Hot off the press : news-implied sovereign default risk.
Date: 2022-05-06
Institutions: European Investment Bank
Subjects: economic indicator, financial risk, machine learning, natural language processing, price index, price of securities, public debt, swap arrangement

ECONOMICS – WORKING PAPERS 2022/06

Hot off the press 

News-implied sovereign default risk

Hot off the press 

News-implied sovereign default risk

April 2022

Hot off the press: News-implied sovereign default risk

© European Investment Bank, 2022. 
EIB Working Paper 2022/06
April 2022

This is a publication of the EIB Economics Department.
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Authors
Chukwuma Dim (Frankfurt School of Finance & Management) 
Kevin Koerner (EIB) 
Marcin Wolski (EIB)
Sanne Zwart (EIB) 

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pdf: QH-BK-22-006-EN-N ISBN 978-92-861-5269-6 ISSN 2599-736X doi: 10.2867/661002

News-Implied Sovereign Default Risk∗

Chukwuma Dim†

Kevin Koerner‡

Marcin Wolski‡

Sanne Zwart‡

This version: March 2022

Abstract

We develop a sovereign default risk index using natural language processing tech-
niques and 10 million news articles covering over 100 countries. The index is a high-
frequency measure of countries’ default risk, particularly for those lacking market-
based measures:
it correlates with sovereign CDS spreads, predicts rating down-
grades, and reﬂects default risk information not fully captured by CDS spreads. We
assess the inﬂuence of sovereign default concerns on equity markets and ﬁnd that
spikes in the index are negatively associated with same-week market returns, which
reverses over the next week, indicating that investors might overreact to default
concerns. Equity markets’ reaction to default concerns is more pronounced and per-
sistent for countries with tight ﬁscal constraints. The response to global, compared
to country-speciﬁc, default concerns is much stronger, underlining the relevance of
global “push” factors for local asset prices.

Keywords: Sovereign default, Credit risk, Equity returns, Machine learning, Natural
language processing, Early warning indicators

JEL: F30, G12, G15

∗We are grateful to Cristian Condrea, Can Gao (discussant), Jochen Schanz, Laurence van Lent, and Grigory
Vilkov for helpful comments and suggestions. We also thank participants at the EBRD/EIB Big Data Seminar, 8th
International Conference on Sovereign Bond Markets, and Frankfurt School of Finance & Management Brownbag
Seminar for their helpful comments. The views expressed in this paper are those of the authors and do not
necessarily reﬂect the position of the European Investment Bank.

†Corresponding author. Chukwuma Dim is aﬃliated with Frankfurt School of Finance & Management, Ger-

many, c.dim@fs.de.

‡European Investment Bank, Luxembourg.

1

Introduction

Global sovereign debt soared to unprecedented levels recently as governments provided ﬁscal

stimulus in response to the Covid-19 pandemic. Government debt reached $83.4 trillion in Q1

2021, compared to $70.1 trillion at end-2019 (IIF, 2021b,a). Due to the economic contraction,

global public debt ratios also rose signiﬁcantly when measured relative to the size of the economy,

reaching 98.6% of GDP in 2020 from 83.6% in 2019 (IMF, 2021). The IMF (2021) notes that

global government debt has now stabilized at just below 100% of GDP, with substantial variation

in ﬁscal and economic developments across countries. The rapid rise in public debt has prompted

warnings about the dangers of excessive debt levels, the drag they will place on economic growth

and the burden they represent for future generations (see, Eichengreen et al., 2021; Mitchener

and Trebesch, 2021).1

The literature on the evolution and impacts of sovereign default risk has traditionally relied

on credit default swap (CDS) spreads, i.e., the price of insurance against sovereign default, as

the daily proxy for default risk (see Augustin (2014), for a review). Despite some concerns that

CDS spreads are partly driven by speculation, they are often preferred to alternative measures

of sovereign default risk.2 However, CDS spreads narrow any analysis to the subset of countries

with (liquid) CDS contracts, excluding many emerging markets and virtually all less developed

countries that pose even higher default concerns to investors.

In this study, we address this

shortcoming by developing a novel text-based measure of sovereign default risk—the News-

implied Sovereign Risk Index (NSRI)—that can be computed for any country by applying

Natural Language Processing (NLP) techniques to socioeconomic, political and ﬁnancial news.

Our use of news text to quantify sovereign risk is predicated on the idea that news media is

observed at a relatively high frequency and contains timely information about countries’ socioe-

conomic and political developments that should be predictive of and salient to agents’ concerns,

including governments’ creditworthiness. This view is consistent with Shiller (2017) who ad-

1High debt levels can constrain governments’ ﬁscal leeway to respond to future crises, such as ﬁnancial crises,
natural disasters, or wars (e.g., Romer and Romer, 2018; Battaglini and Coate, 2016), and can result in default
through explicit debt repudiation or inﬂation (Yared, 2019).

2For example, sovereign bond prices are inﬂuenced by diﬀerences in currency denominations, tenor, covenants
and legal jurisdictions, and they are also often less liquid than CDS contracts. Similarly, sovereign credit ratings
are only adjusted at irregular intervals and do not reﬂect real-time dynamics of sovereign default concerns.

1

vocates for the use of textual analysis to quantify the dynamics of popular narratives in the

news media to better understand economic ﬂuctuations. Moreover, competition among media

outlets ensures that news caters to market participants’ fundamental risk concerns regarding

any country at any point in time. Hence, we expect news text to provide timely and informative

signals speciﬁcally about the future dynamics of countries’ riskiness, just as it reﬂects concerns

about future economic growth and consumption (e.g., Liu and Matthies, 2021).

We exploit the rich information in news text with three main contributions to the literature.

First, we construct the NSRI which is computable for any country in real-time and particu-

larly relevant for developing and emerging markets without liquid market proxies of default risk.

Second, we show that the NSRI is strongly associated with sovereign CDS spreads both contem-

poraneously and in predictive terms, predicts future sovereign rating downgrades, and captures

default risk information not fully reﬂected in CDS spreads. Hence, the NSRI can complement

the use of CDS spreads to track default risk for countries for which CDS data is available and can

serve as a stand-alone high-frequency proxy of default risk for countries that lack market-based

measures of sovereign risk. Third, motivated by the evidence that NSRI is a valuable proxy for

sovereign default risk, we apply the index to study how default concerns inﬂuence equity mar-

kets. We show that equity markets tend to overreact to sovereign default risk increase, but less

so for countries facing tight ﬁscal constraints for which the decline in equity returns is more per-

sistent. The overreaction is more pronounced for global default concerns than country-speciﬁc

concerns, signaling, in general, the relevance of global (push) factors for local asset prices.

Our analysis begins with a large text corpus of 10 million news articles covering over 100

countries from the PressReader News API.3 We apply two relatively simple yet powerful Ma-

chine Learning (ML) techniques—topic modelling and text similarity—to quantify the degree

of sovereign default concerns reﬂected in news articles. Topic modelling allows us to focus on

socioeconomic, political and ﬁnancial news topics that are more likely to reﬂect sovereign default

concerns. To quantify sovereign default risk using text similarity, we follow a similar approach

as Engle et al. (2020) and Hassan et al. (2019) and compile a library of 448 documents focused

3Note, the number of countries used for our analysis depends on the country coverage of our news API provider.
However, our methodology can be extended without modiﬁcation to a larger sample of countries if one has access
to news coverage for those countries through a diﬀerent news API.

