Buy these small-caps set for big gains in the spring, says Oppenheimer
Small-cap stocks took their lumps in what was a difficult March for the market, but Oppenheimer thinks some of these names could be set to surge as spring gets underway. In a Wednesday note, the firm listed 30 small- or mid-cap stocks that it rates as outperform and could be set up for strong rallies. All of the stocks also have market caps between $1 billion and $12 billion. Within those 30 stocks, Oppenheimer highlighted a few that it believes are the “best of the best.” Making the grade is Alumis , which has had the best year-to-date performance of the companies on the list, more than doubling already in 2026. The firm has a $55 price target on the stock, indicating a potential gain of nearly 150% from Tuesday’s close. The biotechnology company’s recent successful trials of its envudeucitinib drug for plaque psoriasis is adding to Oppenheimer analyst Jeff Jones’ bullishness. “We see envu’s demonstration of biologic-like efficacy in a pivotal trial, along with a safety profile devoid of signals that have hampered adoption of the 1st-generation deucra,” a Bristol-Myers Squibb drug, “as highly encouraging.” Also making the “best of the best” category is Nurix Therapeutics . While the stock has fallen about 18% in the year, analyst Matthew Biegler has a $28 price target, which represents an 80% gain from Tuesday’s close. Biegler’s positive outlook comes from the company’s portfolio of drugs to treat cancer and autoimmune diseases. Lastly, DigitalOcean , a cloud platform company, also made the list. Analyst Param Singh has a $100 price target, which would be a gain of more than 16% from Tuesday’s close. “We view DigitalOcean as a turnaround story that is in the early stages of a multi-year expansion,” Singh wrote. “The company has already shown improving traction and momentum with existing and new customers, and we see customers expanding their utilization of DigitalOcean’s infrastructure-as-a-service and platform-as-a-service cloud offerings for their artificial intelligence inferencing workloads.”