The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests
journal contributionposted on 09.10.2018 by Rangan Gupta, Christian Pierdzioch, Andrew Vivian, Mark Wohar
Any type of content formally published in an academic journal, usually following a peer-review process.
We contribute to research on the predictability of stock returns in two ways. First, we use quantile random forests to study the predictive value of various consumption-based and income-based inequality measures across the quantiles of the conditional distribution of stock returns. Second, we examine whether the inequality measures, measured at a quarterly frequency, have out-of-sample predictive value for stock returns at three different forecast horizons. Our results suggest that the inequality measures have predictive value for stock returns in sample, but do not systematically predict stock returns out of sample.
We thank the German Science Foundation for financial support (Project Macroeconomic Forecasting in Great Crises; Grant number: FR 2677/4-1).
- Business and Economics