capital market disruptions - babson collegefaculty.babson.edu/goldstein/teaching... · knight...

Sanjay Dasari

Thomas Gallagher

Aangi Kothari

Sean Wright

December 9, 2013

FIN3560 Section 2

Capital Market Disruptions

Source: iStockphoto

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Table of Contents

Table of Contents Executive Summary ............................................................................................................................................ 2

Introduction to Markets ..................................................................................................................................... 3

Introduction to Market Disruptions & Outcomes .............................................................................................. 3

“Flash Crash” May 2010 ..................................................................................................................................... 5

Twitter caused Market Disruption April 23rd, 2013 ............................................................................................ 7

August 22nd, 2013 Nasdaq Halts Trading Due To Computer Glitch .................................................................... 9

Regression Analysis .......................................................................................................................................... 11

Moving Forward ............................................................................................................................................... 13

Conclusion ........................................................................................................................................................ 15

Works Cited ...................................................................................................................................................... 16

Exhibits: ............................................................................................................................................................ 18

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the completion of this work.”

Sanjay Dasari Thomas Gallagher

Aangi Kothari Sean Wright

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Executive Summary Markets are places where trading on securities takes place both physically and electronically. Virtual trading

companies that trade electronically have recently started using High Frequency trading systems that are based on

complex algorithms and a network of computer software. These algorithms constantly scan the internet for key

words and make several million trades based on that information. The speed with which they makes these trades

and the high turnover rate of their holdings yields low profits per trade, but usually compounds to give bigger

profits.

Market disruptions occur from malfunctions derived by both human error and computer error that cause

disruptions within the trading markets. For the purpose of this paper, we focused on two human errors: Flash

Crash of 2010 and AP Twitter hack of 2013. These two human errors were compared to the August 2013

Disruption which was caused by a computer error. There are three main reasons why these market disruptions

occur: a rise in trading platforms, interconnectivity, and advances in technology. To avoid these disruptions, the

SEC decided to implement Kill Switches, in case of a software error, and Circuit Breakers, to halt trading.

The Flash Crash of 2010 cleared around $1 trillion of market value around 2:45 pm. The crash was provoked by a

trader who sold E-Mini Contracts worth around an estimated $4.1 billion through automated trading. The

algorithm, which only focused on volume, caused the execution of this order to occur over 20 minutes rather than

over a few hours. Simultaneously, buyers of E-Mini Contracts were selling them in equity markets, which caused

the buying and reselling of high frequency trades to occur rapidly.

The Associated Press’ Twitter account was hacked in August 2013, with a tweet that mentioned an attack on the

White House and injury to President Barack Obama. High Frequency trading systems noticed this information and

within 15 seconds trading millions of dollars worth of holdings, leading to a severe dip in the market. The stocks

would rebound that same day, but human error and lack of regulation allowed for such volatility.

The Nasdaq Exchange halted trading on August 22nd

, 2013, for more than three hours when it encountered a

computer glitch in the securities information processor data feed. The computer glitch was resolved before the end

of the day, and no other problems were attributed to the glitch the next few trading days, but confidence in the

fragile systems that support computerized trading was still greatly damaged.

We utilized a regression analysis to determine if the Dow Jones Industrial Average and the S&P 500 react

differently to market disruptions. Our regressions indicate that the indices responded differently to the Flash

Crash, but moved in almost perfect unison in response to the AP Twitter Hack. We also ran regressions to

determine if there was a higher variation in index returns after each disruption compared to before each disruption.

In addition, we ran regressions to compare the effects that these glitches had on the market and investor

confidence through volatility (VIX) analysis. Our regressions determined that after the AP Twitter hack, the

variation in index returns decreased.

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Introduction to Markets Markets are places where buyers and sellers of marketable securities can come together and exchange at a

mutually agreeable price. The most common type of securities exchanged via these markets is stocks,

predominantly of the secondary market. Today, this is possible both in physical exchanges like the New York

Stock Exchange and through online virtual trading systems that make trades using a complex network of computer

software.

Many companies that specialize in stock market trading, speculation and brokering usually run on High-frequency

trading systems that are based on algorithms. They use these algorithms to compute and execute a high number of

trades over a short period of time. As of 2009, approximately 60-73% of all US equity trading volume was

accounted for by High-frequency trading firms. That number fell to about 50% in 2012 due to concerns about risk

and volatility.1

The volatility is most noticeable in cases like Knight Capital. Dubbed “The Knightmare,” the bankruptcy of the

firm was caused when new software they installed experienced glitches and aggressively started buying shares

that the firm would not have bought otherwise. “Knight accidentally bought and sold $7 billion worth of shares”

in 45 minutes, and with each sale the algorithm that malfunctioned kept raising the prices.2 The end result was that

Knight Capital lost 40% of the company’s value undoing what the glitch in the software caused.

For the purposes of this paper, we will be focusing mainly on the secondary market rather than the primary

market.

Introduction to Market Disruptions & Outcomes Software related capital market disruptions are caused by both human and computer error, interrupting investing

and trading on exchanges. For this paper, we looked at two examples of human error in the capital markets,

namely the Flash Crash of May 6th

, 2010 and the Twitter market disruption in April 23rd

, 2013, and one example

of pure computer error on August 22nd

, 2013. “The market has become so complex and so intertwined," said

Dennis Dick, a trader and market structure consultant at Premarketinfo.com, "one little hiccup and everything

1 Lati, Rob. "The Real Story of Trading Software Espionage." Wall Street and Technology. N.p., 10 July 2009. Web. 01 Dec. 2013.

<http://www.wallstreetandtech.com/trading-technology/the-real-story-of-trading-software-espio/218401501>. 2 Phillips, Matthew. "How the Robots Lost: High-Frequency Trading's Rise and Fall."Bloomberg Businessweek Technology. N.p., 06

June 2013. Web. 2 Dec. 2013. <http://www.businessweek.com/articles/2013-06-06/how-the-robots-lost-high-frequency-tradings-rise-and-fall>

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goes down.”3 Today, there are a large variety of trading platforms that need to coordinate and be tightly

interlinked in order to work efficiently and decrease the chance of having trading glitches. Not only is there the

issue of losing capital, but also the issue of traders and investors losing trust and confidence in the competency of

financial markets. There have been increasing instances of computational operations issues over the past 18

months (Exhibit 1). In fact, S&P cited 23 instances of operations issues at global exchanges from March 2012 to

March 2013.4

There are three main causes for the increase in market disruptions: a rise in the number of trading platforms,

interconnectivity between markets, and increasing complexity in the technology utilized. Currently, there are

around 16 securities exchanges that are Securities and Exchanges Commission (SEC) registered and more than 50

ATSs.5 Before 2005, the NYSE and NASDAQ floors dominated most of the equities market (Exhibit 2). Now,

the NYSE has far less traders executing trades on its floor and trade volume is overwhelmingly created by

algorithmic programs that constitute high frequency trading. Experts familiar with the workings of trading

algorithms explain how, “The algorithms look at a price, and then they look at a fixed increment of a price, to see

whether it is decreasing or increasing. Based on that, the algorithm executed a certain amount of trades.”6 The

increase in electronically controlled securities exchanges has caused fragmentation which has “led investors to

rely on interconnectivity among a myriad of fragmented pools of liquidity.”7 The interconnectivity of the

exchanges evident today provides the possibility of a domino effect where a stock market disruption in one

exchange can corrupt trading in other stock exchanges. Exchanges are heavily investing and relying on the speed

and functionality of trading platforms in order to stay competitive. High speed trading accounts for around 50% of

stock trading volume in the US. However, the faster trade speed and the greater interconnectivity are multiplying

the scope and impact of operational glitches that occur.

