Firm-Level Stock Price Reactions to Pandemic News
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Firm-Level Stock Price Reactions to Pandemic News
Steven J. Davis, Stephen Hansen, and Cristhian Seminario-Amez
October 17, 2020
Abstract
COVID-19 will likely restructure economic activity in a variety of ways, and lead to the growth of
some firms and the decline of others. Stock markets provide a natural environment to examine
these effects since share prices are tied to expectations of future earnings growth. On days with
large pandemic-related market moves, there is enormous dispersion in firm-level returns. Firms’
pre-pandemic regulatory filings provide a way to interpret these patterns since they extensively
describe sources of future earnings risk. Text analytic methods uncover dozens of relevant risks
associated with both lower and higher returns, including exposure to activities directly impacted
by social distancing and indirect effects arising from substitution effects and supply-chain
linkages.
Introduction
Amid the aggregate decline in economic activity induced by COVID-19, there are highly varied
impacts in different parts of the economy that will lead to a permanent reallocation of capital
and labor. Survey evidence (Barrero et al., 2020) and historical evidence (Davis and
Haltiwanger, 1992) suggest this reallocation will occur primarily across firms within industries.
Figure 1: Value-Weighted Mean and Cross-Sectional Inter-quartile Range of U.S. Equity Returns,
All Days in 2019 and Days with Large Market-Level Moves in February-March 2020
The behavior of the stock market in the early stages of the pandemic illustrates the vast
differences in firm-level outcomes associated with the arrival of COVID-19. Figure 1 plots the
average US equity market return against the inter-quartile range (IQR) of individual returns for
1all trading days in 2019, and for 20 “jump days” in February and March 2020 on which average
returns rose or fell at least 2.5%. Colors indicate the reason journalists gave for these moves in
next-day newspaper accounts (Baker et al., 2020). A clear pattern emerges, whereby larger
market-level moves correspond to greater variation in firm-level outcomes. On three separate
days (March 16, March 18, and March 24) the IQR is over 15 standard deviations greater than
the average IQR value in 2019. Our recent paper (Davis et al., 2020) explains and interprets the
structure of firm-level returns, and thereby provides evidence of how the pandemic will
reshape the economy.
The primary data source is pre-pandemic 10-K reports, which are filed annually by publicly
traded US firms. Since 2006 10-Ks have contained a Risk Factors (RF) section that provides a
detailed description of known sources of future earnings uncertainty. We match 2,155
individual stock returns on 2020 jump days with corresponding RF texts from 2010 to 2016. The
key idea is that the pre-pandemic RF content that explains returns on the jump days reveals the
forces the market expects will impact post-pandemic earnings.
Explaining Cross-Sectional Returns using Risk-Factor Text
Implementing this idea requires obtaining a quantitative representation of the Risk Factors (RF)
text, for which we use two popular but distinct text-analytic approaches: dictionary methods
and supervised machine learning. This allows us to compare the methods’ strengths and
weaknesses in a concrete setting, and to show how to combine elements of both to
simultaneously explain and interpret outcomes.
Dictionary methods count the frequency of certain terms that relate to concepts of interest.
Our specific dictionaries come from Baker et al. (2019), and their construction relies heavily on
domain expertise. There are 36 categories in all: 16 for economic conditions and 20 for
government policy. The dictionary measures, together with controls for sector and firm
financial variables, explain one-third of the average firm-level return on pandemic fallout days
(those marked red in figure 1). Categories associated with negative returns in reaction to bad
pandemic news include “inflation,” “credit indicators,” “taxes,” and “transportation.” Categories
associated with positive returns include “healthcare policy” and “intellectual property” where
the latter is particularly relevant for pharmaceutical firms. Dictionaries also explain returns on
jump days driven by other types of shocks but in different ways. For example, they explain one-
quarter of the variation in returns on jump days driven by monetary policy news, with an
important role for “inflation,” “interest rates,” and “real estate” in driving positive returns.
2Figure 2: R-squared Achieved by Dictionary and Machine Learning Approaches
(45-degree line is dashed)
While the dictionaries provide an informative characterization of returns, they draw on a small
fraction of RF content. Together they contain 244 unique terms, which make up 2.4% of the RF
corpus (i.e., the collection of RF texts). But, overall, there are more than 18,000 unique terms
in the corpus, and so the dictionaries miss potentially relevant information. Incorporating the
entire corpus into a returns model requires machine learning (ML) methods due to the scale of
the data. We adopt the inverse regression model of Taddy (2013, 2015), which has seen
successful recent applications in economics (e.g., Gentzkow et al., 2020). To compare
approaches in terms of their ability to fit the returns data, we fit returns jump-day-by-jump-day
using both methods and plot the resulting R-squared values in figure 2. The left panel restricts
the ML model to only operate on the 244 terms present in the dictionaries. In this case, both
approaches have equivalent explanatory power. The right panel shows what happens when the
ML model operates on the whole RF corpus. Now the ML approach yields a nearly uniform
increase in goodness-of-fit of twenty percentage points.
