Pursuant to the correction that has taken place in recent weeks with respect to the shares in Tempus AI, Inc, further downside risk now appears limited by close and important technical support levels at 31.83 USD.
Summary
● Overall, the company has poor fundamentals for a medium to long-term investment strategy.
Strengths
● Analysts expect a sharply increasing business volume for the group, with high growth rates in the coming years.
● Analysts covering this company mostly recommend stock overweighting or purchase.
● The difference between current prices and the average target price is rather important and implies a significant appreciation potential for the stock.
● Over the past four months, analysts' average price target has been revised upwards significantly.
Weaknesses
● Low profitability weakens the company.
● For the past year, analysts have significantly revised downwards their profit estimates.
● For the last four months, EPS estimates made by Standard & Poor's analysts have been revised downwards.
● The average consensus view of analysts covering the stock has deteriorated over the past four months.
● Over the past twelve months, analysts' opinions have been revised negatively.
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Tempus AI, Inc. is a technology company. The Company provides next generation diagnostics across multiple disease areas, with technology capabilities, harnessing the data and analytics to help personalize medicine. The Companyâs product lines include genomics, data, and artificial intelligence applications (AI). Its genomics product line leverages its laboratories to provide next generation sequencing, diagnostics, polymerase chain reaction (PCR), profiling, molecular genotyping, and other anatomic and molecular pathology testing. The Company's AI applications are focused on developing and providing diagnostics that are algorithmic in nature, implementing new software as a medical device, and building and deploying clinical decision support tools. Its primary product of AI applications is Next, an AI platform that leverages machine learning to apply an intelligent layer onto routinely generated data to proactively identify and minimize care gaps for oncology and cardiology patients.