Computer Glitches

Tom Callahan

Mike Driscoll FIN 3560 01

Kyle Gietzen Financial Markets and Instruments

Troy Starrett

December 9, 2013

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Contents Executive Summary ...................................................................................................................................... 2

Introduction ................................................................................................................................................... 3

NASDAQ Computer Glitches ....................................................................................................................... 6

August 22nd

, 2013: Freeze Flash ............................................................................................................... 6

September 4, 2013: NASDAQ Outage ..................................................................................................... 9

October 29th, 2013 ................................................................................................................................... 11

Regression Analysis .................................................................................................................................... 12

August 22, 2013 ...................................................................................................................................... 14

September 4, 2013 ................................................................................................................................... 14

October 29, 2013 .................................................................................................................................... 15

Bond ETFs .............................................................................................................................................. 16

Conclusion .................................................................................................................................................. 17

Honor Code ................................................................................................................................................. 18

References: .................................................................................................................................................. 19

Exhibits: ...................................................................................................................................................... 21

2

Executive Summary

Computer glitches, once viewed as a rarity, have become a more prevalent issue during the 21st

century that traders encounter. As a result of the increasing trends in technology, today, trading occurs at

the blink of an eye with highly complicated algorithms. Trading is no longer conducted between two

individuals on the trading floor of the stock market, the developments in technology has drastically

reduced human involvement and interaction. As Wall Street has been forced to adapt to these changes and

convert to High Frequency Trading, these trading glitches have become more and more common. Even if

the glitch only lasts for a few seconds, it still has the ability to create radical impacts. The SEC is

currently seeking innovative methods for companies to monitor these glitches and test their algorithms

before “going live”. Though these measures assist in the reduction of risk, failures in technology continue

to occur.

In order to better comprehend the implications that computer glitches have on Financial

Markets, we gathered the price and volume of the NASDAQ Composite Index and three other indices. To

precisely analyze the computer glitch, we complied hourly data for the five trading days before and after

the glitch. The three main computer glitches that we analyzed occurred on the NASDAQ over the past

four months: August 22nd

, 2013, September 4th, 2013, and October 29

th, 2013. As we ran the regression

output for this data, our focus was to analyze was whether or not computer glitches had a lasting effect on

the market after they occurred. Before running the regression models, we hypothesized that the market

would be extremely volatile after the computer glitches occurred - ensuing an unpredictable fluctuation of

volumes and prices. We thought there would be a much higher correlation (a higher r-squared value)

between the various independent variables before the glitch and there would be a much lower correlation

after the glitch. However, after analyzing the regression output we concluded that there was, in fact, a

higher correlation between the change in NASDAQ price and the five independent variables after the

computer glitch occurred. Therefore, it is evident that there is actually more security and correlation in the

market after a computer glitch. Given the results of our regression models, we believe the observed trends

can be explained by investors trading parallel to ETF’s and the indices rather than buying or selling

individual securities - creating a more diversified portfolio. Traders want to remain highly diversified in

order to protect their portfolios from the possibility of another computer glitch. This is an effective

precautionary measure. Therefore, we can conclude that although computer glitches have the ability to

cause severe negative implications, there is not an alarming lasting effect on the markets after a glitch.

3

Introduction

Computer trading glitches have become more common within recent indices, companies, and the

lives of private investors. As computer glitches seem to occur more frequently, investors will want the

answers to many questions pertaining to how trading will resume efficiently. Are these issues ones that

should greatly worry the investors, or will they occur with such frequency that traders will have to learn

to cope with due to the adoption of high frequency trading? A glitch can be defined in many ways: a

computer malfunction, a computer algorithm misinterpreting information, or human error, where entering

the wrong data into the computers program can cause grave repercussions. Computer glitches can cause

companies to unknowingly buy or sell securities that traders consciously would not, potentially resulting

in the loss of millions of dollars. The event can also lead to a company’s stock price to plummet in a

matter of seconds, or conversely, it can affect the small independent trader. The small independent trader

may not be able to view various stock prices, causing unknown fluctuations in their portfolio. However,

traders are starting to treat these glitches as the “norm”, “but traders are nothing if not pragmatic. They

seem to realize now that these halts are features of the modern market, not random bugs.”1 While glitches

seem to still cause panic in the public’s eye, it seems as though a computer or trading glitch is just another

day on Wall Street.

A prime example of the effects of computer glitches can be examined through the malfunction

that occurred within Knight Capital Group’s software. Knight Capital Group faced a trading glitch that

cost them upwards of $440 million. “Knight Capital Group said the problem was triggered when it

installed new trading software, which resulted in the company sending numerous erroneous orders in 140

stocks listed in the New York Stock Exchange. Those orders were responsible for some sudden swings in

stock prices and surging trading volume shortly after the market opened Wednesday.”2 Knight’s new

software experienced an algorithm glitch which reduced the value of the company by nearly 70%. The

1 Vigna, Paul. "Exchange "Glitches" Are a Feature of the System, Not a Bug." Wall Street Journal. 29 Oct 2013: n. page. Print.