Furthermore, one of every three trades occurs in dark pools or internal trading channels, which further amplify the

problem of transparency within the financial markets. The increasing frequency of trading glitches has caused

tighter regulation and oversight by the SEC, which launched an investigation concerning the relationship between

3 Farrell , Maureen. "Trading glitches a sad new market reality." CNNMoney. N.p., 22 Aug. 2013. Web. 24 Nov. 2013.

<http://money.cnn.com/2013/08/22/investing/nasdaq-trading-glitch/>. 4 Avery, Helen. "Exchanges: Technical glitches threaten stock exchanges." International banking, finance, capital markets news &

analysis | Euromoney magazine. N.p., Oct. 2013. Web. 24 Nov. 2013. 5 ATS stands for Alternative Trading Systems; examples include Call Markets and Dark Pools 6 "Trading 'Kill Switches' Can Avoid Glitches But Need To Be Used With Caution, Experts Say."The Huffington Post. N.p.,

2 Oct. 2012. Web. 24 Nov. 2013. <http://www.huffingtonpost.com/2012/10/03/trading-kill-swtiches-caution-

glitches_n_1934127.html>. 7 "Exchanges' Technical Glitches." S&P. Standard & Poor’s Financial Services LLC, 19 Sept. 2013. Web. 07 Dec. 2013.

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exchanges and high frequency trading firms. The Regulation Systems Compliance and Integrity rule was also

proposed in March 2013 by the SEC. This regulation ensures that the trading systems have the “integrity,

resilience, availability, and capacity to maintain their operational capability”. Members are also required to

schedule test their operations through integrated backup systems. In addition, notices are required regarding any

system disruptions or compliance issues that take place.8

On September 13, 2013, the SEC met with executives from Financial Industry Regulation Authority and Options

Clearing Corporation, among others to discuss the increasing frequency of the exchanges’ operational issues.

Some topics that were discussed in the meeting include oversight of key data feeds, testing standards, and kill

switches.” Kill switches would be used to shut down stock trading if a software error is detected. However, having

a kill switch could increase panic across other traders and investors that may not immediately understand the

reason for the cease in trading. When the market would reopen, there would be a great lack of liquidity.9

Furthermore, circuit breakers were also introduced to halt trading in a case where an index falls below a certain

percentage threshold. The New York Stock-by-Stock circuit breaker was established on June 10, 2010, directly

after the flash crash, which enacts a trading halt in individual stocks which price moves 10% or more in a five

minute period.10

The circuit breakers were introduced in order to decrease unusual volatility in trading, such as the

Flash Crash of May 2010.

“Flash Crash” May 2010 The notorious “Flash Crash” of May 6

th, 2010 caused the Dow Jones Industrial Average to plummet nearly 1000

points, clearing about $1 trillion in market value.11

On May 6, 2010, “over 20,000 trades across more than 300

securities executed at prices more than 60% away from their values just moments before. By the end of the day,

major futures and equities indices “recovered” to close at losses of about 3% from the prior day.”12

Earlier that

day, the Gulf Oil Spill and the European debt crisis caused the premium to rise for buying protection against the

8 "Regulation Systems Compliance and Integrity." U.S. Securities and Exchange Commission | Homepage. Release No. 34-69077;

File No. S7-01-13, n.d. Web. 24 Nov. 2013. <http://www.sec.gov/rules/proposed/2013/34-69077.pdf>. 9 "Is ‘Kill Switch’ the Solution to Market Trading Glitches?" Fox Business. N.p., 19 Dec. 2012. Web. 24 Nov. 2013.

<http://video.foxbusiness.com/v/2046252892001/is-kill-switch-the-solution-to-market-trading-glitches/>. 10 "Investor Bulletin: New Stock-by-Stock Circuit Breakers." U.S. Securities and Exchange Commission | Homepage. N.p.,

9 Aug. 2011. Web. 24 Nov. 2013. <http://www.sec.gov/investor/alerts/circuitbreakers.htm>. 11 Rooney, Ben. "Trading software sparked flash crash." CNNMoney - Business, financial and personal finance news. N.p.,

1 Oct. 2010. Web. 24 Nov. 2013. <http://money.cnn.com/2010/10/01/markets/SEC_CFTC_flash_crash/>. 12

FINDINGS REGARDING THE MARKET EVENTS OF MAY 6, 2010. U.S. Commodity Futures Trading Commission & U.S.

Securities & Exchange Commission, 2010. REPORT OF THE STAFFS OF THE CFTC AND SEC TO THE JOINT

ADVISORY COMMITTEE ON EMERGING REGULATORY ISSUES. Web. 24 Nov. 2013.

<http://www.sec.gov/news/studies/2010/marketevents-report.pdf>.

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risk of the Greek government defaulting on its debt, increasing volatility in the market. Around an hour before the

actual crash, the Euro began to quickly decline in value against the dollar and yen.

The declining Euro caused a rise in volatility in the markets and by around 2:30 pm, the S&P 500’s volatility

index (VIX) was up 22.5% from the opening value, which caused investors to sell; ten year treasury yields began

to decline, causing the price to rise. Simultaneously, buyer side liquidity for the E-Mini S&P 500 futures contracts

and S&P 500 SPDR ETF fell 55% and 20% respectively. Soon after, a trader decided to sell $4.1 billion worth of

E-Mini Contracts through automated trading as a response to the increasing volatility and decreasing liquidity of

the market. The “sell algorithm” that executed the traders sell-action only targeted trading volume and disregarded

price and time, causing the execution of the sell trade to transpire within 20 minutes, rather than spreading the

trades out over a couple of hours, as a normal order would have (Exhibit 3). At the same time, those traders that

were buying the E-Mini Contracts were selling it for equal amounts in the equity markets. This caused the buying

and reselling of high frequency trades to occur rapidly. Between 2:45:13 and 2:45:27, high frequency trades

accounted for about 49 percent of the total trading volume in the market (Exhibit 4). At this time, buy-side market

depth in the E-Mini fell to about $58 million, less than 1% of its depth from that morning’s level (Exhibit 5).

Only 35,000 of the intended 75,000 E-Mini contracts were sold. Dow Jones dropped 9.2% before recovery8

(Exhibit 6). The built-in automated systems paused trading after the sudden price decline. Market makers and

liquidity providers reacted to the pause and decided to either widen their quotes spreads, reduce offered liquidity,

return back to manual trading, or completely withdraw from the markets. An important lesson learned from this

event demonstrates that automatic computer trades can destroy liquidity through high frequency trading.13

David

Lauer, former high frequency trader, said “in the flash crash, high frequency traders were becoming net takers of

liquidity, they were not providing liquidity, they were taking liquidity.”14

The VIX index, which is a measure of

the volatility of the S&P 500 index, increased immediately after the crash and ended higher in the period two

weeks after the crash compared to two weeks before. The Dow Jones and the S&P 500 had an increase in standard

deviation of returns and volume the ten day period after the crash as compared to before, seen in the table below.

13 "What caused the flash crash?: One big, bad trade." The Economist. N.p., 1 Oct. 2010. Web. 24 Nov. 2013.

<http://www.economist.com/blogs/newsbook/2010/10/what_caused_flash_crash>. 14 Farrell , Maureen. "Trading glitches a sad new market reality." CNNMoney. N.p., 22 Aug. 2013. Web. 24 Nov. 2013.

<http://money.cnn.com/2013/08/22/investing/nasdaq-trading-glitch/>.

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Twitter caused Market Disruption April 23rd, 2013 On April 23

rd, 2013, the Associated Press’ twitter account was hacked into by an unknown source and used to

propagate a false news message. The message, or “tweet” mentioned that there were two explosions in the White

House, and that President Obama was injured (Exhibit 7). This unverified tweet caused mass panic, and its effects

were visible in the stock market, most notably the Dow Jones Industrial Average, which rapidly dipped 145 points

(Exhibit 8). The Dow then bounced right back to about previous levels when human traders and those involved in

the markets realized that the information was false. Other markets reacted in similar fashion, with the S&P 500

dropping by 0.9% or $130 billion in stock value, but also recovering all lost value almost as quickly as it lost it.

The reaction in the capital market to the Tweet was caused by an overreaction of High Frequency Trading

algorithmic systems (HFTs), as described above. Trading firms that use these systems have them search the

internet multiple times per second for key words. These systems are designed to authorize and execute millions of

trades per second on that information. These systems are purely computational software designed by humans, and

hence have no actual human being to act as a buffer system to filter out erroneous information and authorize

trades. Irene Aldridge, managing partner at ABLE Alpha Trading, said that the “method is quite simplistic”:

traders at firms command their software which databases or news sources to look at, which words or phrases to

look for, and what actions to take based on that information. Since the amount of information available is too vast

for a group of humans to break down and analyze, these systems allow for a faster, streamlined and efficient way

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of making choices based off of market information. These trades usually yield miniscule profits, but end up being

quite profitable for the firm when they are executed millions of times per second.15

In the particular case of the Associated Press Twitter hack, it is likely that the firms that executed the trades as a

result of the hack had flagged words such as “explosion” in conjunction with “Obama” as triggers to sell U.S.

stock positions. Millions of sales took place based off of this counterfeit information, leading to a massive drop in

all stock markets, compounded by the fact that the original tweet was retweeted several times, flooding the

internet with this same false information. The first algorithm trade was made 15 seconds after the tweet was

published, allowing for the possibility that a human conducted the first trade.16

Theoretically, these algorithms and

“trading bots” are trained to follow market trends, so they could have sold millions of stock when they noticed

human trades.

