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

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Econometric Institute Erasmus University Rotterdam November 2010Abstract 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.

Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

“…More importantly, the specific model is now found to contain significant predictive ability both in and out-of-sample. 8 Given that stock prices serve as a leading indicator and, hence, carries useful information for policy makers as to where the economy might be heading, future research would aim to investigate not only in-sample, but also out-of-sample predictability of real stock returns 9 based on a wider set of financial and macroeconomic variables (Choudhry, 2004;Chancharoenchai et al, 2005;Rapach et al, , 2010aRapach and Wohar, 2006;Ng, 2007, 2009, forthcoming;Carvalhal and de Melo Mendes, 2008;Goyal andWelch, 2008, Cakmakli andvan Dijk, 2010) by extracting factors to serve as explanatory variables in predictive regression models or even based on Bayesian vector autoregressive models, with both these approaches capable of handling huge data sets involving hundreds of variables. In addition, one might also want to delve into multifractal (Balcilar, 2003), long memory models (Franses and van Dijk, 2000;Balcilar, 2004) and even non-linear models 10 (Qi, 1999;McMillan, 2001) to capture stock return movements.…”

“…2 See for example Campbell and Thompson (2008), Cochrane (2008), Goyal and Welch (2008) and Rapach et al, (2009) amongst others. Given the wide variety of possible predictors, studies by Ng (2007, 2009, forthcoming) and Cakmakli and van Dijk (2010) have suggested the use of large-scale factor models to extract common factors, and using them in the predictive regressions to evaluate stock returns predictability. Having said this, valuation ratios do remain important predictors of stock returns, especially given their theoretical importance, and this paper aims to shed further light on the empirical importance of the price-dividend and price-earnings ratios in predicting stock returns by using a different data set from an emerging economy.…”

A comprehensive look at financial volatility prediction by economic variables

“…3 3 Independent contemporaneous work by Cakmakli and van Dijk () also considers the issue of stock return and volatility predictability based on many (macro‐)economic variables. Unlike the BMA framework considered here, the authors extract information from macroeconomic series by dynamic factor models.…”

Tests of equal accuracy for nested models with estimated factors

“…Toward this goal we consider each of the …rst 8 macroeconomic factors (f 1t ; :::;f 8t ) as potential predictors. 9 As shown later, the second factor, which loads heavily on interest rate variables, is clearly the strongest predictor.…”