar cousins of the wires will continue churning out profit till the splice everything into the cyber-DNA of some strange assemblage – a virtual city of code pirates and surf-warriors. Then we will have what many academics these days want anyway: a world without humans – a mini-world of lubricated light-farers roaming the rainbow seas of an endless bitstream narrative of commerce and desire. A machinic world we ourselves once constructed from which we have been banned and made disposable by the very processes we once set free.
Of course I jest and realize such satiric jibes are but the strange premonitions of a science-fictional future dreamed up by an economics of capitalism totally out of control. But, still, one wonders…
1. Patterson, Scott (2012-06-12). Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market (p. 281). Crown Publishing Group. Kindle Edition.