Key Points
- MU shares declined approximately 8% Tuesday amid U.S.-Iran tensions, followed by a 2% Wednesday recovery
- March 18 Q2 FY26 earnings expected to show EPS jumping 447% year-over-year to $8.54
- Stifel increased price target to $550, projecting gross margins comparable to software companies by mid-2026
- UBS elevated target to $475, anticipating DRAM supply constraints extending to 2027–2028
- Company introduced 256GB LPDRAM module — currently the market’s highest-capacity CPU-attached solution
Micron Technology shares experienced an approximately 8% decline Tuesday when U.S.-Iran conflict concerns triggered market-wide selling. Memory competitors Samsung and SK Hynix from Korea similarly retreated as investors worried about potential energy cost impacts on semiconductor manufacturers.
Wednesday brought a 2% recovery for MU shares.
The stock has declined 9% over the trailing month. However, the longer-term picture shows a remarkable 319% gain — a performance that helps explain analyst optimism.
March 18 brings the company’s Q2 FY26 earnings release. Street consensus calls for impressive results — analysts project EPS of $8.54, representing a 447% increase from the prior year, while revenue is forecast to surge beyond 134% to reach $18.88 billion.
These projections represent substantial growth. Analysts covering the memory sector believe the forecasts are achievable — some suggest they might even underestimate actual results.
Brian Chin from Stifel, holding five-star analyst status, elevated his price target from $360 to $550 while maintaining his Buy rating. His rationale centers on memory pricing exceeding previous projections.
“Memory pricing is hitting levels we did not envision,” Chin stated, highlighting a supply-demand imbalance that appears likely to persist.
Chin’s supply chain research indicates memory production capacity remains essentially locked for the coming 12 months. His analysis predicts Micron will achieve gross margins comparable to software companies — reaching the mid-to-upper 70% range — by mid-2026, with those levels sustained through year-end.
He further contends that current consensus estimates remain too conservative and will require upward revision in subsequent quarters.
Price Target Increases Continue
Timothy Arcuri from UBS maintains his bullish stance, raising his price target from $450 to $475. His optimism stems from robust pricing trends across both core DRAM and NAND segments, validated by recent industry channel checks.
Arcuri forecasts DRAM supply constraints continuing through 2027 and extending into 2028 — a more prolonged timeline than earlier predictions suggested.
Heading into the earnings release, MU holds 26 Buy ratings alongside only two Hold recommendations, establishing a Strong Buy consensus from Wall Street analysts. The mean price target stands at $417.81, suggesting approximately 10% appreciation potential from present trading levels.
256GB Module Launch Aimed at AI Infrastructure
Beyond earnings anticipation, Micron announced a product development this week. Customer sampling has begun for the company’s latest 256GB SOCAMM2 LPDRAM module.
Micron describes this as the highest-capacity CPU-attached LPDRAM module available today.
The module incorporates what the company characterizes as the industry’s first monolithic 32Gb LPDDR5X architecture, designed to provide enhanced memory density while improving power efficiency for AI and high-performance computing applications.
This configuration delivers 33% greater capacity compared to the previous 192GB SOCAMM2 top specification. The technology enables configurations reaching 2TB of LPDRAM per 8-channel CPU — supporting expanded context windows and more sophisticated AI inference operations.
Energy usage runs approximately one-third that of comparable RDIMM configurations, potentially enabling hyperscale operators to reduce power expenses and enhance server utilization efficiency.
Micron indicated the product launch demonstrates its commitment to advancing packaging and memory technology to address expanding requirements for high-capacity, power-efficient components in emerging AI data center infrastructure.
