908 Devices (NASDAQ:MASS) Posts Earnings Results

908 Devices (NASDAQ:MASS) announced its quarterly earnings results on Tuesday. The company reported ($0.33) earnings per share for the quarter, beating analysts’ consensus estimates of ($0.36) by $0.03, Yahoo Finance reports. The company had revenue of $9.99 million for the quarter, compared to analyst estimates of $9.29 million. 908 Devices had a negative net margin of 68.57% and a negative return on equity of 20.47%. 908 Devices’s revenue for the quarter was up 5.3% on a year-over-year basis. During the same quarter last year, the business earned ($0.39) EPS. 908 Devices updated its FY 2024 guidance to EPS.

908 Devices Trading Down 3.2 %

Shares of MASS traded down $0.18 during mid-day trading on Thursday, reaching $5.50. The company had a trading volume of 149,759 shares, compared to its average volume of 174,794. The stock has a market cap of $181.39 million, a price-to-earnings ratio of -5.20 and a beta of 1.02. The firm has a fifty day simple moving average of $6.59 and a 200-day simple moving average of $7.08. 908 Devices has a fifty-two week low of $4.85 and a fifty-two week high of $12.51.

About 908 Devices

908 Devices Inc, a commercial-stage technology company, provides various purpose-built handheld and desktop mass spectrometry devices to interrogate unknown and invisible materials in life sciences research, bioprocessing, pharma/biopharma, forensics, and adjacent markets. The company's products include MX908, a handheld, battery-powered, and Mass Spec device that is designed for rapid analysis of solid, liquid, vapor, and aerosol materials of unknown identity; Rebel, a small desktop analyzer that provides real-time information on the extracellular environment in bioprocesses; and Maverick, an optical in-line analyzer that offers real-time monitoring and control of multiple bioprocess parameters, including glucose, lactate, and total biomass in mammalian cell cultures, as well as provides process fingerprint data to support large-scale efforts in predictive bioprocess modeling.