2

on sovereign default risk. We then calculate how similar the language used in a news article is to

the sovereign default library using the cosine similarity algorithm. Intuitively, an article about a

country would have a high score if it uses combinations of “default risk charged words” in similar

proportions as the sovereign default library, which will be the case if the news article signals

elevated sovereign default concern. Finally, we aggregate the news-level scores to a daily fre-

quency for each country to obtain the NSRI. Intuitively, NSRI will be high during elevated news

coverage about a country’s default risk, which will typically coincide with heightened investor

concern about the country’s worsening default risk. This view is consistent with academic stud-

ies (Liu and Matthies, 2021; Trussler and Soroka, 2014; Robinson, 2007) suggesting that news

often focuses on negative events because they are more attention-grabbing.

Figure 1 shows the evolution of the NSRI for two developed countries (Panels A and B) and

two emerging ones (Panels C and D). First, we observe that the NSRI has rich time-series and

cross-sectional variation, spiking during the great ﬁnancial crisis and the subsequent European

sovereign debt crisis, as well as during the Covid-19 pandemic when several sovereigns faced

heightened default concerns. Second, NSRI reasonably tracks the evolution of sovereign CDS

spreads with a correlation coeﬃcient ranging between 0.48 and 0.86. More so, CDS spreads

appear to be more persistent than NSRI. This is likely because CDS spreads capture the level of

risk, whereas NSRI tends to reﬂect changes in risk, consistent with the idea that news captures

changes in market participants’ concerns and expectations.

We conduct a formal analysis of the relationship between CDS spreads and the NSRI using

panel regressions and controlling for global and local macro-ﬁnancial variables, including stock

market returns, volatility, and general news sentiment. Overall, we ﬁnd that the NSRI percentage

change is strongly associated with that of CDS spreads. For instance, a standard deviation

increase in the NSRI percentage change is associated with a 0.21 – 0.40 percent points (pp)

increase in the same-week CDS spread percentage change relative to the previous week. NSRI

is also signiﬁcantly related to CDS spreads at the monthly frequency and predicts an increase

in the CDS spread percentage change by 1.02 – 1.67 pp over the next month for a standard

deviation increase in the NSRI percentage change.

3

A: Italy

B: Spain

C: China

D: Russia

Figure 1: News-implied Sovereign Default Risk and CDS Spreads Dynamics. The ﬁgure shows the
time series evolution of each country’s NSRI (left axis) and the natural log of CDS spreads (right axis). Both
variables are smoothed using a three-month moving average. r is the correlation coeﬃcient between both variables
over the period January 2007 - February 2021.

The use of CDS spreads to demonstrate NSRI’s informativeness about default risk dynamics

raises the question of whether the informativeness extends beyond the sample of countries for

which CDS data is available. Furthermore, it is helpful to understand whether NSRI provides

additional default risk information beyond that already captured by CDS spreads. To address

these two questions, we examine whether the NSRI predicts future sovereign rating downgrades

using the information on all countries rated by the credit rating agencies Moody’s, Standard &

Poor’s, and Fitch. We ﬁnd that NSRI has signiﬁcant explanatory power in predicting sovereign

rating downgrades. A standard deviation increase in NSRI over the past 30-day period ending

two days before a rating change announcement predicts an increase in the probability of a

downgrade by 8.8 pp, which corresponds to a 17% rise in the likelihood of a downgrade relative

to the observed fraction of downgrades in the data.

The NSRI’s ability to predict future rating downgrades holds even after controlling for se-

lected local and global macro-ﬁnancial variables, including CDS spreads and news sentiment.

4

20082010201220142016201820200.150.200.250.300.350.400.45NSRIr=0.76Italy (Daily, NSRI 90 days moving ave.)NSRICDS spread (right)3.54.04.55.05.56.0Log(CDS spread)2010201220142016201820200.100.150.200.250.300.350.400.45NSRIr=0.86Spain (Daily, NSRI 90 days moving ave.)NSRICDS spread (right)4.04.55.05.56.0Log(CDS spread)2010201220142016201820200.250.280.300.330.350.380.400.430.45NSRIr=0.48China (Daily, NSRI 90 days moving ave.)NSRICDS spread (right)3.53.84.04.24.54.85.05.2Log(CDS spread)2010201220142016201820200.140.160.180.200.220.24NSRIr=0.52Russia (Daily, NSRI 90 days moving ave.)NSRICDS spread (right)4.04.55.05.56.06.5Log(CDS spread)Conversely, conditional on NSRI, CDS spreads do not provide signiﬁcant incremental infor-

mation about future rating downgrades. These results indicate that NSRI contains additional

sovereign risk information not fully reﬂected in CDS spreads. A potential reason for this is that

news media reﬂects agents’ default concerns more timely before those concerns are fully incorpo-

rated in CDS spreads, ultimately leading to downgrades. Overall, NSRI can complement CDS

spreads to track default concerns at a high frequency for countries with liquid CDS contracts.

For other countries lacking daily market-based measures of sovereign default risk, NSRI can

serve as a stand-alone high-frequency proxy for default risk.

Equipped with an informative proxy of sovereign default risk for a broad cross-section of

countries, we revisit theoretical predictions on the inﬂuence of sovereign default risk in equity

markets. Jeanneret (2017) suggests a risk-based channel through which an increase in sovereign

default risk negatively impacts equity prices across countries by raising the threat of a future

economic slow-down. However, a diﬀerent channel supported by behavioural explanations (e.g.,

Dremen, 1983; De Bondt and Thaler, 1985; Gennaioli et al., 2015; Bordalo et al., 2019) is

investors’ overreaction to news of sovereign default concerns that may not be strongly associ-

ated with economic fundamentals. Such overreaction can stem from an aversion to an actual

sovereign default’s far-reaching adverse ﬁnancial market impacts. Using panel regressions of

weekly stock market returns and weekly-aggregated NSRI, we ﬁnd that NSRI is associated with

stock market returns in ways that support the overreaction hypothesis. Precisely, a standard

deviation increase in the NSRI percentage change is contemporaneously associated with a 7.1

bps (3.4% annualized) decline in the equity returns and predicts an increase in equity returns

by 6.4 bps (3.1% annualized) over the next week. The reversal of the initial decline in equity

returns suggests that equity investors tend to overreact to news of sovereign default concerns.

Next, motivated by the literature on how countries’ ﬁscal capacity can diﬀerently impact their

ability to withstand adverse economic shocks and avoid default (e.g., Yared, 2019; Romer and

Romer, 2018; Battaglini and Coate, 2016), we examine whether the equity market’s reaction

to sovereign default concerns is heterogeneous across countries. We ﬁnd that countries with

tighter ﬁscal constraints experience a more pronounced and persistent decline in equity returns

following an increase in default concerns. Precisely, for countries facing a high ﬁscal constraint,

5

a standard deviation increase in the NSRI percentage change is contemporaneously associated

with a 10.7 bps (5.5% annualized) decline in weekly equity returns, more than four times the

magnitude of the decline experienced by other countries. At the same time, the equity return

reversal over the next week for the high ﬁscal constraint countries is only about 1.9 bps.

