Has GOOGLE Stock Price Been Manipulated?

Jerry Wenjiu Liu Assistant Professor of Finance California State University - East Bay College of Business and Economics Hayward, CA 94542 Tel: (510) 885-3933 Fax: (510) 885-7175 jerry.liu@csueastbay.edu

February 1, 2009

I thank Michael Brennan, Scott Fung, Robert Geske, Robin Greenwood, Bruce Lehmann, Terry Odean, Neil Pearson, Mark Ready, Stephen Shmanske, Dragon Tang, Pradeeo Yadav, Yi Zhou, and seminar participants at 2008 Bay Area Finance Colloquium, 2008 China International Conference in Finance for their helpful comments and suggestions. I also benefited from conversations with Larry Harris and Xiaoyan Ni. All errors are the sole prpoperty of the author.

1Electronic copy available at: http://ssrn.com/abstract=1337350

Has GOOGLE Stock Price Been Manipulated?

AbstractWe present the evidence that the GOOGLE stock, one of the most important stocks in the 21st century, may have been illegally controlled by large Wall Street firms. We identify a group of smart traders, including financial firm proprietary traders and large traders who trade options with large orders. We find that they have advance information about the closing prices on post earning release and option expiration days. These smart traders sell options to the market which would become worthless after the key events, and their high success rate is difficult to be explained by normal trading behavior. Additional evidence from the tape, such as unusually high quoted depth and extremely low narrow quoted spreads during clustering option expiration days, provide direct proof that the GOOGLE stock price may have been manipulated to coordinate the institutional traders option selling activities.

2Electronic copy available at: http://ssrn.com/abstract=1337350

Has GOOGLE Stock Price Been Manipulated?

1. Introduction In fall of 1998, two doctoral students, Larry Page and Sergey Brin, left Stanford University to start up a company. They rented a garage at the price of $1,700 per month in nearby Menlo Park and moved their computers into the house. On September 7, 1998, Larry and Sergey officially established the company GOOGLE Inc. The world knows the rest of the story: by the end of October 2007, shares of GOOGLE stock, with the NASDAQ ticker GOOG, reached $700 per share and the company has a market value of over $200 billion. However, this is not the complete story about GOOGLE. In this paper, we will share with other GOOGLE users a story that has yet to be told. We think that the GOOGLE stock may have been illegally controlled by Wall Street firms. We show that large financial institutions use small GOOGLE investors as pawns in their pursuits of high returns. This study is of significant value to the stock market. We provide the evidence of illegal activities surrounding the trading of GOOGLE stock and this evidence can be used to track down the manipulators. We tell naive, small investors why they are losing money, and sometimes their retirement money, in the stock market. It is due to malicious stock price manipulations by large financial firms. We will show why large financial firms seem to be always able to generate large amounts of profit and hand out generous bonuses to themselves they have the power to muscle stock price up and down and may use this power to their interest. This paper makes an important contribution to the finance literature. Stock price manipulation has been widely studied by many scholars. Some model the behavior of a stock market manipulator and analyze how he profits from this illegal activity (Mei, Wu, and Zhou (2004), Jarrow (1992)). Many papers examine well-publicized manipulation cases (for example, Jiang, Mahoney, and Mei (2005), Greenwood (2005), Merrick, Naik, and Yadav (2002)). Other papers concentrate on price manipulation during certain periods of stock trading such as the end of the day (Comerton-Forde and Putnins (2007), Hillion and Suominen (2004)), end of the quarter (Mark, Kaniel, Musto, and Reed

3

(2002)), option expiration (Ni, Pearson, and Poteshman (2005)), dividend announcements (John and Lang (1991)), and earning releases (Sivakumar and Waymire (1994)). In this study, we follow traces from different markets, option and stock markets, from different time periods, earning releases and option expirations. We gather evidence from difference sources and show the GOOGLE stock price may have been illegally controlled. We show this case based on public available data. As a matter of fact, it is difficult, even for the market regulators with all data sources that exist, to prove a stock price has been illegally controlled. We use data from both stock and option markets. Using an unique data from the Chicago Board Options Exchange (CBOE), we are able to construct a proxy for option trades conducted by Wall Street firms and other large traders such hedge funds, mutual funds, who we call smart traders. We follow smart-trader trades during all the GOOGLE earning release (ER) events before the end of 2007. We find that they can accurately predict the post-ER price and conduct risky option selling business in which they are almost never wrong. In the few cases that their option selling plan appeared not perfect, the stock price would just turn around and head back to the direction that favors the smart option sellers. In 2006 and 2007, the smart traders only lost money in 3 out 74 major option sales. The high success rate of this smart traders group points to the existence of a superman or possible stock price manipulation. We also study all the option expiration (OE) periods and have two discoveries. First, the GOOGLE stock often stop at a point that is extremely close to an option strike price, which is often termed as clustering or pinning. We provide the proof that stopping on a strike is in the best interest of option sellers: it minimizes value of both call and put options with that strike. It has been documented in the literature that stock price pinning is the joint result of stock price manipulation and option hedging behavior. We find, during the 34 option expiration Fridays between September 2004 and June 2007, the GOOGLE stock had the highest frequency of clustering among all the 1,833 optionable stocks for which we can find closing price data. Our second discovery during OE periods is similar to what we found during ERs. Smart traders appeared to have knowledge, in advance, about the final OE closing price and sold new options to the market which would later become worthless.

4

Further evidence of stock price manipulation is found in the stock market from the intraday trade and quote data. We calculate various trading measurements, such as quoted spread, quoted depth, and number of trades, for the last minute of trading on OE Fridays. We obtain evidence that quoted depth is artificially enhanced during clustering OE Fridays. The quote spread is also being maintained at extremely low level on such days. Our study on the number of trades reject that the clustering is driven by option hedging activities. Our results from the tape are consistent with large institutions manipulates the price near a strike price during option expiration days. The remainder of this paper is organized as follows. In Section 2, we review the GOOGLE stock price history and describe data used in this study. In Section 3, we examine the option selling activities by institution traders before GOOGLEs ERs. In Section 4, we focus on option expiration periods. We discuss direct evidence of stock price manipulation from the tape in Section 5. In Section 6, we discuss confessions of a Wall Street insider and conclude our paper.

2. GOOGLE Stock History and Our Data The GOOGLE Company made its shares available to the public on Thursday, August 19, 2004. A total of 19.6 million shares were offered at a price of $85 per share. The lead initial public offering (IPO) underwriters were Morgan Stanley and Credit Suisse First Boston. On the IPO date, the first trade occurred at 11:55 AM on the NASDAQ market with a price of $100.01, $15.01 higher than its offer price. Share price reached a high of $104.06 at 12:48 PM, and, by the end of the trading day, its share price closed at $100.34. Many of GOOGLE's employees became instant paper millionaires. Options trading on the GOOGLE stock started one week later, on Friday, August 27, 2004. 1 Figure 1 is a price chart for the GOOGLE stock between its IPO and November 2, 2007. In January 2005, 5 months after its IPO, the stock price reached $200. On June 1, 2005, GOOGLE shares gained nearly 4% to $288 after Credit Suisse First Boston raisedAugust 30, 2004 is the first day that GOOGLE option data appears in the Open-Close data that we obtained from the CBOE.1

5

its price target to $350. Six days later, on June 7, 2005, the share price jumped to $299.59 and GOOGLE was valued at nearly $52 billion, making it one of the world's biggest media companies. In another 5 months, on November 17, 2005, GOOGLE share price reached over $400. The Standard & Poors 500 is an important index containing the stocks of 500 large corporations. On March 31, 2006, after months of speculation, GOOGLE was added to this index. On October 9, 2006, GOOGLE bought the popular online video site YouTube for $1.65 billion while the share price closed at $429. One month later, on November 17, 2006, the stock price went above $500 by the first time. The $600 record was broken about one year later on October 8, 2007, and the stock soon reached over $700 per share on October 31, 2007, the last day in our sample. Seven days later, on November 7, 2007, GOOGLE stock reached $747.24 per share. We cover 39 months of GOOGLE stock and option trading from its initial public offering date, August 19, 2004, to October 31, 2007. During this period, there were 15 quarterly ERs and 38 option expiration days. Our main data is from the Options Clearing Corp (OCC) of the Chicago Board Options Exchange. OCC clears transactions for put and call options on common stocks and other equity issues, stock indexes, foreign currencies, interest rate composites and single-stock futures. The Options Clearing Corp provides some clearing data, named as Open-Close data, to the public. The Open-Close data provides information on different order type, order size, and customer categories. Option order types are different from what we normally see from stock trading. There are four option order types: open-buys, open-sells, close-buys, and closesells. At any time, there are a fixed number of option contracts in the market and the total number of contracts is the open interest. When a trader buys an existing option contract from the market, he should choose open-buy. If the same trader wants to close out his position, he can sell the option to the market with closing-sell order. If an option trader wants to create a new option contract and sell it to the market, his order type is opensell and the trade will increase the open interest by one. When the option writer wants to buy back the option that he sold and close out his position, he would choose a closingbuy order.

6

The Open-Close data also break traders into two classes: Firm and Public Customer. An option trader at Goldman Sachs who trades for the banks own account is an example of a firm proprietary trader. Other investors trading through Merrill Lynch or Scottrade are examples of public customers. The OCC Open-Close data provides summary statistics for public customer trades based on the order size: no more than 100 contracts, 101-200 contracts, and more than 200 contracts. OCC suggests that data users classify traders by their trade size: trades with no more than 100 contracts are from retail customers, and trades with more than 200 contracts are from institution traders.2 Our OCC data contains exactly 788 trading days from August 30, 2004 to October 31, 2007. Our second data is the Ivy DB OptionMetrics. This dataset contains historical end-of-day option volume, open interest, and daily closing price for the associated underlying securities. We use this dataset to estimate profits for option transactions. The OptionMetrics only had data up to June 29, 2007 when we started this study. The third data is the Trade-and-Quotes (TAQ) database of NYSE, AMEX, and NASDAQ trading. We use it to obtain intraday trade price of the GOOGLE stock and to calculate various trading measurements such as quote spread, quote depth, etc. The TAQ data is available up to August 31, 2007 when we started this study.