<http://blogs.wsj.com/moneybeat/2013/10/29/exchange-glitches-are-a-feature-of-the-system-not-a-bug/?KEYWORDS=trading

glitches>. 2 Gogoi, Pallavi. "Knight Capital blames software for computer trading glitch." USA TODAY: Money. 02 Aug 2012: n. page.

Print. <http://usatoday30.usatoday.com/money/economy/trade/story/2012-08-02/Knight-Capital-trading-glitch/56692822/1>.

4

stock price fell from $10.32 per share to $2.86 per share in a matter of two days.3 Knight Capital suddenly

came to terms with the possibility of bankruptcy and quickly sought assistance from investors; a capital

infusion was necessary in order for the survival of the company. In the meantime, the company was not

only losing customers, but they were forced to turn down many trades. At that moment, Knight Capital

was financially unstable, “Alarmed at the snafu and worried about further glitches, some customers said

they moved their business elsewhere on Thursday. Knight also requested some customers stop sending it

trades, according to brokers and traders. That is because Knight is required to set aside more capital

against each trade, so the more business it does, the more capital it needs. And, after the loss, the firm was

determined to keep its capital demands to a minimum.”4 Knight ended up finding a group of Wall Street

investors to bail them out for $400 million. These investors included: the Jefferies Group, Blackstone,

GETCO, Stephens, Stifel Financial Corp and TD Ameritrade Holding Corporation.5 Since Knight Capital

failed to take proper precautions in the development of their computer software, the SEC fined them an

additional $12 million.

Another computer glitch that severely affected investors took place on the day that this social

media giant went public. One of the most highly anticipated IPO’s in history, Facebook Inc. (FB), was

affected by a computer glitch which forced the NASDAQ to shut down trading on their first day as a

public company due to a poor software design. Because of the software glitch, investors were given the

impression that people were selling FB due to the lack of updated prices. Again, this was caused by the

computer glitch. “Computer systems used to establish the opening prices were overwhelmed by order

cancellations and updates”6 “NASDAQ's software for initial public offerings allows investors to cancel or

update details of orders until the auction runs. Trade requests received during the 5 milliseconds it took to

operate the auction disturbed the process, leading to an imbalance of buys and sells and sending the

3 Ibid. 4 Strasburg, Jenny, and Jacob Bunge. "Loss Swamps Trading Firm." Wall Street Journal. 02 Aug 2012: n. page. Print.

<http://online.wsj.com/news/articles/SB10000872396390443866404577564772083961412>. 5 Brinded, Lianna. "Knight Capital Fined for $460m NYSE Trading Glitch." International Business Times. 17 October 2013: n.

page. Print. <http://www.ibtimes.co.uk/articles/514583/20131017/knight-capital-trading-glitch-sec-bailout-brokerage.htm>. 6 Mehta, Nina. "Nasdaq blames software for Facebook IPO glitches." SFGate. 19 Jul 2013: n. page. Print.

<http://www.sfgate.com/business/article/Nasdaq-blames-software-for-Facebook-IPO-glitches-3575285.php>.

5

program into a loop.”7 NASDAQ originally proposed to pay nearly $13 million back to investors whose

trades were not executed due to the glitch. However, the final number was much greater. NASDAQ ended

up paying $62 million as a result of their error.8 The SEC also fined the NASDAQ Stock Market $10

million.9 It is estimated that the Wall Street firms who were unable to buy shares of Facebook loss

approximately $500 million.10

As evident from the loss in funds, not only did the software glitch cause

Facebook’s stock to drop, but it also negatively impacted investment banks and the NASDAQ Stock

Market. Given this scenario, we feel as though companies will be more hesitant when deciding whether to

use the NASDAQ for their IPO’s as they want to avoid the possibility of a computer glitch.

Furthermore, the financial giant, Goldman Sachs, even faced a trading glitch this past August

when a programming error caused trading orders to be sent to various exchanges with the wrong prices.

Many of these trades were being executed at a price of $1 instead of the correct market price, “In one

trade of options on the iShares Russell 2000 ETF, Goldman sold 993 call options for $1 apiece, or

$99,300. Based on the price of options at the close Tuesday, those contracts should have sold for about

$35.72 a share, or $3.55 million.”11

The losses that resulted from the trades were expected to amount to

$100 million. However, after looking into the trades, the NYSE Euronext- where the majority of the

trades were placed- decided to cancel the majority of trades in order to avoid unnecessary affects that it

could have on the market.12

However, people who tried to hedge portfolios based upon Goldman Sachs’

order may face huge losses since their orders would not be cancelled. The Financial Times stated,

“…traders that were counterparties to the Goldman orders were worried that if the trades were cancelled,

7 Ibid 8 Constine, Josh. "NASDAQ's Glitch Cost Facebook Investors $500M. It Will Pay Out Just $62M. IPO

Elsewhere.." TechCrunch. 25 Mar 2013: n. page. Print. <http://techcrunch.com/2013/03/25/ip-oh-my-gosh-all-that-money-just-

disappeared/>. 9 Tausche, Kayla. "Nasdaq to Pay $10 Million to Settle SEC Charges Over Facebook IPO." CNBC. n.d. n. page. Print.

<http://www.cnbc.com/id/100736915>. 10 Bunge, Jacob, Justin Baer, and Kaitlyn Kiernan. "Goldman Issues Mistaken Options Orders, Roiling Prices."Wall Street

Journal. 20 Aug 2013: n. page. Print.