These results show that the entire gain in explanatory power from ML is due to its ability to
operate on the full set of terms contained in RF texts. There remains a strong relationship
between goodness-of-fit values achieved by the two approaches, a result we extend in several
ways in Davis et al. (2020). This finding says the information contained in the non-dictionary
terms refines and extends rather than substitutes for the signals captured by the dictionaries.
Targeted Exposure Construction for Interpreting Returns
The ML model greatly increases the ability of the Risk Factors texts to predict firm-level returns
because it draws on a vastly larger set of terms. This very fact makes interpreting its output
difficult, because the fitted ML model involves a huge number of estimated parameters. To
overcome this challenge, we propose and implement an algorithm for targeted risk factor
construction to gain insight into the specific RF content that drives returns on pandemic fallout
3days. First, we use the fitted inverse regression model to identify “seed” terms that relate
especially strongly to firm-level returns. We then find other terms that (1) explain returns
similarly to the seed, according to the ML model, and (2) co-occur with the same surrounding
words in the RF texts, a standard notion of linguistic similarity. The resulting set of terms
associated with the seed effectively defines a new risk exposure category. For example, starting
with the seed word “tantalum,” we find related terms such as “tin” and “tungsten” to form a
new term set that we label Raw Metals and Minerals. Proceeding in this manner, we obtain 38
categories for pandemic fallout days, which we use in place of the baseline, untargeted
dictionaries to explain returns.
Negative Exposures Positive Exposures
Traditional Retail E-commerce
Card Payments
Restaurants Foodstuffs
Gambling Video Games
Travel
Oil and Gas/Energy Infrastructure Raw Metals and Minerals
Electronic Components
Web-Based Services
Mortgages Banking/Deposits
Investment Funds
Table 1: A Selection of Targeted Exposures for Explaining Returns on Pandemic Fallout Days
Table 1 highlights selected negative and positive exposures that are important drivers of returns
on pandemic fallout days. For example, high exposures to traditional retail, restaurants, and
travel – all of which are directly harmed by social distancing – drive negative returns on days
with bad pandemic news. But we also find positive return exposures associated with
substitution away from these activities, such as ecommerce and basic foodstuffs. Moreover,
exposure to intermediate inputs affected by downstream demand shocks drives returns in both
negative (e.g., for energy) and positive (e.g. for the technology supply chain) directions on days
with bad pandemic news. Mortgages drive negative returns, but other financial activities like
banking and investment funds drive positive returns. Most of these effects remain even when
we control for very narrow industry codes, meaning they capture firm-level variation within
industries.
Conclusion
Our results point to a large variety of firm-level risk exposures that drive returns in both
positive and negative directions in response to the arrival of pandemic news , which provides
4insight into how the market expects reallocation activity to take place across firms. In time, we
can assess how these exposures relate to changes in the real economy, but a reasonable
conclusion is that the economic impact of COVID-19 will stretch far beyond the most obviously
exposed sectors and firms.
More broadly, we show how researchers can use the rich textual data in regulatory filings to
account for firm-level outcomes by combining machine learning and human judgment. This
approach has many potential applications in other settings.
References
Baker, S. R., Bloom, N., Davis, S. J., and Kost, K. J. (2019). Policy News and Stock Market
Volatility. National Bureau of Economic Research Working Paper Series, (w25720).
Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C., and Viratyosin, T. (2020). The
Unprecedented Stock Market Reaction to COVID-19. The Review of Asset Pricing Studies,
Forthcoming.
Barrero, J. M., Bloom, N., and Davis, S. J. (2020). COVID-19 Is Also a Reallocation Shock.
Brookings Papers on Economic Activity, Forthcoming.
Davis, S. J. and Haltiwanger, J. (1992). Gross Job Creation, Gross Job Destruction, and
Employment Reallocation. The Quarterly Journal of Economics, 107(3):819– 863.
Davis, S. J., Hansen, S., and Seminario-Amez, C. (2020). Firm-level Risk Exposures and Stock
Returns in the Wake of COVID-19. Centre of Economic Policy Research Discussion Paper 15314.
Gentzkow, M., Shapiro, J. M., and Taddy, M. (2019b). Measuring Group Differences in High-
Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4):1307–
1340.
Taddy, M. (2013). Multinomial Inverse Regression for Text Analysis. Journal of the American
Statistical Association, 108(503):755–770.
Taddy, M. (2015). Distributed Multinomial Regression. The Annals of Applied Statistics,
9(3):1394–1414.
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