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PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST
Author(s) -
Balcilar Mehmet,
Gupta Rangan,
Kyei Clement
Publication year - 2018
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12119
Subject(s) - predictability , econometrics , quantile , economics , stock (firearms) , volatility (finance) , nonparametric statistics , financial economics , mathematics , statistics , mechanical engineering , engineering
Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S BW and S PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al . (2015) respectively are mixed. While, linear predictive models show that only S PLS 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 S BW and S PLS can predict stock returns and its volatility, with S PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.