3. Earning Releases By the end of October 2007, the company had made 13 quarterly ERs. The news always comes out after 4:00 PM, when the normal trading hour is over. As we now know, GOOGLE is a very successful company and has reported many good quarterly earnings. Table 1 Panel A lists all the 13 earning results. Most of the 13 quarterly earnings results were positive or, we should say, significantly positive. For example, stock analysts predicted that earnings per share (EPS)Before the Open-Close data were made available to the publicly, CBOE provided 1996-2001 data to a small number of academic researchers. The early data were in different form. Each option transactions were assigned three codes: C for public customers, F for firm proprietary traders, and M fro market makers. Non-market-maker open interest and volume were also broken down to public customer and firm proprietary trader categories. The public customer category was further divided into customers of discount brokers, customers of full-service brokers, and other public customers. An option trader trading with Scottrade is an example of discount broker customer while customers of full-service brokers are generally regarded as hedge funds.2

7

for the first quarter (Q1) of 2005 should be $0.92. On April 21, 2005, the company reported an EPS of $1.29, beating the market consensus by 40%. Reporting a good EPS number can lead to the upward movement of the share price. In this study, we use the two closing prices before and after the ER to measure the markets reaction to GOOGLE Companys earning report. In 7 out the 13 quarters, the stock price jumped up by more than $10. For example, on October 21, 2005, the stock price leaped up by $36.70, an increased of 12.1% after the company reported an EPS of $1.51 against a market expectation of $1.36. In 4 quarters, Q2 and Q4 of 2005, Q4 of 2006, and Q2 of 2007, the share price dropped on the second day by more than $10. The biggest drop occurred on February 1, 2006: the stock price went down by $30.88, a negative return of 7.13%, after the company missed the market EPS expectation by 22 cents. In the remaining 2 ERs, the stock price

remained basically unchanged after ER. On July 20, 2006, after GOOGLE released its 2006 Q2 results, stock price went up by $2.99, a small fluctuation considering the $390level share price. On October 19, 2007, the stock price moved up by $5.09 after 2007 Q3 numbers were released, which is a change of 0.8%. To profit from GOOGLEs ERs, an investor can either trade the stock or options written on the GOOGLE stock. Due to the high-price nature of the stock, short-term trading of the underlying stock may not bring significant percentage returns. The average magnitude of percentage change in all the 13 ERs was about 6% and the last three ER price changes in our sample period were only 2%, 5%, and 1%. On the other side, trading GOOGLE options can bring high percentage returns, especially those options with the shortest time to maturity. For example, on October 20, 2005, the lowest offer for 2005 October call option with strike price of $330 was $0.6. The next day, the GOOGLE stock price moved up by 36.70 from $303.20 to $339.9, and the October $330 call had a highest bid of $9.5. The return for holding this call option was 1,483% in 24 hours! In this study, we focus on the trading of GOOGLE options written with the shortest time to maturity. Table 1 Panel A lists the number of trading days to the nearest OE Friday. Out of the 13 ERs, 8 were made just one day before the OE Friday. For example, the company made their 2006 Q1 financial results public at 4:30 PM on Thursday, April 20, 2006. The next day, Friday, April 21, 2006, was the last trading day

8

for all April 2006 options. Actually, only first 5 ERs were more than 12 days before the next OE Friday. Next, we move on to study GOOGLE option trading surrounding ER events.

3.1 Option Activities Surrounding April 2007 ER

On April 19, 2007, after the closing of the market, GOOGLE reported an EPS of $3.68, against a market expectation of $3.30. The next trading day, April 20, 2007, was the last trading day for April 2007 options. Therefore, we can observe an interesting play on the last days of April 2007 options. Before the market close on April 19, 2007, investors traded April options based on their prediction of the ER. On the next day, Friday, April 20, option value was decided by the stock closing price. To make things simple, we call Friday, April 20 as +1 day since it is the first day after the ER. Thursday, April 19 is defined as -1 day since it is the last trading day before the ER and Wednesday, April 18 is termed as -2 day. Table 1 Panel B lists the shares prices, including open, close, high, and low for 10 days surrounding the April 2007 ER. The share price was $482.48 at the close of April 20, 2007 (the +1 day). This price is critical--it determines all the April option value at the expiration. On April 19 (the -1 day), just before the earnings result is released, the market closed at $471.65. Figure 2A shows the intraday price movement for 5 days surrounding GOOGLEs April 2007 ER. The share price was fluctuating between high $460s and low $470s. During the second half of April 19 (the -1 day), just before the release, the price had a downward trend. We interpret it as a signal that the market does not know the significantly positive news. However, we will show that a certain group of option traders had information, in advance, about what the closing price will be for April 20, the critical OE day. The CBOE data contains four order types, open-buys, open-sells, close-buys, and close-sells. We concentrate on the open-sell orders, which represent selling new options to the market. Selling options before a major event is highly risky -- an opposite price movement could bring tremendous losses to the seller. Due to the risky nature, retail brokers often impose a high margin requirement for option sellers. An option seller could

9

also hedge his position by holding underlying stocks, but that would impose high capital requirement for them. We all know that each contract of option covers 100 shares of the underlying stock. If a trader sells 1,000 contracts of GOOGLE call option on April 19, 2007, to hedge his option position, he would need to purchase 100,000 shares of the stock with market value of $47,165,000. Following the open-sell orders, it would lead us to a group of highly-skilled option traders. There are 4 classes of trader in the CBOE database: firm proprietary trades, public customer trades with no more than 100 contracts, 101-200 contracts, and more than 200 contracts. We want to examine the trading of Wall Street firms, so we are interested in firm trades. Other large funds, such as hedge funds and mutual funds, are also subject of interest in this paper and they may trade with large order sizes. Therefore, we include trades with more than 100 contracts. We call this trader group as smart traders. In this study, it would be ideal that we can pinpoint the manipulators by disclosing their identities. We want to state which firms are illegally controlling the GOOGLE stock price. However, this is an impossible task since all trader identities are removed from option trading data available to the public. By studying firm trades and trades of more than 100 contracts, we construct a proxy for trades conducted by the possible manipulators who should be large players in the market. However, the sum of firm trades and trades with more than 100 contracts is only a proxy for the smart traders. There are two reasons. First, an institutional trader can split a large order into many small orders with less than 100 contracts. Second, a wealthy retail investor may also buy and sell with more than 100 or even 200 contracts. So, unavoidably, there are noises in our proxy for institutional trades. Figure 2B indicates the smart trader new option selling activities for the GOOGLE stock on April 19, before the ER. In the graph, the horizontal axis represents the strike price and option type, call or put. We use 490C for the call option with strike price $490. The vertical axis is the total number of contracts sold. For April 490C, there were 1,278 contracts sold by smart traders. On the same graph, we see there were 151 new contracts for 520C. We want to ignore these small option activities and only concentrate on sales with a total of more than 500 contracts. Applying this filter, we are left with 430P, 450P, 460P, 470P, 480C,

10

490C, 500C, and 510C. Interesting enough, out of the 8 options, 7 became totally worthless by the end of the second day since the OE closing price was $482.48. The only option that still had positive value was 480C which had $2.48. However, the bid price for 480C on April 18 was $6.5, which means selling 480C on April 19 (the -1 day) can bring profit since the revenue exceed the payoff liability. In sum, smart traders sold 8 options with different strikes just before the ER and made money on all of them. Profit for the option selling was enormous. Assuming the 470P were sold at the best bid price of $9.7 at Thursdays close, by selling 1,105 contracts, the sellers pocketed $9.7*100*1,105=$1,071,850. The one million dollar income is also the net profit since 470P became worthless the next day. The same occurred to 490C. Assuming the 1,278 contracts were sold at the closing bid price of $3.6, the sellers pocketed $3.6*100*1278=$460,080. Again, the 490C became totally OTM on the OE Friday. Adding up all the options sold on April 19 with 500+ contracts, the smart traders pocketed $3,062,080 and the payoff liability was only $158,720 on the next day. The ratio of option selling revenue versus option payoff liability is 19.29 to 1. This means for every $19.29 option selling revenue, the options sellers need to pay only $1. Similar smart option sales also occur on days earlier than the -1 day. Figure 2C shows the situation on Wednesday, April 18, 2007. On this day, 985 contracts of April 500C were sold by the smart trader group. Assuming they were sold at the closing bid price of $2.45, the sellers earned $241,325 and all these 500C options became worthless two days later. The Monday situation is indicated in Figure 2D. On Monday, April 16, 2007, 1,460 contracts of April 440P were sold to the market and all became worthless on the OE Friday. The best bid price on Mondays close was $1.6, therefore, the sellers made $1.6*100*1460=$233,600. On the same day, 736 contracts of 510C were sold and that is $73,600 revenue, without any liability on the OE Friday. The earliest smart option sales went back to Tuesday, April 10 (the -8 day). On this date, 514 new April 490C were sold to the market by firm traders. Assuming that these 514 contracts were sold at the closing bid price of $3.3, the sellers generated a profit of $3.3*100*514=$169,620. Summarizing these option selling activities, we found a group of traders who sell options with amazing accuracy. From the -8 day to the -1 day, there were 12 cases of

11

significant April options sales. 11 out of the 12 sales, the option became OTM after the ER. The only exception is the -1 day selling of $480C. But the selling price of $6.5 was still higher than final OE value of $2.48. Adding the 8 sales together, 10,917 new April option contracts were sold and the institution traders generated total revenue of $3,772,305 with a $158,720 payoff liability. The smart traders created a net profit of $3,613,585. The ratio of option sellings revenue to payoff is 23.77 to 1, which means they only need to pay $1 for each $23.77 that they had collected. The smart traders ability to predict the OE closing price goes up as it gets closer to the ER. On the -8 day, they sold 490C, which means that the smart traders believed that the share price will be below $490. On the -4 day, the predicted range was between $440 and $510. On the -2 day, the prediction narrowed to $440 and $500. On the -1 day, the smart traders were confident that the OE price should be between $470 and $480 and they were right on this forecast. On the OE Friday, the GOOGLE stock opened at $490.52, quickly reached a high of $490.52, and then turned around moving toward the smart traders prediction of $480 and finally closed at $482.48. With this observation, we wonder why the institution traders were so confident that the final OE price would be between $470 and $480. Why did the GOOGLE stock price move down, after an impressive ER, from $494 to $482.48 on the OE Friday?