<http://online.wsj.com/news/articles/SB10001424127887324747104579024964124614096>. 11 Ibid 12 Ritter, Dan. "Goldman Dodges Losses But Concerns Remain After Trading Glitch." Wall St. CheatSheet. 27 Aug 2013: n.

page. Print. <http://wallstcheatsheet.com/stocks/goldman-dodges-losses-but-concerns-remain-after-trading-glitch.html/>.

6

then hedges used to offset the positions could run against the firms and cause them losses.”13

Many

people were affected by this computer glitch, including investors and members of Goldman Sachs.

Goldman Sachs suspended four senior technology specialists and put them on administrative leave. This

glitch is something that not only affected the company responsible for the glitch, but also affected

companies trying to hedge these investments.

NASDAQ Computer Glitches

August 22nd

, 2013: Freeze Flash

The August 22nd

NASDAQ glitch was a problem with the software that caused the market to halt

trading for almost three hours. The halt affected over two thousand stocks that are traded on the exchange

including big names such as Apple, Google, and Facebook. This was a devastating blow as approximately

20% of the companies in the S&P 500 index call NASDAQ their home exchange.14

The NASDAQ

reported that the glitch was caused by a problem in the software that publishes the prices of the stocks

listed on the exchange. It has been reported that the glitch halted approximately $5.7 trillion in shares.

This number is enormous because the NASDAQ acts as a central exchange, other exchanges, such as the

NYSE Euronext, depend on it for pricing. This glitch also affected systems that service professional

investors who deal in large blocks of thousands of shares, rendering them unable to trade as well. Prices

were not being listed for the stocks so they were unable to be traded on any platform, not just the

NASDAQ.15

This glitch cost the brokerage firms a considerable amount of money. Firms get paid when they

execute trades and when the market freezes like this so does their income. The specific problem was the

securities information processor, or SIP. This is the tool that consolidates stock prices, and it was not

disseminating price quotations. It was tabbed as a connectivity issue between an unnamed exchange party

13 Massoudi, Arash, Tracy Alloway, and Neil Munshi. "Goldman Faces Losses on Erroneous Trades."Financial Times. 21 Aug

2013: n. page. Print. <http://www.ft.com/intl/cms/s/0/f95200d6-09ad-11e3-ad07-00144feabdc0.html 14 Moore, Heidi. "Nasdaq Glitch Prompts Two-hour Shutdown and Fears over Stability."The Guardian. N.p., 23 Aug. 2013.

Web. 15 Ibid.

7

and the SIP.16

This system operates by having thirteen exchanges send bid and offer prices on NASDAQ

listed stocks.17

A source close to the matter said that it was a connection issue with NYSE Euronext Inc.’s

Arca Exchange. The SIP was unable to revert to a backup system causing trading to halt.18

NASDAQ did

not provide much detail about what had specifically gone wrong with the system. They did however say

that the issue was resolved within thirty minutes and that they worked with other exchanges, regulators,

and market traders to ensure a smooth transition back in to trading.19

The problem originally surfaced at 12:14:03 PM when all traffic through the NASDAQ suddenly

stopped, including options trading. During this outage trading of shares not listed on NASDAQ were not

affected, but no transactions could be executed on the NASDAQ platform itself. Trading did resume in

normal volume around 3:25 PM.

There seemed to be a general consensus between all the brokers and regulators that NASDAQ

should not rush back to trading because if a problem were to occur again it would be ten times as worse as

before. They wanted NASDAQ to take their time and get the system running correctly before trading

would resume. James Angel a finance professor at Georgetown University said that “we can live with the

market being closed for a little bit, but we can’t live with bad pricing.”20

Angel is on the board of another

exchange operator, Direct Edge, and he also said that NASDAQ “appeared to take steps to ensure that

trading reopened in an orderly fashion and with correct pricing.”21

NASDAQ’s own stock was up 0.8%

before the glitch and finished down 3.4%.

So what is being done to make the system more secure? NASDAQ and the rest of the trading

platforms must decide whether they want to stay with the SIP system or move to something new.

NASDAQ CEO, Robert Greifeld, said that he is supporting possible alternatives such as using multiple

16 Mikolajczak, Chuck, and Campos, Rodrigo. "Nasdaq Market Paralyzed by Three Hour Shutdown." Reuters. Thomson Reuters,

22 Aug. 2013. 17"Trading Glitches a Sad New Market Reality." CNNMoney. Cable News Network, 22 Aug. 2013. Web. 18 McCrank, John. "Glitch Hits Nasdaq System at Center of Trading Outage." Reuters. Thomson Reuters, 04 Sept. 2013. Web. 19 Mikoljczak and Campos 20 Ibid. 21 Ibid.