Finally, we examine the factor structure in countries’ NSRI, following the literature on the

common drivers of sovereign default risk reﬂected in CDS spreads (e.g., Pan and Singleton, 2008;

Longstaﬀ et al., 2011). We ﬁnd that NSRI exhibits a non-negligible factor structure, as the ﬁrst

ﬁve principal components explain roughly 51% of the variation in countries’ NSRI. Motivated by

this evidence, we compare how equity markets respond to global sovereign default concerns ver-

sus country-speciﬁc default concerns. Surprisingly, we ﬁnd that global default concerns are more

strongly associated with local equity returns in terms of magnitude and statistical signiﬁcance.

A standard deviation increase in the global NSRI percentage change is contemporaneously as-

sociated with a decline in weekly equity returns by 21.6 bps, and predicts an increase in returns

by 16.7 bps over the next week. The magnitude of the initial decline and subsequent reversal

is more than twice that of the country-speciﬁc NSRI, indicating that equity investors overreact

even more to global sovereign default concerns than country-speciﬁc concerns. Hence, the re-

sults underline the importance of global (or push) factors for local asset prices, in line with the

existing literature (e.g., Colacito et al., 2018; Fraiberger et al., 2021).

Taken together, our paper shows that textual data from news media contains valuable in-

formation about countries’ default risks that is useful for tracking default concerns in real-time.

Our quantiﬁcation of such default risk concerns from the news media, NSRI, is informative about

observed countries’ default risk dynamics and equity returns, and hence has useful applications

in credit risk early warning systems, credit portfolio management, and for testing theories on

the economic costs and ﬁnancial market impacts of sovereign default risk.

Related Literature: This paper contributes to several strands of research. First, the paper

is related to the literature on the measurement and evolution of sovereign default risk. Several

papers (e.g., Chernov et al., 2020b; Longstaﬀ et al., 2011; Remolona et al., 2008; Pan and

Singleton, 2008) explore the default risk implied by sovereign CDS spreads. Others examine

factors that drive sovereign CDS spreads as default risk proxies, including local factors (e.g.,

6

Augustin et al., 2021; Chernov et al., 2020a; Hilscher and Nosbusch, 2010) and common global

factors (e.g., Ang and Longstaﬀ, 2013; Longstaﬀ et al., 2011; Augustin and T´edongap, 2016).

However, one of the major shortcomings of using CDS premium to proxy sovereign default

risk is that liquid CDS contracts are not available for most countries. Consequently, tracking

default risk concerns for many emerging and developing markets at a daily frequency has proved

impossible. Our contribution is to employ big data techniques to construct a novel real-time

sovereign default risk proxy (NSRI) that depends only on news media data, is not constrained by

market liquidity problems, and can be computed for any country. We show that, as with CDS

spreads, NSRI has a strong factor structure, corroborating the importance of common global

drivers of sovereign default risk (e.g., Longstaﬀ et al., 2011).

This paper also relates to the literature on the economic impacts of sovereign credit risk.

Several papers note that sovereign default risk has real economic eﬀects (Gennaioli et al., 2014;

Adelino and Ferreira, 2016; Bocola, 2016; Almeida et al., 2017; Lee et al., 2016 ), others fo-

cus on stock market implications of sovereign default risk (Andrade and Chhaochharia, 2018;

Jeanneret, 2017). By showing that sovereign default risk concerns reﬂected in the news media

aﬀect global equity markets, our paper is more closely related to the latter strand of literature.

We present new evidence along three dimensions: (i) Investors tend to overreact to sovereign

credit risk news, resulting in stock market eﬀects that typically reverse over the next week. (ii)

Equity markets have a heterogeneous response to sovereign risk news, reacting more strongly

and persistently to news about ﬁscally constrained countries, which supports Jeanneret’s (2017)

risk-based explanation for the eﬀect of sovereign default risk on equity prices. (iii) Common

(systemic) global sovereign risk concerns have a more pronounced inﬂuence on equity returns

than country-speciﬁc sovereign risk concerns, highlighting the importance of global factors for

local asset prices (e.g., Colacito et al., 2018; Fraiberger et al., 2021).

Finally, our paper is related to, but fundamentally diﬀerent from, recent papers that measure

important economic variables from textual data (e.g., Baker et al., 2016; Engle et al., 2020;

Manela and Moreira, 2017; Hassan et al., 2019; Sautner et al., 2020). We diﬀer from these

and other related papers in the following ways: First, to our knowledge, we are the ﬁrst to

leverage news media and NLP to construct a high-frequency proxy of sovereign default risk.

7

Second, the papers that develop country-level variables from text data mainly focus on the US

or a few advanced countries using a hand full of news sources. In contrast, our sample covers

over 100 countries and uses various news sources, including local and global news publishers.

Moreover, we employ a much larger text corpus, over 10 million articles, which enables us to

construct NSRI for a broad sample of countries. Overall, our analysis provides strong support

for applying text data to understand the dynamics of macro-ﬁnancial variables.

The paper proceeds as follows. Section 2 describes the data and key variables. Section 3

covers the NLP framework for quantifying sovereign default concerns from news articles. Sec-

tion 3.3 discusses the NSRI’s validation exercises. Section 4 analyzes the NSRI’s informativeness

about sovereign CDS spreads and downgrades, and how equity markets react to sovereign default

concerns. Section 5 discusses robustness tests, and Section 6 concludes the paper.

2 Data and Variables

Our news data comes from the PressReader News API. The sample period starts from Jan-

uary 2006, the inception of the API data, and ends in February 2021. PressReader is a news

aggregator service provider that uses its proprietary technology to process thousands of newspa-

pers daily, extracting text and images and making articles instantly translatable and searchable

(PressReader, 2021). We use PressReader’s data for this study because it provides historical

news coverage for many countries, including local and global news publishers.

Since we are interested in socioeconomic and political news content that may be informative

about default risk, we proceed as follows. For each country, we retrieve all English language

news articles that mention the country’s name and one of the key words “economy” and “gov-

ernment” and published either in the country in question, the US, UK or China.4 In total, we

retrieved approximately 10 million news articles published by roughly 2,100 publishers covering

184 countries. Table A1 of the Appendix provides the country coverage and number of news

4We use a keyword search for news retrieval because the PressReader news API does not provide the func-
tionality to ﬁlter news by topic. However, after we retrieve news data using the broad search, we train a topic
model to isolate relevant news from noise. Including the US, UK and China when ﬁltering news for the country
of publication allows us to obtain news published by popular international media about each country. This is
particularly helpful in scenarios where PressReader does not have extensive coverage of local news publishers for
a country.

8

articles we obtained for each country. There is substantial heterogeneity in news coverage per

country, arising from our focus on English language news and diﬀerent starting moments of

PressReader’s coverage of local news publishers in each country.5 We measure news sentiment,

i.e., the negativity of each news article, as the number of negative words divided by the sum of

negative and positive words from the Loughran and McDonald (2011) dictionary.

