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: 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: 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).

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

Description: The paper demonstrates that the most popular rationalization for evolving glide paths is flawed and should be discarded. The paper also demonstrates that glide paths should generally evolve for investors that endeavor to optimize outcomes and reevaluate their portfolios regularly.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: Lifetime pension pools convert a lump sum into lifelong income, periodically adjusting benefit payouts based on mortality and investment experience. Volatility-targeting strategies can enhance benefit stability by dynamically adjusting the investment portfolio’s exposure to risky assets in the context of these pools, demonstrating superior performance compared to traditional static asset allocation methods.

Description: Our previous article, “Introduction of Cashflow Matching Strategic Asset Allocation (SAA) Framework,” introduced the concept and included a real-world case study that developed a liability-driven investment strategy for a single line of business (LOB) for a life insurer. This article expands the framework and the case study from a single LOB to simultaneously optimize asset allocation across multiple LOBs. It discusses how this multi-line SAA framework can enable an insurance company to efficiently tag its asset holdings to each LOB, identify opportunities in the public and private asset markets to enhance risk-adjusted returns, and develop a deeper understanding of the trade-off between the degree of cashflow matching and risk-adjusted returns across LOBs.