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macrocephalopod
macrocephalopod
macrocephalopod
@macrocephalopod

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macrocephalopod

@macrocephalopod

Paul Allen, Vice President M&A, Pierce & Pierce (Sie/Hir) | Vegan | Silence is Violence | Women’s Rights Are Human Rights | ACAB (Assigned Cephalopod At Birth)

Joined December 2020

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    1. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      Want to talk about a weird paradox in US equity markets -- 1. Just before the close is by far the most liquid time of day however you measure it (traded volume, bid/ask spread, market depth) 2. It's also when you are least likely to to get filled anywhere near "fair value"pic.twitter.com/D7vqi7WRyo

      4 replies 5 retweets 49 likes
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    2. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      The chart shows ES futures volatility 2013-now measured using 24 hour periods starting/ending at the indicated time. If there was no time of day effect this would be a flat line, in reality the close-to-close return is two percentage points higher than e.g. the 1pm-to-1pm return

      1 reply 0 retweets 2 likes
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    3. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      The reason is huge flows around the close that distort the price away from fair value *despite* the additional liquidity available at this time (e.g. market-on-close orders and yes, maybe even dealer option hedging....)

      1 reply 0 retweets 5 likes
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    4. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      The dumbest possible model for this is to write the observed price P(t) as the sum of a fair price F(t) and a random noise E(t) that represents excess demand at the close on day t

      1 reply 0 retweets 3 likes
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    5. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      This model predicts that the variance of close-to-close returns is larger than the variance of "fair value" returns by 2 * Var(E) -- if we assume the difference between the 1pm-1pm and close-close returns is entirely due to this effect (it's not) than it implies

      2 replies 0 retweets 3 likes
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    6. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      that Var(E) = (0.17^2 - 0.15^2)/2, which is the same as saying that the daily standard deviation of E is ~35 basis points, which is absolutely huge (it's smaller than this in reality but still pretty big -- and potentially tradable?)

      1 reply 0 retweets 3 likes
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    7. macrocephalopod‏ @macrocephalopod 8 Feb 2021

      Another prediction of this really dumb model is that the close-close return on day t should be negatively correlated with close-close return on day t+1, which is something that we observe -- subsequent day returns have a correlation of about -0.07

      3 replies 0 retweets 3 likes
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    8. Garh ☀️卐‏ @GarhNauti 8 Feb 2021
      Replying to @macrocephalopod

      May I request you to elaborate on what there will be -ve corr?

      1 reply 0 retweets 0 likes
      macrocephalopod‏ @macrocephalopod 8 Feb 2021
      Replying to @GarhNauti

      If the noise E is uncorrelated from day to day, then days with higher than average noise will be followed by days with lower noise on average, and this is enough to introduce negative correlation.

      7:25 AM - 8 Feb 2021
      0 replies 0 retweets 0 likes

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