In light of this event, and others like it, the SEC took steps to prevent similar market dips in the future. These steps

included introducing more extensive circuit breakers that automatically cease all trading on a particular stock or

exchange after it falls a certain percentage. For example, should the NASDAQ drop by 20%, all trading on the

NYSE would halt for a period of time to prevent against algorithmic trading following market trends.17

In

addition, the Market Access Rule, Rule 15c3-5 “requires broker-dealers to implement controls and supervisory

procedures to manage the financial and regulatory risks of market access, including the risks of errant trading

algorithms that could seriously disrupt the market.18

Easily the industry leader in HFT, the United States has

billions of trades occurring every day as a result of these systems aiming for the first mover advantage (attempting

to be the first in line for a trade). Therefore, while the SEC tries to collect information and data about these trades

to understand what causes them and what drives these trades explicitly, the task calls for a much larger operation.

The SEC has managed to do so by implementing new software and restricting algorithmic operations to allow for

easier data gathering and analysis through the MIDAS system (Market Information Data Analytics Software), and

others like it. All of this is encapsulated in SEC’s Regulation SCI proposal that would “replace the current

voluntary compliance program with enforceable rules designed to better insulate the markets from vulnerabilities

15 Matthews, Christopher. "Business & Money." Business Money How Does One Fake Tweet Cause a Stock Market Crash

Comments. TIME Business and Money, 24 Apr. 2013. Web. 07 Dec. 2013. <http://business.time.com/2013/04/24/how-

does-one-fake-tweet-cause-a-stock-market-crash/>. 16 Gandel, Stephen. "Tweet Retreat: Did High Frequency Reading Crash the Market?"CNNMoney. Cable News Network, 25 Apr.

2013. Web. 08 Dec. 2013. <http://finance.fortune.cnn.com/2013/04/25/twitter-stock-market-crash/>. 17 Ziliak, Zachary. "Regulation Ahead: Advice and Options for Automated and High-Frequency Traders." Bloomberg Law.

Bloomberg, n.d. Web. 08 Dec. 2013. 18 "MARKET ACCESS RULE 15c3-5." NasdaqTrader.com. NASDAQ, n.d. Web. 4 Dec. 2013.

<http://www.nasdaqtrader.com/content/productsservices/trading/ften/SECRule_15c3_5.pdf>.

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pose by systems technology issues.”19

Germany has taken the lead on imposing HFT regulations, actively passing

its own anti-HFT legislation, constituting “imposed fines for disruptive behavior” and requiring “tougher internal

controls on trading algorithms.”20

The VIX index did not notably change after the Twitter hack, indicating that the

volatility in the market was not affected by the hack.

August 22nd, 2013 Nasdaq Halts Trading Due To Computer Glitch On Thursday, August 22

nd, 2013, the Nasdaq Stock Market ceased trading for more than three hours when it

encountered a computer glitch in the SIP, or securities information processor, data feed. This feed is an integral

component of Nasdaq computerized security trading, it “is the single source of consolidated market data for

Nasdaq-listed securities”, and “provides continuous quotations from all market centers trading Nasdaq-listed

securities”, according to the SIP plan’s website (Exhibit 9).21

Experts E.S. Browning and Scott Patterson

attributed the glitch in The Wall Street Journal later that day to the system’s complexity. Stocks trade on dozens of

venues, about forty dark pools, and countless “internalizers”, or internal trading operations between stockbrokers,

at speeds measured in the millionths and/or billionths of seconds. This complex market relies on the SIP feed to

get prices, which explains how a relatively small computer error in the data feed caused a magnifying chain

reaction throughout the market, eventually prompting a market halt. On the day following the market shutdown,

Friday August 24th, Nasdaq officials blamed the SIP software error on a faulty connection with another exchange,

19 “SEC Proposes Rules to Improve Systems Compliance and Integrity." SEC.gov. SEC, 07 Mar. 2013. Web. 03 Dec. 2013.

<http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171513148>. 20 Alys, Kathy. "High-frequency Traders Face up to Regulatory Clampdown." FX Week. N.p., 23 Aug. 2013. Web. 1 Dec. 2013.

<http://www.fxweek.com/fx-week/analysis/2289771/highfrequency-traders-face-up-to-regulatory-clampdown>. 21 "UTP SIP Plan." UTP SIP Plan. N.p., n.d. Web. 25 Nov. 2013. <http://www.utpplan.com/>.

DJIA Index S&P 500 Index Nasdaq

Standard Deviation of Price 10 days Before 129.2250926 17.94967545 45.821513

Standard Deviation of Price 10 days After 134.2136688 16.5516741 47.319772

Standard Deviation of Volume 10 days Before 271140.859 510945316.4 151336728

Standard Deviation of Volume 10 days After 174099.1212 315129569.8 159425188

Increase in Standard Deviation of Price -3.86% 7.79% -3.27%

Increase in Standard Deviation of Volume 35.79% 38.32% -5.34%

Change in Volatility after Twitter Glitch

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NYSE Arca.22

Nasdaq officials failed to offer more explanation at the time, further frustrating traders and failing

to curtail fears about future glitches.

The interruption in trading on Thursday, August 22nd

was resolved before the days end, and Friday trading

experienced no major glitches, but confidence in the fragile systems that support computerized trading was still

greatly damaged.24

On Thursday between 3:25 p.m. EDT, when full trading resumed on the Nasdaq, and 4:00 p.m.

EDT, at closing time, Nasdaq OMX Group Inc. shares, owner of the Nasdaq exchange, fell over three percent. The

share price closed at $30.46, after trading in the $31.80 range before the halt.23

The market halt three hours prior

to the three percent drop is suspected to be behind the lower investor confidence in Nasdaq OMX Group Inc. and

the drop in its share price. Professor Richard Sylla of New York University’s Stern School of Business is quoted

on the subject as stating, “In an uncertain world, the fact that these computers are failing with greater frequency is

going to scare people away from the market.”24

Critics claim that relatively little money is spent by exchanges on

the order feed system when compared to the hundreds of millions of dollars spent on direct pipelines and superfast

connections, ultimately pointing out that years of neglect by U.S. exchanges and regulators is to blame.25

The VIX

index ended higher in the ten day period following the crash compared to before the crash. However, the VIX

index was not immediately impacted by the crash, indicating that the increase in the VIX was due to other market

factors excluding the crash.

22 Patterson, Scott, Andrew Ackerman, and Jenny Strasburg. "Nasdaq Shutdown Bares Stock Exchange Flaws." The Wall Street

Journal. Dow Jones & Company, 24 Aug. 2013. Web. 25 Nov. 2013.

<http://online.wsj.com/news/articles/SB10001424127887324619504579031270514157580>. 23 "Stock of Nasdaq Exchange Owner Drops after Glitch." The Wall Street Journal. Associated Press, 22 Aug. 2013. Web. 25 Nov.

2013. <http://online.wsj.com/article/APbd6e750bec5d49ef889bd294b0bc355c.html >. 24 Browning, E.S., and Scott Patterson. "Market Size + Complex Systems = More Glitches." The Wall Street Journal. Dow Jones &

Company, 22 Aug. 2013. Web. 25 Nov. 2013. 25 Patterson, Scott, Andrew Ackerman, and Jenny Strasburg. "Nasdaq Shutdown Bares Stock Exchange Flaws." The Wall Street


<http://online.wsj.com/news/articles/SB10001424127887324619504579031270514157580 >.