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data feeds to provide bench-mark pricing information.22

Following another small glitch in the SIP on

September fourth a NASDAQ official stated “Our objective is to learn from the events and work together

to identify appropriate solutions that improve the reliability, governance and operations of the [Securities

Information Processor],”23

NASDAQ officials also said that it has installed a new process that will

automatically disconnect processers servicing the data feed when an outage is experienced. This will help

to ensure the shift to a backup system. Another precautionary measure the NASDAQ is taking is to

implement a system in which data feed operators will manually disconnect exchanges if they see

“unusually high traffic” flooding the system.24

This new process of having the data feed operators

manually disconnect the exchange may not be the best solution because as evident in the other glitches

human error can also play a major role. The implementation of this program would require extensive

training, but could be successful. Before, the system would not cut out the exchange because it did not

sense a problem, but now humans who are trained to spot irregularities will be able to pull the plug before

the glitch effects trading.

The SEC is also looking to implement higher standards for trading systems to help ensure that

glitches do not occur. Back in March of 2013 the SEC proposed a new set of rules called Regulation SCI

that would create new enforceable standards for trading systems. These rules would affect the

maintenance and testing of trading systems used by both exchanges and brokers. If approved by the

agency, these rules would replace the guidelines that are now in place regarding trading technology.

However, these new rules are being opposed by exchanges and brokers due to fears over cost and scope.25

As evident, NASDAQ and other trading platforms want to instill confidence in investors that their

systems are reliable, but they do not seem willing to spend the money to get it done.

22 Bunge, Jacob. "Nasdaq Suffers Another Gllitch." WSJ Online. Wall Street Journal, 4 Sept. 2013. Web. 23 Ibid. 24 Ibid. 25 Bunge, Jacob, and Scott Patterson. "SEC and Exchange Chiefs to Discuss Market Glitches."WSJ Online. Wall Street Journal,

11 Sept. 2013. Web.

9

September 4, 2013: NASDAQ Outage

About two weeks after NASDAQ was hit by the three-hour “Flash Freeze,” the index faced a less

severe glitch, however, still noteworthy. On September 4, 2013, NASDAQ experienced an outage that

put stock quotes in the dark. NASDAQ claims the outage took place between 11:35 AM and 11:41 AM

However, NYSE Arca reported that quotes were unavailable from 11:35:09 AM to 11:44:32 AM26

The

three and a half minute difference in outage durations does not seem like much, but it brings up the

underlying problem of NASDAQ’s inability to prevent glitches and accountability in their security.

Similar to the “Flash Freeze,” both glitches traced back to conflicts with the servers at the UTP SIP, the

security responsible for thirteen exchanges that send quotes to NASDAQ.27

NASDAQ reported that only one of its quote channels was affected by the glitch. This specific

dissemination channel is made up of stocks with ticker symbols from PC to SPZ. Due to the limited

quotes affected and short duration, NASDAQ stated that the market was back to its normal position by

the end of the trading day. However, the September 4, 2013 glitch in addition to the “Freeze Flash”

brought enough attention to committee members, regulators, and traders for review of the SIP.28

Many of

the people affected by the glitch such as Joe Saluzzi, co-founder of Themis Trading, believe the two

recent outages may have caused enough skepticism with NASDAQ’s SIP to lose confidence and reduce

trading. If outages become a problem traders should expect, NASDAQ’s position as a major index should

be reconsidered.29

The six minute glitch had further effects to the stock market among different exchange operators.

Direct Edge reported that NASDAQ’s quotes were unavailable from 11:43 AM to 11:48 AM. The

exchange also faced a wider range of stocks affected from ticker symbols PBCP to ZZZZ. As different

exchange operators presented differing results of the latest glitch, the aftermath was the same throughout.

26 Farrell, Maureen. "Nasdaq: Another Week, Another Glitch." CNNMoney. Cable News Network, 04 Sept. 2013.

<http://money.cnn.com/2013/09/04/investing/nasdaq-trading-glitch/>. 27 IBID. 28 Shell, Adam. "Nasdaq: Tech Glitch Caused 'brief Outage'" USA Today. Gannett, 4 Sept. 2013.

<http://www.usatoday.com/story/money/markets/2013/09/04/nasdaq-reports-another-brief-outage/2762847/>. 29 IBID

10

Unlike the “Freeze Flash,” this minor outage was able to rely on a backup server. After the September 4

glitch, scares of another larger glitch brought a short meeting to brainstorm possible solutions that would

prevent a future catastrophe. Parallel to the conclusions drawn after the “Flash Freeze,” the simplest

solution mentioned would be to disconnect exchanges and traders if traffic becomes high enough to pose

a threat on the SIP’s data limits.30

The causes behind the “Freeze Flash” and September 4 glitches are clearly different from a

software and technology bug to server outage. Both had different responses to the situation along with

NASDAQ’s different explanations. NASDAQ felt no fault for the “Freeze Flash” and labeled

uncontrollable factors as the driving force. For the September 4 outage, NASDAQ had to take the blame

because it was their hardware and server that crashed. The responsibility behind glitches has become a

main focus point for NASDAQ and NYSE Euronext. NASDAQ currently holds the majority of large U.S.

technology stock listings.31

NYSE took advantage of the “Freeze Flash” to highlight the NASDAQ’s

inability to prevent flaws. This battle for highly valued stock listings has seen plenty of other gruesome

attacks on indexes. NASDAQ used a NYSE market maker, Knight Capital, as a target in the tech listing

war. As mentioned on page 3, Knight Capital faced a glitch on August 2, 2012 that resulted in a loss of

