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Overview

Every strategy ranks its holdings using a small set of signals. Each one answers a different question about a company’s relationship to the theme. The holding-level signals you will work with:
  • Conviction score: the default ranking metric, for overall thematic strength.
  • Linguistic beta: how strongly the company ties to the theme.
  • Market buzz: recent thematic media momentum around the company.
One strategy-level metric, the Linguistic Strength Indicator (LSI), tells you about the quality of the strategy as a whole. Sector concentration shows how the strategy is distributed.

Conviction score (0–1)

The conviction score represents the overall strength of a company’s thematic connection. It is the default sort for holdings. Conviction is independent of company size. A small company with a strong, focused thematic connection can outrank a much larger one, so you can compare companies on thematic merit.
RangeLabelMeaning
> 0.5High convictionStrong thematic fit across multiple signals
0.2–0.5ModerateDecent fit, worth investigating further
< 0.2Low convictionWeak overall signal, candidate for exclusion
How to use it:
  • Rank holdings: sort by conviction to see the strongest thematic names first.
  • Set an inclusion bar: drop holdings below a conviction threshold to tighten a strategy.
  • Compare across sizes: because conviction is size-independent, a high score on a smaller name is a genuine thematic signal.

Linguistic beta (0–1)

Linguistic beta is a company’s stable, structural exposure to a theme. It captures how persistently the company ties to the theme over time. A higher value means a stronger, more direct tie.
RangeLabelMeaning
> 0.5CoreStrong, direct connection to the theme
0.2–0.5SupportingModerate connection, often through a business segment
< 0.2TangentialWeak connection, candidate for exclusion
How to use it:
  • Defend an inclusion: use a holding’s linguistic beta as the evidence that it belongs in the theme.
  • Judge strategy quality by reading the distribution across holdings:
    • Most holdings above 0.5 → a tightly focused strategy.
    • Spread across the whole range → a broad or dilute strategy.
    • Clustered around 0.2–0.3 → the theme may be too niche, with few strong matches.

Market buzz

Market buzz has no fixed 0–1 scale, so there is no range table. Compare it within a strategy, not across strategies. Market buzz reflects recent thematic news and media activity around a company. A higher value means more current coverage related to the theme. How to use it:
  • Spot momentum: find companies gaining or losing thematic relevance right now.
  • Surface trending names worth investigating.

Linguistic Strength Indicator (LSI)

The LSI is a strategy-level metric. It reflects how well the overall theme is represented in current market narratives. How to use it:
  • Judge strategy quality: a stronger LSI means the theme is well supported by current narratives across its holdings.
  • Compare iterations: watch the LSI as you refine an objective to see whether your changes sharpen or dilute the theme.

Sector concentration

Read sector concentration from the holdings data to understand how the strategy is distributed. Watch for:
  • Over-concentration (above 40%): the strategy may really be a sector bet.
  • Unexpected sectors: could be genuine thematic connections or noise; check the inclusion reasoning.
  • Missing expected sectors: the strategy might be too narrow, or its exclusions too aggressive.

Display conventions

  • Conviction is the default ranking metric. Use that label in table headers.
  • Say linguistic beta, not “beta” alone, because “beta” by itself means financial beta. In tight headers, “Ling. Beta” or “Ling. β” is fine.
  • Market buzz can be shortened to “buzz” or “Mkt Buzz”.
  • When you narrate these scores in prose, display conviction, linguistic beta, and market buzz as percentages (0.17 → 17%, 0.0001 → 0.01%). Holdings tables from the API are already pre-formatted as percentages, so leave them as-is.

Next step

Once you can read these signals, see Portfolio optimization for how they translate into holding weights.