Every figure this model derived for KAYS, the value it took, and
the element in the company's own XBRL it was read from — so any of it can be checked
against the filing rather than taken on trust.
As at 2024-12-31.
The figures, and where each came from
This is the part no other screener shows. Each figure below names the XBRL element it
was read from, so it can be checked against the filing itself.
The figures themselves rather than their scores — what somebody looking up
KAYS on one particular measure came for.
Ratio
Value
current ratio
0.04
current ratio reported
0.04
dilution pct
26.43
interest cover
-19.3
revenue growth
-96.37
revenue growth per share
-97.13
roic
-13816.46
rule of 40
-16090.13
sbc pct revenue
0.0
What to be careful about
What this model itself distrusts about KAYS's figures. Written by the
derivation as it ran, not added afterwards.
WITHHELD: operating margin, cash operating margin, owner earnings margin, fcf margin, net margin, ocf margin, ebitda margin -- net margin came out at -164,600% against a scale that tops out near 1,500%, which is arithmetic on a revenue figure of 7,134 rather than a margin. The figure is absent rather than corrected: there is no right value to put here.
revenue is annual, not TTM
revenue period ends 2024-12-31 (607 days ago) - the tag chain found nothing recent, so treat this row as unscored
no current stock compensation tagged (last tagged 2020-12-31); treated as zero, so owner earnings are overstated
no market cap; set `market_cap` in overrides.yaml
share count is from 2025-11-17 (286 days old, dei cover page); a stock split since then would make the market cap wrong. Set `market_cap` in overrides.yaml to be sure.
no forward EPS estimate; growth rests on filings only. Analyst consensus is not published in XBRL, so it has to be fetched from a data provider or set by hand in overrides.yaml as `eps_growth_2y`
quality DROPPED from the total: only 2 of 5 inputs could be computed (operating margin, owner earnings, cash operating margin not tagged), so the score is not meaningful. Its weight is spread over the other dimensions
valuation DROPPED from the total: only 0 of 2 inputs could be computed (ev sales, ev owner earnings not tagged), so the score is not meaningful. Its weight is spread over the other dimensions
durability rests on 3 of 4 inputs; not tagged: contracted coverage
2 of 5 dimensions could not be measured (quality, valuation -- 55% of the model), so this row is not comparable with the others; its total rests on what was left