Study:Google Earnings Stock Manipulation EVERY Time?

by Darwin on June 4, 2009

Today, I came across this damning paper from Jerry Liu, a Finance professor at Cal State on the overt manipulation of Google’s share prices around earnings and options expiry timeframes.  Despite occasional grammatical errors throughout, this is the most influential paper of its sort I’ve read in years, including the assigned literature of this sort from my MBA program I finished last year.  I have to admit, I’ve been an advocate for selling options around earnings time for highly volatile tech stocks like Apple and Google, and I’ve profited handsomely from doing so (I had recently authored “A Lesson in Volatility prior to Earnings” wrt GOOG and have been selling GOOG Credit Spreads for Income); but I have to admit that I was completely naïve to the potential for broader market manipulation by institutional players (apparently I’ve just been lucky and noticed the trend early).  Presumably, this activity will subside to some degree given the implications of this paper and the author’s submission of data to the SEC for investigation.  I will likely continue to undertake the same activity though – with AAPL’s wild swings in expectations, earnings, new product cycles, and overall investor enthusiasm and with Google’s refusal to provide meaningful earnings estimates, it leaves both stocks open to immense implied volatility going into earnings, which are often then met with a yawn, allowing for a nice easy option premium capture literally overnight.

Interesting Quotes from the Paper:

“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 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.”

While he makes a very compelling case (really, I don’t see how you could explain it otherwise) for advanced knowledge of future share price levels, he doesn’t actually demonstrate how to facilitate the actual closing price; but it is amazing that the “smart traders” can predict a day or more in advance exactly where shares will close post-earnings in the future.  Was it public statements and price target revisions post-earnings from the large investment houses tied to the same in-house traders?  It would have been interesting to pair up the numerical data with public comments and visits on CNBC that day by institutional colleagues. The only reason I’m a bit surprised by the findings is that we’re only talking a few million dollars here, a few million dollars there, which for the largest investment houses, doesn’t seem like it would be worth the risk, fines, damage to reputation, etc.  Perhaps there are some individual rogue traders out there, but I’d be surprised if the manipulation were endorsed as a conspiracy at high levels across multiple large investment houses.  If we were talking Billions, I might feel a bit differently.

The question is, by using publicly available data, and hence, legal trading activity, can you find a way to profit from the next earnings announcement by looking at the tape?  If you could, would you trade on it?

I have some thoughts; interested in yours.

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Canadian Personal Finance Blog » Blog Archive » Carnival of Financial Planning #94
June 20, 2009 at 8:03 am


1 Mapkaminihinc June 5, 2009 at 3:54 am

Hi, Congratulations to the site owner for this marvelous work you’ve done. It has lots of useful and interesting data.

2 Matt August 18, 2010 at 2:09 pm


Sure would trade on that sort of scheme…in fact that is part of my strategy, we just played NFLX puts for 200% gains overnight by using a hybrid strategy that incorporates the “max pain” theory.

Good blog post.

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