Absci (NASDAQ:ABSI) Posts Earnings Results, Misses Expectations By $0.05 EPS

Absci (NASDAQ:ABSI) issued its quarterly earnings results on Tuesday. The company reported ($0.24) earnings per share (EPS) for the quarter, missing the consensus estimate of ($0.19) by ($0.05), Zacks reports. Absci had a negative net margin of 2,838.89% and a negative return on equity of 44.24%. The business had revenue of $1.70 million during the quarter, compared to analyst estimates of $1.77 million. During the same quarter in the previous year, the firm posted ($0.24) earnings per share.

Absci Stock Performance

NASDAQ ABSI traded down $0.10 during trading on Wednesday, reaching $3.54. The company’s stock had a trading volume of 509,513 shares, compared to its average volume of 1,318,742. The firm has a 50 day moving average price of $3.98 and a 200 day moving average price of $4.08. Absci has a 12 month low of $1.28 and a 12 month high of $6.72. The firm has a market capitalization of $402.00 million, a price-to-earnings ratio of -4.08 and a beta of 2.18. The company has a current ratio of 6.49, a quick ratio of 6.49 and a debt-to-equity ratio of 0.01.

Analyst Upgrades and Downgrades

Several research firms recently weighed in on ABSI. HC Wainwright reissued a “buy” rating and issued a $7.00 target price on shares of Absci in a research note on Thursday, August 15th. Guggenheim began coverage on shares of Absci in a research note on Wednesday, October 2nd. They issued a “buy” rating and a $10.00 target price for the company. Six research analysts have rated the stock with a buy rating, According to data from MarketBeat.com, the stock has a consensus rating of “Buy” and an average target price of $8.67.

About Absci

Absci Corporation operates as a data-first generative artificial intelligence (AI) drug creation company in the United States. The company combines AI with scalable wet lab technologies to create biologics for patients. Its integrated drug creation platform is designed to improve upon traditional biologic drug discovery by using AI to simultaneously optimize multiple drug characteristics that may be important to development and therapeutic benefit.