Firm-Level Political Risk: Measurement and Effects

Abstract

We adapt simple tools from computational linguistics to construct a new measure of political risk faced by individual US firms: the share of their quarterly earnings conference calls that they devote to political risks. We validate our measure by showing it correctly identifies calls containing extensive conversations on risks that are political in nature, that it varies intuitively over time and across sectors, and that it correlates with the firm's actions and stock market volatility in a manner that is highly indicative of political risk. Firms exposed to political risk retrench hiring and investment and actively lobby and donate to politicians. These results continue to hold after controlling for news about the mean (as opposed to the variance) of political shocks. Interestingly, the vast majority of the variation in our measure is at the firm level rather than at the aggregate or sector level, in the sense that it is neither captured by the interaction of sector and time fixed effects, nor by heterogeneous exposure of individual firms to aggregate political risk. The dispersion of this firm-level political risk increases significantly at times with high aggregate political risk. Decomposing our measure of political risk by topic, we find that firms that devote more time to discussing risks associated with a given political topic tend to increase lobbying on that topic, but not on other topics, in the following quarter.

Firm-level political risk and asymmetric volatility

“…Note that the start and end dates are purely driven by the availability of firm-level political risk data. Hassan et al, (2017) use textual analysis of quarterly earnings conference-call transcripts to construct a firm-level measure of the extent and type of political risk faced by individual firms listed in the U.S. exchanges. This metric is simply based on the share of the conversation between participants and firm management that centers on risks associated with politics, i.e., PRisk.…”

“…In the figures, the line corresponding to each political risk variable shows the rejection (non-rejection) of the null of no Granger causality from the political risk variable to each S&P 500 realized volatility measure at a specific quantile captured in the horizontal axis. Note that, as in Hassan et al, (2017), for the sake of comparability, we also include the economy-wide news-based measure of economic policy uncertainty (EPU) index (as developed by Baker et al, 2016) in our set of predictors. 5 Given that the test statistic is standard normal, the figures also present the lines capturing the critical values of 2.575, 1.96, and 1.645 across the quantiles corresponding to 10%, 5%, and 1% levels of significance respectively.…”

“…Pastor andVeronesi, 2012, 2013), we contribute to this debate from a novel angle. Utilizing the concept of political risk exposure at the firm level, recently developed by Hassan et al (2017), we explore the role of political risk on volatility asymmetry in stock market returns. By doing so, we contribute to the literature on (i) volatility asymmetry by providing an alternative risk-based explanation to its cause; and (ii) the EPU-stock market nexus by exploring an alternative channel in which volatility might be related to political uncertainty.…”

“…While volatility prediction has historically relied on high-frequency univariate models, more recently, Engle and Rangel (2008), Rangel et al, (2011) and Engle et al (2013) have highlighted the importance of low-frequency financial and macroeconomic variables in capturing future movements in the volatility process of financial assets, based on mixed-frequency modelling. In this context, Hassan et al (2017) adapt simple tools from computational linguistics to construct a new measure of political risk faced by individual U.S. firms, i.e., the share of their quarterly earnings conference calls that they devote to political risks. They then show that this proxy of political risk is able to predict quarterly (implied and realized) U.S. firm-level volatility in a panel of 9,481 firms.…”

“…They then show that this proxy of political risk is able to predict quarterly (implied and realized) U.S. firm-level volatility in a panel of 9,481 firms. While Hassan et al (2017) relate the effect of political risk on volatility to market frictions and inefficiencies that hinder growth at the firm level, the recent evidence by Andrei and Hasler (2015) and Dzieliński et al (2018) create an interesting opening suggesting that investor attention and dispersion of forecasts by analysts may also play a role in how political risk exposure relates to volatility.…”