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The OECD’s recent upgrade of world‑GDP forecasts to 2.9 % signals that growth remains surprisingly robust despite geopolitical shocks. For quantitative traders, this macro backdrop alters the statistical environment in which big‑data and machine‑learning models operate. Understanding how resilient growth interacts with high‑dimensional inference can improve factor selection, risk estimation, and portfolio construction. From Macro Resilience to Model Design When global output is steadier than expected, the variance of macro‑driven predictors (such as industrial production, PMI, or trade flows) tends to shrink. A lower variance reduces the signal‑to‑noise ratio for models that rely on these variables, making over‑fitting more likely. In high‑dimensional settings—where the number of predictors can exceed the number...