Valuation : NetraMark Holdings Inc.

Market Cap 6.62Cr 4.81Cr 4.13Cr 3.89Cr 3.55Cr 455.85Cr 6.66Cr 46Cr 18Cr 233Cr 18Cr 18Cr 741.46Cr P/E 2026 *
-
P/E 2027 * -
Enterprise Value 6.62Cr 4.81Cr 4.13Cr 3.89Cr 3.55Cr 455.85Cr 6.66Cr 46Cr 18Cr 233Cr 18Cr 18Cr 741.46Cr EV / Sales 2026 *
165x
EV / Sales 2027 * -
Free-Float
91.14%
Yield 2026 *
-
Yield 2027 * -
1 day-7.88%
1 week-7.88%
Current month-7.88%
1 month-26.15%
3 months-32.09%
6 months-36.75%
Current year-49.89%
1 week 0.4
Extreme 0.3985
0.4
1 month 0.4
Extreme 0.3985
0.51
Current year 0.4
Extreme 0.3985
0.89
1 year 0.4
Extreme 0.3985
1.15
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.($)
-7.88%-7.88% - - 4.81Cr
-1.15%-1.41%-0.85%+47.77% 3,66800Cr
-2.31%-5.35%+9.10%+1,025.58% 41TCr
-2.93%-9.24%-21.22%+19.38% 7.82TCr
-1.27%-6.08%+54.08%+115.27% 7.55TCr
-0.46%-5.49%-35.64%-14.62% 7.51TCr
+1.11%+0.60%-40.53%+196.29% 6.94TCr
-6.94%-5.67%+2.15%-14.61% 4.47TCr
-0.65%-1.56%-14.27%+49.39% 3.99TCr
-3.04%-1.21%+107.09%+244.98% 3.47TCr
Average -0.63%-4.74%+6.66%+185.49% 49.94TCr
Weighted average by Cap. +0.09%-2.13%+0.17%+139.61%

Financials

2026 *2027 *
Net sales 4L 3L 3L 2L 2L 2.76Cr 4L 27.89L 10.79L 1.41Cr 10.93L 10.69L 4.49Cr -
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

Quarterly revenue - Rate of surprise

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