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Regression Analysis We utilized the SPDR S&P 500 (SPY) ETF as a proxy for the S&P 500 index because we do not have access to

intraday index returns. To confirm that the ETF is a strong proxy for the index, we ran a regression with the ETF

returns as the predictor and the index returns as the dependent variable (Exhibit 10). We chose to run the

regression with daily data from May 6th

2009 to May 6th

, 2011 because this allows for enough observations around

the event that we are concerned with (May 6, 2010). The results confirm that the ETF is a strong proxy for the S&P

500 index, with a 1.00948 coefficient for the SPY ETF term and a constant term very close to zero (-.00008368).

R-squared is at 99%, indicating that 99% of the variation in the index return can be explained by the SPY return,

on average. The p-value of the SPY Return is 0.000, indicating that the model is statistically significant. We

repeated the process for the DJIA index, utilizing the SPDR DJIA ETF as the proxy, and produced approximately

the same results (as seen in the regression output below). The NASDAQ Composite proxy (Fidelity NASDAQ

Comp. Index ETF) is not as strong of a fit as the other ETFs, but still is very (.982 coefficient) correlated to the

index, and therefore was going to be used as the proxy. However, the frequency of trades disables us from using it

in the minute data analysis because trades don’t occur every minute.

We regressed the SPY ETF minute returns versus the DIA ETF minute returns during a two-hour period around

the time of the Flash Crash and the Twitter Crash to reveal if the ETFs, which represent the indices, reacted to

each crash in the same way and if the same patterns are seen in the Flash Crash as in the Twitter Crash.

The Flash Crash analysis revealed that the ETFs were highly correlated before the time of the crash, but then

diverged dramatically at the time of the crash. During the 2:00-2:30pm period, the t-stat (38.46) is much greater

than the critical t-value of 1.96 at a .05 level of significance, indicating that the model is statistically significant.

The coefficient of DIA returns is 1, meaning that the returns of the SPY and DIA move almost identically. The r-

squared of 98.1% reveals that 98.1% of the variation in the SPY ETF returns can be explained by the DIA returns,

on average (Exhibit 11). This trend of strong fit continues in the time period of 2:30-2:40. The fit decreases

during the 2:40-2:45pm time period, but the model is still statistically significant. During the 2:45-2:50pm time

DJIA Index S&P 500 Index Nasdaq

Standard Deviation of Price 10 days Before 224.1719271 21.82689208 36.957399

Standard Deviation of Price 10 days After 75.58080003 11.88731649 32.617351

Standard Deviation of Volume 10 days Before 231104.4894 200778206.8 142756533

Standard Deviation of Volume 10 days After 166688.8455 408391811 169728495

Increase in Standard Deviation of Price 66.28% 45.54% 11.74%

Increase in Standard Deviation of Volume 27.87% -103.40% -18.89%

Change in Volatility after Glitch

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period, however, the returns move in different directions and magnitudes, as revealed by the t-stat of 1.07 (less

than t-critical of 2.228 at .05 level of significance), making the model statistically insignificant. This reveals that

the indices reacted drastically different to the market disruption. The difference in reaction continues during the

2:50-2:55pm time period, but the 2:55-3:00pm indicates that the indices begin to move together again, with the

model being statistically significant. By the 3:30-4:00pm period, the indices are again highly correlated, with a r-

squared of 97% and a t-stat of 30.45 (Exhibit 11).

The Twitter Crash analysis revealed a different pattern than the Flash Crash analysis. The ETFs were the most

correlated immediately following the crash. During the 12:00-12:30pm period, the t-stat (48.10) is much greater

than the critical t-value of 1.96 at .05 level of significance, indicating that the model is statistically significant. The

.978 coefficient of DIA indicates that for every 1% change in the SPY ETF, the DIA changes .978% on average

(Exhibit 12). The r-squared is 95.1%. The 12:30-12:40pm period sees a large drop in r-squared however, to

68.9%, with the model still being statistically significant. R-squared improves during the 12:40-12:45pm and

12:45-12:50pm. The periods leading up to the disruption show a varying degree of correlation with the 12:55-

1:00pm period showing a t-stat below the critical value. However, when the disruption occurs and the markets

react, r-squared jumps to 99.8% from 1:05-1:10pm and 97.2% from 1:10-1:15pm. By 1:30pm, the r-squared has

jumped back down to 55.4% (Exhibit 12).

This analysis exposes that there is not a pattern from the Flash Crash to the Twitter Crash, and that the indices

reacted drastically different. In the Flash Crash, the SPY ETF and the DIA ETF moved differently in reaction to

the crash, whereas in the Twitter Crash, the ETFs were the most correlated during their reaction to the crash

(Exhibit 13). It is possible that the SPY was directly part of the flash crash and was hence directly affected by it;

but the DIA was more volatile given that the algorithms acting on the information were constantly racing against

each other and acting on each other’s trades.

In order to test the effect each market disruption may have had on returns before and after each event, we

regressed daily returns of a random base month versus the daily returns of the Dow Jones Industrial Average,

Nasdaq Composite, and S&P 500 30 trading days before and after each event, all of which can be found in

Exhibit 14. We chose our base month as 30 trading days after July 1st, 2011 because there was no market

disruption during this period and it is roughly between our events. Our results for the AP Twitter Hack indicate

that the returns of each index decrease in variation from our base month returns after the crash occurs. The

coefficients for the Dow Jones, Nasdaq, and S&P 500 decrease from 1.35, 1.24, and 1.44 to 1.13, 1.11, and 1.16,

Page | 13

respectively, before to after the crash. This indicates that the variation in returns of each index decreases after the

crash in relation to our base month. For example, the DJIA base month returns increase 1.35% for every 1%

increase in the DJIA returns before the AP Twitter Hack, on average. The 1.35% decreases to 1.13% after the

crash, thus exhibiting a decrease in variation. The explanatory power of our regressions for the AP Twitter Hack

are relatively weak, however, with an average r-squared of 15.27%, indicating that only an average of 15.27% of

the variation in the base month returns can be explained by the variation in the returns before and after the crash.

Each regression (with the exception of ^IXIC Base Month versus ^IXIC After) for the AP Twitter Hack is

statistically significant with all p-values less than .10. Both the Flash Crash and August 22nd

Glitch regressions for

the base month versus one month before and after periods are statistically insignificant with p-values greater than

.10 and have very little explanatory power with low r-squareds, and therefore should not relied on for insights.

Moving Forward The continuously increasing occurrence of stock market disruptions and computer glitches is a center for

discussion between markets and regulators. Investors and brokers are very apprehensive of the future, believing

that glitches across all major exchanges are becoming more common and intense in disruption. The amount of

significant market disruptions has increased since 2010, when the markets were shocked with the Flash Crash as

the Dow plummeted about 1,000 points, $862 billion, in minutes, regaining most of the loss within half an hour. In

2012, there were three significant market disruptions that interrupted U.S. trading, causing issues with Facebook

IPO, and the bankruptcy of Knight Capital. In 2013, the number of significant U.S. market trading disruptions

jumped to eighteen, including the Associated Press Twitter hack mini flash crash on April 23rd

, the Nasdaq trading

halts on August 22nd

and November 1st, and the OTC market Failure on November 7

th.26

Chris Nagy, president of

the market consulting firm KOR Trading and the former head of trading at TD Ameritrade, has stated in reference

to the Thursday, August 22nd

, 2013 glitch, “In 26 years in the industry, I’ve never seen the SIP have a catastrophic

failure like this”.27

In response to these recent glitches, the Securities and Exchange Commission set a sixty-day

deadline for U.S. exchanges to modify and strengthen their technology at a meeting on September 12

th, 2013.

The most popularized solution to mitigate the effects of such disruptions and glitches among NYSE Euronext,

Nasdaq OMX Group Inc., and other U.S. exchanges is for each of the market platforms to mutually protect each

other in the event of a corrupting trade or computer based breakdown. In the event that one of the group’s data

26 "Chronology of Flash Crashes and Software Glitches Threatening the Stability of Global Markets." The Keystone Speculator™.

The Keystone Speculator™, 10 Nov. 2013. Web. 26 Nov. 2013. 27 Farrell, Maureen. "Trading Glitches a Sad New Market Reality." CNNMoney. Cable News Network, 22 Aug. 2013. Web. 26 Nov.

2013. <http://money.cnn.com/2013/08/22/investing/nasdaq-trading-glitch/>.