3.2 Option Activities Surrounding April 2006 ER

If you wonder whether the institution traders ability to accurately predict the market closing price after the ER in April 2007 may be random luck, we will show that institution traders do have the same ability in almost all other ER events. One of such examples is April 2006. On Thursday, April 20, 2006, GOOGLE reported an EPS of $2.29, against a market expectation of $1.97. This is 16.2% positive earnings surprise. Activities in the option market were basically the same as in April 2007. Panel B of Figure 3 indicates that, on April 20, just before the news was made available to the public, 3,161 contracts of April 440C were sold to the market. The highest bid price for April 440C was $3.8,

12

and the 3,161 contracts had value of $3.8*100*3161=$1,201,180. As in the April 2007 case, the 440C became worthless on the next day when the share price closed at $437.10. On Wednesday, April 19, 2006, 1,550 contracts of April 370P were sold to the market by smart traders. With the bid price of $2.4 at the close, the 1,550 contracts brought smart traders total revenue of $372,000. Certainly, by Friday, April 21, 2006, all the April 370P became waste paper. On Wednesday, April 12, 2006, six trading days ahead of the ER, 600 contracts of new April 370P were sold to the market. The market closing bid price for this put option was $2.4 and we estimate that the 600 contracts brought $144,000. Again, all of the 600 contracts became worthless by the market close. For the month of April 2006, our calculation shows total revenue of $1,717,180 for the smart option sellers. On the liability side, all the 5,311 options became worthless on the market close of Friday, April 21, 2006, and the smart traders needed to pay $0. Another interesting observation from the April 2006 earning event is the strange price movement on Friday, April 21, 2006. The institution traders sold 3,161 contracts of April 440C to the market on April 20. However, due the significant positive nature of GOOGLEs profit level in the first quarter of 2006, the stock price opened at $448.90 on Friday, up by $33.9 from Thursdays closing price. The share price even moved up to a high of $450.72 shortly after the market open. This is probably the worst scenario for the option writers who sold the large numbers of April 440C. Assuming the market will close at $450.72, the call option writers would face a total liability of ($450.72$440)*100*3161=$3,388,592. However, astonishingly, the share price started to drop after reach the high of $450.72. The downward movement was strong and fast, and by 4:00 PM, the share price dropped $13 to $437.10, just enough to make the 440C options worthless!

3.3 Profitability of Smart Trader Option Selling Activities Surrounding ERs

We have examined the option market activities surrounding the two GOOGLE ERs. The next question is whether these option selling activities are profitable just because of random luck. In this subsection, we show that the profitability of institution

13

option selling is consistent across the eight GOOGLE ERs between January 2006 and October 2007. To calculate profit of institution option selling before ERs, we use the following methods to screen the data. In 2006 and 2007, 6 out of 8 GOOGLE ERs occurred on Thursday of the option expiration week. We look at the 9-day trading period between the second Monday of the month and the Thursday on which date the news is released after the market close. For these 6 ERs, current month options will expire on the next day, Friday of the OE week. During January 2006 and January 2007, GOOGLE made the news available at the last day of the month. Since we consider option expiration as another major event, we only consider the period between the Fourth Monday of the month and the ER date. Therefore, our sample period has 7 trading days for the January 2006 ER and 8 trading days for January 2007 ER. To minimize noise in the data, we only consider option selling with more than 500 contracts, likely conducted by institution traders. For example, as shown in Figure 2E, option selling occurred for six April options on Tuesday, April 10, 2007. Five out of the six option sales, such as 510C and 520C, are with very small volume, and only one of the six, the 490C, has a volume of 514 contracts. We only include the selling of the 514 contracts of 490C in our sample and ignore other smaller option sales. We estimate the sellers revenue using the closing bid price provided by the OptionMetrics database. For the selling of 514 contracts of 490C on April 10, 2007, the closing bid price was $3.3 and the offer price was $3.5. We estimate the option-selling revenue as $3.3*100*514=$169,620. The option sellers liability is estimated using the closing option price on the second day after the ER. We treat the options value as zero if the bid price is zero and as the average of bid and ask price if the bid is positive. Recall that out of the eight ERs, six occurred just one day before the OE Friday. So for the six ERs, the options final value is just the options the intrinsic value. For the two ERs occurred on the last day of January of 2006 and 2007, the final value of the option, although they may become OTM, still contains some time value. Going back to our example, the final value of the 514 contracts of 490C became zero since GOOGLE share closed at $482.48 the next day.

14

Table 1, Panel D summarizes the option selling activities surrounding the eight ERs. As explained earlier, we consider a selling of options with more than 500 contracts on a day as a major option selling event. During the eight ERs, there were, altogether, 74 major sales. If the options value on the second day after ER is lower than the original selling price, we term the selling as a Good Selling. Amazingly, 71 out of the 74 major option sales were good and only 3 were bad. This achievement is indeed a miracle by any standard. Out of the 74 option sales, the sellers pocketed about $39.5 million and their liability is just $9.2 million, 23% of the revenue. This liability-revenue ratio is strictly 0% for three ER events, April 2006, July 2006, and October 2007. In the April 2007 ER event, this liability-revenue ratio is as low as 4%. Basically, during these ER events, the institutional traders had perfect foresight of the price movement after the news release, and they managed it well and all the options that they sold became worthless. We estimate that this group of option traders has made $30.3 million out of their pre-event knowledge of post-ER stock prices. However, $30.3 million is an underestimation since we only considered open-sell orders, trades with more than 100 contracts, and options with the shortest expiration. If a trader knew how much the post-ER closing price would be, he could also three other orders: open-buys, close-buys, and close-sells. He can also split his large orders into smaller orders with less than 100 contracts per trade. Finally, the super trader can also increase his profit by selling options with longer maturities, in addition to those options with the closest expiration dates.

3.4 How Did Institution Option Writers Make Money by Selling Options before ER

Making 71 good trades out of 74 highly risky options selling could be seen as a miracle in the world of option trading. But how did this occur? We provide our view after examining the option selling before 8 ERs in 2006 and 2007. First, the sellers seemed to know, before the event, the direction of earning result, good or bad. About one week in advance, they sell put options if the news is good and call options otherwise. Using the April 2006 ER as example, the news came out after the market close on Thursday, April 20, 2006. On Tuesday, April 11, 8 trading days prior to the news release, the stock price was around $409 and 350 contracts of April 400P were

15

sold. On the next day, 600 contracts of April put were sold (Figure 3D). Again, on Wednesday, April 19, 1550 contracts of April 370P were newly opened. Certainly, all these puts became worthless after the ER release. Secondly, as it gets closer to the release date, the smart sellers seem to be able to confine the stock prices to a range and they sold OTM call options before good news and puts before bad news. On Thursday, April 20, 2006, when GOOGLE was around $415, 3,161 new April 440C options were opened and certainly, they all became worthless since the stock closed at $437.1 on the OE day just after the ER. Thirdly, when it came really close to the ER, the sellers appeared to know exactly where the post-ER stock price would be. Panel B of Figure 1 shows the option selling on Thursday, April 19, 2007, the very day where the news was released after the market close. They were selling 1,105 contracts of $470P, 640 contracts of 480C (at around $3.6), and 1,278 contracts of 490C. To make all these options worthless, the next-day, option expiration closing price should fall between $470 and $490, most likely at $480. The final price after ER is actually closed at $482.48! Finally, when the large sellers predictions were not perfect, the stocks price movements on the next day were always to their favor. Before the April 19, 2007 ER, the smart traders were selling April 480C. As shown in Figure 2F, on the next day, April 20, the last trading day for April 2007 options, the stock price opened at $490.52, quickly reached a high of $490.52. The stock price then gradually moved down and closed at $482.48, making 480C only slightly in-the-money. As shown in Figure 3B, the big sellers were selling 440C options on Thursday, April 20, 2006. The next day, which was also the last trading day for April 2006 options, the stock price opened at $448.90, reached a high of $450.72, then moved down and closed at $437.10, making 440C worthless. The stock price also moved up on after July 2007 ER and moved down after January 2007 ER, all to the option sellers favor! Based on these observations, we think that both news leak and price control may have occurred during GOOGLEs eight ER events in 2006 and 2007. It is obvious that the smart traders know, at least one week in advance, the direction of earning result before the official release. These leaks consistently occurred in all the eight ERs between 2006 and 2007. However, the ability for the smart traders to narrow the post-ER price to

16

a range of $20 points to the existence of illegal price control in the trading of GOOGLE stock. Anyone who has traded in ER events should know the market reaction to the new is a wild card. Factors outside the company such as a news medias coverage on the company may affect the post-ER stock movement. It is often happens that the stock price may move down (up) after a positive (negative) earning result. To know the post-ER price in advance, someone needs to know both the news and the market reaction to the news. For the smart traders, they seem to have both. They know what market reaction would be, in additional to the advanced access to the confidential information. Certainly, you know what the market reaction to the ER if you are powerful enough to create the market reaction. It appears to us that, during most of the events, the smart traders can control the GOOGLE stock price according to an advanced plan. They may get the confidential ER information from some sources. They could contemplate a target postER price and sell options to the innocent small investors accordingly. After the news release, the mighty institutions may muscle the stock price to their original target so they can keep the option premium. In the remainder of the paper, we will discuss the smart traders knowledge about future prices when there is no major news event.

