The Text Premium and Stock Returns

Abstract

This thesis proposes a novel approach to portfolio sorting based on the following: (i) the time-varying structure of company lings, and (ii) its exposure towards a common text source. We construct a similarity measure which allows us to identify under- and over- performing stocks in a way such that we can construct portfolios with an increasing rate of return. We discover that a long-short strategy based on these portfolios will yield a signi cantly higher risk-adjusted return than the benchmark index of 10.05% annually, and is not accounted for by the risk-factors in the conventional ve-factor model proposed by Fama and French (2015).