Description: Comments on how US insurers have compensated for low interest rates by investing in a range of less liquid and lower quality assets.

Description: Provides an overview of special characteristics of private equity and infrastructure asset classes, based on the perspective of experienced investors

Description: Investment Correspondent's report for the Opening session of the 2017 Annual Meeting & Exhibit in Boston, MA.

Description: Crossword Puzzle

Description: Recaps April 2015 seminar on derivatives use for fund managers and insurers in China. Describes growth in Chinese derivatives market including CSI 300 futures

Description: Provides a summary of investment sessions at the March 2015 Investment Symposium in Philadelphia

Description: GDP growth has slowed as numerous headwinds have emerged. This article summarizes a research paper by Mark Alberts and Max Rudolph that addresses reasons why this is so and how risk managers can address using scenarios.

Description: All things considered, caution needs to be applied to the use of so-called “standard” mortality tables. Sharpening up these blunt estimates by using individual assumptions reveals material differences in the structure of longevity, both between plans and within plans.

Description: While the major market crash subsequent to the widespread contagion of the COVID-19 virus was readily predictable, the rapid recovery and ongoing growth subsequent to it was not. Market dynamics around the recovery resist characterization by most traditional analytical approaches. In examining the circumstances around the emergence of a large retail class of investors, one sees that ingrained human tendencies, particularly optimism bias, have fueled much investor behavior responsible for the recovery. Understanding this makes it possible to predict potential future crashes and develop behavioral interventions for subsequent market recoveries.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).