Page | 14

feed fails, such as what happened on August 22nd

, 2013, for Nasdaq, then traders would be able to switch to its

competitor for data while the problem is being fixed, effectively simulating a market halt but allowing trading to

continue.28

The exchanges would need to run backups of the other exchange’s benchmark stock-pricing data in

order for the protection to work (Exhibit 15). Even though the U.S. exchanges are in competition, they will

collectively benefit from protecting one another in the case of another disruption or glitch. The heavily integrated

stock market as a whole could potentially be disrupted when a single exchange encounters a computer error,

encouraging all exchanges to pursue stronger technological shields against problems like extreme stock-price

volatility that could endanger their own operations. There are currently sixteen such exchanges registered with the

Securities and Exchange Commission, and six more registered with the purpose of trading futures.29

Obstacles to implementing the mutual backup system include the need for standardized computer languages

between exchanges where communication is currently different, more sophisticated data feed technology to handle

to the extra strain on running duplicate feeds, and the added cost of executing the changes that will likely be borne

by the customers.29

The Depository Trust & Clearing Corporation could potentially act as an independent and

centralized entity capable of running the backup feeds for the U.S. exchanges, because it already “processes all

U.S. stock trades through its clearinghouse”28

, which would help alleviate challenges such as extra strain on

duplicate data feeds carried by exchanges; but this plan carries with it its own consequences of computer failure.

The most likely course for exchanges will be implementing their own “internal hot backups” that are real time

data repositories that individual exchanges build, instead of backups across exchanges.30

In addition to running back up data feeds, the exchanges have discussed adding more representatives from banks

and financial firms to the industry committee that advise the data feeds31

. Ideally, these additions to the committee

will offer insight into how to avoid disruptions in the future and mediate discussions between exchanges as new

policy is implemented. Policies that are currently sought after include uniform procedures focused on identifying

and cancelling mistaken trades between exchanges. In the past, glitches have caused erroneous price quotes on

securities that vary greatly from market rates. U.S. Exchanges have also agreed on “basic principles” that

28 Bunge, Jacob. "NYSE, Nasdaq Consider Cooperating to Address Glitches." The Wall Street Journal. Dow Jones & Company, 25

Sept. 2013. Web. 26 Nov. 2013. 29 "Exchanges." U.S. Securities and Exchange Commission. U.S. Securities and Exchange Commission, 30 Aug. 2012. Web. 26 Nov.

2013. <http://www.sec.gov/divisions/marketreg/mrexchanges.shtml>. 30 Rosenbush, Steven, and Bradley Hope. "Exchanges Look to Strengthen Their Own Backup Systems." The Wall Street Journal.

Dow Jones & Company, 18 Nov. 2013. Web. 26 Nov. 2013. 31 Bunge, Jacob, and Bradley Hope. "Exchanges Offer Proposals to SEC to Strengthen Market Infrastructure." The Wall Street

Journal. Dow Jones & Company, 12 Nov. 2013. Web. 26 Nov. 2013.

Page | 15

coordinate stock-trading halts and communication between exchanges and traders in an effort to minimize

resulting confusion from serious system glitches.

Exchanges are currently working on a “kill switch” proposal, a device that will prevent market disruption by

stopping trading during emergencies. The kill switch’s intention would be to address problems with quotes and

orders being sent to the SIP, which consolidates before and after trade pricing information31

, while disregarding

the issues apparent with any one processor32

. A kill switch would have likely diminished the disruption caused by

a number of glitches, namely the August 22nd

2013 Nasdaq SIP error. For the proposed kill switch agreed upon by

exchanges and regulators, individual broker-dealers would likely set their own exposure limits that, once passed,

would signal the kill switch to halt trading and isolate errors from being unleashed onto the broader market.32

Attempts to eliminate computer based market failures are a top priority for U.S. exchanges and regulators. A

senior research analyst at TABB Group, Paul Rowady, considers the period from the Flash Crash in 2010 to

current day as “glitchapalooza” due to the number of trading interruptions32

. If computer glitches do not cease or

at least slowdown in occurrence soon, then exchanges and regulators will continue to come under public pressure.

Paul Rowady has also stated, and he is not alone in his judgment, that "the exchange brands have been suffering

because of these problems.”32

Trader confidence in computerized and automated markets has dropped, and

demands for greater technology are rapid.

Conclusion Disruptions in Equity Markets can occur both by human error, evident in the Flash Crash and Twitter incident, and

by computer error, evident in the Nasdaq SIP glitch. To understand the relationship between these occurrences,

our group performed regression analysis and found that indices, a prime example for movements in the general

market, responded differently to the three different market disruptions we investigated. From our results, we

determined that the ETF is a strong proxy for the S&P 500 index. For the Flash Crash analysis, the ETFs were

highly correlated before the time of the crash, and then deviating dramatically at the time of the crash. For Twitter,

the ETFs were the most correlated immediately following the crash. Lastly, we tested the effect each market

disruption may have had on returns before and after each event. Today, market disruptions are one of the most

significant issues that exchanges, traders, and markets are trying to investigate, understand, and hopefully solve.

32 Lynch, Sarah N., and Herbert Lash. "U.S. Exchanges to Create Kill Switches following Nasdaq Outage." Reuters. Thomson

Reuters, 12 Sept. 2013. Web. 26 Nov. 2013. <http://www.reuters.com/article/2013/09/13/us-nasdaq-halt-sec-

idUSBRE98A13K20130913>.

Page | 16

Works Cited

"Chronology of Flash Crashes and Software Glitches Threatening the Stability of Global Markets." The Keystone

Speculator™. The Keystone Speculator™, 10 Nov. 2013. Web. 26 Nov. 2013.

"Exchanges." U.S. Securities and Exchange Commission. U.S. Securities and Exchange Commission, 30 Aug.

2012. Web. 26 Nov. 2013. <http://www.sec.gov/divisions/marketreg/mrexchanges.shtml>.

"Investor Bulletin: New Stock-by-Stock Circuit Breakers." U.S. Securities and Exchange Commission |

Homepage. N.p., 9 Aug. 2011. Web. 24 Nov. 2013.

<http://www.sec.gov/investor/alerts/circuitbreakers.htm>.

"Is ‘Kill Switch’ the Solution to Market Trading Glitches?" Fox Business. N.p., 19 Dec. 2012. Web.

24 Nov. 2013. <http://video.foxbusiness.com/v/2046252892001/is-kill-switch-the-solution-to-market-

trading-glitches/>.

"MARKET ACCESS RULE 15c3-5." NasdaqTrader.com. NASDAQ, n.d. Web. 4 Dec. 2013.

<http://www.nasdaqtrader.com/content/productsservices/trading/ften/SECRule_15c3_5.pdf>.

"Regulation Systems Compliance and Integrity." U.S. Securities and Exchange Commission | Homepage. Release

No. 34-69077; File No. S7-01-13, n.d. Web. 24 Nov. 2013. <http://www.sec.gov/rules/proposed/2013/34-

69077.pdf>.

"Stock of Nasdaq Exchange Owner Drops after Glitch." The Wall Street Journal. Associated Press, 22 Aug. 2013.

Web. 25 Nov. 2013. <http://online.wsj.com/article/APbd6e750bec5d49ef889bd294b0bc355c.html >.

"Trading 'Kill Switches' Can Avoid Glitches But Need To Be Used With Caution, Experts Say."The Huffington

Post. N.p., 2 Oct. 2012. Web. 24 Nov. 2013. <http://www.huffingtonpost.com/2012/10/03/trading-kill-

swtiches-caution-glitches_n_1934127.html>.

"UTP SIP Plan." UTP SIP Plan. N.p., n.d. Web. 25 Nov. 2013. <http://www.utpplan.com/>.

"What caused the flash crash?: One big, bad trade." The Economist. N.p., 1 Oct. 2010. Web. 24 Nov. 2013.

<http://www.economist.com/blogs/newsbook/2010/10/what_caused_flash_crash>.

“SEC Proposes Rules to Improve Systems Compliance and Integrity." SEC.gov. SEC, 07 Mar. 2013. Web. 03

Dec. 2013. <http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171513148>.

Alys, Kathy. "High-frequency Traders Face up to Regulatory Clampdown." FX Week. N.p., 23 Aug. 2013. Web. 1

Dec. 2013. <http://www.fxweek.com/fx-week/analysis/2289771/highfrequency-traders-face-up-to-

regulatory-clampdown>.

