Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility
This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only include valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are both statistically and economically significant. The factor-augmented predictive regressions have superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. An important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s.
|dynamic factor models, model uncertainty, return predictability, variable selection|
|Time-Series Models; Dynamic Quantile Regressions (jel C22), Forecasting and Other Model Applications (jel C53), Portfolio Choice; Investment Decisions (jel G11), Asset Pricing (jel G12)|
|Tinbergen Institute Discussion Paper Series|
|Discussion paper / Tinbergen Institute|
Cakmakli, C, & van Dijk, D.J.C. (2010). Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility (No. TI 2010-115/4). Discussion paper / Tinbergen Institute. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/21861