We obtain data on sovereign CDS spreads and countries’ stock market indices from Datas-

tream. We use the more liquid CDS contracts with ﬁve years tenor, full restructuring clause,

denominated in USD, and referencing external debt. For stock market returns, we use country-

level MSCI total return indices and consider both USD and local-currency denominated indices

for each available country. Table A2 of the Appendix lists the index ticker per country consid-

ered in this study. We collect historical sovereign credit rating changes and the announcement

dates from Trading Economics.6

We follow a similar approach as Augustin et al. (2021) to characterize a country’s ﬁscal

constraint using variables that capture ﬁscal capacity and economic conditions. We obtain

annual data on countries’ government debt-to-GDP ratio, public external debt-to-GDP ratio,

interest-to-GDP ratio, unemployment rate, GDP growth, and credit ratings from the World

Bank.7 Since most of these variables are available only with a signiﬁcant delay, we use them

with a two-year lag. Next, for each of the variables and each year, we calculate a country’s score,

such that the country with the largest ﬁscal constraint (e.g., the highest debt-to-GDP ratio) has

a value of one, and the country with the lowest ﬁscal constraint has a value of zero. In doing so,

variables (GDP growth and credit ratings) for which higher values mean better economic/ﬁscal

conditions are signed so that the rankings are comparable across metrics. Finally, we construct

a single measure of ﬁscal constraint by averaging across the ranks of all six metrics.

5A possible extension of our framework, which we do not pursue in this study, is to use machine translation
of news in languages other than English to signiﬁcantly expand the news coverage of some non-English speaking
countries with little news coverage in English. Note, however, that the framework we develop in this paper will
work with little or no modiﬁcation for such machine-translated news, making our work more appealing.

6https://tradingeconomics.com/country-list/rating.
7We use the average rating of foreign currency long-term sovereign debt from rating agencies Moody’s, Standard
& Poor’s, and Fitch, as provided by the World Bank (Kose et al., 2017). Each agency’s rating is converted to
a numerical scale, with 1 representing the least creditworthy country and 21 the most creditworthy. The other
variables are from the World Development Indicators.

9

We use the following global macroeconomic and ﬁnancial indicators: world stock market

index from MSCI, US Economic Policy Uncertainty Index (EPU) from Baker et al. (2016),8 US

macroeconomic activity index (ADS) of Aruoba et al. (2009) from the Federal Reserve Bank of

Philadelphia,9 and the VIX volatility index from the Chicago Board Options Exchange (CBOE).

To mitigate the inﬂuence of outliers, we winsorize all continuous variables in our study at the

1% and 99% levels.

3 Measuring Sovereign Default Risk from News Articles

The sheer size of our news corpus, with roughly 10 million articles, presents a large text corpus

to construct a text-based multi-country measure of sovereign default risk. However, text data

is inherently high-dimensional and may contain substantial noise. Therefore, we start by pre-

processing the text corpus to reduce noise by eliminating uninformative text as described in

Appendix A.1. Next, we employ two NLP techniques to extract sovereign default risk signals

from text. First, we train a topic model to further reduce noise and dimensionality. Second, we

employ text similarity to quantify the sovereign default concern reﬂected in news articles.

3.1 Topic Model

Our news data relies on a broad search of news that uses the words “economy” or “government”.

However, some of the resulting news articles may only be tangentially related to the broad

economy, business, and sociopolitical topics likely relevant to sovereign risk. Therefore, we use a

topic model to isolate articles that belong to topics that are more likely to be informative about

sovereign risk, hence reducing noise and dimensionality.

We employ the Latent Dirichlet Allocation (LDA) algorithm of Blei et al. (2003), which is

widely used in the economics and ﬁnance literature for topic modelling (e.g., Bellstam et al., 2021;

Bybee et al., 2021; Hanley and Hoberg, 2019; Huang et al., 2018). LDA gives text a hierarchical

structure, where documents (news articles) are composed of topics which in turn contain words.

Precisely, each document has a probability distribution over latent topics, with parameter α > 0,

8https://www.policyuncertainty.com/.
9https://www.philadelphiafed.org/surveys-and-data/real-time-data-research/ads.

10

and each topic is deﬁned by a probability distribution over words with parameter β > 0. α

controls the sparsity of topics in a document, while β controls the sparsity of words in a topic.

LDA treats a document as a mixture of topics and a topic as a mixture of words, such that

documents overlap each other rather than being separated into discrete groups.

Training the model essentially boils down to ﬁnding the optimal number of latent topics T

that best ﬁt the data. A topic model ﬁtted on a corpus of documents with a chosen T yields

two outputs: (i) the distribution of word frequencies for each topic, common across documents;

and (ii) the distribution of topics across documents. For each document, the topic distribution

is a vector of loadings that describe how intensively the topic is reﬂected in a given document,

such that if a document has a higher loading for a particular topic, it is more likely associated

with the topic.

We train the topic model by randomly selecting 70% of the news corpus as the training set

and the remainder as the test set. Next, we convert the training set into a document term matrix

whose rows are the news articles (documents) and columns the unique single words (unigrams)

and two-word combinations (bigrams) in the training set corpus. We then search for the number

of topics T that minimizes the test set perplexity score (maximizes the log-likelihood). We search

for a low-level T ∈ {4, 5, ..., 10}, as we are interested in broad topical themes, and ﬁnd that the

test set likelihood score increases with T , although for T > 9 the resulting additional topics are

arguably sub-topics of broader themes. Hence, we set T = 9 and manually assign the resulting

topics meaningful labels based on their word distributions.

Next, we classify each news article into a topic based on its maximum probability score across

the nine topics. Table 1 shows the topics and their most important terms. The “Economy”,

“Business”, “Security”, and two “Governance” topics—which are more likely to matter for

sovereign default risk—account for 70% of the text corpus.

In the next section, we restrict

attention to these topics to compute the NSRI.

3.2 Text Similarity

The second component of our framework is quantifying how much sovereign risk concern each

news article reﬂects. We use a lexical text similarity algorithm, namely cosine similarity (as in

11

Hanley and Hoberg, 2010; Engle et al., 2020), because of its simplicity, low computational cost

on large data sets, and the diﬃculty of mapping our problem to supervised learning.10 Cosine

similarity relies on the word combinations in documents to quantify the similarity between

documents. The algorithm quantiﬁes the distance between two documents’ vectors such that a

value close to 1 indicates a high degree of similarity between the documents and a value close to

0 a low similarity. Cosine similarity is calculated as the inner product of two documents’ vectors

divided by the product of the vectors’ euclidean lengths:

CS = cos(u, v) =

u · v
|u| × |v|

∈ [0, 1].

(1)

Let v in Eq. (1) be the vector of a representative sovereign default risk document whose language

usage reﬂects elevated sovereign default concerns, and u that of an arbitrary news article. Then,

for any article represented by u, CS will be close to 1 if the article signals elevated sovereign

risk by using default risk-related language in similar proportions as in v.

