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Valuation: NetraMark Holdings Inc.

Market Cap 72.34M 51.71M 44.57M 41.04M 38.53M 4.89B 73.11M 485M 189M 2.39B 194M 190M 8.3B P/E Ratio 2026 *
-
P/E Ratio 2027 * -
Enterprise Value 72.34M 51.71M 44.57M 41.04M 38.53M 4.89B 73.11M 485M 189M 2.39B 194M 190M 8.3B EV / Sales 2026 *
180x
EV / Sales 2027 * -
Free-Float
89.03%
Yield 2026 *
-
Yield 2027 * -
1 day-10.98%
1 week-10.98%
Current month-21.46%
1 month-21.46%
3 months-31.50%
6 months-37.70%
Current year-30.46%
1 week 0.55
Extreme 0.553
0.56
1 month 0.46
Extreme 0.4649
0.64
Current year 0.46
Extreme 0.4649
0.89
1 year 0.46
Extreme 0.4649
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 - 17/02/2022
President - 04/07/2022
Director of Finance/CFO 57 18/07/2022
Director TitleAgeSince
Chairman 40 09/06/2025
Director/Board Member - -
Director/Board Member - 16/06/2022
Change 5-day change 1-year change 3-year change Capi.($)
-10.98%-10.98% - - 51.71M
-1.48%-2.37%-17.80%+15.04% 2,926B
-1.08%+0.89%-5.77%+717.48% 319B
-2.10%+2.91%+225.90%+773.87% 121B
-1.69%-0.78%+28.94%+64.45% 107B
-1.32%-3.63%-6.47%+1.63% 85.86B
-0.85%+1.66%+89.54%+141.37% 82.27B
+0.17%+2.29%-45.12%+39.84% 53.17B
+6.35%+8.45%-3.62%+65.88% 42.7B
+8.06%+14.74%-50.88%+18.04% 35.33B
Average -1.27%+3.05%+23.86%+204.18% 419.19B
Weighted average by Cap. -0.34%-1.37%-5.55%+103.75%

Financials

2026 *2027 *
Net sales 401K 287K 247K 227K 214K 27.1M 405K 2.69M 1.05M 13.27M 1.08M 1.05M 45.98M -
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
15/06/26 0.5530 $ -10.98% 12,000

Quarterly revenue - Rate of surprise

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