{"id":456,"date":"2012-03-26T10:00:00","date_gmt":"2012-03-26T10:00:00","guid":{"rendered":"https:\/\/blog.alexanderhiggins.com\/?p=456"},"modified":"2024-04-24T12:55:44","modified_gmt":"2024-04-24T12:55:44","slug":"study-18520-cases-hft-stock-market-manipulation-5-years-105781","status":"publish","type":"post","link":"https:\/\/blog.alexanderhiggins.com\/economy\/study-18520-cases-hft-stock-market-manipulation-5-years-105781\/","title":{"rendered":"Study: 18,520 Cases Of Black Swan Stock Market Manipulation In Last 5 Years"},"content":{"rendered":"
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Researchers planning a system to completely computerize stock market trading uncovered 18,520 instances of stock market manipulation between 2006 and 2011.<\/h3>\n

If you are walking down the streets of New York City and you suddenly see a crowd rushing your way what to you do?<\/p>\n

Most likely you will assume the crowd is in panic because they fear for their lives and join them in running to save your own life and ask questions later.<\/p>\n

The same kind of herd mentality exists on Wall Street.<\/p>\n

With millions of dollars on the line the shares of the stock you are holding start to plummet into a free fall.<\/p>\n

The commotion grabs your attention and only thing you can decipher is a room full of people shouting at the market maker to sell, sell, sell.<\/p>\n

When everyone else in the room is dumping unloading their positions as the price tanks, you assume there must be some horrible news and jump in on the frenzy and dump your shares to limit your losses and ask what the bad news was after the fact.<\/p>\n

Mix tens of thousands of start of the art high-speed computers into the equation, with the ability to execute tens of thousands of trades per second and running highly complex algorithms designed to prey on this herd mentality, and you suddenly enter the shadowy underworld of Wall Street known as High Frequency Trading (HFT).<\/p>\n

These trading bots take advantage small differences in prices on the markets along with flaws in the market exchanges while roaming in a realm were executing a trade a few milliseconds faster than the next guy is equivalent to humans having insider information days in advance.<\/p>\n

As much as these automated borgs enjoy snacking on the savory taste of the retail investor\u2019s life savings the real feast comes from cannibalizing their own kind.<\/p>\n

By flooding the system with thousands of sell orders, often spoofed or cancelled,\u00a0 in just mere milliseconds, they trigger other bots to into panic and drive the price of whatever shares they target either up or down.<\/p>\n

In less time than a human can even react these bots earn their wage for the day and have changed the trajectory of the the shares they have targeted in a manner invisible to the human eye.<\/p>\n

Only by using other computers and examining the sequence of trades in the aftermath can one detect the ulfrafast financial black swan event that has just occurred.<\/p>\n

On March 23rd, the real-time data feed company NANAX did just that in catching high frequency traders using the same sophisticated computer trading algorithms to manipulate and crash silver prices.<\/p>\n

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Traders Caught Using NASDAQ Exploit To Manipulate Silver Prices<\/a><\/h2>\n

Hedge funds caught red-handed using flaws in the NASDAQ market exchange to manipulate the price silver using high-speed computer trading algorithms.<\/h3>\n

Despite documented flaws in the way market exchanges handles high speed trades at high volumes<\/strong><\/a>, which are attributed to the DOW flash crash, electronic trading systems still have not been patched to fix the issue.<\/p>\n

The vulnerability stems from bad timestamps assigned to orders when traders flood the system which causes trades to executed on quotes before they even yet exist in the system.<\/p>\n

Earlier today high frequency traders were caught in act by NANEX taking advantage of the flaw to exploit the NASDAQ silver ETF by barraging the system with a whopping 75,000 trades per second.<\/p>\n

The exploit then triggered other trading robots to execute trades based on the high volume of trades quickly plummeting silver price.<\/p>\n

[\u2026]<\/p>\n<\/blockquote>\n

While I was under the impression that such things did indeed happen I assumed that they were a rare exception.<\/p>\n

I assumed only the greediest or most desperate traders would be foolish enough to risk getting caught engaging in such behavior, take for example the case of JP Morgan whose short position on silver would bankrupt the company if they allowed the price of silver to rise to high.<\/p>\n

However, I was completely taken off guard when Financial Sense followed up on March 23 silver crash and reported on an interview with Ted Butler.<\/p>\n

