"Hey market what are you up to?"
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@futurebird @cykonot this is to the tune of Pumps and a Bump by MC Hammer?
I was thinking something more like the
1800 VICTIM 2 song:
(This ad is very funny but it was also very effective in NYC ... we really are like this I guess)
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I was thinking something more like the
1800 VICTIM 2 song:
(This ad is very funny but it was also very effective in NYC ... we really are like this I guess)
@futurebird @eeentropyyy @cykonot I love how their shirts say VictimA, clearly this was the second go at it
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@futurebird @eeentropyyy @cykonot I love how their shirts say VictimA, clearly this was the second go at it
@neckspike @eeentropyyy @cykonot
OMG I didn't catch that!
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"Hey market what are you up to?"
"I have this nice AAA bond very safe can't go wrong."
"Wow THREE As?"
"Yes."
"So uh... what's in it?"
"Well... you know. Bundles."
"Bundles of what?"
"Bundles of bundles."
"Ok what is in those bundles?"
"Loans."
"That's nice. What kind of loans?"
"..."
"Well?"
"I'm lending cash to teenagers over the internet to buy NFTs... LISTEN I know it sounds risky... but ALL the loans can't fail!"@futurebird every generation learns that variances don't cancel
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@futurebird every generation learns that variances don't cancel
@PizzaDemon@mastodon.online @futurebird@sauropods.win what does that mean exactly?
is it like "if E(x) is negative then n*E(x) is even more negative"?
but if your E(x) is zero or positive, the variance doesnt really matter no? -
"Hey market what are you up to?"
"I have this nice AAA bond very safe can't go wrong."
"Wow THREE As?"
"Yes."
"So uh... what's in it?"
"Well... you know. Bundles."
"Bundles of what?"
"Bundles of bundles."
"Ok what is in those bundles?"
"Loans."
"That's nice. What kind of loans?"
"..."
"Well?"
"I'm lending cash to teenagers over the internet to buy NFTs... LISTEN I know it sounds risky... but ALL the loans can't fail!"@futurebird Isn't that what basically happened during the 2008 financial crisis? Millions of bad sub-prime mortgage loans re-packaged as collateralized debt obligations for basically the sole purpose of bumping up the debt by one or more letter grades?
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@futurebird Isn't that what basically happened during the 2008 financial crisis? Millions of bad sub-prime mortgage loans re-packaged as collateralized debt obligations for basically the sole purpose of bumping up the debt by one or more letter grades?
@Intaglio_Dragon
yes. and they are doing it again -
"Hey market what are you up to?"
"I have this nice AAA bond very safe can't go wrong."
"Wow THREE As?"
"Yes."
"So uh... what's in it?"
"Well... you know. Bundles."
"Bundles of what?"
"Bundles of bundles."
"Ok what is in those bundles?"
"Loans."
"That's nice. What kind of loans?"
"..."
"Well?"
"I'm lending cash to teenagers over the internet to buy NFTs... LISTEN I know it sounds risky... but ALL the loans can't fail!"@futurebird Oh, have we reverted to 2008 and the system of bundling and rebundling and securitizing bundles of bad loans that brought down so many firms and banks and cost many people their homes?
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@futurebird Oh, have we reverted to 2008 and the system of bundling and rebundling and securitizing bundles of bad loans that brought down so many firms and banks and cost many people their homes?
YES
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"Hey market what are you up to?"
"I have this nice AAA bond very safe can't go wrong."
"Wow THREE As?"
"Yes."
"So uh... what's in it?"
"Well... you know. Bundles."
"Bundles of what?"
"Bundles of bundles."
"Ok what is in those bundles?"
"Loans."
"That's nice. What kind of loans?"
"..."
"Well?"
"I'm lending cash to teenagers over the internet to buy NFTs... LISTEN I know it sounds risky... but ALL the loans can't fail!" -
@PizzaDemon@mastodon.online @futurebird@sauropods.win what does that mean exactly?
is it like "if E(x) is negative then n*E(x) is even more negative"?
but if your E(x) is zero or positive, the variance doesnt really matter no?@m @futurebird
https://statproofbook.github.io/P/var-lincomb.htmlIf both RVs are drawn from a similar population, they are likely to covary together. My long ago recollection from undergrad.
So two subprime mortgages are probably not independent risks. It's likely the same factors that influence one home owners ability to make payments also affects others in the same tranche. I.e. the macroeconomic conditions.
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@futurebird every generation learns that variances don't cancel
@PizzaDemon @futurebird
I think their bad assumption was "housing prices always go up!" They assumed that even if people foreclosed the banks would make money selling the houses. -
@PizzaDemon @futurebird
I think their bad assumption was "housing prices always go up!" They assumed that even if people foreclosed the banks would make money selling the houses.I view the source of the nonsense as the idea that just because you can do math to show two sets of debts are âequivalentâ that means itâs wise to treat them like they really are identical. But, in the end, behind every loan contract there are people and there is no math to make people equivalent.
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I view the source of the nonsense as the idea that just because you can do math to show two sets of debts are âequivalentâ that means itâs wise to treat them like they really are identical. But, in the end, behind every loan contract there are people and there is no math to make people equivalent.
