Compare SHMD & ZSQR Stocks: Price Trends, ML Decisions, Charts, Trends, Technical Analysis and more.
| Metric | SHMD | ZSQR |
|---|---|---|
| Founded | 1864 | 2022 |
| Country | Germany | United States |
| Employees | N/A | N/A |
| Industry | | Finance: Consumer Services |
| Sector | | Finance |
| Exchange | Nasdaq | Nasdaq |
| Market Cap | 198.2M | 204.6M |
| IPO Year | 2023 | 2020 |
| Metric | SHMD | ZSQR |
|---|---|---|
| Price | $4.51 | $4.04 |
| Analyst Decision | | |
| Analyst Count | 0 | 0 |
| Target Price | N/A | N/A |
| AVG Volume (30 Days) | ★ 1.4M | 417.7K |
| Earning Date | 05-15-2026 | 11-20-2026 |
| Dividend Yield | N/A | N/A |
| EPS Growth | N/A | ★ 50.27 |
| EPS | ★ N/A | N/A |
| Revenue | N/A | ★ $1,363,045.00 |
| Revenue This Year | $49.82 | N/A |
| Revenue Next Year | $36.39 | N/A |
| P/E Ratio | N/A | ★ N/A |
| Revenue Growth | N/A | N/A |
| 52 Week Low | $2.34 | $2.11 |
| 52 Week High | $10.65 | $16.62 |
| Indicator | SHMD | ZSQR |
|---|---|---|
| Relative Strength Index (RSI) | 59.49 | 55.69 |
| Support Level | $3.80 | $2.81 |
| Resistance Level | $7.36 | $4.64 |
| Average True Range (ATR) | 0.44 | 0.49 |
| MACD | 0.16 | 0.22 |
| Stochastic Oscillator | 70.77 | 73.11 |
Schmid Group NV is a supplier of equipment, software and services for various industries such as printed circuit board (PCB), substrate manufacturing, photovoltaics, and glass and energy storage with a focus on the highest end of this market in terms of technology and performance. It focuses on a modular product portfolio of machinery to use in the manufacturing of high-end PCB equipment and semiconductor packaging devices which includes common flexible circuit fabrication techniques such as subtractive, semi-additive processes (SAP) and modified semi-additive processes (mSAP).
Z Squared Inc is a computing infrastructure company operating computing equipment and expanding into AI infrastructure. The Company's idea is to build on three principles: guide with power by acquiring operating sites where power is already flowing; build for AI workloads by converting that capacity into AI-ready colocation where the customer brings the compute and runs what it needs; and scale with discipline by deploying conversion capital site by site, against signed contracts and operational readiness.