Factor Coverage: Check Which Securities and Dates Reach Evaluation

Coverage is the proportion of an intended sample with the values required for a particular task. The denominator might be every listed security or a research universe defined in advance. Record the universe, dates and exclusion rules before examining results; changing the denominator afterwards can conceal missing observations.

For factor evaluation, the presence of a price row is insufficient. Count securities with valid features separately from those with both valid features and future labels. The first describes feature availability; the second describes the usable evaluation sample for that date.

All numbers here are fictional teaching examples. Suppose the intended universe is A, B, C, D and E. Features are available for A through D, so feature coverage is 4 / 5 = 80%. Replacing the denominator with four because E is missing would make that missingness disappear from the statistic.

Suppose labels are available for A, B, C and E. Only A, B and C have both inputs, giving evaluation coverage of 3 / 5 = 60%. Two separate availability rates of 80% do not tell you the size of their intersection.

Insufficient history at the beginning of a window differs from unavailable future observations at its end. Trading interruptions and malformed input columns are other issues. Record the reason, security and date alongside the count to distinguish expected blanks from problems that need investigation.

In a fictional final session with no future labels, evaluation coverage could be 0% while feature calculation remains valid. Labels are evaluation inputs. Do not repair coverage by feeding future values into features or filling gaps with the next observation.

Reading IC only on high-coverage dates may select an unrepresentative set of observations. A correlation calculated from five securities and one calculated from two securities have different information content. Inspect intended counts, valid counts and invalid-day reasons beside IC and quantile plots.

Removing frequently missing securities after viewing results changes the research universe. If a revision is necessary, retain the original and revised definitions as separate conditions. A better-looking coverage percentage does not itself establish better evidence.

Demo 2 in the public GUI courses examines daily IC, cross-sectional counts and coverage, quantile diagnostics and invalid reasons together. Match the selected build's display to its receipt. Do not turn a missing or invalid statistic into a valid observation merely to complete a chart.

Your deliverable should explain counts such as intended five, valid features four and common evaluation three, with reasons for exclusions. Retain the selected input identity and recipe. If the chart looks orderly but its sample or input identity is unclear, defer comparisons.

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

Explore the factors learning path

• Rank Normalization: Comparable Scales Still Need Clear Conventions
• Factor Correlation: Check Whether Features Repeat the Same Information
• ICIR: What the Mean and Variation of IC Can Tell You