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Correlation, Regression, and Time Series

Valuation-modeling terms for correlation, covariance, cointegration, regression analysis, and time-series analysis.

Correlation, Regression, and Time Series covers valuation-modeling terms for correlation, covariance, cointegration, regression analysis, and time-series analysis.

Use these pages when a statistical assumption, model structure, or risk distribution changes the analytical result. It sits inside Statistical Relationships and Time-Series Analysis, so readers can move up when the broader valuation context matters.

Use the table below to choose the narrower valuation branch before relying on a model input, market multiple, forecast, risk premium, price signal, or recommendation.

What This Branch Covers

AreaUse it for
CointegrationCointegration refers to a statistical property indicating a stable, long-run relationship between two or more time series variables, despite short-term deviations.
CorrelationCorrelation measures how strongly two variables move in relation to each other.
CovarianceCovariance measures how two variables move together and helps calculate portfolio risk, diversification effects, and factor relationships.
Regression AnalysisRegression analysis estimates relationships between variables and is used to explain returns, forecast metrics, and test drivers.
Time Series AnalysisTime series analysis studies data ordered through time to identify trends, cycles, volatility, and forecasting patterns.

What to Check

  • Forecast source, valuation date, market data, accounting adjustments, and model version.
  • Cash-flow input, discount rate, multiple, growth assumption, terminal value, balance-sheet adjustment, and scenario range.
  • Comparable set, transaction set, sector, geography, size, leverage, margin profile, and accounting basis.
  • Effect on intrinsic value, relative value, price target, margin of safety, impairment view, deal price, or recommendation.
  • Sensitivity to growth, margins, reinvestment, discount rate, exit multiple, leverage, and market conditions.

Common Mistakes

  • Treating a valuation output as a precise fact instead of a range of estimates.
  • Comparing multiples without normalizing earnings, leverage, accounting policy, growth, and risk.
  • Ignoring valuation date, source quality, cyclicality, nonrecurring items, and sensitivity analysis.
  • Using valuation terminology as personalized investment, tax, legal, or appraisal advice.

Valuation content is educational and does not provide investment, tax, legal, accounting, appraisal, or valuation advice.

In this section

Choose a subsection first. Deeper term pages live inside each subsection, which keeps large topic hubs readable.

Cointegration

Cointegration refers to a statistical property indicating a stable, long-run relationship between two or more time series variables, despite short-term deviations.

Correlation

Correlation measures how strongly two variables move in relation to each other.

Covariance

Covariance measures how two variables move together and helps calculate portfolio risk, diversification effects, and factor relationships.

Regression Analysis

Regression analysis estimates relationships between variables and is used to explain returns, forecast metrics, and test drivers.

Time Series Analysis

Time series analysis studies data ordered through time to identify trends, cycles, volatility, and forecasting patterns.

Revised on Sunday, June 21, 2026