ATS stands for Alternative Trading Systems; examples include Call Markets and Dark Pools

Avery, Helen. "Exchanges: Technical glitches threaten stock exchanges." International banking, finance, capital markets news & analysis | Euromoney magazine. N.p., Oct. 2013. Web. 24 Nov. 2013.

Browning, E.S., and Scott Patterson. "Market Size + Complex Systems = More Glitches." The Wall Street


Bunge, Jacob, and Bradley Hope. "Exchanges Offer Proposals to SEC to Strengthen Market Infrastructure." The

Wall Street Journal. Dow Jones & Company, 12 Nov. 2013. Web. 26 Nov. 2013.

Page | 17

Bunge, Jacob. "NYSE, Nasdaq Consider Cooperating to Address Glitches." The Wall Street Journal. Dow Jones

& Company, 25 Sept. 2013. Web. 26 Nov. 2013.

Farrell , Maureen. "Trading glitches a sad new market reality." CNNMoney. N.p., 22 Aug. 2013. Web.

24 Nov. 2013. <http://money.cnn.com/2013/08/22/investing/nasdaq-trading-glitch/>.

Gandel, Stephen. "Tweet Retreat: Did High Frequency Reading Crash the Market?"CNNMoney. Cable News

Network, 25 Apr. 2013. Web. 08 Dec. 2013. <http://finance.fortune.cnn.com/2013/04/25/twitter-stock-

market-crash/>.

Lati, Rob. "The Real Story of Trading Software Espionage." Wall Street and Technology. N.p., 10 July 2009.

Web. 01 Dec. 2013. <http://www.wallstreetandtech.com/trading-technology/the-real-story-of-trading-

software-espio/218401501>.

Lynch, Sarah N., and Herbert Lash. "U.S. Exchanges to Create Kill Switches following Nasdaq Outage." Reuters.

Thomson Reuters, 12 Sept. 2013. Web. 26 Nov. 2013. <http://www.reuters.com/article/2013/09/13/us-

nasdaq-halt-sec-idUSBRE98A13K20130913>.

Matthews, Christopher. "Business & Money." Business Money How Does One Fake Tweet Cause a Stock Market Crash Comments. TIME Business and Money, 24 Apr. 2013. Web. 07 Dec. 2013.

<http://business.time.com/2013/04/24/how-does-one-fake-tweet-cause-a-stock-market-crash/>.

Patterson, Scott, Andrew Ackerman, and Jenny Strasburg. "Nasdaq Shutdown Bares Stock Exchange Flaws." The

Wall Street Journal. Dow Jones & Company, 24 Aug. 2013. Web. 25 Nov. 2013.

<http://online.wsj.com/news/articles/SB10001424127887324619504579031270514157580 >.

Phillips, Matthew. "How the Robots Lost: High-Frequency Trading's Rise and Fall."Bloomberg Businessweek Technology. N.p., 06 June 2013. Web. 2 Dec. 2013. <http://www.businessweek.com/articles/2013-06-

06/how-the-robots-lost-high-frequency-tradings-rise-and-fall>

Rosenbush, Steven, and Bradley Hope. "Exchanges Look to Strengthen Their Own Backup Systems." The Wall

Street Journal. Dow Jones & Company, 18 Nov. 2013. Web. 26 Nov. 2013.

Ziliak, Zachary. "Regulation Ahead: Advice and Options for Automated and High-Frequency

Traders." Bloomberg Law. Bloomberg, n.d. Web. 08 Dec. 2013.

Page | 18

Exhibits: Exhibit 1

Source: Standard and Poor

Page | 19

Exhibit 2

Source: Standard and Poor

Exhibit 3

Source: sec.gov

Page | 20

Exhibit 4

Source: sec.gov

Exhibit 5

Source: sec.gov

Page | 21

Exhibit 6

http://money.cnn.com/2010/10/01/markets/SEC_CFTC_flash_crash/

Exhibit 7:

http://money.cnn.com/2010/10/01/markets/SEC_CFTC_flash_crash/

Page | 22

Exhibit 8:

Source: Yahoo Finance

Exhibit 9:

Page | 23

Exhibit 10: ETF Proxy Regressions

Regression Analysis: S&P 500 Index Return versus S&P 500 ETF (SPY) Return

The regression equation is

S&P 500 Index Return = - 0.000084 + 1.01 S&P 500 ETF (SPY) Return

Predictor Coef SE Coef T P

Constant -0.00008368 0.00004810 -1.74 0.083

S&P 500 ETF (SPY) Return 1.00948 0.00445 226.87 0.000

S = 0.00107737 R-Sq = 99.0% R-Sq(adj) = 99.0%

Regression Analysis: DJIA Index Return versus DJIA ETF (DIA) Return The regression equation is

DJIA Index Return = - 0.000101 + 1.01 DJIA ETF (DIA) Return


Constant -0.00010139 0.00004123 -2.46 0.014

DJIA ETF (DIA) Return 1.00922 0.00424 238.17 0.000

S = 0.000922303 R-Sq = 99.1% R-Sq(adj) = 99.1%

Regression Analysis: Nasdaq Composite versus Nasdaq ETF (ONEQ) The regression equation is

Nasdaq Composite Index = - 0.000009 + 0.982 Nasdaq ETF (ONEQ) Return


Constant -0.00000859 0.00009001 -0.10 0.924

Nasdaq ETF (ONEQ) Return 0.982023 0.007565 129.81 0.000

S = 0.00201499 R-Sq = 97.1% R-Sq(adj) = 97.1%

Exhibit 11: Flash Crash Regressions Regression Analysis: Flash Crash: SPY Returns (2:00-2:30) versus DIA Returns (2:00-2:30) The regression equation is

SPY Returns (2:00-2:30) = - 0.000047 + 1.00 DIA Returns (2:00-2:30)


Constant -0.00004677 0.00003468 -1.35 0.188

DIA Returns (2:00-2:30) 1.00377 0.02610 38.46 0.000

S = 0.000187976 R-Sq = 98.1% R-Sq(adj) = 98.0%

Page | 24

Regression Analysis: Flash Crash: SPY Returns (2:30-2:40) versus DIA Returns (2:30-2:40) The regression equation is

SPY Returns (2:30-2:40) = 0.000024 + 1.05 DIA Returns (2:30-2:40)


Constant 0.00002381 0.00004101 0.58 0.576

DIA Returns (2:30-2:40) 1.04990 0.01781 58.95 0.000

S = 0.000113684 R-Sq = 99.7% R-Sq(adj) = 99.7%

Regression Analysis: Flash Crash: SPY Returns (2:40-2:45) versus DIA (2:40-2:45) The regression equation is

SPY Returns (2:40-2:45) = - 0.00053 + 0.887 DIA (2:40-2:45)


Constant -0.000534 0.001196 -0.45 0.678

DIA (2:40-2:45) 0.8868 0.1436 6.18 0.003

S = 0.00201972 R-Sq = 90.5% R-Sq(adj) = 88.1%

Regression Analysis: Flash Crash: SPY Returns (2:45-2:50) versus DIA (2:45-2:50) The regression equation is

SPY Returns (2:45-2:50) = 0.00356 + 0.238 DIA (2:45-2:50)


