Marvell Technology shares surged on Tuesday after CEO Matt Murphy outlined an aggressive long-term growth plan at the company’s Investor Day in New York. The stock closed at $287.01, up 5.81%, after touching an intraday high of $301.27. Trading volume reached 51.3 million shares, more than four times the prior session’s 12.3 million.
Murphy introduced Marvell’s first fiscal 2031 revenue target of $70 billion to $90 billion, a dramatic expansion from the $8.2 billion reported for fiscal 2026. Hitting that range would imply annual growth of roughly 55% to 60% over five years. The company also raised its fiscal 2028 revenue guidance to about $20 billion, up from $18 billion, and above Wall Street’s previous estimate of about $18.2 billion. Management additionally pointed to earnings per share of $30 or more by fiscal 2031, compared with fiscal 2026 diluted EPS of $3.07.
The growth story is built on AI data center demand. Marvell expects roughly $37.5 billion in interconnect and networking revenue and about $30 billion from custom silicon at the midpoint of the fiscal 2031 range. The company counts Alphabet and Amazon among its custom chip customers. Marvell also said it sees a roughly $400 billion AI market by 2030, more than four times its prior $94 billion opportunity estimate for 2028, and forecast data center capital spending could reach $3 trillion by 2030.
Wall Street responded quickly. Jefferies raised its target to $450 from $325, Evercore ISI lifted its target to $433 from $275, and TD Cowen upgraded the stock to Buy with a $350 target, up from $245. Raymond James kept its Strong Buy rating and $295 target. The broader analyst consensus remained bullish, with roughly 26 Buy and 4 Hold ratings and an average target near $314.
Still, valuation remains a key debate. At $287.01, Marvell trades above 90 times trailing earnings based on fiscal 2026 EPS, but only about 9.6 times the company’s targeted $30 in fiscal 2031 EPS. The bull case rests on execution of the long-range AI infrastructure plan, while the bear case argues the targets remain distant and vulnerable to any slowdown in AI capital spending.