To specify v, we follow a similar approach as Engle et al. (2020) and Hassan et al. (2019) to

build a training library of documents that signal elevated sovereign default risk. In building this

library, we take special care to focus on texts that are primarily centered on sovereign credit

risk issues and default concerns. This is important because we are interested in constructing

a measure that reﬂects sovereign default risk dynamics. Hence, we collect 448 documents,

comprised of three textbooks on sovereign default,11 31 sovereign credit rating downgrade reports

by the rating agencies Moody’s, Standard & Poors and Fitch, 367 excerpts from the European

Investment Bank’s sovereign rating downgrade rationales and reports for low rated sovereigns,

22 country risk analysis reports by the Economist Intelligence Unit, and 28 debt sustainability

analysis reports by the World Bank and IMF as well as news articles on sovereign defaults and

10We experimented with supervised learning by training linear classiﬁers (penalized logistic regression and
support vector machine) on a binary transformation of CDS spreads (outcome variable) on a feature space
comprising unigrams and bigrams. However, we found that supervised learning could not eﬀectively separate
elevated sovereign risk concern periods reﬂected in CDS spreads from other periods. For example, the best
classiﬁer achieved an out-of-sample Area Under the Curve (AUC) score of 0.66. Reasons for this weak performance
include micro-structure noise in CDS spreads and news re-iterations from diﬀerent media outlets, which creates
misalignment in the dating of news events and the CDS market’s potential reactions.

11The books are (i) “Sovereign Debt: from Safety to Default” by Robert W. Kolb; (ii) “Sovereign Debt: A
Guide for Economists” by Ali Abbas, Alex Pienkowski, And Kenneth Rogoﬀ; (iii) “Why not Default? The Political
Economy of Sovereign Debt” by Jerome Roos.

12

debt problems. We preprocess the documents using steps 2 – 5 in Appendix A.1 and then

aggregate them into a representative sovereign default risk library d.

Next, to extract v from d, we ﬁrst convert our news corpus into a “term frequency–inverse

document frequency” (tf-idf ) normalized matrix X using the Bag of Words text vectoriza-

tion.12,13 The rows of X are the u’s in (1) and the columns are the unique single words (unigrams)

and two-word combinantions (bigrams) in our news corpus. We then transform d to v using

the text vectorizer and tf-idf weights ﬁtted on X, which ensures that the document-frequency

weights of d match that of our news corpus.

Finally, we score each news article using Eq. (1) and re-scale the resulting CS scores to have a

maximum value of 1 and a minimum of 0 in the sample. We then obtain the country-level News-

implied Sovereign Risk Index (NSRI) by aggregating CS over a time interval. The preponderance

of socioeconomic news served in real-time by several news feed providers suggests that the NSRI

aggregation interval can range anywhere from a few minutes to several days, depending on

the frequency of news availability. In this paper, we aggregate NSRI at a daily frequency by

averaging across all news that belongs to the sovereign risk-relevant topics:

NSRIj,t =

1
nr
j,t

nr
j,t
(cid:88)

i

CSi,j,t,

(2)

where CSi,j,t is the cosine similarity score of article i published on day t about country j, and nr
j,t

is the number of news articles in the sovereign risk-relevant topics published about country j on

day t. We consider news articles in the “Economy”, “Business”, “Governance”, and “Security”

topics relevant for sovereign risk and use only those in (2).

Note that although our framework and its implementation rely solely on English language

news and countries with news data from PressReader, the framework is applicable with little or

no modiﬁcation to alternative news data and non-English language news. The fact that our news

12Following standard NLP practice aimed at reducing noise and dimensionality, we omit terms (unigrams and

bigramns) that occur very rarely in the text corpus — terms that occur in less than 10,000 documents.

13For a given document i, tf-idf normalization gives low weights to terms that are commonplace in all documents
It is computed as tf-idf(x, i) =
and high weights to terms that appear frequently in that speciﬁc document.
tf ∗ log(N + 1)/(Nx + 1) + 1, where N is the number of documents in our news corpus, Nx is the number of
documents in which term x appears, and tf is the number of times x appears in document i.

13

data covers several media outlets from various countries allows our framework to not depend

on speciﬁc news outlets, enhancing generalization. Therefore, one can seamlessly apply our

framework to other news sources, besides PressReader, with broader coverage. For non-English

language news, one option is to translate the news to English using language translation APIs,

then feed the resulting English text through our framework. Given that language translation

algorithms are still developing, we anticipate that this option may result in potentially noisier

default signals which will get less noisy with improvement in translation algorithms. We leave

this interesting application for future research.

3.3 Framework Validation

We conduct a multi-pronged validation exercise to establish NSRI’s robustness and informa-

tiveness about sovereign default risk. Starting with the topic model, Table 1 shows the top-20

relevant unigrams and bigrams for the identiﬁed topics. We measure term relevance using the

approach of Sievert and Shirley (2014) based on the rescaled topic-speciﬁc term probabilities and

setting the scaling parameter λ = 0.5. Table 1 shows that the most relevant words for each topic

are distinct enough to permit assigning meaningful labels to the topics, which allows us to ﬁlter

out news unlikely to matter for sovereign risk. For instance, Topic 9, which we label “Economy”,

includes terms such as “market”, “bank”, “economy”, and “debt”, which are likely relevant for

sovereign risk. In contrast, Topic 8, which we label “Tourism”, includes terms such as “tourist”,

“food”, “ﬂight”, and “hotel”, which are unlikely to be informative about sovereign risk. There-

fore, by isolating news on the Environment, Health, Tourism and Miscellaneous topics, the topic

model allows us to eliminate noise that can swamp the information about sovereign default risk

potentially reﬂected in the news on the Security, Governance, Business, and Economy topics.

Next, we examine the most inﬂuential terms in our sovereign default risk library d used to

obtain article-level scores. Figure 2 depicts the unigrams and bigrams with the highest weights

(in vector v in Eq. (1)), indicating that words such as “sovereign”, “default”, “public debt”

“crisis”, “risk”, “ﬁscal”, “borrowing”, “creditor”, “currency”, “repayment”, “credit rating”,

and “downgrade”, which are likely to ﬁgure prominently in discussions about sovereign credit

risk, rank highly. We also observe other economic and ﬁnancial terms that are likely to be used

14

S/n

Topic 1
Environment

Topic 2
Security

Topic 3
Topic 5
Topic 4
Miscellaneous Governance I Health

Topic 6
Business

Topic 7
Topic 8
Governance II Tourism

Topic 9
Economy

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

oil
energy
climate
climate change
fuel
coal
mining
oil gas
emission
electricity
oil price
carbon
power
renewable
natural gas
solar
natural
petroleum
crude
gas

police
attack
kill
rebel
force
investigation
intelligence
report
security
arrest
government
terrorist
millitary
millitant
violence
army
civilian
crime
protest
soldier

% of News

4%

12%

woman
world
school
family
team
sports
game
cup
play
love
ﬁlm
book
friend
football
player
young
life
home
old
child

