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Securities, M&A & Corporate Governance Litigation
In today’s market, litigators with experience in securities, corporate governance, and M&A-related (collectively, “securities litigation”) matters need to leverage their broad experience to serve as both advocate and as counsel and strategist, focused on helping clients to overcome not just a legal issue but a collective business problem. Given the frequent interplay between things such as corporate-control transactions, public offerings, activist shareholders, the use of complex derivatives and other financial instruments, bad news impacting stock performance, regulatory inquiries and investigations, and insurance coverage, strategic and tactical litigation decisions can have a wide-ranging impact on the success of a given transaction, as well as on companies, their boards, senior management, and advisors.
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Trial skills matter—even in a world where few disputes ever see the inside of a courtroom. Winston has built a reputation as a trial lawyers’ firm, featuring seasoned litigators who leverage extensive courtroom experience to meet our clients’ business and legal objectives. Our long history of taking cases to trial—and winning—provides our clients with tremendous settlement leverage with their adversaries, as well as a substantial likelihood of a favorable resolution if, and when, they go to trial.
Experience 200 results
Experience
|September 9, 2025
Winston & Strawn LLP represented Greenland Exploration Limited in a landmark business combination with Pelican Acquisition Corporation (NASDAQ: PELI) and March GL Company. The transaction, structured as a merger agreement, will result in the formation of Greenland Energy Company, expected to be publicly listed on NASDAQ under the ticker symbol “GLND.” Winston advised Greenland Exploration on all aspects of the deal, including regulatory strategy, corporate structuring, and negotiation of the merger terms.
Experience
|July 30, 2025
Represent Norman W. Fries, Inc. d/b/a Claxton Poultry Farms in a series of 15+ antitrust class actions consolidated in the Northern District of Illinois and brought by plaintiffs who allege that Claxton and the nation’s other largest poultry producers conspired to fix the price of broiler chickens in a scheme from 2008 to 2016 that raised the price for broiler chickens by artificially reducing supply.
Experience
|July 1, 2025
We served as Remarketing Agent's counsel to Barclays and J.P. Morgan as remarketing agents, in connection with the reoffering by Delmarva Power & Light Company of $78,400,000 Gas Facilities Refunding Revenue Bonds (Delmarva Power & Light Company Project) 2020 Series A (Non-AMT) initially issued on July 1, 2020 by the Delaware Economic Development Authority.
Insights & News 4,418 results
Seminar/CLE
|November 13, 2025
AI in Action: Legal Strategies for Healthcare Innovation
We are bringing together stakeholders across the healthcare ecosystem—from front-line providers to financial backers—for a panel discussion on AI in healthcare.
Winston’s Environmental Law Update
|October 30, 2025
|5 Min Read
E15 Gasoline: Legal Landscape and Recent Legislative Updates
The EPA establishes a national standard for gasoline volatility (Reid vapor pressure, or RVP) via section 211(h) of the Clean Air Act to reduce emissions of volatile organic compounds.
Winston’s AI Top 10
|October 2025
|5 Min Read
Winston’s AI Monthly Recap - October 2025
Winston’s AI Top 10 summarizes the latest AI developments in the legal industry.
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Winston is proud to partner with organizations that expand access to meaningful connections and opportunities for lawyers across the legal industry, and strengthen the pipeline of talent in law.
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Generative AI tools can create new content, such as text, computer code, images, audio, sound, and video, in response to a user’s prompt, often in the form of a short written description of the desired output. Generative AI tools are based on machine learning, trained using enormous amounts of data.[1] Generative AI tools are built on a system of inputs and outputs. First, the tool goes through a machine learning period whereby it is trained to generate predictive models and creative outputs through a large data set, often varied and diverse but tailored to the goal of the tool (i.e., customer service, generating scientific or marketing models, etc.). For in-house tools, this can be done with the company’s own data; for larger tools such as ChatGPT, this is done with the creator’s data set.[2] Once the tool has been trained, the individual user “inputs” a short prompt for the tool to synthesize and produce an “output.” Inputs are often retained on the servers controlled by the company that supports the tool, for monitoring of the tool’s performance and, in some cases, continued learning. The “outputs” are created by combining the machine learning during the training period with the inputs to produce an output.[3]