This paper examines whether incorporating various investor sentiment measures in conditional asset pricing models can help to capture the impact of size, value, liquidity, and momentum effects on risk-adjusted returns of U.S. individual stocks. Using monthly data for the period January 1980 to December 2014, we determine the significance of equity fund flow, initial public offering (IPO) first day returns, IPO volume, closed-end fund discount, equity put-call ratio, dividend premium, change in margin debt, and sentiment index, by including them as conditioning information in asset pricing models. Our results show that sentiment augmented asset pricing models significantly capture the impacts of size, value, liquidity, and momentum effects on risk-adjusted returns. In particular, we observe that conditioning beta on equity fund flow, IPO first day return, and put-call ratio capture the predictive power of equity characteristics for all the asset pricing models.
Bathia, D., & Bredin, D. (2018). Investor sentiment: Does it augment the performance of asset pricing models? International Review of Financial Analysis, 59, 290–303. https://doi.org/10.1016/j.irfa.2018.03.014