I notice: some US/UK crashes are the same year and same color. Most of them are tightly correlated but the red 2020 lines don’t stay close to each other, and it causes me to feel sympathy for the UK… and then have second thoughts about whether maybe the US recovery is just due to the US money printer?
One thing I wonder is what the sampling method looked like? Is there a cutoff for picking a crash that was generated via query, or is this hand curated? How did you boil it down to those 17 modern crashes? I tracked down your May 2020 post and it has slightly different crashes, so my hunch is that this is hand curated :-)
It’s the same set of crashes, just that on the previous post the US and UK were on separate charts. The criterion for a crash is in footnote 1, viz. a real total return fall of 20% or more in 8 weeks or less.
I don’t know the specific reasons for the US recovery being unusually fast (I’m not an economist alas).
This data is pretty great!
I notice: some US/UK crashes are the same year and same color. Most of them are tightly correlated but the red 2020 lines don’t stay close to each other, and it causes me to feel sympathy for the UK… and then have second thoughts about whether maybe the US recovery is just due to the US money printer?
One thing I wonder is what the sampling method looked like? Is there a cutoff for picking a crash that was generated via query, or is this hand curated? How did you boil it down to those 17 modern crashes? I tracked down your May 2020 post and it has slightly different crashes, so my hunch is that this is hand curated :-)
It’s the same set of crashes, just that on the previous post the US and UK were on separate charts. The criterion for a crash is in footnote 1, viz. a real total return fall of 20% or more in 8 weeks or less.
I don’t know the specific reasons for the US recovery being unusually fast (I’m not an economist alas).