4. Option Expiration Dates In this section, we will examine GOOGLE stocks movement near the option expiration dates. We first look at the clustering, which is defined as stock price coming extremely close to a strike price. We then analyze the institution traders option selling before option expiration dates.

4.1 GOOGLE Stock Price Clustering near Option Expiration Dates

Having the stock price closed on or within a short distance from a strike price has the direct effect of destroying the value of both the call and put options with that strike price. Table 3 shows the GOOGLE open interest data on May 19, 2006, the last trading day for May 2006 options. There are 10,346 contracts of call options and 11,350 contracts of put options with the strike price of $370. On that day, the stock closed at $370.02. This price made all of the 370P worthless. On the other side, the 370C only

17

have intrinsic value of $0.02. According to rules made by the Options Clearing Corporation (OCC), there is a threshold of $0.25, in May 2006, for those ITM options to be automatically exercised. Therefore, the 370C with $0.02 intrinsic value will not be automatically exercised by the OCC. It is then up to the holder of the call option to decide whether he wants to exercise the option and get the 2 cents difference. Since there are trading commissions associated with each option exercise, the call holder may choose not to exercise the option if the trading costs exceed the exercise profit. Then, the person who sold the call option ends up with paying nothing to the call holder. Why does the stock price come extremely close to the strike price? We will show that, for option sellers, the strike price is better than or the same as any point between two strike prices.

Proposition 1: If K1 and K 2 ( K1 < K 2 ) are neighboring strike prices, and M is a price

point

with

K1 K 0 > K 1 >) is either ATM or ITM, and let us assume the total call open interests are TCOI . All the put options with strike prices K 2 , K 3 , K 4 ( K 2 < K 3 < K 4 TPOI , and TCOI< TPOI .

and TPOI :

Case A, TCOI= TPOI :

If we move from K1 to M, each ITM/ATM call option value increases by M- K1 , then CallPayOff ( M ) CallPayOff ( K1 ) = ( M K1 ) * TCOI *100 . (1.1)

18

Each ITM/ATM put option value decreases by M- K1 , PutPayOff ( M ) PutPayOff ( K1 ) = ( M K1 ) * TPOI *100 . Adding (1.1) and (1.2), with TCOI= TPOI , we have CallPayOff ( M ) + PutPayOff ( M ) = CallPayOff ( K1 ) + PutPayOff ( K1 ) , It is the same as PayOff ( M ) = PayOff ( K1 ) . Similarly, we have PayOff ( M ) = PayOff ( K 2 ) , so, PayOff ( M ) = PayOff ( K1 ) = PayOff ( K 2 ) . (1.4) (1.3) (1.2)

Case B, TCOI> TPOI :

In this case, we have more ATM/ITM call options K1 at than ATM/ITM put options at K 2 (the same as in Table 3). Let us assume that we start at price point K1 , and as we move up from K1 to K 2 by $1, the ATM/ITM call option payoff will increase by TCOI * 100 and total ATM/ITM put option payoff will decrease by TPOI * 100 . The net change is (TCOI TPOI ) * 100 > 0 . It becomes obvious that as we move up from K1 to K 2 , the total payoff increases. So we have PayOff ( K1 ) < PayOff ( M ) < PayOff ( K 2 ) . (1.5)

Case C, TCOI< TPOI :

In this case, we have more ATM/ITM put options at K 2 than ATM/ITM call options at K1 . It is a mirror image of case B. As we move down from K 2 to K1 , total put payoff increases more than the reduction in total call payoff. So K 2 is the price point with the lowest total payoff for all options, PayOff ( K 2 ) < PayOff ( M ) < PayOff ( K1 ) . (1.6)

Summarizing cases A, B, and C, we know the minimum payoff price point should always be at one of the two strike prices.

19

Proposition 1 indicates that if stock prices can be manipulated by option writers, they will push the stock price to a strike price near the option expiration date. In the following discussions, we see that among all optionable stocks, GOOGLE has the highest frequency of clustering near a strike price on option expiration dates. To check whether the GOOGLE stock price has been manipulated at the option expiration dates, we do a cross-section comparison by comparing the frequency that GOOGLE clusters at a strike price with all other optionable stocks. Option trading for GOOGLE stock started on August 27, 2004 and the first option expiration Friday is September 17, 2004. We use the OptionMetrics Database to check the list of optionable stocks and closing prices for the underlying security. The OptionMetrics Database only has data up to the end of June 2007 when we worked on this section; therefore Friday, June 15, 2007 is the last OE Friday in our sample. In OptionMetrics, there are 2,351 stocks for September 17, 2004 and 3,083 stocks for June 15, 2007. Comparing the two stock lists we find 1,921 common stocks for these two days. We use the ticker list and get closing price data from OptionMetrics and we find 1,833 stocks with complete closing price data for the 34 OE Fridays between September 17, 2004 and June 15, 2007. Therefore, we have 62,322 closing prices for the 1,833 security over the 34 option expiration Fridays. The next question is how to define clustering. To get the clustering frequency variable, we need to consider the price level of each stock. For example, the share of Chicago Mercantile Exchange (CME) is traded above $650 per share in December 2007 and shares of Intel Corp (INTC) have market value around $25. By common sense, if CME closes at $651.00 we probably say that CME is pinned to $580 since the next strike price is $660. However, for INTC, closing at $26.00 makes the price fall almost in the middle of two strike prices, $25 and $27.5. Based on an OE closing price of $26, no one can make the claim that the price is too close to a strike price. For each optionable stock, many call and put options with different strike prices are traded on the market. Option strike prices greater than $200 are at $10 intervals. For stocks with prices between $200 and $20, the strike prices are integer multiples of $5. Exercise prices for stocks below $20 include odd integer multiples of $2.50. We, in the rest of the study, choose to measure the performance based on the relative scale to the strike intervals. We define a

20

stock as clustered to a strike price if its distance from a strike is less or equal to 10% of the strike interval. Therefore, for a stock trading higher than $200, the strike distance is $10 and one dollar is the line to decide whether the stock is pinned. For stocks between $200 and $20, strike interval is $5 and our standard reduces to 50 cents. For stock below $20, strike interval is $2.5 and we set 25 cents as the cut-off point. Based on our definition of clustering, for the 1,833 security over the 34 option expiration Fridays ending on June 2007, we found the average number of clustering is 7.22 times and the median is 7 times. The GOOGLE stock, however, has the highest number of clustering with 21 times. Table 4 Panel B shows the clustering frequency distribution for the 1,833 stocks. We can see that 1,825 out of the total of 1,833 stocks clustered less or equal to 14 times in the 34-month period. Among the 8 stocks clustered more than 14 times, 3 stocked pinned 15 times, 2 pinned 16 times, and 2 pinned 17 times. The GOOGLE stock, however, went far beyond the normal range and had a clustering number of 21 times. To control the differences in strike interval, we show the number of clustering for those high-price stocks with an average price of more than $100 during the 34 month sample period between September 17, 2004 and June 15, 2007. For this group of stocks, the average number of clustering is 9.35 and the median is 8 times, less than half of GOOGLEs number. There is also a comparable stock to GOOGLE, which is the CME stock. CME has an average price of $377 while GOOGLE is $345. On the clustering side, CME had pinned to a strike price 8 times while GOOGLE did it for 21 times. To check the robustness of our results, we also used alternative definitions. We define a stock is clustered to a strike price if its distance from a strike is less or equal to 7.5%, 5%, or 2.5% of the strike interval. Among all the trials that we made, GOOGLE always stood out as the stock with the highest number of clustering.

4.2 Smart Traders Selling of Options before Option Expiration

In Section III, we find that the smart traders, which include Wall Street firms, traders who buy or sell options with order sizes greater than 100, sell options before the ER and almost all of these options became worthless afterwards. If a group of stock price

21

manipulators could control GOOGLE price during volatile ER events, they probably could do the same when there is no major news in the market. In this subsection, we examine whether such activities exist during normal trading periods before option expiration. For the GOOGLE stock, there were 38 option expiration Fridays before November 2007. During 7 out of the 38 months3, the company reported earning on Thursday of the OE week, just one day before the expiration date. In this subsection, we concentrate on the 31 option expiration weeks during which there is no ER events before the Friday. After examining the 31 option expiration weeks, we find significant option trading activities by the smart group in all the 31 months except in the first two months that GOOGLE option market opened. The pattern is consistent across all the 29 months: the smart traders knew almost exactly, in advance, what the option expiration Friday closing price would be and they sold new options to the market which would become worthless on OE Friday close. We use December 2005 as an example to illustrate smart traders activity before an ordinary option expiration period. December 16, 2005 is the third Friday of the month and it is the last trading day for all December 2005 options. Options written on GOOGLE stock expire at 10:59 PM Chicago Time on the third Saturday of the month. Since the option cannot be traded after the market close on the third Friday of each month, we make our life easier by treating the third Friday of the month as the option expiration (OE) date. On December 16, 2005, GOOGLE stock price closed at $430.15. This price is critical since all December 2005 option values are decided by the difference between the options strike price and the $430.15. We now proceed to show that the smart traders knew the option expiration closing price $430.15 in advance. First, we look at how stock moved during the OE period. Figure 4A indicates The GOOGLE stock price movements for the five-day period from Monday, December 12, 2005 to Friday, December 16, 2005. The GOOGLE stock had an overall upward trend during the December 2005 OE week: share price was about $410 in early hours of Monday trading, and, on Friday, the stock price closed at $430.15, after reaching a dayhigh of $432.5 around 2:00 PM East Coast time. From Tuesday to Thursday, the closing3

The seven months are April and July of 2006 and 2007, October of 2005, 2006, and 2007.

