Factor Correlation: Check Whether Features Repeat the Same Information

Factor correlation measures how two features vary together across securities. IC compares a feature with future returns, so the objects being compared differ. Read the row and column definitions and the correlation method before interpreting a matrix.

Strong positive correlation may indicate similar values or rankings; strong negative correlation may indicate reversed ordering. Low correlation does not establish useful information. Both features could have little relationship with future returns.

For fictional features X=(1,2,3) and Y=(2,4,6), the means are 2 and 4. Centered values are (−1,0,1) and (−2,0,2). The Pearson numerator is 4 and its denominator is √(2×8)=4, producing correlation 1.

Changing Y to (6,4,2) makes the numerator −4 with the same denominator, producing −1. Matching security identities matters more than the numeric units. These extreme three-security examples do not establish stability in a realistic, larger sample.

If missing securities differ between features, each comparison uses observations valid for both. With pairwise missing-value handling, matrix cells may have different sample sizes. Report the valid count and missing-value convention with each comparison rather than assuming one common sample.

An average of daily cross-sectional correlations differs from a correlation calculated by pooling all dates. If one fictional date has three securities and another ten, weighting dates differently can change the summary. Identify the date window and aggregation rule.

If one feature is constant across securities, it has zero variance and Pearson correlation is undefined. Treating its blank result as zero confuses an unmeasurable relationship with an observed absence of linear association. Preserve the invalid reason separately.

Rank correlation compares ordering rather than distances. It need not match Pearson correlation on raw values, and ties require a convention. Check whether the displayed calculation uses original features or normalized scores.

Checking repeated information helps explain a research design. Correlation alone cannot choose deletions or combination weights without considering the purpose, stability and missingness of each feature. Separate proposed changes from the period used to evaluate them.

For the factor correlation chart in public Demo 2, inspect input identity, feature definitions, common samples and invalid reasons first. Your deliverable should explain which period, convention and valid observations produced the matrix. Do not turn the matrix alone into an investment decision.

Read What is a factor? · Information coefficient · Quantile analysis to place this topic alongside other research checks. Confirm documented operations and limits in the official GUI research courses. Use documentation for your selected build; if public main differs from your environment, defer unverified claims.

This article teaches research procedures. Numerical examples are fictional teaching illustrations, not actual investment performance. It does not recommend securities or trades or guarantee future returns. Historical public courses do not establish complete historical information availability. Disclaimer

SciPy correlation specification。 This specification explains Pearson correlation and constant inputs. It supports the definition, not a claim that KabuForge uses this implementation.

Explore the factors learning path

• ICIR: What the Mean and Variation of IC Can Tell You
• What Is a Factor? Comparing Security Characteristics with Numbers
• What Is IC? Reading the Relationship Between Factors and Future Returns