Series 6 – Finance, Bubbles & Crises

Series 6: Finance, Bubbles & Crises — 400 Years of Speculative Madness

Finance, Bubbles & Crises: The Unchanging Mechanics of 400 Years of Speculation

In 1637, the Dutch paid the price of a house for a single tulip bulb.
In 2021, people paid $500,000 for an NFT monkey picture.

In 1720, Newton lost £20,000 in the South Sea Bubble — equivalent to millions today.
In 2000, NASDAQ evaporated $5 trillion.

In 1929, the Great Depression took 25 years to recover from.
In 2008, the financial crisis took 5 years — because policymakers had read history.

The mechanics of bubbles never change; only the speed of collapse accelerates.


Dissecting 400 Years of Bubbles and Collapses — With Data

Bubble Trajectory Comparison
Using Python DTW (Dynamic Time Warping) to compare bubble trajectories spanning 384 years — 87.3% price pattern similarity, Minsky’s five stages perfectly replicated

Leverage Death Spiral
Using Agent-Based Models to simulate bank run chain reactions — SVB’s 93% uninsured deposits + social media + mobile banking = perfect storm

Trust Collapse Modeling
Tracking the trust cycle from tulips to FTX — 400-year pattern: narrative → greed → herding → leverage → collapse


Six Deep Analyses, One Historical Thread

Tulip Mania vs Bitcoin Bubble — 384 years of strikingly similar bubble trajectories

South Sea Bubble vs Dot-Com Bubble — 280 years of P/E deviation index tracking

1929 Great Depression vs 2008 Financial Crisis — How institutional design shortened recovery time

Japan’s Lost Decades vs Taiwan’s Housing Crisis — Acute collapse vs chronic suffocation

Bank Runs: Northern Rock to SVB — 150 years of accelerating bank runs

The FTX Collapse & the Bankruptcy of Trust — Finale: summarizing 400 years of lessons


Who is this series for?

  • Investors wanting to understand historical bubble patterns
  • Citizens concerned about financial stability
  • Anyone wanting to learn risk management from history
  • Those with firsthand experience of housing market issues
  • People wanting to understand financial crises through data
  • Anyone curious about “why humans don’t learn from history”

6 in-depth analyses | 400 years of financial data | Python DTW, ABM simulation & leverage modeling

The mechanics of bubbles never change, but we can choose not to repeat the same mistakes


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