In the interview, Butler explains that the practices is not only limited to the silver market but,\u00a0 quite to contrary, all markets are being manipulated by High Frequency Traders and very frequently.<\/p>\n

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Silver Manipulation Caught in the Act; HFT Swamps NASDAQ with 75K SLV Sell Orders Per Second<\/h2>\n

Ironically, just days after noted analyst Ted Butler came on the show<\/a><\/strong> to explain how silver and other markets are manipulated through the use of high frequency trading, the real-time data feed company, Nanex<\/a><\/strong>, showed how the silver ETF (SLV) was forced downwards by a rapid number of machine-generated quotes exceeding a rate of 75,000 per second. Before you start to think that this was merely a bunch of people hitting the sell button all at once, consider this: They were all launched within the space of 25 milliseconds\u2014ten times faster than you and I can blink!<\/p>\n

Here\u2019s a chart of the second by second market activity in SLV where you can see the massive lightning-quick spike occurring at 13:22:33.<\/p>\n

\"slv<\/a>
\nSource: Nanex<\/p>\n

Ted Butler Explains the Whole Process<\/h4>\n
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\u201cWhat\u2019s happening is that these commercials [or large traders], through HFT, can set the price suddenly down. It didn\u2019t go down because there was massive selling from the commercials, they just set the price down. They know how to do it with their computers by putting in actual orders, and faking it, and spoofing, canceling them right away; but what happens is when the price moves down then the selling comes, which is the intended effect and result. Commercials basically put the price down in order to set off stops because everybody seems to be some type of technical trader in the market that reacts to prices.\u201d<\/p>\n

(Click here for interview and full transcript<\/a><\/strong>)<\/p>\n<\/blockquote>\n

[\u2026]<\/p>\n

Read The Rest\u2026<\/strong><\/a><\/p>\n<\/blockquote>\n

In the process of pointing the widespread and rampant manipulation of Wall Street stocks, the article references a newly released study Financial Black Swans Driven by Ultrafast Machine Ecology<\/a><\/em><\/strong> which truly provides some stunning insight.<\/p>\n

The study \u201clooked at 600 different markets around the world and found that these sort of events happen routinely. Over the most recent five years of market data analyzed, 18,520 crashes and spikes occurred at a speed far exceeding human origin.\u201d<\/p>\n

The report documented the\u00a018,520 \u201culfrafast financial black swans\u201d during the time period between 2006 and 2011.<\/p>\n

Given there are approximately 252 trading days in a year, that equates to an average of\u00a0 approximately 14.7 instances of stock market manipulation per day.<\/p>\n

With the trading day being 6 and half hours these stock manipulations are occurring, on average, 2.26 times per hour.<\/p>\n

Almost ironically, the purpose of the paper is to propose a system to control these rogue market manipulations so humans can be eliminated from the stock trading system and the entire platform can be placed under the control of automated computers systems.<\/p>\n

From the study:<\/p>\n

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Financial black swans driven by ultrafast machine ecology<\/h2>\n

Neil Johnson1<\/sup>, Guannan Zhao1, Eric Hunsader2<\/sup>, Jing Meng1<\/sup>, Amith Ravindar1<\/sup>, Spencer Carran1<\/sup> and
\nBrian Tivnan3,4<\/sup><\/p>\n

1<\/sup> Physics Department, University of Miami, Coral Gables, Florida 33124, U.S.A.
\n2<\/sup> Nanex LLC, Evanston, Illinois, U.S.A.
\n3<\/sup> The MITRE Corporation, McLean, VA 22102, U.S.A.
\n4<\/sup> Complex Systems Center, University of Vermont, Burlington, VT 05405, U.S.A.<\/p>\n

ABSTRACT<\/h3>\n

Society\u2019s drive toward ever faster socio-technical systems1-3<\/sup>, means that there is an urgent need to understand the threat from \u2018black swan\u2019 extreme events that might emerge4-19<\/sup>. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash4<\/sup>. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly20,21<\/sup>. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system\u2019s internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast \u2018fractures\u2019 and the slow \u2018breaking\u2019 of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.<\/p>\n<\/blockquote>\n

From the study:<\/p>\n

\u00a0<\/p>\n

Figure 1: Traded price during black swan events.<\/strong><\/h3>\n
\"HFT<\/a><\/p>\n

