Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility

Cem Çakmaklı and Dick van Dijk

No 10-115/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: 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.

Keywords: return predictability; model uncertainty; dynamic factor models; variable selection (search for similar items in EconPapers)
JEL-codes: C22 C53 G11 G12 (search for similar items in EconPapers)
Date: 2010-11-22
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20100115

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