Constant 0.003555 0.006618 0.54 0.620

DIA (2:45-2:50) 0.2382 0.2223 1.07 0.344

S = 0.0160272 R-Sq = 22.3% R-Sq(adj) = 2.9%




Constant -0.001207 0.005057 -0.24 0.823

DIA Returns (2:50-2:55) 0.0205 0.1853 0.11 0.917

S = 0.0123388 R-Sq = 0.3% R-Sq(adj) = 0.0%




Constant -0.0000756 0.0008391 -0.09 0.933

Page | 25

DIA Returns (2:55-3:00) 0.8084 0.2205 3.67 0.021

S = 0.00200509 R-Sq = 77.1% R-Sq(adj) = 71.3%




Constant 0.0000418 0.0003852 0.11 0.919

DIA Returns (3:00-3:05) 0.85329 0.07962 10.72 0.000

S = 0.000937259 R-Sq = 96.6% R-Sq(adj) = 95.8%




Constant -0.0000619 0.0004675 -0.13 0.901

DIA Returns (3:05-3:10) 1.1692 0.1610 7.26 0.002

S = 0.00109754 R-Sq = 92.9% R-Sq(adj) = 91.2%




Constant 0.0001311 0.0001286 1.02 0.366

DIA Returns (3:10-3:15) 1.10281 0.03549 31.07 0.000

S = 0.000281647 R-Sq = 99.6% R-Sq(adj) = 99.5%




Constant -0.0000649 0.0001302 -0.50 0.644

DIA Returns (3:15-3:20) 1.08335 0.05123 21.15 0.000

S = 0.000307793 R-Sq = 99.1% R-Sq(adj) = 98.9%

Page | 26




Constant -0.00000940 0.00007220 -0.13 0.897

DIA Returns (3:20-4:00) 1.06034 0.03624 29.26 0.000

S = 0.000461613 R-Sq = 95.6% R-Sq(adj) = 95.5%




Constant 0.0001193 0.0002048 0.58 0.574

DIA Returns (3:20-3:30) 1.07936 0.08460 12.76 0.000

S = 0.000676602 R-Sq = 94.8% R-Sq(adj) = 94.2%




Constant -0.00004026 0.00007129 -0.56 0.577

DIA Returns (3:30-4:00) 1.08462 0.03562 30.45 0.000

S = 0.000389675 R-Sq = 97.0% R-Sq(adj) = 96.9%

Exhibit 12: Twitter Regressions Regression Analysis: Twitter: SPY Returns (12:00-12:30) versus DIA Returns (12:00-12:30) The regression equation is



Constant 0.00000204 0.00001678 0.12 0.903

DIA Returns (12:00-12:30) 0.97793 0.02033 48.10 0.000

S = 0.000184572 R-Sq = 95.1% R-Sq(adj) = 95.1%

Page | 27

Regression Analysis: Twitter: SPY Returns (12:30-12:40 versus DIA Returns (12:30-12:40) The regression equation is



Constant 0.00000210 0.00001739 0.12 0.906

DIA Returns (12:30-12:40) 1.2692 0.2840 4.47 0.002

S = 0.0000573928 R-Sq = 68.9% R-Sq(adj) = 65.5%

Regression Analysis: Twitter: SPY Returns (12:40-12:45) versus DIA Returns (12:40-12:45) The regression equation is



Constant 0.00001782 0.00002974 0.60 0.581

DIA Returns (12:40-12:45) 1.7147 0.4367 3.93 0.017

S = 0.0000686904 R-Sq = 79.4% R-Sq(adj) = 74.3%




Constant 0.00000926 0.00002798 0.33 0.757

DIA Returns (12:45-12:50) 0.9219 0.1154 7.99 0.001

S = 0.0000543941 R-Sq = 94.1% R-Sq(adj) = 92.6%




Constant 0.00002773 0.00005423 0.51 0.636

DIA Returns (12:50-12:55) 0.8558 0.2141 4.00 0.016

S = 0.000115546 R-Sq = 80.0% R-Sq(adj) = 75.0%

Page | 28




Constant 0.00000306 0.00007091 0.04 0.968

DIA Returns (12:55-1:00) 0.9543 0.6275 1.52 0.203

S = 0.000103148 R-Sq = 36.6% R-Sq(adj) = 20.8%




Constant 0.00002809 0.00004286 0.66 0.548

DIA Returns (1:00-1:05) 0.6804 0.3253 2.09 0.105

S = 0.000104662 R-Sq = 52.2% R-Sq(adj) = 40.3%




Constant 0.00001932 0.00005994 0.32 0.763

DIA Returns (1:05-1:10) 0.99547 0.02152 46.26 0.000

S = 0.000120812 R-Sq = 99.8% R-Sq(adj) = 99.8%




Constant 0.0001178 0.0002771 0.43 0.693

DIA Returns (1:10-1:15) 0.96995 0.08182 11.85 0.000

S = 0.000671417 R-Sq = 97.2% R-Sq(adj) = 96.5%

Regression Analysis: Twitter: SPY Returns (1:15-1:20) versus DIA Returns (1:15-1:20)

Page | 29

The regression equation is



Constant -0.0000107 0.0001012 -0.11 0.921

DIA Returns (1:15-1:20) 1.0148 0.2073 4.90 0.008

S = 0.000239542 R-Sq = 85.7% R-Sq(adj) = 82.1%




Constant -0.00003970 0.00005988 -0.66 0.524

DIA Returns (1:20-1:30) 0.7275 0.2042 3.56 0.006

S = 0.000193155 R-Sq = 58.5% R-Sq(adj) = 53.9%




Constant -0.00000296 0.00003034 -0.10 0.923

DIA Returns (1:30-2:00) 0.8594 0.1450 5.93 0.000

S = 0.000168603 R-Sq = 54.8% R-Sq(adj) = 53.2%

Page | 30

Exhibit 13:

102

104

106

108

110

112

114

116

96

98

100

102

104

106

108

14

:00

:00

14

:04

:00

14

:08

:00

14

:12

:00

14

:16

:00

14

:20

:00

14

:24

:00

14

:28

:00

14

:32

:00

14

:36

:00

14

:40

:00

14

:44

:00

14

:48

:00

14

:52

:00

14

:56

:00

15

:00

:00

15

:04

:00

15

:08

:00

15

:12

:00

15

:16

:00

15

:20

:00

15

:24

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15

:28

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15

:32

:00

15

:36

:00

15

:40

:00

15

:44

:00

15

:48

:00

15

:52

:00

15

:56

:00

16

:00

:00

DIA vs SPY Flash Crash

DIA SPY

155

155.5

156

156.5

157

157.5

158

158.5

144.5

145

145.5

146

146.5

147

12

:00

:00

12

:04

:00

12

:08

:00

12

:12

:00

12

:16

:00

12

:20

:00

12

:24

:00

12

:28

:00

12

:32

:00

12

:36

:00

12

:40

:00

12

:44

:00

12

:48

:00

12

:52

:00

12

:56

:00

13

:00

:00

13

:04

:00

13

:08

:00

13

:12

:00

13

:16

:00

13

:20

:00

13

:24

:00

13

:28

:00

13

:32

:00

13

:36

:00

13

:40

:00

13

:44

:00

13

:48

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DIA vs SPY Price on April 23,2013

DIA SPY

Page | 31

Exhibit 14:

Base Month versus Before and After AP Twitter Hack Returns on ^DJI, ÎXIC, ^GSPC

Regression Analysis: ^DJI Base Month versus ^DJI Before Twitter The regression equation is

^DJI Base Month = 0.00370 + 1.35 DJI^ Before Twitter


Constant 0.003699 0.003709 1.00 0.328

DJI^ Before Twitter 1.3461 0.6156 2.19 0.038

S = 0.0199563 R-Sq = 15.0% R-Sq(adj) = 11.9%

Analysis of Variance

Source DF SS MS F P

Regression 1 0.0019043 0.0019043 4.78 0.038

Residual Error 27 0.0107528 0.0003983

Total 28 0.0126571

Regression Analysis: ^DJI Base Month versus ^DJI After Twitter The regression equation is

^DJI Base Month = 0.00406 + 1.13 DJI^ After Twitter


Constant 0.004063 0.003799 1.07 0.294

DJI^ After Twitter 1.1274 0.5982 1.88 0.070

S = 0.0203541 R-Sq = 11.6% R-Sq(adj) = 8.4%


Source DF SS MS F P

Regression 1 0.0014713 0.0014713 3.55 0.070

Residual Error 27 0.0111858 0.0004143

Total 28 0.0126571

Regression Analysis: ÎXIC Base Month versus ÎXIC Before Twitter The regression equation is

ÎXIC Base Month = 0.00348 + 1.24 ÎXIC Before Twitter


Constant 0.003476 0.004264 0.82 0.422

ÎXIC Before Twitter 1.2418 0.4590 2.71 0.012

S = 0.0229556 R-Sq = 21.3% R-Sq(adj) = 18.4%

Page | 32


Source DF SS MS F P

Regression 1 0.0038575 0.0038575 7.32 0.012

Residual Error 27 0.0142279 0.0005270

Regression Analysis: ÎXIC Base Month versus ÎXIC After Twitter The regression equation is

