Lantern Pharma Inc Up 34.64% To $3.77 After Earnings Beat

Thursday, November 9, 2023 - Lantern Pharma Inc (LTRN) reported upside earnings and in line revenues.

Analyst expectations for Lantern Pharma Inc earnings per share (EPS) were at a loss of $0.45 per share, with the company surpassing those estimates with a reported EPS loss of $0.29 per share, leading to a positive surprise of $0.16 per share (36%). Lantern Pharma Inc's earnings were down 38% year-over-year as the firm reported an EPS loss of $0.21 per share in its year-ago quarter. The negative annual growth shows the Healthcare company isn't performing well amid current economic conditions.

Revenues were in line at $0.00. That revenue figure is unchanged from the year ago report from the year ago quarter.

The stock is up 34.64% to $3.77 after the report.

The firm's higher revenue growth to earnings signals that the firm has not been able to reduce costs and has seen its profit margin decrease.

The average recommendation from Wall Street analysts was a Buy which may get revised based on this new data.

Trading in the five days leading up to the report earned Lantern Pharma Inc a Bearish Sentiment Rank from InvestorsObserver.

Lantern Pharma Inc has performed poorly during the past few months, garnering a low Long-Term Technical Rank by InvestorsObserver of 20, putting Lantern Pharma Inc in the bottom 25% of stocks. The firm was recently trading at a 52-week low of $2.38 on November 1, 2023 and set a 52-week high on January 3, 2023 at $6.2.

Lantern Pharma Inc is a clinical-stage biotechnology company. It is focused on leveraging artificial intelligence (A.I), machine learning and genomic data to streamline the drug development process and to identify the patients that will benefit from targeted oncology therapies. The firm's pipeline of development programs involves four drug candidates and an Antibody Drug Conjugate (ADC) program directed towards eight therapeutic targets. Its A.I. platform, known as RADR, includes more than 18 billion data points, and uses big data analytics and machine learning to rapidly uncover biologically relevant genomic signatures correlated to drug response, and then identify the cancer patients that we believe may benefit most from our compounds.