Can the Variance After-Effect Distort Stock Returns?

36 Pages Posted: 25 Feb 2021

See all articles by Tony Berrada

Tony Berrada

University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute

Date Written: February 23, 2021


Variance after-effect is a perceptual bias in the dynamic assessment of variance. Experimental evidence shows that perceived variance is decreased after prolonged exposure to high variance and increased after exposure to low variance. We introduce this effect in an otherwise standard financial model where information about variance is incomplete and updated sequentially. We introduce a variance after- effect adjustment factor in a bayesian learning model and derive the associated predictive variance. We show theoretically how this adjustment factor affects both average and volatility of excess returns. We construct a proxy of the adjustment factor using the sequence of dispersion of analysts earnings forecast. We provide empirical evidence using US stock data over the sample 1982 - 2019, that fluctuations in this measure are significantly and positively related to excess volatility as predicted by the model. Further confirming the model's implications, we also show how stock returns are positively impacted by the adjustment factor and construct long short strategies that generate significant positive alpha with respect to the Fama-French 5 factor model.

Keywords: Variance after-effect, learning, turnover, volatility, earnings forecasts

JEL Classification: G11, G12, G41

Suggested Citation

Berrada, Tony, Can the Variance After-Effect Distort Stock Returns? (February 23, 2021). Swiss Finance Institute Research Paper No. 21-16, Available at SSRN: or

Tony Berrada (Contact Author)

University of Geneva - Geneva Finance Research Institute (GFRI) ( email )

40 Boulevard du Pont d'Arve
Geneva 4, Geneva 1211

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics