Analysts must be cautious with historical data because:
Past data may not represent future conditions due to changes in technology, politics, regulation, or major disruptions (e.g., wars, policy shifts).
Statistical estimates from historical data can be unreliable, especially if the underlying risk–return dynamics have shifted.
These shifts are called regime changes, leading to nonstationarity—where different periods in the data reflect different statistical properties.
To address these issues, analysts can:
Use models to impose structure on past and expected future data behavior.
Apply statistical techniques to detect and adjust for regime shifts.
Evaluate whether the full dataset is relevant by asking:
Has there been a fundamental regime change?
Do the data support that such a change occurred?
If both are true, the analyst should either:
Use only the relevant segment of the data, or
Employ methods that account for regime changes to enhance forecast reliability.