Relative-value macro research
Ralph Sueppel’s work on 'unlocking relative value across asset classes' uses composite macro support scores to predict returns and simulated PnLs, offering an econometric template for multi-asset alpha. The piece includes simulated performance metrics you can replicate for a portfolio project. (x.com)
Macrosynergy published the research note “Unlocking relative value across asset classes” as a Jupyter Notebook on March 21, 2026. (macrosynergy.com) The methodology equalizes expected volatility across 12 conventional asset classes by using leveraged derivatives and then constructs relative-position targets for each class versus the rest of the universe. (macrosynergy.com) Signals are formed from small sets of macro-quantamental factors computed on a point-in-time basis using the JPMaQS framework developed with J.P. Morgan, transforming macro indicators into tradable scorecards. (macrosynergy.com) The post includes naïve PnL simulations and downloadable Jupyter notebooks intended for audit and replication, and community ports of those scorecard ideas (Python kits) are available on GitHub for fixed‑income scorecard workflows. (macrosynergy.com) Replication requires JPMaQS/DataQuery access for the proprietary point‑in‑time macro inputs; Macrosynergy’s notes state institutional access and an academic support program are available for qualifying researchers. (macrosynergy.com) Ralph Sueppel, listed as Managing Director for Research and Trading Strategies at Macrosynergy and a co‑author of multiple macro‑quantamental posts, previously worked as a portfolio manager at Graham Capital and discussed JPMaQS collaboration on a J.P. Morgan podcast recorded September 26, 2025. (youtube.com) The notebooks specify implementation details that matter for a portfolio project—naïve PnL construction, rebalancing frequency, volatility targeting via leverage, and cross‑asset relative weights—each element included to enable empirical validation and auditability. (macrosynergy.com)
