Algos

I remember watching Kevin Slavin’s talk last year. After the Flash Crash of 2010, before the Knight Capital fiasco of 2012. I am still very surprised as to what seems to be the lack of public awareness regarding what can now be called the pervasive use of algorithms in decision-making. It’s quite obvious that what visionaries of the 20^th century were referring to as ‘Artificial Intelligence’ will finally manifest itself as ‘augmented intelligence’, in the form of high-frequency trading, for example.

“No place for a human”…

Raging Bulls: How Wall Street Got Addicted to Light-Speed Trading

BY JERRY ADLER 08.03.12

http://www.wired.com/business/2012/08/ff_wallstreet_trading/all/

examines how Wall St. got to the point where flash failures come with increasing frequency, and how much farther traders seem willing to go in pursuit of ever-greater speed.

clipped:

“For the first time in financial history, machines can execute trades far faster than humans can intervene,” said Andrew Haldane, a regulatory official with the Bank of England, at another recent conference. “That gap is set to widen.”

This movement has been gaining momentum for more than a decade. Human beings who make investment decisions based on their assessment of the economy and on the prospects for individual companies are retreating. Computers—acting on computer-generated market trend data and even newsfeeds, communicating only with one another—have taken up the slack.

One common algo strategy is to look for pairs of stocks whose prices are historically correlated. The canonical examples are the stock prices of oil companies, which rise with the price of crude, and those of airlines, which do the opposite. But they may not move all at the same time, so one strategy is to buy or sell the one that’s trailing and wait for it to catch up. Similarly, “derivative” equities such as options and futures may get out of equilibrium with the underlying stocks. Some algorithms are “market makers” in a stock—they attempt to buy at a low bid price and quickly sell at a slightly higher asking price, pocketing the difference, or spread. The people who did this used to be called specialists, and it was a nice living when spreads were an eighth of a dollar. Since the New York Stock Exchange instituted “decimalization” in 2001, spreads have gone down to a penny or two, meaning you have to trade a lot more stock, a lot faster, to make the same amount of money. It’s no place for a human being.

“By the time the ordinary investor sees a quote, it’s like looking at a star that burned out 50,000 years ago,” says Sal Arnuk, a partner in Themis Trading and coauthor of a book critical of high-frequency trading titled Broken Markets. By some estimates, 90 percent of quotes on the major exchanges are canceled before execution. Many of them were never meant to be executed; they are there to test the market, to confuse or subvert competing algorithms, or to slow trading in a stock by clogging the system—a practice known as quote stuffing. It may even be a different stock, but one whose trades are handled on the same server. On the Internet, this is called a denial-of-service attack, and it’s a crime. Among quants, it’s considered at most bad manners.

And it’s not just the words of central bankers that matter in this world. Almost any kind of data that in any way bears on economic activity, no matter at what remove, is being aggregated and tested for its potential impact on stock prices. GPS data, showing concentrations of cell phone users in malls or office buildings, has been used to get a real-time read on economic activity. Even the most ephemeral shards in the digital scrap heap, old Twitter posts, are proving of value, if you can get your hands on enough of them. For their starkly titled paper “Twitter Mood Predicts the Stock Market,” Johan Bollen, an informatics researcher at Indiana University, and two colleagues collected almost 10 million tweets from 2008, aggregating phrases that indicate emotional state and analyzing them along dimensions of feeling such as “calm,” “alert,” “sure,” “vital” and “happy.” They then looked for correlations with stock prices and discovered that a surge in “calm” sentiment reliably predicted an increase in the Dow Jones Industrial Average two to six days later. No one, including Bollen, knows what this means or why it should be so, but if quants had a coat of arms, it would say, “If it works, trade on it.”

further:

A New Kind of Socio-Inspired Technology

Edge conversation, Dirk Helbing [6.19.12]

