Forecasting economic recessions remains a fundamental challenge in macroeconomic research and investment decision-making. Financial markets often signal recessions before economic data visibly deteriorate, making indicators such as yield spreads and credit spreads valuable early-warning tools. However, market-based indicators can also generate costly false alarms when financial conditions reflect temporary shocks rather than sustained economic weakness.
To capture both market expectations and underlying economic conditions, we develop a framework that integrates financial indicators with a broad set of macroeconomic variables. By integrating financial indicators with measures of consumption, housing, labor markets, production, and financial health, our framework improves explanatory power from 0.38 to 0.54 and increases classification accuracy from 84% to 89%, while reducing false recession signals. Our analysis suggests that recession forecasts become substantially more reliable when financial market signals are combined with measures of real economic activity.
In the United States, recession dates are determined by the National Bureau of Economic Research (NBER) Business Cycle Dating Committee, which evaluates a broad range of economic indicators to assess the depth, duration, and diffusion of economic downturns.
While widely regarded as the definitive record of business cycles, the NBER process is inherently backward-looking. Historically, official recession announcements have been delayed by four- to twenty-one months, with an average lag of approximately eleven months (see Exhibit 1).
By the time a recession is officially identified, markets and economic conditions have often already adjusted, highlighting the need for forward-looking models that can assess recession risk over investor-relevant horizons.
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