Which will be the lowest range Palantir stock price will reach in the next 2 years?
2
1kṀ3937
2027
2%
$100 or higher
13%
$80-100-
22%
$60-80-
22%
$40-60-
22%
$20-40-
19%
Below $20

Background

Palantir Technologies (NYSE: PLTR) is a software company specializing in big data analytics and AI solutions. The stock has shown significant volatility since its public debut in 2020. Current analyst forecasts for 2025-2026 vary widely, with price targets ranging from $44.90 to $81, reflecting different views on the company's growth potential and market conditions.

Resolution Criteria

  • The market will resolve based on the lowest price range that Palantir's stock reaches at any point during the next 2 years (from market creation date)

  • The stock price must be inside the lowest range at market close time (not during main, pre or after hours) to qualify

  • The top value for the ranges ending with a minus sign is excluded

  • Price data will be based on official NYSE trading data

  • Stock splits or reverse splits will be adjusted for accordingly

  • If the stock is delisted or the company is acquired before the 2-year period ends, the market will resolve based on the lowest range reached before such event

Considerations

  • The stock's performance could be significantly impacted by:

    • Overall market conditions and tech sector performance

    • Company's AI initiatives and government contracts

    • Broader economic factors like interest rates and inflation

    • Competitive landscape in the data analytics and AI space

  • Historical volatility suggests the stock price could experience substantial fluctuations within the 2-year timeframe

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bought Ṁ450 NO

$100 or higher should resolve NO

@MingCat In half an hour, when the market closes

@MingCat Do you have any idea how I resolve one to NO? I can only see the option of resolving the final choice to YES

bought Ṁ858 NO

@ikoukas I've actually never run a market like this, maybe it's not possible to resolve early on linked probability markets? I'm not sure

@MingCat Could be, but seems silly

@MingCat maybe we should ask someone else like @Bayesian

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