Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely SBW and SPLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only SPLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality†in†quantiles model of Balcilar et al., (forthcoming), in fact, both SBW and SPLS can predict stock returns and its volatility, with SPLS being a relatively stronger predictor of excess returns during bear and bull regimes, and SBW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.