12%

right
law
party
political
people
court
public
election
vote
corruption
government
act
bill
freedom
state
rule
voter
legal
democracy
president

health
pandemic
virus
medical
coronavirus
vaccine
covid
hospital
disease
patient
outbreak
infection
lockdown
people
doctor
death
treatment
health care
worker
test

business
development
project
technology
service
company
sector
industry
cooperation
programme
opportunity
digital
international
local
partnership
innovation
system
economic
infrastructure
country

president
minister
leader
prime minister
war
peace
millitary
sanction
opposition
administration
state
meeting
deal
political
nation
conﬂict
diplomatic
international
negotiation
ally

water
food
ﬂight
tourist
passenger
tourism
airline
ship
airport
hotel
road
island
transport
travel
traﬃc
sea
park
vessel
boat
route

market
bank
growth
economy
rate
investor
tax
ﬁnancial
trade
economic
debt
price
stock
investment
income
currency
export
sale
gdp
business

15%

6%

11%

15%

7%

17%

Table 1: Most Important Terms for Topic Labeling. The table shows the most important terms for the
nine latent topics identiﬁed by our topic model. The columns indicate the topics and their assigned labels based
on the word distributions. The last row shows the fraction of articles in our news corpus that belong to each
topic. Each news article is assigned to the topic for which it has the highest probability score.

in context when discussing sovereign credit risk, including “interest”, “lender”, “economic”,

“GDP”, and “bank”. Intuitively, a news article that uses combinations of these terms in a high

proportion as our sovereign default risk library will have a high CS score.

To examine whether our news-level scores suﬃciently diﬀerentiate news articles signalling

elevated sovereign risk concerns from others, we randomly select three sets of news articles that

are publicly available on the internet. We narrowed the search as follows: the ﬁrst set comprises

news articles related sovereign default concerns; the second relates to news articles on socio-

economic problems; and the third comprises miscellaneous news unrelated to the former two.

Importantly, these articles were not part of our training library. Next, we score each news article

using Eq. (1) and assign it to a topic using our topic model. Furthermore, to evaluate whether

our algorithm simply picks up positive/negative sentiment in the news, we compute a sentiment

score for each article.14

Table 2 shows the result of this exercise. As the article titles in the table indicate, Panel

A contains results for the set of articles speciﬁcally discussing sovereign default and credit risk

issues. Panel B contains articles on socioeconomic problems which are not directly related to

sovereign credit risk, and Panel C contains miscellaneous news unrelated to sovereign credit risk.

14We compute sentiment using the Loughran and McDonald (2011) dictionary as the number of negative words

in a news article divided by the sum of positive and negative words in the article.

15

Figure 2: Most Important Terms in the Sovereign Default Risk Library. The ﬁgure shows the terms
with the highest tf-idf scores in our Sovereign Default Risk Library. Font size is proportional to term weight.

Indeed, our algorithm assigns higher CS scores (Column (2)) to the default risk-related articles

in Panel A compared to the rest (Panels B and C). Furthermore, as expected, the articles in

Panel B are on average scored higher than the miscellaneous articles in Panel C. Comparing the

sentiment scores (Column (3)) to the CS scores in (Column (2)), it is clear that CS is markedly

diﬀerent from sentiment, indicating that our algorithm does not simply capture negative news

sentiment. Finally, Column (3) shows that our topic model correctly classiﬁes news articles into

reasonable topics, providing strong support for our reliance on the model for reducing noise.

We now turn to the NSRI’s time-series dynamics to examine whether our algorithm picks up

important periods of elevated global sovereign default concerns. Figure 1 already showed that

this is the case for selected countries. Next, we aggregate the country-level NSRIs to the global

level by taking a weighted average of the daily values using countries’ stock market capitalization

(in dollars) as of the end of the last calendar year as weights.15 Figure 3 depicts the resulting

Global New-implied Sovereign Risk Index (GNSRI). GNSRI has rich time-series dynamics,

15We use market capitalization as weights for computing GNSRI to give more importance to countries with
larger capital markets whose ﬁnancial market and economic policy developments have stronger eﬀects on the
global economy. If a country’s market capitalization is missing for the last calendar year, we use the most recent
value before the last calendar year. If the market capitalization is missing throughout the sample period, we use
the cross-sectional median of market capitalization for each year.

16

News Article Title

Panel A: News signaling sovereign default concerns

(1)
CS

(2)
Sentiment

(3)
Topic

Zambia defaults, economically and politically
Zambia on brink of defaulting on foreign debt
Zambia’s Eurobond default - What we have learned
Zambia’s default fuels fears of African ’debt tsunami’ as Covid impact bites
Argentina Defaults on Sovereign Debt Amid Coronavirus Crisis
Argentina defaults yet again, but hopes to get oﬀ lightly
Already in default, Argentina hits an impasse with creditors over debt restructuring
’Painful’ downgrades will raise South Africa’s borrowing costs, minister says
What the latest rating downgrades mean for the average South African
Moody’s, Fitch further downgrade South Africa
World Bank says world leaders moving away from debt cancellation for Africa
In a surprise move, Fitch upgraded South Africa’s banks - here’s why

Panel B: News on socioeconomic problems

Myanmar: Military Coup Kills Fragile Democracy
Myanmar coup: No sign of end to brutal crackdown on all fronts
Nigeria’s Economy Faces Worst Recession in Four Decades, says New Report
Recession: Nigeria’s Economic Crisis Requires a Political Solution
Turkey’s Erdogan sacks central bank governor after rate hike
Venezuela hyperinﬂation hits 10 million percent. ’Shock therapy’ may be only chance
Venezuela crisis in 300 words
Venezuela crisis: How the political situation escalated
Chadian President Idriss Deby dies on frontline, rebels vow to keep ﬁghting
Nigeria’s inﬂation rises again, hit four-year high February

Panel C: Miscellaneous news

Minister orders intelligence-led operation against violence in South-East
Man who drove into News Cafe in Rosebank appears in court
Five arrested for murder of KZN farmer
Uber pledges to boost safety for SA drivers as accidents rise
UK imposes sanctions on Russians, Guptas in ﬁrst use of anti-corruption law
Naomi Osaka withdraws from French Open amid row over press conferences
Chelsea beat Man City to win Champions League

0.54
0.81
0.69
0.80
0.69
0.63
0.62
0.55
0.64
0.40
0.67
0.52

0.13
0.12
0.23
0.31
0.11
0.42
0.08
0.08
0.13
0.12

0.03
0.03
0.02
0.07
0.09
0.09
0.07

0.79
0.72
0.82
0.74
0.98
0.94
0.82
0.90
0.87
0.75
0.88
0.66

0.91
0.80
0.73
0.69
0.50
0.83
0.57
0.87
0.78
0.50

0.89
0.98
0.77
0.67
0.86
0.80
0.58

Governance I
Economy
Economy
Economy
Economy
Economy
Governance II
Economy
Economy
Economy
Business
Economy

Security
Security
Economy
Economy
Economy
Economy
Governance II
Governance I
Governance I
Economy

Security
Security
Security
Health
Security
Miscellaneous
Miscellaneous

Table 2: News-level Scores and Topics. The table shows the cosine similarity (CS) scores, sentiment scores
and topics for a randomly selected sample of news articles that were not part of our training library. Panel
A contains articles highlighting sovereign default concerns, Panel B contains articles highlighting socioeconomic
problems, and Panel C shows miscellaneous news unrelated to the economy or sovereign credit risk. “Sentiment”
captures the negativity of each news and is computed as the number of negative words divided by the sum of
negative and positive words using the Loughran and McDonald (2011) dictionary.

spiking during the 2009 ﬁnancial crisis that sowed the seeds of the European debt crisis. GNSRI

then attained its peak at the height of the European debt crisis (2011 – 2012). It spiked again

in 2020 at the height of the Covid-19 pandemic during which the global economy experienced

an unprecedented shutdown and governments launched massive ﬁscal packages, plunging many

countries into ﬁscal problems that created sovereign debt concerns and a few defaults. Overall,

the evolution of GNSRI provides strong evidence that our news-based measure of sovereign

default risk captures major developments in global sovereign credit risk.

