Palladyne AI (NASDAQ:PDYN – Get Free Report) is expected to announce its Q4 2025 results before the market opens on Thursday, February 19th. Analysts expect the company to announce earnings of ($0.18) per share and revenue of $0.8090 million for the quarter. Individuals may visit the the company’s upcoming Q4 2025 earning results page for the latest details on the call scheduled for Friday, February 27, 2026 at 4:00 PM ET.
Palladyne AI Stock Performance
Shares of PDYN stock opened at $6.38 on Wednesday. The firm’s 50 day moving average price is $5.90 and its 200-day moving average price is $6.95. Palladyne AI has a one year low of $4.14 and a one year high of $13.00. The firm has a market cap of $268.20 million, a P/E ratio of -3.78 and a beta of 3.80.
Analysts Set New Price Targets
Several equities analysts have commented on PDYN shares. Wall Street Zen upgraded Palladyne AI from a “sell” rating to a “hold” rating in a research note on Saturday, November 15th. Weiss Ratings reiterated a “sell (d-)” rating on shares of Palladyne AI in a 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 presently has a consensus rating of “Reduce” and an average price target of $8.00.
Insiders Place Their Bets
In other Palladyne AI news, insider Kristi Martindale sold 6,753 shares of the stock in a transaction on Friday, November 21st. The stock was sold at an average price of $5.11, for a total value of $34,507.83. Following the completion of the transaction, the insider owned 305,483 shares in the company, valued at approximately $1,561,018.13. This represents a 2.16% decrease in their ownership of the stock. The transaction was disclosed in a legal filing with the SEC, which can be accessed through this link. Also, CTO Denis Garagic sold 15,611 shares of the business’s stock in a transaction on Friday, November 21st. The stock was sold at an average price of $5.11, for a total transaction of $79,772.21. Following the sale, the chief technology officer owned 569,248 shares in the company, valued at $2,908,857.28. The trade was a 2.67% decrease in their ownership of the stock. The disclosure for this sale is available in the SEC filing. Over the last quarter, insiders sold 28,423 shares of company stock worth $145,242. 13.40% of the stock is currently owned by insiders.
Institutional Investors Weigh In On Palladyne AI
A number of hedge funds and other institutional investors have recently modified their holdings of the stock. Goldman Sachs Group Inc. boosted its position in Palladyne AI by 507.2% during the first quarter. Goldman Sachs Group Inc. now owns 115,407 shares of the company’s stock worth $679,000 after purchasing an additional 96,402 shares in the last quarter. Focus Partners Wealth acquired a new position in shares of Palladyne AI during the 1st quarter worth $138,000. Geode Capital Management LLC boosted its position in shares of Palladyne AI by 172.9% in the 2nd quarter. Geode Capital Management LLC now owns 592,640 shares of the company’s stock worth $5,133,000 after buying an additional 375,440 shares in the last quarter. JPMorgan Chase & Co. grew its stake in Palladyne AI by 94.3% in the 2nd quarter. JPMorgan Chase & Co. now owns 24,862 shares of the company’s stock valued at $215,000 after acquiring an additional 12,065 shares during the last quarter. Finally, Rhumbline Advisers acquired a new stake in Palladyne AI in the 2nd quarter valued at about $330,000. 26.02% of the stock is currently owned by institutional investors and hedge funds.
Palladyne AI Company Profile
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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