Managerial sentiment, investor sentiment and stock returns
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It is well established that investor sentiment plays a vital role in global ﬁnancial markets. However, the sentiment of other economic agents has received less attention in the behavioural ﬁnance literature. This thesis aims to address the impact of managerial sentiment on the UK stock market. It investigates the performance of managerial sentiment in predicting stock returns relative to investor and consumer sentiments. In addition, it examines how sentiment is transmitted from managers to investors and whether the response of investor sentiment is asymmetric towards positive versus negative managerial sentiment. Finally, this thesis provides a comparative study of traditional and sentiment-augmented asset pricing models. Using monthly data from January 1985 to December 2014 and a sample of consumer and business conﬁdence indicators provided by the European Commission, the ﬁrst chapter provides novel evidence on how managerial and consumer sentiment indicators aﬀect stock returns. The ﬁndings show no support for consumer conﬁdence as a predictor of stock returns. However, managerial sentiment shows a signiﬁcant impact on aggregate market and sector return indices. Furthermore, results indicate that parameter estimates for sector groupings are not consistent, implying that the sentiment-return relationship diﬀers across sectors and that parameters are sensitive to industry characteristics. In the second chapter, the investigation extends to assess the long and short run dynamics of the sentiment transmission from managers to investors. Using threshold autoregressive (TAR), momentum threshold autoregressive (MTAR), and asymmetric threshold vector error correction (ATVECM) models, the ﬁndings provide evidence on the impact of managerial sentiment on investor sentiment in support of the Catering Theory. Results show that investors’ sentiments converge with long-run equilibrium relationships in response to positive rather than negative shocks in managerial sentiment. Furthermore, ﬁndings indicate that investor sentiment reacts negatively to positive managerial sentiment with a delay of four months, suggesting an over-conﬁdence in managers’ expectations of their future business outcomes. Finally, the third chapter examines the ability of managerial and investor sentiment to explain cross-sectional variation in stock returns. It compares the performance of CAPM, Fama & French (1993) Three factor model and Carhart (1997) four factor model to sentiment-augmented asset pricing models, which incorporate measures of both managerial and investor sentiment. The ﬁndings indicate that inclusion of sentiment factors signiﬁcantly adds to the power of traditional asset pricing models to explain the cross-sectional variation in stock returns. In addition, results show that managerial sentiment outperforms investor sentiment in explaining three out of four test portfolios formed on size, book-to-market, volatility and size/momentum factors. Moreover, ﬁndings show that managerial sentiment exhibits stronger prediction power for size premium over short (1-3 months) forecasting horizon relative to investor sentiment. However, value premiums respond to changes in managerial and investor sentiment over the relatively longer time of 12 months. In addition, the investigation failed to ﬁnd any signiﬁcant relationship between sentiment indices and momentum premium. This study has several implications for empirical researchers, practitioners and policy makers. It provides academics who are concerned with the empirical tests of asset pricing models with new insights on how the inclusion of managerial sentiment impacts the performance of longer term investigated models. For practitioners, our ﬁndings suggest that managerial sentiment and its impact on sector returns provide new opportunities for enhancing trading as well as asset allocation strategies. In developing investment strategies, practitioners may consider sectors that are more or less prone to sentiment in response to investor risk preferences. In addition, results on sentiment-augmented asset pricing models may be of interest to regulators who are concerned with the estimation of businesses’ cost of capital when pricing public services.