$440 million.32

In response to NASDAQ’s September 4 outage, NYSE made it clear that all their equities

were to operate normal throughout the glitch.33

As soon as the September 4, 2013 outage hit NASDAQ, formal plans to change their current

operating system began to make way. The Financial Industry Regulatory Authority and various U.S.

exchanges sought out possible solutions to fix the bugs and potential outages.34

There have been a few

key areas that will gain more focus in the future to prevent glitches such as IPOs. Attempts to backup

30 McCrank, John. "Glitch Hits Nasdaq System at Center of Trading Outage." Reuters. Thomson Reuters, 04 Sept. 2013.

<http://www.reuters.com/article/2013/09/05/us-nasdaq-outage-idUSBRE9830Y920130905>. 31 Trefis Team. "Nasdaq’s Problems Continue After Another Software Glitch." Trefis. N.p., 5 Sept. 2013.

<http://www.trefis.com/stock/ndaq/articles/204567/nasdaqs-problems-continue-after-another-software-glitch/2013-09-05>. 32 Strasburg, Jenny. “Loss Swamps Trading Firm: Knight Capital Searches for Partner as Tab for Computer Glitch Hits $440

Million.” The Wall Street Journal. WSJ Online, 02 Aug. 2013 <

<http://online.wsj.com/news/articles/SB10000872396390443866404577564772083961412> 33 Trefis Team. 34 McCrank, John.

11

NYSE and NASDAQ data to each other’s SIPs has been brought up in discussion. However, the

likelihood of collaboration has proven inexistent in recent meetings.35

It would seem as if the NYSE and

the NASDAQ are the ones truly against the collaboration, but it is truly the formatting of their SIP’s data.

Although the formatting is different, both stock exchanges share Intercontinental Exchange Group as their

SIP operator. It is likely to see both exchanges make changes to their SIP to strengthen their backup

plans. This could allow more time to focus on a larger plan to enhance their overall SIP.36

October 29th

, 2013

The NASDAQ experienced another computer glitch on October 28th, 2013. This time trading

halted between 11:52 AM and 12:37 PM, approximately 45 minutes. Representatives from the NASDAQ

index said that the “glitch” was a result of human error and was not due to any problems with the

computers themselves. Another representative stated, “The disruption was caused by a human error

performing an operational function which resulted in the incorrect delivery of data to the index

distribution system.” While representatives claim that no individual stocks traded on the exchange were

affected, they did halt the, “trading in contracts linked to the affected indexes for about an hour. Rival

exchanges, including the Chicago Board Options Exchange and CME Group Inc. continued to trade

NASDAQ’s index-linked products, according to officials at those exchanges.”37

While this is not the

same type of computer glitch that has happened in the past, it is still an error that will make investor more

hesitant and cautious when trading. Many investors believe that the “human error glitch” continues to

show how poorly run NASDAQ’s IT department is. Neil Kinson, the vice president of EMEA Redwood

Software said, “Blaming its most recent disruption on ‘human error’, NASDAQ’s string of technology

mishaps raises questions about just how seamless its IT processes really are.”38

Kinson goes on to say that

35 Lash, Herbert. "Analysis: U.S. Exchanges Grapple for Solutions to Trading Glitches." Reuters. Thomson Reuters, 26 Nov.

2013. <http://www.reuters.com/article/2013/11/26/us-exchanges-glitches-solutions-analysis-idUSBRE9AP11L20131126>. 36 IBID. 37 Kiernan, Kaitlyn, and Jacob Bunge. "Nasdaq Glitch Prompts Trading Halt in Some Markets." Wall Street Journal. 29 Oct

2013: n. page. Web.

<http://online.wsj.com/news/articles/SB10001424052702304200804579165703242921222?KEYWORDS=NASDAQ glitch >. 38 Finnegan, Matthew. "Nasdaq Hit by Another Computer Glitch, Blames 'Human Error." ComputerWorld. 13 Ocotber 2013: n.

page. Web. <http://www.computerworlduk.com/news/applications/3476353/nasdaq-hit-by-another-computer-glitch-blames-

human-error/>.

12

NASDAQ needs to make these technical glitches a rarity, not something that their IT department has to

deal with on a normal basis. He believes that by using more automated manual checks within the system

for these “glitches” that the chances of human error will be greatly reduced, “By using tightly automated

processes to keep everything running smoothly, IT teams will only need to handle technical glitches on

exception. Collaboration across the business model will be key if NASDAQ is to sustain market

confidence – it’s far more than just reputations on the line here.”39

Therefore, even though the glitch on

October 28th did not have severe consequences, there is always the possibility that a glitch as short as 45

minutes could lead to extreme outcomes. If a problem like this occurred when the markets were

experiencing extremely high trading volumes, the possible effect could turn into a more long term market

panic.40

Regression Analysis

As mentioned above, a slight glitch in any stock market has the ability to lead to immense gains

or losses for both corporations and individuals. As an investor and or as a trader, it is crucial to be able to

recognize and understand the effects of computer glitches on the overall market and trade accordingly. In

order to fully comprehend the impact on the market as a whole, we chose to analyze three NASDAQ

computer glitches: August 22, 2013, September 4, 2013, and October 29, 2013. Bloomberg’s hourly

prices and volumes of four indices of similar nature were gathered in order to capture a broad array of

market data. The four indices include: NASDAQ Composite Index (CCMP), Dow Jones Industrial