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@futurebird a fun fact for those marketroids: P(A and B) only equals P(A) x P(B) if A and B are independent events!
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@cyberlyra @cshlan @PizzaDemon
What is it called?
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I view the source of the nonsense as the idea that just because you can do math to show two sets of debts are âequivalentâ that means itâs wise to treat them like they really are identical. But, in the end, behind every loan contract there are people and there is no math to make people equivalent.
@futurebird @cshlan @PizzaDemon
That normally doesnât matter. The estimation of the probability that someone will default has error margins, but most of the time theyâre independent variables and so large enough sample sets mean they donât matter. There are two cases where it typically goes wrong, neither is particularly to do with the humans on the customer side.
The first, which caused the 2008 crash, Enron, and so on (including, I suspect, the AI Bubble Crash) is that itâs possible to just lie. When a mortgage customer lies, it doesnât matter that much because a load of other customers donât and the ones that do are just another kind of outlier thatâs averaged out. But when the bank lies and shuffles paper trails enough that it looks like their loans are lower risk than they are, and then they sell them on that basis, it causes problems. Similarly, if the loans are smaller numbers and are to massive companies reporting ârevenueâ and not telling you that they are getting that revenue only because the loan is âinvestedâ in companies that then use that money to buy their products, then itâs a problem.
You couldnât convince banks that a bundle of loans backed by NFTs are AAA rated, but you possibly could if you mixed them in with a load of mortgages to the lowest-risk customers and gradually diluted the mortgage ones. Or if youâre actually loaning money to a company that is selling NFTs and is reporting revenue that exceeds the loan amount, while quietly moving things from the capex column to the revenue column by investing in their own customers.
The second, which is more interesting (to me, at least. I donât find lying that interesting) is that we remain very bad at reasoning about correlated risk. Prior to Katrina, a load of insurance companies did reasoning like âthese two businesses are in completely different markets in different towns, so the risk of them both needing to claim on insurance at the same time is lowâ. Only it turns out that they both depended on the same electricity substation, or the same water treatment plant. When the hurricane took out their common dependency, both claimed at once. Suddenly a load of those independent variables turned out not to be independent and that caused, as I recall, six insurance companies to go out of business. This is still a big problem with things like cybersecurity. How do you find two things to insure that are not both more likely to claim in the case of a critical Windows vulnerability, for example? Itâs also a problem now, because âthe country elects a president who actively attacks the economyâ was the kind of thing that everyone knew was a common risk for most businesses and individuals (most of whom get their income via employment at businesses), but not something people estimating insurance claim rates or loan defaults thought was high enough probability to bother modelling.
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@futurebird @cshlan @PizzaDemon
That normally doesnât matter. The estimation of the probability that someone will default has error margins, but most of the time theyâre independent variables and so large enough sample sets mean they donât matter. There are two cases where it typically goes wrong, neither is particularly to do with the humans on the customer side.
The first, which caused the 2008 crash, Enron, and so on (including, I suspect, the AI Bubble Crash) is that itâs possible to just lie. When a mortgage customer lies, it doesnât matter that much because a load of other customers donât and the ones that do are just another kind of outlier thatâs averaged out. But when the bank lies and shuffles paper trails enough that it looks like their loans are lower risk than they are, and then they sell them on that basis, it causes problems. Similarly, if the loans are smaller numbers and are to massive companies reporting ârevenueâ and not telling you that they are getting that revenue only because the loan is âinvestedâ in companies that then use that money to buy their products, then itâs a problem.
You couldnât convince banks that a bundle of loans backed by NFTs are AAA rated, but you possibly could if you mixed them in with a load of mortgages to the lowest-risk customers and gradually diluted the mortgage ones. Or if youâre actually loaning money to a company that is selling NFTs and is reporting revenue that exceeds the loan amount, while quietly moving things from the capex column to the revenue column by investing in their own customers.
The second, which is more interesting (to me, at least. I donât find lying that interesting) is that we remain very bad at reasoning about correlated risk. Prior to Katrina, a load of insurance companies did reasoning like âthese two businesses are in completely different markets in different towns, so the risk of them both needing to claim on insurance at the same time is lowâ. Only it turns out that they both depended on the same electricity substation, or the same water treatment plant. When the hurricane took out their common dependency, both claimed at once. Suddenly a load of those independent variables turned out not to be independent and that caused, as I recall, six insurance companies to go out of business. This is still a big problem with things like cybersecurity. How do you find two things to insure that are not both more likely to claim in the case of a critical Windows vulnerability, for example? Itâs also a problem now, because âthe country elects a president who actively attacks the economyâ was the kind of thing that everyone knew was a common risk for most businesses and individuals (most of whom get their income via employment at businesses), but not something people estimating insurance claim rates or loan defaults thought was high enough probability to bother modelling.
@david_chisnall @cshlan @PizzaDemon
I think you are making much more sophisticated points. And risk evaluation is fascinating.
Frankly I just have a trauma response to the word "bundle" in the context of debt trading and that's just as someone who likes to read about markets who didn't even lose anything. Just seeing it happen was enough.
So I was shocked to find out that it's still going on and people are getting burned again.
