We use nonparametric dimension-reduction methods to extract from a set of 15 macroeconomic variables the risk factors that are priced in the stock market. The dominant factor moves with the business cycle but, because it is a nonlinear function of observed macroeconomic variables, it
captures a rich set of interactions. Low-credit risk and low-inflationary expectations have a greater positive effect on stock returns when leading macroeconomic indicators are high relative to current economic activity, i.e. early in the business cycle as the economy emerges from recession.
High-stock returns also arise in periods when the economy is booming relative to its leading indicators, but such periods tend to portend crashes.
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