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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 233 results
Experience
|April 11, 2025
Cuprina Holdings (Cayman) Limited Announces Closing of Initial Public Offering
Experience
|April 10, 2025
Titan Acquisition Corp Announces Closing of $276,000,000 Initial Public Offering
Insights & News 4,263 results
Seminar/CLE
|May 13, 2025
2025 Health Care & Life Sciences Summit
Winston & Strawn is pleased to host its fifth annual Health Care & Life Sciences Summit. Clients and friends of the firm are invited to join us to network with peers and gain insights from industry leaders and legal experts. The summit will take place live at our Chicago office, and CLE-eligible recordings of the Summit’s sessions will be available shortly following the event.
Speaking Engagement
|May 9, 2025
Join Winston & Strawn partners M. Imad Khan and Keerthika M. Subramanian for the upcoming online program “Hot Topics in International Arbitration and Strategic Transactions: Recent Developments and Key Trends in India” hosted by the Practising Law Insititute.
Competition Corner
|April 24, 2025
|1 Min Read
Spotlight on Japan: Our Japanese Legal Trainee Series
We are proud to launch a short series of articles written by our current Japanese legal trainees—bengoshi who have completed U.S. LLM programs and are now training with Winston & Strawn LLP as part of their professional development.
Other Results 74 results
Site Content
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.
Site Content
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]