Linear Regression for Market Data: Correlation, Fit, and Assumptions

Summary

This introductory article explains simple linear regression as a straight-line model relating one predictor to a target. It presents the slope and intercept, then uses Pearson correlation to assess linear association before choosing predictors. Its worked market-data example collects NASDAQ prices, a moving average, RSI, and S&P 500 values, and reports correlations: NASDAQ is strongly associated with the S&P 500 and its moving average, while RSI has a weaker linear association in that sample.

The discussion proceeds toward estimating regression coefficients and interpreting the model, while noting assumptions such as linearity and normally distributed errors. It also identifies sensitivity to outliers and the limitations of linear boundaries. The article proposes regression as potentially useful for trading research, including pair relationships, but says its library example does not yet include model training and testing. Correlation alone does not establish predictive value or a tradable relationship, and the reported measurements are specific to the collected data rather than evidence of out-of-sample performance.

Key ideas

• Simple linear regression models a target as a linear function of one predictor using a slope and intercept.
• Pearson correlation measures the direction and strength of linear association, ranging from negative to positive values.
• The example finds much stronger sample correlation between NASDAQ and the S&P 500 than between NASDAQ and RSI.
• Regression relies on assumptions including linearity and an appropriate error distribution, and outliers can affect its estimates.
• The article does not provide completed model training, testing, or evidence that the sample correlations yield a profitable strategy.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.