Average (INDU), S&P 500 Index (SPX) and the Russell 3000 Index (RAY). In order to normalize each of

the four indices, the percentage change in price from the previous time period was calculated and used as

inputs when running the regression models. Since the four indices are not traded and are solely used as an

indicator of the performance of stocks, daily and hourly volume are not discoverable. Therefore, we used

four ETF’s, which closely tracked the corresponding indices, as proxies for the volume inputs. The ETF’s

39Ibid. 40 Kiernan, Kaitlyn, and Jacob Bunge. "Nasdaq Glitch Prompts Trading Halt in Some Markets." Wall Street Journal. 29 Oct

2013: n. page. Web. <http://online.wsj.com/news/articles/SB10001424052702304200804579165703242921222?KEYWORDS=NASDAQ glitch>.

13

used include: Fidelity NASDAQ Composite ETF (ONEQ), SPDR Dow Jones Industrial Average ETF

(DIA), SPDR S&P 500 ETF (SPY), and iShares Russell 3000 ETF (IWV). Again, in order to normalize

the trading volume, the percentage change in price from the time period before was calculated (time

periods where volume traded was not available were excluded from the regression model).

Using the NASDAQ Composite Index as the base, our goal was to run regressions in order to see

whether or not the computer glitches had a lasting effect on the stock markets beyond the day that the

glitch occurred. To measure this we gathered hourly data for five trading days prior to the glitch, ran

regressions, and compared it to the regression models of the data five trading days after the glitch. In

doing so our goal was to determine how trends varied before and after the various computer glitches.

Having this in mind, we set the change in the NASDAQ Composite Index price as the dependent variable

(input Y variable), and used the percentage change in volume of the NASDAQ Composite Index and

percentage change of volume and prices of the other three indices as the dependent variables (input x

variable). Then, we ran another regression using a lag of one time period for each of the five independent

variables. In doing so, we wanted to see if there was any correlation from the previous time periods. Once

regression outputs were calculated for each of the three dates, we found the three most significant

variables by using a p-value of less than 0.3 and ran another set of regressions. The three most common

significant variables included: the percentage change in NASDAQ volume, the percentage change in Dow

Jones Industrial Average Price, and the percentage change in the Russell 3000 price. The last set of

regressions that we ran used only the percentage change in volume as the independent variables. The

percentage change of the NASDAQ volume was used as the dependent and the other three indexes were

used as the independent variables. The goal in running this regression was to see if there was an extreme

change in volatility after the computer glitch occurred; however, for this particular regression model we

were unable to conclude any statistical significance (Exhibit 1, 2, 3, 4, 5, 6). A further analysis of each of

the regression outputs for the various dates is explained below.

14

August 22, 2013

On August 22, 2013, a computer glitch in the NASDAQ Stock Market occurred which led to a

trading halt that lasted for three straight hours (12:14 PM – 3:25 PM) 41

. During this time, traders were

unable to trade, which led to a constant flat line in the NASDAQ Index as illustrated in Exhibit 7. As

evident in the regression output calculated by Excel (Exhibit 8), prior to the glitch, the NASDAQ price

was highly correlated with the other five variables. The r-squared value that excel calculated was

0.877827. Given the similarities of the various indices, this was an output that we expected to see.

Therefore, we continued to run a regression on the same variables, however, for the time period of five

trading days after the glitch. To our surprise, the NASDAQ price had an even higher correlation with the

five independent variables. As highlighted in Exhibit 9, the r-squared value that excel calculated when

running the regression was 0.973492. The high r-squared value proves that there is a higher correlation

with the indices for the trading days beyond the actual computer glitch. In order to further analyze the

data, we put a lag on the independent variables of one full time period. After running the regression, we

concluded that there was no statistical significance. The r-squared variables for before and after the

computer glitch with a lag were, 0.261651 (Exhibit 10) and 0.201127 (Exhibit 11) respectively. After

running these two regressions, we ran a third regression on the three most significant variables. Much to

our surprise, the r-squared output came out to be lower for both before and after the glitch, 0.864034

(Exhibit 12) and 0.968092 (Exhibit 13) respectively. Originally we believed that the most significant

variables would have a higher correlation; however, we were proven wrong.

September 4, 2013

On September 4, 2013, a computer glitch occurred in the NASDAQ Stock Market and traders

were unable to see the price of stocks for six minutes (11:35 AM - 11:41AM) 42

. Unlike the computer

glitch that occurred on August 22, 2013, the NASDAQ Stock Market did not experience a trading halt

(Exhibit 14). Stocks were still able to be traded; however, the price was unknown for the short period of

41 Mikolajczak, Chuck, and Campos, Rodrigo. 42

Farrell, Maureen.

