Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence

TL;DR

This paper reviews empirical evidence on how investor sentiment measures influence asset returns, highlighting that complexity improves model fit but not necessarily predictive power, with significance varying across assets and periods.

Contribution

It provides a comprehensive review of 71 studies, analyzing the impact of sentiment measure complexity on asset pricing models and their predictive capabilities.

Findings

Complex sentiment measures increase model fit.

No clear advantage of complex measures for prediction.

Sentiment significance varies by asset and time period.

Abstract

This study conducted a comprehensive review of 71 papers published between 2000 and 2021 that employed various measures of investor sentiment to model returns. The analysis indicates that higher complexity of sentiment measures and models improves the coefficient of determination. However, there was insufficient evidence to support that models incorporating more complex sentiment measures have better predictive power than those employing simpler proxies. Additionally, the significance of sentiment varies based on the asset and time period being analyzed, suggesting that the consensus relying on the BW index as a sentiment measure may be subject to change.