Description: Traditional predictive tools are not built to deal with the variability the world currently faces. The COVID-19 pandemic amounts to a prolonged black swan event, for which one must look past conventional assessments to arrive at any level of confident predictions. Behavioral analysis factors for biases and heuristics designed specifically for uncertain evolutionary contexts, and which are the primary drivers of market participants’ decisions at present. Through understanding the most likely behaviors of individuals, it becomes possible to arrive at an aggregate understanding of market outcomes.

Description: As tariff policies are rapidly changing throughout 2025, the U.S. markets have been reacting in complex ways. In addressing the difference in short and long-term impacts of these policy changes, the importance of resilient and holistic risk management is highlighted in order for companies and investors to find opportunities and mitigate sudden losses.

Description: This article speculates on the presence and magnitude of the benefits attributable to regular rebalancing of one's investment portfolio. It surveys the extant literature, discusses certain implications, and presents conclusions.

Description: Demographic challenges due to longevity increases and low fertility rates will lower economic growth as measured by GDP. By deconstructing GDP growth between population and productivity growth the article describes the problem and presents potential solutions. The article builds off an SOA research paper published in November 2023, Demographics and Productivity: Drivers of Economic Growth.

Description: This article is part of a 2 part series on real options analysis (ROA). Before you can understand the limitations of ROA, you must first understand the difference between risk and uncertainty. This article goes into detail explaining the vast differences between the two phenomena.

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: In *From Distress to Distinction*, Chris Droussiotis explores a novel approach to valuing distressed companies using option pricing theory, drawing parallels to the financial challenges faced by startups. With over 30 years in banking and startup advising, he emphasizes that both distressed firms and early-stage ventures endure periods of negative cash flow but retain potential for recovery. He combines Discounted Cash Flow (DCF) and the Black-Scholes Option Pricing Model to evaluate distressed firms’ recovery prospects and debt sustainability. A case study on Spirit Airlines illustrates this approach: though its current equity value is negative, projected future cash flows suggest significant potential recovery. Droussiotis concludes that strategic valuation, using trend, peer, and expected analysis, can reveal hidden value in distressed companies, guiding effective investment and restructuring decisions.

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.

Description: This publication presents an exploratory framework that applies volatility signals—specifically the VIX and MOVE indices—to asset allocation decisions. Rather than prescribing a definitive model, the work highlights a potential pathway for linking equity and bond market risk expectations to portfolio construction. The analysis demonstrates how relative volatility between asset classes can inform dynamic weighting, opening the door to more adaptive strategies in shifting market environments. Importantly, this is not intended as a finished methodology, but as a foundation on which more experienced professionals can build. Readers with advanced backgrounds in finance, risk management, or quantitative analysis are encouraged to refine, stress-test, and extend the framework to suit their own investment contexts. By positioning the concept as a starting point rather than a conclusion, the publication invites collaboration and deeper exploration into how volatility-based metrics may enhance long-term allocation models.

Description: A review of changes in the last two years with respect to investing, geopolitical risks and the markets.