15

time. As evident in the regression output calculated by Excel (Exhibit 15) prior to the glitch, the

NASDAQ price was highly correlated with the other five variables. The r-squared value that excel

calculated was 0.967457. We continued to run a regression on the same data, however, for the five trading

days after, and unlike the August 22, 2013 glitch, the r-squared output was lower after the glitch. As

highlighted in Exhibit 16, the r-squared value that excel calculated was 0.952079. The lower correlation

after the glitch may be due to the fact that the glitch did not halt trading, it simply did not display stock

prices for a few minutes. Another factor that may have led to a lower r-squared value after the computer

glitch on September 4, 2013, may have resulted because the markets were still experiencing the high

correlation effects that took place after the computer glitch on August 22, 2013. In order to further

analyze the data, we put a lag on the independent variables of one full time period. After running the

regression, we concluded although they were higher than the previous glitch, there was no statistical

significance when interpreting these results. The r-squared variables for before and after the computer

glitch with a lag were, 0.431003 (Exhibit 17) and 0.419740 (Exhibit 18) respectively. After running these

two regressions, we ran a third regression on the three most significant variables. Again, much to our

surprise, the r-squared output came out to be lower for both before and after the glitch, 0.948092 (Exhibit

19) and 0.940323 (Exhibit 20) respectively.

October 29, 2013

On October 29, 2013, a similar occurrence to the August 22, 2013 computer glitch took place. On

this day, trading halted in the NASDAQ Stock Market for about an hour (11:52 AM - 12:37 PM) 43

as

illustrated by the flat line in Exhibit 21. In the regression output calculated in Excel (Exhibit 22), prior to

the glitch, the NASDAQ price was highly correlated with the other five independent variables. Excel

calculated an r-squared value of 0.897660. We proceeded by running a regression on the same variables

for the time period of five trading days after the glitch. Similar to the glitch that occurred on August 22,

the NASDAQ price had an even higher correlation with the other five variables. As highlighted in Exhibit

43 Kiernan, Kaitlyn, and Jacob Bunge.

16

23, the r-squared value calculated by excel was 0.902757. Much like the glitch that took place on August

22, 2013, the higher r-squared value, even though it was minor, proves that there is a higher correlation

between the indices for the trading days beyond the actual computer glitch. In order to further analyze the

data, we put a lag on the independent variables of one full time period. After running the regression,

again, we concluded that there was no statistical significance. The r-squared variables for before and after

the computer glitch with a lag were, 0.099176 (Exhibit 24) and 0.221651 (Exhibit 25) respectively. After

analyzing these two regression models, we ran a third regression using the three most significant

variables. Similar to the other two glitches, the r-squared output calculated was lower for both before and

after the glitch, 0.856569 (Exhibit 26) and 0.889378 (Exhibit 27) respectively. Given that the independent

variables that excel calculated to be the most significant did not result in higher r-squared values for each

of the three dates that we analyzed, we concluded that the more variables that are used in the regression

model, the better the relationship will be.

Bond ETFs

Since we did not discover any extreme result when analyzing the regression models on the four

indices, we ran another set of regressions using bond indices as the dependent variables. The goal behind

running these regressions was to see if the computer glitch at the NASDAQ Stock Market pushed

investors away from trading stocks and into the bond market. The dependent variable remained the same,

change in price of the NASDAQ Composite Index, however, the independent variables changed. The

three independent variables were composed of various bond ETFs: SPDR Barclays Aggregate Bond ETF

(LAG), iShares 20+ year Treasury Bond ETF (TLT), and iShares iBoxx $ Investment Grade Corporate

Bond ETF (LQD). Similar to the regressions that were performed on the stock indices, we calculated the

hourly percentage change in price for each of the three independent variables. We then ran a regression

for the time period before and after the glitch. We concluded that there was no statistical relationship

between the percentage change in NASDAQ price and the percentage change of the prices of the three

17

bond ETF’s. The highest r-squared value calculated was 0.5581 (Exhibit 28, 29, 30, 31, 32, 33). As

evident, there was no relationship between the variables.

Conclusion

Data has given us insight that glitches are more prevalent in this time of great technological

innovation. In order to better protect investors and traders, further safety measures need to be made to

prevent these glitches from occurring. After thoroughly analyzing each of the three computer glitches

that occurred on the NASDAQ Stock Market we were able to discover trends that allowed us to draw

conclusions. As mentioned above, the computer glitches that occurred on August 22 and on October 29

were very similar in nature. Unlike the glitch that took place on September 9th, these two glitches caused

trading to completely stop for a certain time period. Given their comparability, the regression outputs

produced very similar results; the given variables for the five days after the computer glitch were proven

to have a higher r-squared value than before the glitch. We can conclude that after computer glitches

investors and traders are more cautious when trading, so they trade parallel to the other indices. One

would expect after a market scare, like a glitch, the market would be extremely volatile and the r-squared

value would be closer to zero- thus, insinuating that there is no correlation between the indices. However,

based on our analysis of the regression output, we can conclude that there is actually slightly more

security in the market after a glitch. We believed that this was caused from traders paying more attention

to ETF’s and to the indices rather than buying or selling individual securities. Given the negative effects

that a glitch can have on a portfolio, we believe that traders want to remain highly diversified in order to

protect their portfolios from the possibility of another computer glitch. Overall, we can conclude that even

though computer glitches have the ability to severely impact investors, there is not an alarming lasting

effect on financial markets after a glitch.

