A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics

TL;DR

This paper introduces a new multivariate sentiment model that separates long-term and short-term components, improving understanding of sentiment dynamics and their impact on asset returns, especially during extreme market events.

Contribution

It presents a novel dynamic factor model combining a random walk and VAR(1) processes for sentiment, estimated via Kalman filtering, applicable to large portfolios.

Findings

Long-term sentiment component co-integrates with market factors.

Short-term sentiment captures transient market swings.

Strong statistical link between sentiment and extreme negative returns.

Abstract

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.