"...computers of tomorrow are basically creating artificial social systems. Just take financial trading today, it's done by the most powerful computers. These computers are creating a view of the environment; in this case the financial world. They're making projections into the future. They're communicating with each other. They have really many features of humans. And that basically establishes an artificial society, which means also we may have all the problems that we are facing in society if we don't design these systems well. The flash crash is just one of those examples that shows that, if many of those components — the computers in this case — interact with each other, then some surprising effects can happen. And in that case, $600 billion were actually evaporating within 20 minutes."

http://edge.org/conversation/a-new-kind-of-social-inspired-technology

Kevin Slavin: How algorithms shape our world

FILMED JUL 2011 • POSTED JUL 2011 • TEDGlobal 2011

Kevin Slavin argues that we're living in a world designed for -- and increasingly controlled by -- algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can't understand, with implications we can't control.

http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world.html

NYSE looks into 'irregular trading' in 140 stocks

1 August 2012

http://www.bbc.co.uk/news/business-19087748

Decision-making, Public Oversight, and Privatization

Aug 2012

http://networkaddress.blogspot.com/2012/08/decision-making-public-oversight-and.html


Math algorithm tracks crime, rumours, epidemics to source
10 August 2012
http://phys.org/news/2012-08-math-algorithm-tracks-crime-rumours.html

U.S. Cities Relying on Precog Software to Predict Murder
KIM ZETTER 01.10.13
The software parses about two dozen variables, including criminal record and geographic location. The type of crime and the age at which it was committed, however, turned out to be two of the most predictive variables.
“People assume that if someone murdered then they will murder in the future,” Berk told the news outlet. “But what really matters is what that person did as a young individual. If they committed armed robbery at age 14 that’s a good predictor. If they committed the same crime at age 30, that doesn’t predict very much.”
-Richard Berk, criminologist at the University of Pennsylvania who developed the algorithm
http://www.wired.com/threatlevel/2013/01/precog-software-predicts-crime/

Supercomputers could generate warnings for stock crashes
Lisa M. Krieger, Apr 19, 2013
http://phys.org/news/2013-04-supercomputers-stock.html

High-frequency traders face speed limit on deals
29 April 2013

High-frequency trading tactic lowers investor profits

Jun 17, 2013

"Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model."

Provided by University of Michigan

[pdf]

Doing the math 'predicts' which movies will be box office hits
Aug 22, 2013
based on an analysis of the activity on Wikipedia pages: number of page views for the movie's article, number of human editors contributing to the article, number of edits made, and diversity of online users

New Algorithm Can Spot the Bots in Your Twitter Feed

Lee Simmons, Wired, 10.17.13

I Liked Everything I Saw on Facebook for Two Days
Wired, Aug 2014

[...] My News Feed took on an entirely new character in a surprisingly short amount of time. After checking in and liking a bunch of stuff over the course of an hour, there were no human beings in my feed anymore. It became about brands and messaging, rather than humans with messages.

[...] By the next morning, the items in my News Feed had moved very, very far to the right. I’m offered the chance to like the 2nd Amendment and some sort of anti-immigrant page. I like them both. I like Ted Cruz. I like Rick Perry. The Conservative Tribune comes up again, and again, and again in my News Feed. I get to learn its very particular syntax.

[...] The next morning, my friend Helena sent me a message. “My fb feed is literally full of articles you like, it’s kind of funny,” she says. “No friend stuff, just Honan likes.” I replied with a thumbs up. This continued throughout the experiment. When I posted a status update to Facebook just saying “I like you,” I heard from numerous people that my weirdo activity had been overrunning their feeds. “My newsfeed is 70 percent things Mat has liked,” noted my pal Heather.

Patents provide insight on Wall Street 'technology arms race'

phys.org, Jan 2015

"A 'technology arms race' is well underway as firms vie to shave even more time off trading and maintain their competitive edge. But it's not just about trading speed. We're seeing technology used more when firms are first issuing securities and even the use of neural networks in portfolio selection."