ÎXIC Base Month = 0.00527 + 1.11 ÎXIC After Twitter


Constant 0.005268 0.004673 1.13 0.269

ÎXIC After Twitter 1.1077 0.6770 1.64 0.113

S = 0.0246862 R-Sq = 9.0% R-Sq(adj) = 5.7%


Source DF SS MS F P

Regression 1 0.0016315 0.0016315 2.68 0.113

Residual Error 27 0.0164540 0.0006094

Total 28 0.0180854

Regression Analysis: ^GSPC Base Month versus ^GSPC Before Twitter The regression equation is

^GSPC Base Month = 0.00406 + 1.44 ^GSPC Before Twitter


Constant 0.004058 0.003971 1.02 0.316

^GSPC Before Twitter 1.4378 0.4973 2.89 0.007

S = 0.0213829 R-Sq = 23.6% R-Sq(adj) = 20.8%


Source DF SS MS F P

Regression 1 0.0038223 0.0038223 8.36 0.007

Residual Error 27 0.0123451 0.0004572

Total 28 0.0161674

Regression Analysis: ^GSPC Base Month versus ^GSPC After Twitter The regression equation is

^GSPC Base Month = 0.00463 + 1.16 ^GSPC After Twitter


Constant 0.004629 0.004303 1.08 0.292

^GSPC After Twitter 1.1557 0.6299 1.83 0.078

S = 0.0230744 R-Sq = 11.1% R-Sq(adj) = 7.8%

Page | 33

Base Month versus Before and After August 22nd Glitch Returns on ^DJI, ÎXIC, ^GSPC

Regression Analysis: ^DJI Base Month versus ^DJI Before August 22 The regression equation is

^DJI Base Month = 0.00468 + 1.05 ^DJI Before August 22


Constant 0.004680 0.004110 1.14 0.265

^DJI Before August 22 1.0550 0.9457 1.12 0.274

S = 0.0211691 R-Sq = 4.4% R-Sq(adj) = 0.9%


Source DF SS MS F P

Regression 1 0.0005577 0.0005577 1.24 0.274

Residual Error 27 0.0120995 0.0004481

Total 28 0.0126571

Regression Analysis: ^DJI Base Month versus ^DJI After August 22 The regression equation is

^DJI Base Month = 0.00332 + 0.147 ^DJI After August 22


Constant 0.003316 0.004018 0.83 0.417

^DJI After August 22 0.1467 0.6617 0.22 0.826

S = 0.0216317 R-Sq = 0.2% R-Sq(adj) = 0.0%


Source DF SS MS F P

Regression 1 0.0000230 0.0000230 0.05 0.826

Residual Error 27 0.0126341 0.0004679

Total 28 0.0126571

Regression Analysis: ÎXIC Base Month versus ÎXIC Before August 22 The regression equation is

ÎXIC Base Month = 0.00353 + 1.15 ÎXIC Before August 22


Constant 0.003528 0.004638 0.76 0.454

ÎXIC Before August 22 1.1547 0.8104 1.42 0.166

S = 0.0249597 R-Sq = 7.0% R-Sq(adj) = 3.5%

Page | 34


Source DF SS MS F P

Regression 1 0.0012647 0.0012647 2.03 0.166

Residual Error 27 0.0168207 0.0006230

Total 28 0.0180854

Regression Analysis: ÎXIC Base Month versus ÎXIC After August 22 The regression equation is

ÎXIC Base Month = 0.00316 + 0.439 ÎXIC After August 22


Constant 0.003165 0.004858 0.65 0.520

ÎXIC After August 22 0.4390 0.6628 0.66 0.513

S = 0.0256733 R-Sq = 1.6% R-Sq(adj) = 0.0%

Regression Analysis: ^GSPC Base Month versus ^GSPC Before August 22 The regression equation is

^GSPC Base Month = 0.00458 + 1.03 ^GSPC Before August 22


Constant 0.004580 0.004487 1.02 0.316

^GSPC Before August 22 1.0275 0.9300 1.10 0.279

S = 0.0239352 R-Sq = 4.3% R-Sq(adj) = 0.8%


Source DF SS MS F P

Regression 1 0.0006993 0.0006993 1.22 0.279

Residual Error 27 0.0154681 0.0005729

Total 28 0.0161674

Regression Analysis: ^GSPC Base Month versus ^GSPC After August 22 The regression equation is

^GSPC Base Month = 0.00383 + 0.130 ^GSPC After August 22


Constant 0.003829 0.004561 0.84 0.409

^GSPC After August 22 0.1297 0.7383 0.18 0.862

S = 0.0244563 R-Sq = 0.1% R-Sq(adj) = 0.0%


Source DF SS MS F P

Regression 1 0.0000185 0.0000185 0.03 0.862

Residual Error 27 0.0161490 0.0005981

Base Month versus Before and After Flash Crash Returns on ^DJI, ÎXIC, ^GSPC

Page | 35

Regression Analysis: ^DJI Base Month versus ^DJI Before Flash Crash The regression equation is

^DJI Base Month = 0.00346 - 0.578 ^DJI Before Flash Crash


Constant 0.003464 0.003926 0.88 0.385

^DJI Before Flash Crash -0.5781 0.4991 -1.16 0.257

S = 0.0211327 R-Sq = 4.7% R-Sq(adj) = 1.2%


Source DF SS MS F P

Regression 1 0.0005991 0.0005991 1.34 0.257

Residual Error 27 0.0120580 0.0004466

Regression Analysis: ^DJI Base Month versus ^DJI After Flash Crash The regression equation is

^DJI Base Month = 0.00359 + 0.282 ^DJI After Flash Crash


Constant 0.003592 0.003933 0.91 0.369

^DJI After Flash Crash 0.2823 0.2475 1.14 0.264

S = 0.0211481 R-Sq = 4.6% R-Sq(adj) = 1.1%


Source DF SS MS F P

Regression 1 0.0005816 0.0005816 1.30 0.264

Residual Error 27 0.0120755 0.0004472

Total 28 0.0126571

Regression Analysis: ÎXIC Base Month versus ÎXIC Before Flash Crash The regression equation is

ÎXIC Base Month = 0.00403 - 1.17 ÎXIC Before Flash Crash


Constant 0.004028 0.004445 0.91 0.373

ÎXIC Before Flash Crash -1.1691 0.5459 -2.14 0.041

S = 0.0239283 R-Sq = 14.5% R-Sq(adj) = 11.4%


Page | 36

Source DF SS MS F P

Regression 1 0.0026263 0.0026263 4.59 0.041

Residual Error 27 0.0154592 0.0005726

Total 28 0.0180854

Regression Analysis: ÎXIC Base Month versus ÎXIC After Flash Crash The regression equation is

ÎXIC Base Month = 0.00378 - 0.169 ÎXIC After Flash Crash


Constant 0.003785 0.004782 0.79 0.436

ÎXIC After Flash Crash -0.1685 0.3203 -0.53 0.603

S = 0.0257494 R-Sq = 1.0% R-Sq(adj) = 0.0%


Source DF SS MS F P

Regression 1 0.0001835 0.0001835 0.28 0.603

Residual Error 27 0.0179019 0.0006630

Total 28 0.0180854

Regression Analysis: ^GSPC Base Month versus ^GSPC Before Flash Crash The regression equation is

^GSPC Base Month = 0.00400 - 0.753 ^GSPC Before Flash Crash


Constant 0.003998 0.004370 0.91 0.368

^GSPC Before Flash Crash -0.7528 0.5070 -1.48 0.149

S = 0.0235286 R-Sq = 7.5% R-Sq(adj) = 4.1%


Source DF SS MS F P

Regression 1 0.0012204 0.0012204 2.20 0.149

Residual Error 27 0.0149470 0.0005536

Total 28 0.0161674

Regression Analysis: ^GSPC Base Month versus ^GSPC After Flash Crash The regression equation is

^GSPC Base Month = 0.00391 + 0.089 ^GSPC After Flash Crash


Constant 0.003910 0.004535 0.86 0.396

^GSPC After Flash Crash 0.0889 0.2715 0.33 0.746

S = 0.0244218 R-Sq = 0.4% R-Sq(adj) = 0.0%


Page | 37

Source DF SS MS F P

Regression 1 0.0000639 0.0000639 0.11 0.746

Residual Error 27 0.0161035 0.0005964

Total 28 0.0161674

Exhibit 15:

capital market disruptions - babson collegefaculty.babson.edu/goldstein/teaching... · knight...

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