Forecasting works where the data have structure.
Stocks have weak structure.
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- Weak patterns
Trends break on news.
Seasonality is minimal.
Cycles are unstable.
Autocorrelations are low.
The data behave like noise.
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- The main drivers are outside the time series
Future prices depend on what is not in the history:
regulator decisions, earnings, liquidity, events, market sentiment.
The time series does not contain causes.
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- Predictors are unknown or unpredictable
It’s not enough to know the variables — you must forecast them.
With stocks, that’s hard or impossible.
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- Regimes change abruptly
Low rates → tightening → crisis → recovery.
Each regime invalidates the last one’s conclusions.
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- The forecast changes the market
Expectations become part of price.
Any pattern disappears when it’s exploited.
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- Noise dominates
Large outliers.
News jumps.
Local, short‑lived regularities.
Signal is weak; noise is strong.
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Conclusion
Stocks violate the key conditions for predictability:
- no stable structure,
- no robust drivers,
- no repeatability,
- no stationarity,
- and the forecast feeds back into the object.
Therefore, forecasting stocks is one of the hardest problems.
Uncertainty exceeds information.
— S. Praevis