Morgan Stanley (United States)

Abstract: This paper examines the Federal Reserve's communication strategy to see how well it has worked and how it can be improved. It argues that Federal Reserve communication when short-term interest rates are no longer constrained by the zero lower bound should be focused on relaying a data-based reaction function which informs market participants how interest rates will adjust as new information arrives. Instead, the Federal Reserve in recent years has relied more heavily than desired on “time-based” forward guidance, focusing on when interest rates are likely to rise rather than under what circumstances. We argue that, except under unusual circumstances, this is an imprudent strategy, as it mutes the effect of macroeconomic news on interest rates and unnecessarily places restrictions on future Federal Reserve action when new information arrives. We argue that the Federal Reserve can improve communication in the current environment by moving away from time-based forward guidance, clarifying how interest rates are likely to change given new information, and providing more information in the Summary of Economic Projections.

Abstract: The ever-increasing volume, variety, and velocity of threats dictates a big data problem in cybersecurity and necessitates deployment of AI and machine-learning (ML) algorithms. The limitations and vulnerabilities of AI/ML systems, combined with complexity of data, introduce a new adversarial model, which is defined and discussed in this article.