Palladyne AI Corp. (NASDAQ:PDYN – Get Free Report) was the recipient of a significant decline in short interest in February. As of February 13th, there was short interest totaling 6,755,528 shares, a decline of 22.4% from the January 29th total of 8,710,257 shares. Approximately 18.6% of the company’s stock are short sold. Based on an average trading volume of 3,438,591 shares, the days-to-cover ratio is currently 2.0 days. Based on an average trading volume of 3,438,591 shares, the days-to-cover ratio is currently 2.0 days. Approximately 18.6% of the company’s stock are short sold.
Analyst Upgrades and Downgrades
PDYN has been the topic of several research analyst reports. Wall Street Zen raised shares of Palladyne AI from a “sell” rating to a “hold” rating in a research report on Saturday, November 15th. Weiss Ratings reiterated a “sell (d-)” rating on shares of Palladyne AI in a research report on Monday, December 29th. One equities research analyst has rated the stock with a Hold rating and one has issued a Sell rating to the stock. According to MarketBeat.com, the company currently has an average rating of “Reduce” and an average target price of $8.00.
Check Out Our Latest Stock Analysis on PDYN
Hedge Funds Weigh In On Palladyne AI
Palladyne AI Stock Performance
Shares of PDYN stock opened at $7.49 on Monday. Palladyne AI has a 1 year low of $4.14 and a 1 year high of $13.00. The firm has a market cap of $314.90 million, a price-to-earnings ratio of -4.43 and a beta of 3.80. The stock’s 50-day moving average is $6.29 and its two-hundred day moving average is $6.90.
About Palladyne AI
Palladyne AI Corp., a software company, focuses on delivering software that enhances the utility and functionality of third-party stationary and mobile robotic systems in the United States. Its Artificial Intelligence (AI)/ Machine Learning (ML) software platform enables robots to observe, learn, reason, and act in structured and unstructured environments. The company's software platform enables robotic systems to perceive their environment and quickly adapt to changing circumstances by generalizing from their experience using dynamic real-time operations without extensive programming and with minimal robot training.
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