Valuation: NetraMark Holdings Inc.

Market Cap 76.98M 54.25M 47.47M 43.75M 40.55M 5.15B 78.22M 525M 204M 2.54B 204M 199M 8.78B P/E 2026 *
-
P/E 2027 * -
Enterprise Value 76.98M 54.25M 47.47M 43.75M 40.55M 5.15B 78.22M 525M 204M 2.54B 204M 199M 8.78B EV / Sales 2026 *
192x
EV / Sales 2027 * -
Free-Float
91.14%
Yield 2026 *
-
Yield 2027 * -
1 day+22.05%
1 week+29.53%
Current month+39.90%
1 month-4.33%
3 months-5.33%
6 months-28.72%
Current year-29.40%
1 week 0.46
Extreme 0.46
0.56
1 month 0.4
Extreme 0.4013
0.62
Current year 0.4
Extreme 0.4013
0.89
1 year 0.4
Extreme 0.4013
1.26
3 years 0.11
Extreme 0.114
1.26
5 years 0.1
Extreme 0.1045
1.61
10 years 0.1
Extreme 0.1045
1.61
Manager TitleAgeSince
Chief Executive Officer - 2022-02-16
President - 2022-07-03
Director of Finance/CFO 57 2022-07-17
Director TitleAgeSince
Chairman 40 2025-06-08
Director/Board Member - -
Director/Board Member - 2022-06-15
Change 5-day change 1-year change 3-year change Capi.($)
+22.05%+29.53% - - 54.14M
+0.81%+4.75%-21.49%+15.87% 2,873B
+2.36%+17.47%-1.49%+793.42% 318B
+1.33%-6.34%+277.76%+899.96% 127B
-1.38%-0.80%+16.24%+64.10% 104B
+0.89%+0.82%+72.23%+167.80% 90.9B
-1.05%-1.89%-18.43%+1.47% 84.69B
-0.26%+9.66%-30.45%+91.14% 64.31B
-0.45%+3.00%+6.16%+79.82% 47.98B
-0.56%+4.13%-47.45%+37.61% 40.83B
Average +2.36%+4.51%+28.12%+239.02% 416.62B
Weighted average by Cap. +0.77%+5.23%-6.39%+118.66%

Financials

2026 *2027 *
Net sales 401K 283K 247K 228K 211K 26.82M 407K 2.73M 1.06M 13.23M 1.06M 1.04M 45.74M -
Net income - -
Net Debt - -
Logo NetraMark Holdings Inc.
NetraMark Holdings Inc. is a Canada-based company, which is focused on the development of Generative Artificial Intelligence (Gen AI)/Machine Learning (ML) solutions targeted at the pharmaceutical industry. The Company’s product offering uses a novel topology-based algorithm that has the ability to parse patient data sets into subsets of people that are strongly related according to several variables simultaneously. This allows the Company to use a variety of ML methods, depending on the character and size of the data, to transform the data into powerfully intelligent data that activates traditional AI/ML methods. The result is that it can work with smaller datasets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment. The typical molecular data used is RNASeq, microarray, single nucleotide polymorphism (SNP) and methylation.
Employees
-
Date Price Change Volume
26-07-06 $0.5614 +22.05% 10,000
26-07-02 $0.4600 +14.62% 5,000
26-06-30 $0.4013 -7.40% 4,020

Quarterly revenue - Rate of surprise