Finally, Table 3 shows the descriptive statistics for NSRI by income level, indicating sub-

stantial cross-sectional heterogeneity. For instance, the mean (median) daily NSRI for the

17

Figure 3: Global News-implied Default Risk. The ﬁgure shows the weekly time series evolution of the
Global News-implied Risk Index (GNSRI). GNSRI is computed as the time t weighted average of the country-
speciﬁc NSRIs, with the country weights based on the preceding year’s dollar stock market capitalizations. The
time series is then re-scaled to range between 0 and 1. The plotted values are three-month moving averages.

Countries

Obs.

Mean

SD

Min

5%

10% 25% 50% 75% 90% 95% Max

High income
Upper middle income
Lower middle income
Low income

57
48
51
27

150,156
110,106
89,120
46,231

0.151
0.116
0.113
0.095

0.075
0.051
0.049
0.044

0.008
0.008
0.007
0.007

0.064
0.056
0.051
0.044

0.078
0.067
0.063
0.054

0.102
0.085
0.084
0.069

0.136
0.107
0.107
0.087

0.180
0.136
0.134
0.109

0.241
0.169
0.164
0.141

0.295
0.197
0.191
0.169

0.894
0.953
0.841
0.825

Table 3: NSRI Summary Statistics by Income Level. The table shows the summary statistics of the daily
NSRI by income level, following the World Bank’s country classiﬁcation. “Countries” indicates the number of
countries in each group for which we have news data. The sample period is January 2006 – February 2021.

high-income countries is 0.15 (0.14), which declines to around 0.11 (0.11) for the middle-income

countries and 0.10 (0.09) for the low-income countries. It is not surprising that NSRI, on average,

is higher for the high-income countries. In our sample period (2006 – 2021), the major sovereign

debt crises and the associated elevated default concerns mainly occurred among high-income

countries, such as Spain, Italy, Portugal, and Greece. In summary, the validation exercises in-

dicate that NSRI eﬀectively reﬂects sovereign default concerns by exploiting narratives about

sovereign credit risk in the news media.

4 Empirical Analysis

4.1 NSRI’s Informativeness about CDS Spreads and Rating Downgrades

We saw in the previous section that NSRI has exciting properties, exhibiting cross-sectional

and time-series variations that align with reasonable priors. We now turn to an analysis of

18

20082010201220142016201820200.450.500.550.600.650.700.750.800.850.90Peak of Euro debt crisisFinancial crisis & onset of Euro debt crisisCovid-19 & debt concerns3-month MA of weighted average NSRIthe relationships between NSRI and the two standard indicators of sovereign credit risk—CDS

spreads and credit ratings—to understand if and to what extent NSRI captures information

contained in these variables.

Informativeness about CDS Spreads. CDS spread data is available only for a limited

cross-section of countries with liquid CDS contracts. For these countries, we examine how

the percentage change of the weekly/monthly averaged CDS spreads relate to the percentage

change of the weekly/monthly averaged NSRI, using panel contemporaneous and predictive

regressions. We use percentage changes instead of levels to prevent cross-country diﬀerences

in the levels and volatilities of CDS spreads from inﬂuencing our results. Furthermore, we use

a weekly/monthly frequency because the news data used to compute NSRI are from various

sources with diﬀerent time zones. By aggregating to a lower frequency, we side-step potential

complications surrounding whether the news were published before or after the market trading

hours. We use the following regression speciﬁcation:

∆CDSj,t = β0 + β1∆NSRIj,t−τ + γX + ξj + λt + (cid:15)j,t,

(3)

where ∆CDSj,t is the percentage change of country j’s CDS spread in period t relative to t − 1,

∆NSRIj,t−τ is the percentage change of country j’s NSRI relative to period t − 1 or its past

three-month median. τ = 0 for contemporaneous regression and τ = 1 for one-period predic-

tive regression. X is a vector of control variables: one-period lag of the dependent variable,

percentage changes of global macro-ﬁnancial variables—the implied volatility index (VIX), US

economic policy uncertainty Index (EPU), US economic activity index (ADS), and the world

stock market return—that may drive international CDS spreads’ dynamics (e.g., Pan and Sin-

gleton, 2008; Longstaﬀ et al., 2011; Ang and Longstaﬀ, 2013). X also includes country-speciﬁc

controls, namely stock market return, volatility and news sentiment. λt is the year-month (year)

ﬁxed-eﬀect for the weekly (monthly) frequency panel to absorb slower-moving common trends

in CDS spreads. ξj is the country ﬁxed-eﬀect to absorb time-invariant characteristics that may

inﬂuence CDS spread dynamics. We normalize all continuous regressors to unit variance. The

sample includes only countries with liquid CDS contracts. We further restrict the analysis to

19

country-week observations with at least 60 news articles in a calendar month to reduce noise

and ensure suﬃcient information to compute the NSRI.16,17

Table 4 shows the regression results. Panel A shows results for the weekly frequency, while

Panel B shows results for the monthly frequency. Starting with percentage changes relative to

the previous period, we see that NSRI has a statistically signiﬁcant and economically sizeable

association with CDS spreads. Columns (1) and (2) of Table 4 show that a standard deviation

Dep. var.: CDS Spread % change

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

NSRI % change rel. to t − 1

NSRI % change rel. to 3-month median

Contemporaneous

Lagged

Contemporaneous

Lagged

Panel A: Weekly frequency
NSRI % change

Controls
R2 (%)
Obs.
Panel B: Monthly frequency
NSRI % change

Controls
R2 (%)
Obs.

