Chasing noise in the stock market: an inquiry into the dynamics of investor sentiment and asset pricing.

This study explores the inclusion of sentiment measures as a risk factor in asset pricing. Using UK market data for the period January 1993 to December 2020, we create a new sentiment variable, and construct both raw and clean sentiment indices from a principal component analysis of a variety of literature-acknowledged sentiment proxies. Essentially, the model estimations are categorized into two: first, the study documents the performance of the traditional pricing models on portfolios formed on different characteristics. Second, the study augments the first category by iterating sentiment variables into the model specification. The findings reveal that sentiment-augmented asset pricing models outperform the traditional models in explaining the excess returns of the portfolios. Furthermore, using Hansen & Jagannathan (1997) non-parametric model performance technique, we observe that the sentiment-induced models produce a small distance error compared to the traditional models, thus validating the use of sentiment measures in our pricing mechanism. It is therefore opined that extant asset pricing models may not be sufficient to explain market or pricing anomalies. Investors' sentiment is an important systematic risk factor that possesses useful information, and by implication, market analysts and stakeholders must take serious cognizance of its propensities when forecasting risk-adjusted returns. [ABSTRACT FROM AUTHOR]