HFT Stock Market Manipulation \u2013 Traded price during black swan events<\/p>\n<\/div>\n


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(A) Crash. Stock symbol is ABK. Date is 11\/04\/2009. Number of sequential down ticks is 20. Price change is -0.22.\u00a0 Duration is 25ms (i.e. 0.025 seconds). Percentage price change downwards is 14% (i.e. crash magnitude is 14%).<\/p>\n

(B) Spike. Stock symbol is SMCI. Date is 10\/01\/2010. Number of sequential up ticks is 31. Price change is +2.75. Duration is 25ms (i.e. 0.025 seconds). Percentage price change upwards is 26% (i.e. spike magnitude is 26%). Dots in price chart are sized according to size of trade.<\/p>\n

(C) Cumulative number of crashes (red) and spikes (blue) compared to overall stock market index (Standard & Poor\u2019s 500) in black, showing daily close data from 3 Jan 2006 until 3 Feb 2011.<\/p>\n

\u00a0<\/p>\n

\u00a0<\/p>\n

Figure 2: Empirical transition in size distribution for black swans with duration above threshold r, as function of r.<\/strong><\/h3>\n
\"HFT<\/a><\/p>\n

HFT Stock Market Manipulation \u2013 Empirical transition in size distribution for black swans<\/p>\n<\/div>\n

Top: Scale of times. 650 ms is the time for chess grandmaster to discern King is in checkmate.<\/p>\n

Plots show the results of the best-fit power-law exponent (black) and goodness-of-fit (blue) to the distributions for size of crashes and spikes separately, as shown in the inset schematic.<\/p>\n

\u00a0<\/p>\n

Figure 3: Theoretical transition.<\/strong><\/p>\n

\"HFT<\/a><\/p>\n

HFT Stock Market Manipulation \u2013 Theoretical transition.<\/p>\n<\/div>\n

Model output for the two regimes of strategy distribution among agents ( n<\/em> = 2m+1<\/sup> \/ N) together with timescales from Fig. 2 (top).<\/p>\n

n < 1 implies many agents per strategy, hence large crowding which produces frequent, large and abrupt price-changes, i.e. high number of short-duration (<< 1 second) black swans, as observed empirically.<\/p>\n

n> 1 implies very few, if any, agents per strategy, hence small crowding. Therefore large changes are rarer and last longer, i.e. low number of longer-duration black swans, as observed empirically.<\/p>\n

\u00a0<\/p>\n

Figure 4: Prediction and mitigation of ultrafast black swans.<\/strong><\/h3>\n
\"HFT<\/a><\/p>\n

HFT Stock Market Manipulation \u2013 Prediction and mitigation of ultrafast black swans<\/p>\n<\/div>\n

(A) Theoretical black swan produced by our model, similar to Fig. 1A on an expanded timescale. Below are model\u2019s node weights for m = 3 as a function of time, with blue and red denoting the weight values (see text and SI). The more blue the weight on node 0 (or the more red the weight on node 7) the more likely that a large price-drop (or rise) will occur when the model\u2019s trajectory (green curve) hits that node.<\/p>\n

(B) Nodes shown as their binary and decimal equivalents (e.g. 000 is 0 in decimal) for m = 3. Red (blue) arrows represent transitions generating a bit-string update of 0 (1) and hence a price drop (rise).<\/p>\n

(C) Proposed mitigation scheme. Blue boxes show histogram of strategy possession for existing population, as an \u2018occupancy\u2019 in a two-dimensional space spanned by the possible strategy combinations for s = 2 strategies with m = 2. Red boxes are inserted agents, which can induce a steering effect to avoid the crash29 (red dotted line in Fig. 4A showing the mean of the resulting price movement). See text for discussion.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Researchers planning a system to completely computerize stock market trading uncovered 18,520 instances of stock market manipulation between 2006 and 2011. If you are walking down the streets of New York City and you suddenly see a crowd rushing your way what to you do? Most likely you will assume the crowd is in panic … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/posts\/456"}],"collection":[{"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/comments?post=456"}],"version-history":[{"count":2,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/posts\/456\/revisions"}],"predecessor-version":[{"id":1054,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/posts\/456\/revisions\/1054"}],"wp:attachment":[{"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/media?parent=456"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/categories?post=456"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.alexanderhiggins.com\/wp-json\/wp\/v2\/tags?post=456"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}