IR Strategy

Your Press Releases Are Leaving Money on the Table

After analyzing over 500 press releases from NYSE and Nasdaq small-cap firms, the findings are consistent: the average algorithmic score is 47 out of 100. Most small-cap releases fail to activate algorithmic attention from institutional trading systems — and the gap is almost entirely structural, not substantive.

47/100
Average algo score across 500+ analyzed small-cap press releases

What the Scoring Analysis Revealed

The performance gap between top and bottom quartile releases stems from language choices, not company quality. Bottom quartile releases share a predictable set of characteristics:

Bottom quartile characteristics:

Top quartile characteristics:

Post-release trading patterns for releases scoring 65+:

The Language Changes That Move Scores 20+ Points

Three specific modifications account for most of the score improvement seen in releases that moved from below-average to top-quartile performance:

1. Headline Specificity

Replacing generic statements with data-driven headlines consistently increases NLP scores. Algorithms parse the headline first and weight it heavily. A headline like "Company Reports Record Revenue of $24.2M, Up 38% Year-Over-Year" scores dramatically higher than "Company Provides Business Update."

2. Executive Attribution

Active constructions with named executives performing specific actions score significantly higher on attribution models than passive voice. "CEO Jane Smith directed the team to accelerate production" versus "Production was accelerated" represents a meaningful difference in how algorithmic systems assess management confidence.

3. Operational Causation

Every metric requires a causal explanation linking the outcome to a business driver. "Revenue increased 23% driven by enterprise license expansions in the healthcare vertical" scores higher than "Revenue increased 23%." The causation tells the algorithm — and the human reading behind it — that management understands their own business.

The performance gap between top and bottom quartile releases is almost entirely structural, not substantive. Companies with identical business quality produce dramatically different algo responses based solely on language choices.

What a 20-Point Score Improvement Means for Your Stock

A release moving from a 47 to a 67 score is not an abstract improvement. It corresponds to measurable changes in how institutional systems classify your company: lower short-seller alert probability, higher inclusion in momentum screens, tighter spreads on the trading day, and improved next-quarter visibility to screening tools that inform institutional targeting.

For a small-cap company where institutional attention is scarce and must be earned, the difference between a 47 and a 67 on every release compounds significantly over time.

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This article is informational and not investment or legal advice. Past performance of algorithmic signal scores does not guarantee future results. See our Disclaimer.