Two Ways To Hedge Apple and Research In Motion
The following article is from one of our external contributors. It does not represent the opinion of Benzinga and has not been edited.
Back in November, when Apple was about 40 points higher than it is now, hedge fund manager and market technician Tim Knight wrote what now looks like a prescient post about how its stock's best days were behind it (See How Apple Became Japan). Here's how Tim concluded that post:
As for the stock price, I suspect it'll resemble the Nikkei chart found way above, although perhaps not as dramatically. I imagine AAPL will be in the low 400s next year and will meander around relatively trendlessly for years to come. Its multi-thousand percent gain will be a part of financial history, just like similar gains enjoyed many years ago by RIMM, CSCO, and YHOO.
Although Apple has slid since then, surprisingly, shares of its smart phone maker competitor Research In Motion (RIMM) have nearly doubled.
Hedging AAPL With Optimal Puts in August
Back in August, I tweeted from the Portfolio Armor Twitter account the optimal put* to hedge Apple against a greater-than-20% drop over the next six months:
— Portfolio Armor (@PortfolioArmor) Aug. 17, 2012
That put is getting a little long in the tooth now, but since AAPL has dropped 22.9% since then, it has done its job: if an AAPL long who purchased that put on Aug. 17 were to exercise it now, his loss would be limited to 20%, taking into account the initial cost of the put. Since the put still has some time value left (see the screen cap of the Yahoo quote page for it below), he'd have a smaller loss if he sold the put and his AAPL shares now -- in that case, his loss would be limited to 17.7%.
Hedging AAPL and RIMM with Optimal Puts Now
If an AAPL long wanted to hedge against another 20%+ drop in his shares from here, he'd be able to find optimal puts to do that now -- and for a fairly low cost, considering Apple's recent slide. But despite RIMM's recent rise, it is currently too expensive to hedge against a greater-than-20% drop from here:
Since the cost of hedging RIMM against a greater-than-20% drop was itself greater than 20% on Friday, Portfolio Armor indicated there were no optimal puts available for it (there was an optimal put available to hedge RIMM against a >21% drop, but the cost of it, as a percentage of position value, was 20.15%). This is a case where -- for longs willing to cap their potential upside -- being able to scan for an optimal collar** would come in handy.
That capability is now live on the website, and coming soon to the iOS app (where it will be available as an in-app subscription). It has been generating some interesting results, including a number of instances where the optimal collars are zero cost, or even have negative net costs (i.e., the income from the calls you sell as part of the collar is greater than the cost of the puts you buy, so you are getting paid to hedge). Below we'll take a look at the optimal collars to hedge $50,000 positions in AAPL and RIMM against greater-than-20% declines from here, for investors willing to cap their potential upsides from here at 15%.
Hedging AAPL and RIMM With Optimal Collars Now
As you can see in the screen captures below, for longs willing to cap their potential upsides at 15% while limiting their potential downside risk to drops of no more than 20%, the optimal collars are negative cost collars in both cases: the investors would get paid to hedge.
*Optimal puts are the ones that will give you the level of protection you want at the lowest possible cost. Portfolio Armor (available on the web and as an Apple iOS app), uses an algorithm developed by a finance Ph.D to sort through and analyze all of the available puts for your stocks and ETFs, scanning for the optimal ones.
**Optimal collars are the ones that will give you the level of protection you want at the lowest net cost, while not limiting your potential upside by more than you specify. The extension to the Portfolio Armor algorithm to find optimal collars was developed by a post-doctoral fellow in the financial engineering department at Princeton University.
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