  We propose a novel approach to sentiment data filtering for a portfolio of
assets. In our framework, a dynamic factor model drives the evolution of the
observed sentiment and allows to identify two distinct components: a long-term
component, modeled as a random walk, and a short-term component driven by a
stationary VAR(1) process. Our model encompasses alternative approaches
available in literature and can be readily estimated by means of Kalman
filtering and expectation maximization. This feature makes it convenient when
the cross-sectional dimension of the portfolio increases. By applying the model
to a portfolio of Dow Jones stocks, we find that the long term component
co-integrates with the market principal factor, while the short term one
captures transient swings of the market associated with the idiosyncratic
components and captures the correlation structure of returns. Using quantile
regressions, we assess the significance of the contemporaneous and lagged
explanatory power of sentiment on returns finding strong statistical evidence
when extreme returns, especially negative ones, are considered. Finally, the
lagged relation is exploited in a portfolio allocation exercise.
