The 1987 market crash highlighted a critical flaw in the Black-Scholes options pricing model: it assumes a world without large, sudden crashes. This intellectual gap spurred Jean-Philippe Bouchaud to move into finance and develop new quantitative models that could account for these real-world "jumps."

Financial crises are rarely caused by risks everyone is watching, like inflation (known knowns). The true danger comes from unforeseen events (unknown unknowns) like 9/11 or the Lehman collapse, which aren't priced into risk models and cause systemic panic.

Unlike physical sciences where observation doesn't change the subject, the stock market's behavior is influenced by participants watching it. A market can rise simply because it has been rising, creating momentum loops. This "self-awareness" means price and value are not independent variables, a key distinction from more rigid scientific models.

A bewildering disconnect exists between high market enthusiasm and extreme geopolitical and economic uncertainty. This suggests investors are either willfully ignorant of the risks or believe they are insulated, creating a fragile environment where a materialized risk could trigger a sudden, severe, and nonlinear market crash.

Bitcoin's recent crash is attributed to extreme leverage unique to crypto, with platforms letting users buy $100 of Bitcoin with only $1 of their own money. This amplifies gains, creating bubbles, but more dangerously, it amplifies losses, forcing panic selling and cascading liquidations that can erase huge gains almost instantly.

A market enters a bubble when its price, in real terms, exceeds its long-term trend by two standard deviations. Historically, this signals a period of further gains, but these "in-bubble" profits are almost always given back in the subsequent crash, making it a predictable trap.

Even a highly systematic quant shop like CFM acknowledges the need for human intervention. For truly unprecedented events like the Brexit vote or sudden tariff announcements, the firm concluded its models were blind to the unique context, requiring a manual human judgment call to manage risk appropriately.

CFM operates on the belief that in the short-to-medium term (up to a year), market prices are driven primarily by investor flows, not fundamental value. This "inelastic market hypothesis" means their strategy focuses on predicting what people will buy and sell, rather than analyzing company balance sheets.

To combat unreliable backtests, CFM is building "meta-models" that quantitatively predict whether a new model's results are overfitted. This systematic approach aims to replace human judgment with a data-driven process for deciding if a trading model is robust enough for production.