Abnormal Distribution
ที่อัปเดต:
Going against the norm. Modeling stock markets using non-normal distributions.
That is the only way to take into account the massive volatility that markets can reach.
arxiv.org/pdf/1906.10325.pdf
Laplace Distributions manage to analyze prices better than normal ones.
It is, as if, stock market is not normal, as in abnormal. As if it defies all physics.
2020 surely suggests that. Rest assured, 2020 was not the worst violation of "normal" physics.
The 2020 Swan may have made headlines, but 2008 is surely unforgettable for young and old alike. It is an example of how deep and unpredictable a crisis can become.
Before the crisis, a plausible risk assessment would give us the following region.
This assumption would end up catastrophic.
A 2-sigma difference may not seem much, but it ended up being a 40% gap.
The 2020 crash shown before, reached a 6-sigma deviation in a shorter timeframe than the following ones. Comparing apples-to-apples with the other charts, 2020 was less than 2-sigma on 300-month length. Suddenly, 2008 looks, and was in fact, more painful than 2020.
Sticking to the same interval (monthly) of analysis and with the same length, we go back in time, in 1980s, just after stagflation ended.
Once again, investors were baffled to see markets grow and grow, above all expectations.
Curiously, Black Monday occurred on the exact 4-sigma limit on the 300M length.
Moving swiftly on, we reach the "Roaring '20s".
Spoiler Alert, the same happened.
Price reached above any possible expectation.
An investor in 1926 would do an analysis based on their historical data. They could not have known the future price action. The 2-sigma channel we drew in 1926 ended up deviating up to 6-sigma.
As we all know, The Great Depression followed up. That was a similar >6-sigma event.
Price reached below any possible expectation.
If you believe 2-sigma is all that Dow can do, don't think twice...
...think quadruply (as in 4-sigma)
The 2-sigma limit was dwarfed...
...from the scale of the events that followed.
After all, stock markets must go in places where nobody believes possible.
No second thoughts must flood our minds to reach the top.
All hope must be lost to reach the bottom.
Extra Charts:
A small-looking but deadly bear trap could be all it takes to create a massive bubble.
Dividing by M2SL reveals that equities are not that overpriced. They are sitting comfortably at the mean.
Has Big Tech grown enough?
For 20 years, consumers were the result of the growth of these companies. Now, governments need digital payment systems, digital identities, IoT. All of these will come mainly from existing corporations, not so much from government production.
The .com bubble was uncharted territory for technology.
Now, supply has developed, and believe it or not, we are still in charted territory. The IXIC/SPX ratio (technology dominance) hasn't even managed to make an all-time high.
Tread lightly, for this is hallowed ground.
-Father Grigori
That is the only way to take into account the massive volatility that markets can reach.
arxiv.org/pdf/1906.10325.pdf
Laplace Distributions manage to analyze prices better than normal ones.
It is, as if, stock market is not normal, as in abnormal. As if it defies all physics.
2020 surely suggests that. Rest assured, 2020 was not the worst violation of "normal" physics.
The 2020 Swan may have made headlines, but 2008 is surely unforgettable for young and old alike. It is an example of how deep and unpredictable a crisis can become.
Before the crisis, a plausible risk assessment would give us the following region.
This assumption would end up catastrophic.
A 2-sigma difference may not seem much, but it ended up being a 40% gap.
The 2020 crash shown before, reached a 6-sigma deviation in a shorter timeframe than the following ones. Comparing apples-to-apples with the other charts, 2020 was less than 2-sigma on 300-month length. Suddenly, 2008 looks, and was in fact, more painful than 2020.
Sticking to the same interval (monthly) of analysis and with the same length, we go back in time, in 1980s, just after stagflation ended.
Once again, investors were baffled to see markets grow and grow, above all expectations.
Curiously, Black Monday occurred on the exact 4-sigma limit on the 300M length.
Moving swiftly on, we reach the "Roaring '20s".
Spoiler Alert, the same happened.
Price reached above any possible expectation.
An investor in 1926 would do an analysis based on their historical data. They could not have known the future price action. The 2-sigma channel we drew in 1926 ended up deviating up to 6-sigma.
As we all know, The Great Depression followed up. That was a similar >6-sigma event.
Price reached below any possible expectation.
If you believe 2-sigma is all that Dow can do, don't think twice...
...think quadruply (as in 4-sigma)
The 2-sigma limit was dwarfed...
...from the scale of the events that followed.
After all, stock markets must go in places where nobody believes possible.
No second thoughts must flood our minds to reach the top.
All hope must be lost to reach the bottom.
Extra Charts:
A small-looking but deadly bear trap could be all it takes to create a massive bubble.
Dividing by M2SL reveals that equities are not that overpriced. They are sitting comfortably at the mean.
Has Big Tech grown enough?
For 20 years, consumers were the result of the growth of these companies. Now, governments need digital payment systems, digital identities, IoT. All of these will come mainly from existing corporations, not so much from government production.
The .com bubble was uncharted territory for technology.
Now, supply has developed, and believe it or not, we are still in charted territory. The IXIC/SPX ratio (technology dominance) hasn't even managed to make an all-time high.
Tread lightly, for this is hallowed ground.
-Father Grigori
บันทึก
Pro Tip: You cannot spot long-term support/resistance levels on the standard SPX chart, since it is inflationary. Instead, divide by CPIAUCSL (inflation) to pinpoint retracements and support/resistance aka supply/demand levels.Supply levels in equities, are, after all, inflationary similar points in time.
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