The Ornstein–Uhlenbeck (OU) process models a quantity that fluctuates randomly but is continually pulled back toward a long-term mean — Quantustik uses it as the honest baseline its quantum models must beat.
Sentiment gauges like the Fear & Greed Index are natural OU candidates: extreme readings historically tend to normalize. Quantustik fits an OU model to these series specifically to serve as the honest, simple baseline its more elaborate quantum-inspired models must beat — if a fancier model can't outperform a well-fit OU baseline on out-of-sample data, that's a signal the extra complexity isn't earning its keep.
Live example: the CNN Fear & Greed Index currently reads 38 (fear) — an Ornstein–Uhlenbeck baseline treats this as a mean-reverting series, so an extreme reading carries an implicit pull back toward its long-run average. See the Fear & Greed model page for how the OU baseline compares to Quantustik's fancier models on this exact series.
Where OU always pulls toward one fixed equilibrium, the Schrödinger-based approach can fit a potential with more than one basin, allowing for genuinely multi-modal forecasts the single-equilibrium OU process cannot represent.
In discrete time, yes — the OU process is the continuous-time analogue of the AR(1) autoregressive model.
Because a simple, well-understood baseline is the fairest test: if a more complex model can't beat a properly-fit OU process on out-of-sample data, the added complexity isn't earning its keep.
It can, but is a better fit for series with a stable long-run mean, like a bounded sentiment index, than stock prices, which can trend for extended periods.
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Educational research only — not investment advice.