22

prices were $417.49, $418.96, and $422.55 with a steady growth trend. Secondly, we check the option selling before the option expiration. Figure 4B shows the smart traders option selling activities on Friday, December 16. If we concentrate those options with significant volume, the smart traders sold, using open-sell orders, 2,025 contracts of 430P, 1,165 contracts of 430C, and 742 contracts of 440C. With the closing price of $430.15, all of these 3,932 contracts of options became worthless after the option expiration.4 There are two reasons why large amount of options could not be sold at the last minute before the market close. First, the option market is not liquid enough to take 3,932 contracts of option in a short period. Second, selling these options one minute before the market close does not make sense since the time value of the options should be extremely low near the market close. It is most likely that these 3,932 contracts of options were sold over the six and half trading hours on Friday, December 16. However, on Friday, December, 16, the market opened at only $425.34, reached a low of $422.75 soon after the market open, then moved up to a high of $432.50 around 2:00 PM. With the high volatility and wide trading range of $9.75, how did the smart traders become so confident that they sold $3,932 contracts of option on the last trading day? This was indeed a highly risky behavior since this group of option traders would have suffered huge losses had the market closed at other prices, say $420. The only explanation for this miracle is that this group of smart traders knew in advance that the GOOGLE would close extremely close to $430. Actually, this kind of superman-style option selling occurred in almost all option expiration Fridays between November 2004 and September 2007. Figure 4C illustrates the smart option selling on Thursday, December 15, 2005. On this day, the GOOGLE share price was fluctuating around $420, and too hard to ignore, the smart traders sold 2,887 contracts of December 420P, which soon became worthless given the $430.15 closing price on the next day.5 This indicates that the smart traders knew, one

Strictly speaking, the $430 call option is $0.15 in the money. However, in December 2005 the Options Clearing Corporation was imposing a threshold of $0.25 for those ITM options to be automatically exercised. Therefore, the $430 call option with $0.02 intrinsic value will not be exercised by the Options Clearing Corporation unless the holder of the option request so. Given the trading commissions associated with each option exercise, small option traders may not choose to exercise the option if the trading costs exceed the exercise profit. 5 On the same day, there were three other option selling, 32 contracts of 410P, 502 contracts of 420C, and 165 contracts of 430C. The selling of 502 contracts of 420C turned out to be a bad trade. However, since the sell

4

23

day in advance, that the Friday closing price would be higher than $420. Figure 4D and 4E shows that the smart traders sold 500 contracts of 430C on Tuesday and 1,522 contracts of the same call option on Monday, December 12, 2005. The two days selling of the same 430C indicates certain smart traders knew that the final closing price on Friday, December 15 would be lower than $430. Going back further, on Wednesday, December 7, 800 contracts of new 380P were sold to the market and on that day, the stock price was about $400. This shows that the smart traders knew that the stock price would not go below $380 by Friday, December 16, 2005. Panel G is the selling picture for Monday, December 5, 2005, 10 trading days before the option expiration. Although the option selling sizes were relatively small (around 200 contracts or less), the strike distribution, calls with strike prices $430, $440, $450, $460 and puts with strike prices $410, $400, $380, $370, defines a narrow range of $410 to $430 for the final option expiration closing price. This prediction is right: the final OE closing price fits this range well at $430.15! Having seen the smart traders super ability to predict stock prices ten days in advance, we have to believe that the GOOGLE stock price is likely planned by a group of manipulators, instead of the natural force of market supply and demand. Before finishing the discussion on December 2005 option expiration, we want readers to look at the strange, intraday price movements on Friday, December 16, 2005. On this day, the trading of GOOGLE stock opened at $425.34, reached a low of $422.75 around 9:45 AM. Then the share prices slowly moved up to around $426 before 11:00 AM then came back to about $424 around 12:00 PM. If the price fluctuation before noon is nothing uncommon, then what happened after 12:00 PM is clearly unusual. Starting around 12:45 PM, the stock suddenly gained momentum and shot up about $10 in a matter of 90 minutes, the price jumped from about $424 to almost $433. Only after 2:20 PM, the share price pulled back and finally came to rest at the predicted strike price of $430. We have to ask, what force made the sudden extreme movement of GOOGLE stock price just before the option expiration? Having seen the large selling of 2,025 contracts of 430P on Friday, we can only conclude that the stock price is very likely pulled up by some manipulators to make the 430P worthless. They did it and did it so well, with $430.15 to make 430P worthless. As we will discuss later, the volatile sudden pull up or down of GOOGLE stock price occurred in manyvolume is relatively low and since we cannot know exactly who made the trade due to our customer classification, we think the bad trade could be the result of an uninformed rich trader.

24

months in the period before option expiration. In one extreme case, on Friday, January 20, 2006, the stock price was pulled down $40 in the short period of six and half trading hours, just before the option expiration. We next move on to show that the smart traders ability to accurately predict option expiration closing price is not by accident. Out of the 29 months, we find 21 months in which the smart traders were able to make perfect trades on the final OE Friday. During these 21 months, the smart traders made highly risky option selling before the option expiration and all the options, both call and put, became totally worthless or almost worthless after the market is closed. Table 5 summaries our results. For example, the closing price on Friday, February 16, 2007, was $469.94. On the same day, during the six and half trading hours, the smart traders sold 7,383 contracts of 470C and 669 contracts of 460P options, all expiring on Saturday, February 17, 2007. Amazingly, all of the 8,052 options became totally worthless after the market close of Friday, February 16, 2007. The smart traders ability to profit is superman-like and consistent from 2004 to 2007. The question is: how did they know the OE closing price in advance in the 21 months?

5. Evidence of Manipulation from the Tape

We have discussed that smart traders may know the closing prices on post-ER and OE days in advance. In this section, we provide further evidence of stock price manipulation in actual trading. We use the Trade-and-Quotes (TAQ) database of NYSE, AMEX, and NASDAQ trading to calculate various trading measurements and we discover footprints of illegal activities. In addition to what we have seen in the option market, the direct evidence from stock trading gives a complete picture of stock price manipulation. Many researchers have used trade and quote data to study stock price manipulation. Carhart, Kaniel, Musto, and Reed (2002) study quarter end price manipulation by mutual funds. They find that there is a surge of trading volume in the last minutes of trading and stocks close more at the ask price. Comerton-Forde and Putnins (2007) suggest that methods of manipulation detection should vary with the manipulators incentive. They use variables such as return, trade size, and price reversion,

25

to create an index gauging manipulation. In this section, we focus on how the manipulators control the price to a narrow range near a strike price on option expiration days.

5.1 Working with TAQ Data The TAQ database contains information for GOOGLE from August 19, 2004 to August 31, 2007 when we work on this section. The trading of Google stock is very active. For example, there were 38 quote updates and 20 transactions within the second of 15:59:54 on August 17, 2008. While working with TAQ data, we find that the original quote data contains many errors. We apply a series of filters to ensure data integrity. We use BBO eligible quotes only. We also require that all quotes and depths must be positive, and asks must exceed bids. Occasionally, we see an ask price exceeds the two adjacent ask prices by a significant amount leading to a spike in the ask prices. This could be the result of a typo such as typing $483.71 as $487.31. Therefore, we eliminate an ask price if it exceeds the two adjacent ask prices by more than $1. We did the same to a bid price if it is lower than two adjacent bids by more than $1. While working with trade data, we use only transactions coded as regular trades with no corrections and we require that both transaction price and size must be positive. We delete a transaction if its price is higher (or lower) than both of the two adjacent transaction prices by $10.

5.2 Testable Hypotheses In Section IV, we find that during the 34 OE Fridays between September 17, 2004 and June 15, 2007, GOOGLE clustered 21 times. The Google stock is by far the No. 1 clustering stock, compared to an average number of 9.35 for all the 1,833 optionable stocks. Clustering near a strike causes wealth transfer between option buyers and sellers. Holders of those options with the same strike see their money evaporates while sellers minimize their payoff liability and keep the option premium that they have collected. Different theories have been proposed in the literature on what lead to stock clustering on option expiration days. Ni, Pearson, and Poteshman (2005) study 4,395 stocks from 1996 to 2002 and find strong evidence that stock price clustering on option expiration date. They suggest the following three possible causes for clustering: (1) delta

26

hedging of option traders, (2) illegal price control by institutional traders, and (3) nondelta-hedge traders unwind their combined option and stock position, such as covered call and protective puts. For delta-hedging, if an investor has a net purchased option position, the hedging rebalance should drive stock toward the strike price. Assuming market makers are the main delta-hedgers, Ni, Pearson, and Poteshman (2005) find that clustering is only partly, not entirely, to delta hedging. They conclude that clustering is consistent with stock price manipulation by large institutions who sold options to the market. Which makes GOOGLE the No. 1 clustering stock, option trader hedging or illegal price manipulation? We want to seek more evidence from the tape. If option hedging activity is the driving force of GOOGLE clustering, we should see more trades during the last minute of trading on those clustering OE Fridays. Hedging activity is associated with frequent trades. If such activities have pushed to the stock price to a strike, number of trades should be higher on clustering days than on other non-clustering days.

