A slew of articles has appeared recently about the growing use of artificial intelligence (“AI”) and neural networks for securities trading. Most of these articles conflate basic algorithm refinement with deep learning and neural networks. As I noted in an article I wrote last December, I am confident that some forms of deep learning are being used by a few hedge funds and proprietary traders, and that such use will grow quickly over time. The notion, however, that it is taking hold on a widespread basis seems grounded on a misunderstanding of the nature of deep learning.
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.
Howard Kramer Comments - Early this year FINRA released a thoughtful white paper (or concept release) on distributed ledger technology (“DLT”), also referred to as blockchain, and its implications for securities regulation. The white paper provided an overview of DLT and its securities industry applications and potential impact. It also described the factors to consider when implementing DLT and the attendant regulatory considerations.
The purpose of this paper is to explain and interpret the Securities and Exchange Commission's (SEC's) recently announced charges against 23 firms for violation of short selling restrictions set out in Rule 105 under Regulation M of the Securities Exchange Act of 1934.
Supervisory obligations of broker-dealer legal and compliance personnel after the Urban case
SEC Adopts Large Trader Reporting System