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Dropping
Dropping-0.mp3
Dropping-0.mp4
Dropping-I.mp3
Dropping-I.mp4
Dropping-intro.mp3
[Intro]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Verse 1]
A chaotic decline
Kind to break a spine
Falling faster (and faster)
Exponential disaster
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Bridge]
A falling knife
(Best think twice)
Have to sell your wife
(Rolling the dice)
[Verse 2]
A chaotic fall
A fall for us all
Falling faster (and faster)
Exponential disaster
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Bridge]
A falling knife
(Best think twice)
Have to sell your wife
(Betting with your life)
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Outro]
A falling knife
(Best think twice)
Have to sell your wife
(Betting with your life)
A SCIENCE NOTE
The stock market — especially during a crash — behaves like a chaotic system, not a linear or purely random one.
Here’s How Chaos Theory Explains a Market Crash:
1. Sensitive Dependence on Initial Conditions (Butterfly Effect)
Tiny changes → outsized effects.
In normal markets: news moves prices somewhat predictably.
In panics: anything (bad earnings, policy tweet, random rumor) can trigger cascading selling.
This is why crashes often start small — then suddenly snowball.
2. Feedback Loops Amplify Instability
Chaos systems are full of feedback loops.
Market Example:
Price drops → triggers margin calls → triggers forced selling → drives price lower → triggers more margin calls → repeat.
Other Feedback Loops:
Algorithmic selling.
Stop-loss triggers.
ETF outflows.
Option hedging gone wrong (gamma squeezes in reverse).
Result → Violent, non-linear moves.
3. Fractal Patterns in Price Movement
Market crashes often show self-similarity at different time scales — a classic fractal trait.
1-minute chart → sharp drops & rebounds.
Daily chart → same jagged patterns.
Weekly chart → still looks like chaos.
Chaos theory predicts this — because the forces driving action at all scales are structurally similar.
4. No Predictable Floor
In chaotic systems:
Patterns emerge…
But exact outcomes cannot be predicted.
→ This explains why technical support levels sometimes work — but often fail spectacularly in a true crash.
“The floor only exists until everyone agrees it doesn’t.”
5. Order Emerges After Disorder
Chaos systems often self-organize into new stable patterns — but not on a predictable schedule.
In markets:
Stabilizers eventually overpower panic.
Valuation buyers step in.
Forced selling exhausts itself.
But when this happens is unknowable in advance.
In Summary:
A market crash is the perfect real-world example of chaos theory in action.
→ Small triggers lead to huge consequences.
→ Feedback loops accelerate instability.
→ Non-linear, jagged price moves dominate.
→ Short-term randomness — long-term pattern formation.
→ Order only emerges after volatility burns itself out.
From the album “Collapse”
The Human Induced Climate Change Experiment
View all episodes
Dropping-0.mp3
Dropping-0.mp4
Dropping-I.mp3
Dropping-I.mp4
Dropping-intro.mp3
[Intro]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Verse 1]
A chaotic decline
Kind to break a spine
Falling faster (and faster)
Exponential disaster
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Bridge]
A falling knife
(Best think twice)
Have to sell your wife
(Rolling the dice)
[Verse 2]
A chaotic fall
A fall for us all
Falling faster (and faster)
Exponential disaster
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Bridge]
A falling knife
(Best think twice)
Have to sell your wife
(Betting with your life)
[Chorus]
Dropping
(Like a rock)
Rocking
(Best take stock)
[Outro]
A falling knife
(Best think twice)
Have to sell your wife
(Betting with your life)
A SCIENCE NOTE
The stock market — especially during a crash — behaves like a chaotic system, not a linear or purely random one.
Here’s How Chaos Theory Explains a Market Crash:
1. Sensitive Dependence on Initial Conditions (Butterfly Effect)
Tiny changes → outsized effects.
In normal markets: news moves prices somewhat predictably.
In panics: anything (bad earnings, policy tweet, random rumor) can trigger cascading selling.
This is why crashes often start small — then suddenly snowball.
2. Feedback Loops Amplify Instability
Chaos systems are full of feedback loops.
Market Example:
Price drops → triggers margin calls → triggers forced selling → drives price lower → triggers more margin calls → repeat.
Other Feedback Loops:
Algorithmic selling.
Stop-loss triggers.
ETF outflows.
Option hedging gone wrong (gamma squeezes in reverse).
Result → Violent, non-linear moves.
3. Fractal Patterns in Price Movement
Market crashes often show self-similarity at different time scales — a classic fractal trait.
1-minute chart → sharp drops & rebounds.
Daily chart → same jagged patterns.
Weekly chart → still looks like chaos.
Chaos theory predicts this — because the forces driving action at all scales are structurally similar.
4. No Predictable Floor
In chaotic systems:
Patterns emerge…
But exact outcomes cannot be predicted.
→ This explains why technical support levels sometimes work — but often fail spectacularly in a true crash.
“The floor only exists until everyone agrees it doesn’t.”
5. Order Emerges After Disorder
Chaos systems often self-organize into new stable patterns — but not on a predictable schedule.
In markets:
Stabilizers eventually overpower panic.
Valuation buyers step in.
Forced selling exhausts itself.
But when this happens is unknowable in advance.
In Summary:
A market crash is the perfect real-world example of chaos theory in action.
→ Small triggers lead to huge consequences.
→ Feedback loops accelerate instability.
→ Non-linear, jagged price moves dominate.
→ Short-term randomness — long-term pattern formation.
→ Order only emerges after volatility burns itself out.
From the album “Collapse”
The Human Induced Climate Change Experiment