H1: Number of trades is higher on clustering days than on non-clustering days.

From TAQ, we can calculate the quoted spread. If the stock price is illegally controlled by large institutions, the manipulators want to make sure the spread is narrow so the closing price will be near a strike price. On the other hand, option hedging activities involves trading when stock price moves. In our view, option hedging should not have a direct impact on the width of the quotes spread. If clustering is caused by option-hedging activities, quoted spread should be approximately the same on clustering days as on non-clustering days. Alternatively, if clustering is a direct result of illegal price control, then we may see spread artificially narrowed on clustering days.

H2: Quoted spread is the same on clustering or non-clustering OE days.

The quoted depth could be another indicator to look at. If the manipulators want to control the price to a strike, they may post significant depths on the lowest ask and the

27

highest bid. Doing so, they can prevent the GOOGLE stock price moved away by the arrival of a large order. Therefore, we may see differences in market depths on clustering and non-clustering OE days. Option-hedging activities may not have significant

differential limit order depth effects on clustering or non-clustering days. If the optionhedgers use market orders to unwind their positions, the depth in the limit order book should not be affected. If they choose to use limit orders when stock price moves significant, the limit orders that they post should be posted away from the inside quotes. Therefore, we could use the quoted depth as a test on what causes the clustering.

H3: Quoted depth is the same on clustering as on non-clustering OE days.

To test these hypotheses, we calculate trading measurements from the TAQ data. We concentrate on the last one minute of trading before market close which is from 3:59PM to 4:00PM. We use quoted spread to illustrate how to find the measurements. First, we calculate each quoted spread as the difference between ask and bid prices for each quotes during the one minute interval in each day. Secondly, we find the median of the quoted spreads for each day and use it to represent the width of quotes spread. Our first choice is the median, instead of the mean, since mean could be affected by extreme values. Mean is used later in our robust tests. Therefore, we get the median quoted spread for all the OE Fridays in our sample period. For quote depth, we first find ask depth and bid depth, then find the average of these two as the quote depth. Then we follow the same steps as we calculate the quoted spreads.

5.3 Graphic Comparison of Trading Measurements During the 36-month period (September 17, 2004 and August 17, 2007) that we have the TAQ data, GOOGLE clustered 22 times, which means the closing price is within 10% of the strike interval from a strike price. During the other 14 months, the GOOGLE stock stayed away from a strike price by more than 10% of the strike interval. We divide the 36 months into two groups, clustering and non-clustering. We then find the average trading measurements within each group. To see whether the trading

28

measurements are same on the OE days and other non-OE days, we calculate the measurements for 2 trading days before the OE Friday and 3 trading days after. Figure 5 Panel A shows number of trades on option expiration Fridays during clustering and non-clustering months respectively. The two bar groups are for clustering months (left-side) and non-clustering months (right-side). The six vertical bars indicate the median number of trades on the OE Friday (the bar in color) and 5 trading days surrounding the OE Friday. In both groups, the number of trades on OE Fridays is obviously higher than on other trading days. It is interesting to see that the number of trades for clustering months is significantly less than that for non-clustering months (334 vs. 393). This observation is certainly not consistent with the theory that option-hedging is the driving force for GOOGLE stock clustering. Figure 5B shows the situation for quoted spreads. It is striking to see the narrow quoted spread on clustering OE Fridays. The 7.2-cent spread is narrower than the 9-cent spread on non-clustering OE Fridays, and it is much lower than the quoted spreads during clustering months on +1 day (13.4 cents) and -1 day (11.9 cents). To us, the unusually narrow quoted spread is most likely caused by price manipulation. It looks like that some institutions are controlling the quoted spread to a narrow range, and then make the closing price stop at a strike price. Quoted depth is presented in Figure 5 Panel C. The chart shows the median depth of the quotes. The depth of 364 shares on clustering OE Fridays is the highest among all the days in the chart. We observe two things. First, in non-clustering months, the depth on non-clustering Fridays is actually lower than those on other trading days. On clustering Fridays, the depth is much higher than those depths on the 5 trading days surrounding the OE Friday. This indicates that the inside depth on the limit order book is unusually high on clustering OE Fridays. Again, it points to the direction of illegal price control by setting up high depths and limit the price into a narrow range.

5.4 Pair-wise Comparison of Trading Measurements In this subsection, we conduct paired t-test comparing trading measurements on option expiration days with those normal trading days. We first divide the 36 months into two groups, the 22 clustering months and the 14 non-clustering months. Within the

29

clustering-month group, we pair up the number of trading on the OE Friday and the number of trading on the next Monday. We select the Monday after the OE Friday since this date should be relatively free of stock price manipulation. We directly compare number of trades on OE Friday with that on the following Monday. We do it first for the 22 clustering months. Then we do the paired tests for the 14 non-clustering months. In Table 7 Panel A, we see that numbers of trades on OE Fridays are both higher than the normal trading day of Monday after OE. This suggests that option expiration brings more trades compared to other normal trading days. For the quoted spread, we see that only during clustering months, the quoted spread is significantly narrower than that on the Monday after OE, with p-value of 0.0061. But when it is not clustering, the quoted spread is basically the same on OE Friday or on the Monday after OE. Again, it provides the evidence that quoted spread is artificially narrowed on clustering OE Fridays. The quoted depth has a strike p-value of 0.0002 in the clustering months. This means that the quoted depth is substantially higher on a clustering OE Friday than on the normal Monday after OE. Similar to the quoted spread, there is no difference on depth between OE Friday and the following Monday in non-clustering months. This points that the high quoted depth on clustering OE Fridays is not usual.

5.5 Regression Analysis of Trading Measurements during Clustering OE Fridays In this subsection, we conduct regression analysis to test the difference of trading measurement during clustering and non-clustering OE Fridays. We calculate trading measurements in the last minute of trading during the 36 OE Fridays from September 17, 2004 and August 17, 2007, for which we have the TAQ data. Then we use these trading measurements, such as quoted depth, quoted spread, number of trades, as the dependent variable in our regressions. We use one of these two regression models,

MODEL I: Mt =b0+b1*PRICEt+b2*CLUSTERINGt +t

(5.1)

MODEL II: Mt =b0+ b1*CLUSTERINGt+b2*Y2005t+b3*Y2006t+b4*Y2007t+t (5.2)

30

In both equations, M is trading measurement. The CLUSTERING is equal to 1 if the GOOGLE stock closed with 10% of the strike distance to a strike price; otherwise it is 0. In the first model, PRICE is the price level of the GOOGLE and we use the average of bid and ask prices. In the second model, we treat each of GOOGLEs fouryear trading as a different stage. The year 2004 was Googles IPO year and the stock was only traded for 5 months. In year 2005, GOOGLE experienced a price jump from $200 to over $400. The stock remained basically flat in year 2006 and it rapidly increased from sub-$500 to $747.24 in 2007. Since PRICE and the year indicators may be correlated, we keep them separate in different models. We use Model I to test the impact of clustering on quoted depth. The result is listed in Table 7 Panel A. We can see that the CLUSTERING indicator has a coefficient of 1.43 with p-value of 0.009. Here, we find strong statistical evidence that the quoted depth is enhanced in the last minute of trading during clustering OE Fridays. The result on quoted spread is also significant. We use model II in this case since spread should be directly related to the stock price. The spread is narrower on clustering OE Fridays than non-clustering ones by about 2.8 cents with a p-value of 0.01. Number of quote updates is lower by 528 (p-value=0.0314) on clustering OE Fridays than on non-clustering OE Fridays. Similarly, number of trade decreases by 84 on clustering OE Fridays compared to that on non-clustering OE Friday. This result is not consistent with the theory that GOOGLE clustering on OE Fridays is the outcome of option hedging which requires more trades. We also do sensitivity analyses to ensure the robustness of our results. So far, we have only used the last one minute of trading before the market close (3:59 to 4:00PM). We also used 30 seconds (3:59:30 to 4:00:00PM), 2 minutes, 3minutes, and 5 minutes. Results based on these lengths generally agree with the 1-minute result, but they become less significant when the length of trading increases. This is consistent with our expectation. If a price manipulators goal is to control the closing price, the closer to the market close, the stronger the market manipulation. We have used the median of the trading measurements of each day. As a sensitivity test, we try the daily means and obtain the same result. To ensure our result on quoted depth is robust, we also do the same analysis based on ask depth and bid depth separately and the result remains the same.

31

In sum, based on both of our paired t-test and regression analysis, we are rejecting all of the three hypotheses. We find that (1) number of trades is lower on clustering days than on non-clustering days, (2) quoted spread is narrower on clustering OE days than on non-clustering OE days, and (3) quoted depth is bigger on clustering OE days than on non-clustering OE days. The three joint results state that manipulation, instead of option hedging, is the driving force that makes GOOGLE the No. 1 clustering stock on option expiration days.