18

Honor Code

“We pledge our honor that we have neither received nor provided any unauthorized assistance during the

completion of this work.”

“The authors of this paper hereby give permission to Professor Michael Goldstein to distribute this paper

by hard copy, to put it on reserve at Horn Library at Babson College, or to post a PDF version of this

paper on the internet.”

Tom Callahan: ____________________________________

Mike Driscoll: ____________________________________

Kyle Gietzen: _____________________________________

Troy Starrett: ______________________________________

19

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capital-trading-glitch-sec-bailout-brokerage.htm>.

Bunge, Jacob, and Scott Patterson. "SEC and Exchange Chiefs to Discuss Market Glitches."WSJ Online.

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KEYWORDS=Nasdaq+Glitch>

Bunge, Jacob, Justin Baer, and Kaitlyn Kiernan. "Goldman Issues Mistaken Options Orders, Roiling

Prices."Wall Street Journal. 20 Aug 2013: n. page. Print.

<http://online.wsj.com/news/articles/SB10001424127887324747104579024964124614096>.

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http://online.wsj.com/news/articles/SB10001424127887324123004579055000015684192>

Constine, Josh. "NASDAQ's Glitch Cost Facebook Investors $500M. It Will Pay Out Just $62M. IPO

Elsewhere.." TechCrunch. 25 Mar 2013: n. page. Print. <http://techcrunch.com/2013/03/25/ip-oh-

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Farrell, Maureen. "Nasdaq: Another Week, Another Glitch." CNNMoney. Cable News Network, 04 Sept.

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glitch-blames-human-error/>.

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Kiernan, Kaitlyn, and Jacob Bunge. "Nasdaq Glitch Prompts Trading Halt in Some Markets." Wall Street

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WORDS=NASDAQ glitch >.

Lash, Herbert. "Analysis: U.S. Exchanges Grapple for Solutions to Trading Glitches." Reuters. Thomson

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solutions-analysis-idUSBRE9AP11L20131126>.

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Trades."Financial Times. 21 Aug 2013: n. page. Print.

<http://www.ft.com/intl/cms/s/0/f95200d6-09ad-11e3-ad07-00144feabdc0.html

20

McCrank, John. "Glitch Hits Nasdaq System at Center of Trading Outage." Reuters. Thomson Reuters, 04

Sept. 2013. Web.< http://www.reuters.com/article/2013/09/05/us-nasdaq-outage-

idUSBRE9830Y920130905>

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idUSBRE9830Y920130905>.

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3575285.php>.

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Shutdown." Reuters.

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shutdown-fears-stock-markets >

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21

Exhibits:

Exhibit 1: August 22nd

Volume Regression Output on Change in Volume before Glitch

Exhibit 2: August 22nd

Volume Regression Output on Change in Volume after Glitch

22

Exhibit 3: September 4th Volume Regression Output on Change in Volume before Glitch

Exhibit 4: September 4th Volume Regression Output on Change in Volume after Glitch

23

Exhibit 5: October 29th Volume Regression Output on Change in Volume before Glitch

Exhibit 6: October 29th Volume Regression Output on Change in Volume after Glitch

24

Exhibit 7: NASDAQ Composite Index August 22nd

*Bloomberg

Exhibit 8: August 22nd

Regression Output before Glitch

25

Exhibit 9: August 22nd

Regression Output after Glitch

Exhibit 10: August 22nd

Regression Output before Glitch with a lag

26

Exhibit 11: August 22nd

Regression Output after Glitch with a lag

Exhibit 12: August 22nd

Regression Output before Glitch with 3 Significant Variables

27

Exhibit 13: August 22nd

Regression Output after Glitch with 3 Significant Variables

Exhibit 14: NASDAQ Composite Index September 4th

*Bloomberg

28

Exhibit 15: September 4th Regression Output before Glitch

Exhibit 16: September 4th Regression Output after Glitch

29

Exhibit 17: September 4th Regression Output before Glitch with a lag

Exhibit 18: September 4th Regression Output after Glitch with a lag

30

Exhibit 19: September 4th Regression Output before Glitch with 3 Significant Variables

Exhibit 20: September 4th Regression Output after Glitch with 3 Significant Variables

31

Exhibit 21: NASDAQ Composite Index October 29th

*Bloomberg

Exhibit 22: October 29th Regression Output before Glitch

32

Exhibit 23: October 29th Regression Output after Glitch

Exhibit 24: October 29th Regression Output before Glitch with a lag

33

Exhibit 25: October 29th Regression Output after Glitch with a lag

Exhibit 26: October 29th Regression Output before Glitch with 3 Significant Variables

34

Exhibit 27: October 29th Regression Output after Glitch with 3 Significant Variables

Exhibit 28: August 22nd

Regression Output with Bond ETFs before Glitch

35

Exhibit 29: August 22nd

Regression Output with Bond ETFs after Glitch

Exhibit 30: September 4th Regression Output with Bond ETFs before Glitch

36

Exhibit 31: September 4th Regression Output with Bond ETFs after Glitch

Exhibit 32: October 29th Regression Output with Bond ETFs before Glitch

37

Exhibit 33: October 29th Regression Output with Bond ETFs after Glitch