Palantir's 9% Jump Boosts Appeal of Call Options as Bulls Pile In
$Palantir Technologies Inc.(PLTR)$
Palantir shares climbed above $194 Monday, rebounding from an 11% slump last week, their worst performance since the five days ended April 4. That was the same week when President Donald Trump announced "Liberation Day," unleashing a slew of tariffs on U.S.'s trading partners and fueling a stock market sell-off.
The gains came after hedge fund manager Michael Burry, famous for his "Big Short" during the housing bubble over a decade ago, hinted that he may no longer be short Palantir shares.
Call options that give their holder the right to buy Palantir shares at $200 each by the end of the week attracted the heaviest trading among contracts tied to the software company that provides artificial intelligence (AI) systems. Volume on that contract more than doubled to 54,470 options as of 1:47 p.m., from the previous session.
Trading advanced as the stock price climbed near that strike price, increasing the odds that the call option could be in-the-money by the time it expires Friday. Volume on those $200 call options expiring in four days is already more than double the 24,040 open interest.
Burry's social media post overnight came after Palantir CEO said in a CNBC interview that the two stocks that the hedge fund manager is shorting are the ones "making all the money, which is super weird - like the idea that chips and ontology is what you want to short is batsh** crazy."
While his company, Scion Asset Management, filed a 13F document showing it held put options that give their holder the right to sell 5 million Palantir shares and put options on 1 million $NVIDIA (NVDA.US)$ shares, the document was based on positions as of Sept. 30 and the holdings in the portfolio may have changed since then.
"Doesn't surprise me one bit that Alex Karp and his "ontology" @PalantirTech cannot crack a simple 13F," Burry said in his post. "A fundamental principle of any rigorous ontological/epistemological model- whether philosophical or in data science - is recognizing when your information set is insufficient for valid conclusions."
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