6. Conclusion

In this paper, we have studied several phenomena in GOOGLEs option and stock markets that point to the possible price manipulation by a group of institution traders. We have presented are adequate evidence to establish the existence of manipulation. We briefly summarize our results as following: 1. Earning Releases. A group of institutional traders appeared to know the postER stock price in advance. They opened new options which would become OTM later and sold them to the public. The high success rate of their risky option selling activities is too good to be normal. 2. Option Expirations. GOOGLE has the highest frequency of clustering on a strike price among all optionable stocks in our sample period. Clustering brings profit to option sellers, which often happen to be large financial institutions, and loss to option buyers, most of them public traders. Moreover, option sales by a small group of institution traders will abnormal positive returns also occur before option expirations. 3. Evidence from the Tape. We have calculated various trading measurements from the TAQ data. We find that quoted depth is unusually high and quoted spread is unusually narrow on clustering option expiration days. On such days, number of trades also is lower. These results reject the hypothesis that option hedging drives the stock price to the strike. They are consistent with the hypothesis that price manipulation lead to the high frequency of clustering for the GOOGLE stock. Examining stock price manipulation would be more interesting if we could hear directly from a stock manipulator. In March 2007, Jim Cramer, host of CNBCs Mad Money, openly bragged about manipulating stock prices in a video interview with

32

TheStreet.com. He discussed how easy it was to manipulate the markets by pushing stocks up, down and all around virtually at will with the enlisted cooperation of the financial press, who would print whatever spin he wanted: "This is blatantly illegal but when you have 6 days (to end the quarter) and your company may be in doubt because you are down, I think its really important to foment, if I were one of these guys, an impression that RIMM isnt any good." Jim Cramer described in great detail how he could spend just $15 million to $20 million to "knock RIMM down, which would be fabulous because it would beleaguer all the moron longs. He also discussed how to manipulate the Apple stock: "Apple, its very important to spread the rumor that both Verizon and AT&T decided they didnt like the phone. Cramer says he would do this in conjunction with placing large put orders to create the impression that something was happening. Cramer said, "Whats important when youre in that hedge fund mode, is not to do anything remotely truthful, because the truth is so against your view that its important to create a new truth, to develop a fiction. And the fiction is developed by almost anybody who is down like 2% to up 6% a year" Mr. Cramers confession about his involvement in stock price manipulation confirmed what we have discussed in this paper. For large financial institutions, it only cost them about $20 million to control the stock price while we find the gain could reach $100 million or more. It may be difficult for the market regulation agents to investigate stock price manipulation. First, there could be many complaints of manipulation and not all of them are true. Secondly, it takes a lot of work to prove that someone is illegally controlling the stock price and the regulators may not have enough resources. Our study presents a complete case for the market regulation agents and the manipulators could be tracked down by following the option sellers and examining those institutions involved in GOOGLE stock trading. Cleaning up the stock market is a challenging and interesting task. Keep the manipulators out of the market will protect the interest of small investors and the long-term health of the stock market.

33

References

Carhart, M., Kaniel, R., Musto, D., and Reed A., 2002. Leaning for the Tape: Evidence of Gaming Behavior in Equity Mutual Funds. Journal of Finance 46, 733-746. Comerton-Forde, C., and Putnins, T., 2007. Measuring Closing Price Manipulation. University of Sydney working paper. Greenwood, R., 2005. Float Manipulation and Stock Price. Harvard University working paper. Hillion, P., and Suominen, P., 2004. The Manipulation of Closing Prices. Journal of Financial Markets 7, 351-375. Hotchkiss, E., and Strickland, D., 2003. Does Shareholder Composition Matter? Evidence from the Market Reaction to Corporate Earnings Announcements. Journal of Finance 58, 1469-1498 Jarrow, R. A., 1992. Market Manipulation, Bubbles, Corners, and Short-Squeezes. Journal of Financial and Quantitative Analysis 27, 311-336. Jiang, G., Mahoney, P., and Mei, J., 2005. Market Manipulation: A Comprehensive Study of Stock Pools. Journal of Financial Economics 77, 147-170. John, K., and Lang, L., 1991. Strategic Insider Trading around Dividend Announcements: Theory and Evidence. Journal of Finance 46, 1361-1398. Khwaja, K. and Mian, A., Unchecked Intermediaries: Price Manipulation in an Emerging Stock Market. Journal of Financial Economics 78, 203-241. Lee, C., and Ready, M., Inferring Trade Direction from Intraday Data. Journal of Finance 46, 733-746. Mark C. M., Kaniel, R., Musto, D. K., and Reed A. V., 2002. Leaning for the Tape: Evidence of Gaming Behavior in Equity Mutual Funds. Journal of Finance 57, 661-693. Mei, J. P., Wu, G. J., and Zhou, C. S., 2004. Behavior Based Manipulation: Theory and Prosecution Evidence. New York University working paper. Merrick, J., Naik, N. Y., and Yadav, P. K., 2005. Strategic Trading Behavior and Price Distortion in a Manipulated Market: Anatomy of a Squeeze. Journal of Financial Economics 77, 171-218.

34

Meulbroek, L. K., 1992. An Empirical Analysis of Illegal Insider Trading. Journal of Finance, Volume 47, 1661-1699 Ni, X. Y., Pearson, N., and Poteshman, A., 2005. Stock Price Clustering on Option Expiration Dates. Journal of Financial Economics 78, 49-87. Seyhun, H.N., 1998. Investment Intelligence From Insider Trading. MIT Press. Sivakumar, K., and Waymire, G., 1994. Insider Trading Following Material News Events: Evidence from Earnings. Financial Management 23, 23-32. Roulstone, D., 2006. Insider Trading and the Information Content of Earnings Announcements. University of Chicago working paper. Vise, D., and Malseed, M., 2005. The GOOGLE Story. Bantam Dell Publishing.

35

Table 1 GOOGLE Earnings ReleasesThe following tables summarize market events surrounding all of GOOGLEs ERs before November 2007. Panel A lists the earnings results and the market price reactions. No is the sequence number of GOOGLEs ERs since its IPO. For example, the release made on July 19, 2007 is GOOGLEs 12th ER. Days to OE is the number of trading days to the next option expiration Friday. EPS estimate is the average analyst forecast of GOOGLEs earnings per share while EPS Actual is the real earnings number reported by the company. EPS Surprise is the difference between EPS Actual and EPS estimate. Pre-ER Close is the closing price at 4:00 PM, right before the company made the result available to the public at 4:30 PM. Post-ER Close is the following days closing price. Price Reaction equals to Post-ER Close minus Pre-ER Close.

Panel A: Summary of the Earning Results

No

Quarter

Release Date

Days to OE

EPS Estimate

EPS Actual

EPS Surprise

Pre-ER Close

PostER Close

Price Reaction

1 2 3 4 5 6 7 8 9 10 11 12 13

Q3 2004 Q4 2004 Q1 2005 Q2 2005 Q3 2005 Q4 2005 Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007

10/21/04 02/01/05 04/21/05 07/21/05 10/20/05 01/31/06 04/20/06 07/20/06 10/19/06 01/31/07 04/19/07 07/19/07 10/18/07

21 13 21 1 1 13 1 1 1 12 1 1 1

$0.56 $0.77 $0.92 $1.21 $1.36 $1.76 $1.97 $2.22 $2.42 $2.92 $3.30 $3.59 $3.78

$0.70 $0.92 $1.29 $1.36 $1.51 $1.54 $2.29 $2.49 $2.62 $3.18 $3.68 $3.56 $3.91

$0.14 $0.15 $0.37 $0.15 $0.15 -$0.22 $0.32 $0.27 $0.20 $0.26 $0.38 -$0.03 $0.13

$149.38 $191.90 $204.22 $313.94 $303.20 $432.66 $415.00 $387.12 $426.06 $501.50 $471.65 $548.59 $639.62

$172.43 $205.96 $215.81 $302.40 $339.90 $401.78 $437.10 $390.11 $459.67 $481.75 $482.48 $520.12 $644.71

$23.05 $14.06 $11.59 -$11.54 $36.70 -$30.88 $22.10 $2.99 $33.61 -$19.75 $10.83 -$28.47 $5.09

36

Panel B: GOOGLE Stock Prices around April 2007 Earning Releases

Sequence 1 -1 -2 -3 -4 -5 -6 -7 -8 -9

Date Friday, April 20, 2007 Thursday, April 19, 2007 Wednesday, April 18, 2007 Tuesday, April 17, 2007 Monday, April 16, 2007 Friday, April 13, 2007 Thursday, April 12, 2007 Wednesday, April 11, 2007 Tuesday, April 10, 2007 Monday, April 09, 2007

Open $490.52 $474.50 $471.26 $473.80 $468.46 $468.45 $464.00 $466.06 $467.09 $472.98

High $492.50 $481.95 $479.90 $476.39 $476.99 $468.77 $468.00 $469.40 $470.79 $473.00

Low $482.02 $469.59 $469.53 $471.60 $468.15 $463.36 $462.24 $462.61 $465.16 $465.59

Close $482.48 $471.65 $476.01 $472.80 $474.27 $466.29 $467.39 $464.53 $466.50 $468.21

Panel C: GOOGLE Stock Prices around April 2006 Earning Releases

Sequence 1 -1 -2 -3 -4 -5 -6 -7 -8

Date Friday, April 21, 2006 Thursday, April 20, 2006 Wednesday, April 19, 2006 Tuesday, April 18, 2006 Monday, April 17, 2006 Thursday, April 13, 2006 Wednesday, April 12, 2006 Tuesday, April 11, 2006 Monday, April 10, 2006

Open $448.90 $411.01 $412.57 $407.93 $403.45 $408.63 $409.00 $416.42 $407.08

High $450.72 $416.00 $413.64 $409.83 $412.50 $409.76 $411.33 $419.10 $417.17

Low $436.17 $408.20 $406.73 $401.50 $400.84 $400.50 $405.19 $406.22 $405.25

Close $437.10 $415.00 $410.50 $404.24 $406.82 $402.16 $408.95 $409.66 $416.38

37

Panel D: High Profitability of Institutional Option Selling Before Earning Releases

