Deep learning is another technological advance that has important implications for securities regulation. In simple terms, deep learning is a form of machine learning that involves learning data representations and patterns using simulated neural networks. Deep learning requires access to a very large amount of data and immense computing power. I expect deep learning to be used by certain sophisticated traders, such as hedge funds and proprietary trading firms.
Steve Crimmins to speak at the Practising Law Institute (PLI) seminar on November 30th, 2017 in New York, NY. A live webcast of the program is available for those not able to attend in person.
Two developments in technology, blockchain and deep learning, have implications for securities trading regulation. The two technologies are different in scope and purpose and will raise different issues for securities regulators. Both demonstrate how technological advances in the trading area can outpace current rules and regulations and cause regulators to rethink how to handle so-called “disruptive” technologies without impeding new structures and ideas.
Schemers and Scammers, 2017 Edition: The Never Ending Battle Against Fraud
Murphy & McGonigle, P.C., a leading provider of legal services to the financial services industry, has launched a dedicated website to track the U.S. Department of the Treasury’s new recommendations that seek to reform capital markets regulation.