Forecasting Macro with Finance

While financial markets are known to contain information about future economic developments, the channels through which asset prices enhance macroeconomic forecastability remain insufficiently understood. We develop a structured set of like-for-like experiments to isolate which data and model properties drive forecasting power. Using U.S. data on inflation, industrial production, unemployment and equity returns, we test eight hypotheses along two dimensions: the contribution of financial data given different estimation methods and model classes, and the role of model choice given different financial inputs. Data aspects include cross-sectional granularity, intra-period frequency, and real-time, revisionless availability; model aspects include sparsity, direct versus indirect specification, nonlinearity, and state dependence on volatile periods. We find that financial data can deliver consistent and economically meaningful gains, but only under suitable modeling choices: Random Forest most reliably extracts useful signals, whereas an unregularised VAR often fails to do so; by contrast, expanding the financial information set along granularity, frequency, or real-time dimensions yields little systematic benefit. Gains strengthen somewhat under elevated policy uncertainty, especially for inflation, but are otherwise fragile. The analysis clarifies how data and model choices interact and provides practical guidance for forecasters on when and how to use financial inputs.