No

Release Date

Major Option Selling

Good Option Selling

Revenue

Liability

Profit

Liability Ratio

1 2 3 4 5 6 7 8

1/31/2006 4/20/2006 7/20/2006 10/19/2006 1/31/2007 4/19/2007 7/19/2007 10/18/2007

11 4 3 8 9 12 9 18

11 4 3 7 9 12 7 18

$12,150,210 $1,860,445 $752,385 $3,728,820 $4,738,330 $3,770,375 $3,833,510 $8,663,435

$2,877,463 $0 $0 $2,388,000 $2,019,230 $166,400 $1,791,000 $0

$9,272,748 $1,860,445 $752,385 $1,340,820 $2,719,100 $3,603,975 $2,042,510 $8,663,435

24% 0% 0% 64% 43% 4% 47% 0%

Sum

74

71

$39,497,510

$9,242,093

$30,255,418

23%

38

Table 2 GOOGLE Stock Closing Prices on Option Expiration Dates

No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Sum

Date Friday, September 17, 2004 Friday, October 15, 2004 Friday, November 19, 2004 Friday, December 17, 2004 Friday, January 21, 2005 Friday, February 18, 2005 Friday, March 18, 2005 Friday, April 15, 2005 Friday, May 20, 2005 Friday, June 17, 2005 Friday, July 15, 2005 Friday, August 19, 2005 Friday, September 16, 2005 Friday, October 21, 2005 Friday, November 18, 2005 Friday, December 16, 2005 Friday, January 20, 2006 Friday, February 17, 2006 Friday, March 17, 2006 Friday, April 21, 2006 Friday, May 19, 2006 Friday, June 16, 2006 Friday, July 21, 2006 Friday, August 18, 2006 Friday, September 15, 2006 Friday, October 20, 2006 Friday, November 17, 2006 Friday, December 15, 2006 Friday, January 19, 2007 Friday, February 16, 2007 Friday, March 16, 2007 Friday, April 20, 2007 Friday, May 18, 2007 Friday, June 15, 2007 Friday, July 20, 2007 Friday, August 17, 2007 Friday, September 21, 2007 Friday, October 19, 2007

Closing Price $117.49 $144.11 $169.40 $180.08 $188.28 $197.95 $180.04 $185.00 $241.61 $280.30 $301.19 $280.00 $300.20 $339.90 $400.21 $430.15 $399.46 $368.75 $339.79 $437.10 $370.02 $390.70 $390.11 $383.36 $409.88 $459.67 $498.79 $480.30 $489.75 $469.94 $440.85 $482.48 $470.32 $505.89 $520.12 $500.04 $560.10 $644.71

Strike Intervals $5 $5 $5 $5 $5 $5 $5 $5 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10 $10

Next Strike $115 $145 $170 $180 $190 $200 $180 $185 $240 $280 $300 $280 $300 $340 $400 $430 $400 $370 $340 $440 $370 $390 $390 $380 $410 $460 $500 $480 $490 $470 $440 $480 $470 $510 $520 $500 $560 $640

Distance to Next Strike $2.49 $0.89 $0.60 $0.08 $1.72 $2.05 $0.04 $0.00 $1.61 $0.30 $1.19 $0.00 $0.20 $0.10 $0.21 $0.15 $0.54 $1.25 $0.21 $2.90 $0.02 $0.70 $0.11 $3.36 $0.12 $0.33 $1.21 $0.30 $0.25 $0.06 $0.85 $2.48 $0.32 $4.11 $0.12 $0.04 $0.10 $4.71

Clustering 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 1 1 1 0 24

39

Table 3 Clustering and Open InterestThis table lists the GOOGLE option open interest data for Friday, May 19, 2006. This day is the last trading day for May 2006 GOOGLE options and the stock price closed at $370.02. The table only lists the options with the nearest expiration date, April 20, 2006. Strike Prices are listed in the middle of the table from $185 to $490. Call (Put) open interest numbers are on the left (right) of the strike prices. Based on the closing price of $370.02, all the call options with strike price $370 or lower and all the put options with strike $380 or higher are ITM and they are highlighted in the table. The cumulative call (put) open interest numbers are divided into two parts. For options with strike less or equal to $370, it represents the total open interests running from $185 to the current strike price. For options with strike price of $380 or higher, cumulative open interest is the sum of all option open interests between the current strike and the strike of $660.

Cumulative Call Open Interest

Call Open Interest

Strike Price

Put Open Interest

Cumulative Put Open Interest

357 674 982 1234 1478 1694 1892 2057 2139 2213 2322 2516 2760 3473 3830 4398 5077 5765 7253 11068 21414 178414 166109 153235 136973 123252 108036 87856 76599 63023 54249 43687 34966

357 317 308 252 244 216 198 165 82 74 109 194 244 713 357 568 679 688 1488 3815 10346 12305 12874 16262 13721 15216 20180 11257 13576 8774 10562 8721 3071

$185 $190 $195 $200 $210 $220 $230 $240 $250 $260 $270 $280 $290 $300 $310 $320 $330 $340 $350 $360 $370 $380 $390 $400 $410 $420 $430 $440 $450 $460 $470 $480 $490

30 55 36 0 26 163 175 188 473 392 613 855 710 2289 1641 2619 3840 5035 8573 9497 11350 13676 6580 6337 2777 1234 1610 1696 572 112 60 48 46

30 85 121 121 147 310 485 673 1146 1538 2151 3006 3716 6005 7646 10265 14105 19140 27713 37210 48560 34883 21207 14627 8290 5513 4279 2669 973 401 289 229 181

40

31895 16634 13395 9996 8352 6506 4463 3441 2645 1836 1269 586 368 145 61 30 10

15261 3239 3399 1644 1846 2043 1022 796 809 567 683 218 223 84 31 20 10

$500 $510 $520 $530 $540 $550 $560 $570 $580 $590 $600 $610 $620 $630 $640 $650 $660

14 0 10 0 1 0 9 10 1 0 0 0 0 0 20 20 50

135 121 121 111 111 110 110 101 91 90 90 90 90 90 90 70 50

41

Table 4 Cross-Section Comparison of Clustering of Different Stocks

Panel A Summary Statistics

Num of Months

Number of Clustering Mean Min Q1 Median Q3 Max 21 (GOOGLE)

34

7.22

0

5

7

9

Panel B Clustering Frequency Distribution across 1833 Stocks

Option Expiration Months

Times of Clustering

Num of Stocks

Cumulative Num of Stocks

Distribution Percent

Cumulative Distribution Percent

34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21

2 10 30 60 151 216 286 251 281 224 148 90 32 33 11 3 2 2 1 (GOOGLE)

2 12 42 102 253 469 755 1006 1287 1511 1659 1749 1781 1814 1825 1828 1830 1832 1833

0.11% 0.55% 1.64% 3.27% 8.24% 11.78% 15.60% 13.69% 15.33% 12.22% 8.07% 4.91% 1.75% 1.80% 0.60% 0.16% 0.11% 0.11% 0.05%

0.11% 0.65% 2.29% 5.56% 13.80% 25.59% 41.19% 54.88% 70.21% 82.43% 90.51% 95.42% 97.16% 98.96% 99.56% 99.73% 99.84% 99.95% 100.00%

SUM

1833

100.00%

42

Panel C Number of Clustering for Stocks with over $100 Average Price (34-Month)

Option Expiration Months

34-Month Avg Price

Stock

Num of Clustering

34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34

$377.17 $344.80 $177.61 $175.74 $156.64 $146.05 $145.88 $140.66 $134.76 $126.76 $124.57 $122.95 $116.31 $112.49 $110.94 $109.57 $108.09 $106.39 $104.68 $102.18 $100.98 $100.87 $100.05

CME GOOG RTP BBH FFH GS RKH SHLD MDY VTI BSC OIH UTH DIA MTB ACL CI DB STRA POT FDX TOT HAR

8 21 11 8 5 9 5 17 10 6 13 10 6 6 6 6 10 8 14 11 11 7 7

Average

9.35

43

Table 5 Smart Traders Selling New Options That Became Worthless After OEThis table lists the smart traders option selling activities on normal option expiration dates without major event. The smart trader group includes Wall Street firms and traders who buy and sell with order sizes larger than 100 contracts. We select the 21 option expiration periods from the 29 ordinary option expiration periods during which there were no major events such as earning release. We select the month if we see the smart traders have perfect foresight of the OE closing price by selling options that became worthless(or almost worthless later). The options were sold using open-sell order. OE Closing Price is the critical closing price on the option expiration Friday. All the options were sold on the Friday, before the option expiration. Strike indicates the strike price for the call or put options. OE value is the final value of the call or put options at the option expiration.

No

OE Closing Price

Call Option Trading Date Strike Volume OE Value Strike

Put Option Volume OE Value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

$560.10 $500.04 $470.32 $440.85 $469.94 $489.75 $480.30 $498.79 $409.88 $390.70 $339.79 $430.15 $400.21 $280.00 $301.19 $241.61 $185.00 $180.04 $197.95 $188.28 $169.40

Friday, September 21, 2007 Friday, August 17, 2007 Friday, May 18, 2007 Friday, March 16, 2007 Friday, February 16, 2007 Friday, January 19, 2007 Friday, December 15, 2006 Friday, November 17, 2006 Friday, September 15, 2006 Friday, June 16, 2006 Friday, March 17, 2006 Friday, December 16, 2005 Friday, November 18, 2005 Friday, August 19, 2005 Friday, July 15, 2005 Friday, May 20, 2005 Friday, April 15, 2005 Friday, March 18, 2005 Friday, February 18, 2005 Friday, January 21, 2005 Friday, November 19, 2004

$560 $500 $470 $440 $470 $490 $480 $500 $