0.399
(5.05)
NO
25.45
14,004

3.117
(7.11)
NO
10.81
3,015

0.258
(4.39)
YES
38.73
14,004

1.713
(9.59)
YES
46.33
3,015

-0.055
(-1.15)
NO
25.19
14,004

1.495
(3.53)
NO
8.66
3,015

-0.095
(-2.12)
YES
28.71
14,004

1.021
(3.47)
YES
18.99
3,015

0.391
(5.35)
NO
24.86
13,779

4.804
(9.08)
NO
14.67
3,073

0.213
(3.58)
YES
38.04
13,779

2.384
(8.62)
YES
47.64
3,073

-0.132
(-1.91)
NO
24.64
13,779

1.673
(3.87)
NO
8.97
3,073

-0.158
(-2.43)
YES
28.32
13,779

1.100
(3.27)
YES
19.27
3,073

Table 4: CDS Spread and News-implied Sovereign Risk Index. The table shows results for the panel
regression of percentage changes in countries’ CDS Spreads (in %) on percentage changes in NSRI at the weekly
(Panel A) and monthly (Panel B) frequencies. Percentage changes are based on average values over each frequency.
For the ﬁrst set of results (Columns (1) – (4)), the percentage change in NSRI is relative to the previous period.
For the second set of results (Columns (5) – (8)), the percentage change in NSRI is relative to the past three-
month median. The regressors are either contemporaneous or one-period lagged, as indicated in the column
headers. Control variables are the one-period lag of the dependent variable, the weekly average of country j’s
news sentiment, country j’s weekly stock market return and realized volatility, the weekly return of the MSCI world
stock market index, weekly percentage changes of the US economic policy uncertainty index, the US economic
activity index, and the implied volatility index. Continuous regressors are normalized to unit variance, and all
regressions include country and time ﬁxed eﬀects (year-month ﬁxed eﬀects in Panel A and year ﬁxed eﬀects in
Panel B). Shown in parentheses are t-statistics based on standard errors clustered by country. The sample period
is January 2006 – February 2021.

increase in the NSRI percentage change relative to the previous week (month) is associated with

an increase in CDS spreads by between 0.26% and 0.40% (1.71% and 3.12%) in the same period.

Furthermore, Columns (3) and (4) show that NSRI also predicts CDS spreads dynamics. At the

weekly frequency, the prediction is negative and small in magnitude, while the predictability is

16The ﬁnal sample includes the following 39 countries Austria, Brazil, Chile, China, Colombia, Costa Rica,
Croatia, Denmark, Egypt, France, Germany, Greece, Hungary, Indonesia, Israel, Italy, Japan, Malaysia, Mexico,
Netherlands, Norway, Panama, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Slovakia,
Slovenia, South Africa, Spain, Sweden, Thailand, Turkey, UK, USA, and Venezuela

17Requiring at least 60 news articles per month is equivalent to an average of two articles per day, which is a
reasonable requirement to avoid noise in the data. Our analysis is, however, robust to altering the threshold as
we show in the robustness Section 5.

20

positive, sizeable, and strongly signiﬁcant at the monthly frequency. For instance, a standard

deviation increase in the NSRI percentage change predicts an increase in CDS spreads by 1.02%

– 1.50% over the next month. This suggests that shocks to sovereign default risk concerns

reﬂected in the news trigger insurance sellers to demand a higher premium for insurance against

default, resulting in higher CDS spreads. At short windows (one week), the initial increase in

CDS premiums tends to subside, indicating a potential overreaction to news, before increasing

further as more news about default concerns emerge.

Columns (5) – (6) of Table 4 present further evidence that the association between NSRI

and CDS spreads is robust. In these latter speciﬁcations, we use more persistent changes in

NSRI by computing NSRI percentage change relative to the past three-month median. The

results are even stronger in magnitude and statistical signiﬁcance. For example, at the monthly

frequency, a standard deviation increase in the NSRI percentage change relative to its past

three-month median is associated with an increase in CDS spreads by between 2.38% and 4.80%

contemporaneously and predicts an increase in CDS spreads by 1.10% – 1.67% over the next

month. Overall, the analyses indicate that NSRI reﬂects information about sovereign default

risk captured by CDS spreads — the predominantly used proxy for sovereign default risk at

a daily frequency.18 The results, therefore, point towards the possibility of using NSRI as a

high-frequency proxy of default risk for countries without liquid CDS contracts.

Informativeness about Sovereign Rating Downgrades. To reinforce our claim that NSRI

can be used as a proxy of sovereign default risk, we test if NSRI is informative about future

sovereign credit rating downgrades. Like CDS spreads, sovereign credit ratings reﬂect the like-

lihood that a sovereign will default on its debt obligations in the future, such that sovereigns

with poor credit ratings are perceived to have higher default risk. However, unlike CDS spreads,

credit ratings are available for a much broader cross-section of countries and do not exhibit daily

time-series variation. Instead, rating agencies update credit ratings typically during pre-speciﬁed

intervals and signiﬁcant economic fundamentals changes that weaken a sovereign’s creditwor-

thiness. Hence, if NSRI is truly informative about sovereign default risk, it should aggregate

18We conduct additional robustness analysis, using only observations for which the country has at least 90 news
articles in the calendar month, to further reduce noise, and by dropping one country at a time from the panel
regression to ensure that an outlier country does not drive the results. The results presented in Tables A3, A4
and A5, and discussed in Section 5, strongly reinforce the main results.

21

information about default concerns leading up to a sovereign rating downgrade announcement

and therefore should predict rating downgrades. We test whether this is the case using historical

sovereign rating information for all available countries.

For each country j we construct a dummy variable Downgradej,t that equals 1 if any of rating

changes on day t is a downgrade and 0 otherwise. We consider ratings by the three main credit

rating agencies, Moody’s, Standard & Poor’s and Fitch, and deﬁne downgrade as any change

in credit ratings towards a lower credit quality, including changes in rating outlook. We then

estimate the following regression speciﬁcation:

Downgradej,t = β0 + β1NSRIj,t−τ + γX + ξj + (cid:15)j,t,

(4)

where j and t index country and time respectively, Downgradej,t is the dummy variable for

downgrade conditional on rating change on day t, and NSRIj,t−τ is country j’s average NSRI

over the past 30-day window ending t − τ , where τ = 2 in the baseline analysis. To ensure

that diﬀerences in the level of NSRI across countries do not drive the results, we normalize

the time series of each country’s daily NSRI to have a minimum value of 0 and a maximum

value of 1 before computing the averages. X is a vector of control variables computed over the

same window as NSRIj,t−τ : global macro-ﬁnancial variables as before (VIX, EPU, ADS, and

world stock market return) and country-speciﬁc controls, namely stock market return, volatility,

news sentiment, and CDS spread. ξj captures time-invariant country ﬁxed-eﬀect. We normalize

continuous regressors to unit variance and estimate the regression using ordinary least squares

(OLS), which allows us to include ﬁxed eﬀects without raising an incidental parameters issue.

We provide robustness results based on logistic regression in Section 5.

Table 5 shows the estimation results. Across diﬀerent speciﬁcations, NSRI signiﬁcantly

predicts sovereign rating downgrades. A standard deviation increase in average NSRI over the

30-day window ending t − 2 predicts an increase in the probability of a downgrade by 8 – 12

pp, which corresponds to a 15% – 22% increase in the likelihood of a downgrade relative to the

observed fraction of downgrades in the data. Importantly, this result holds after controlling for

a set of global and country-speciﬁc macro-ﬁnancial variables, including news sentiment and CDS

spreads.

22

NSRI

Sentiment

Ret

Vol

World Ret

EPU

ADS

VIX

CDS Spread

R2 (%)
Obs.

(1)

0.114
(6.51)

(2)

0.115
(6.63)
0.051
(3.12)

(3)

0.092
(4.31)
0.048
(1.80)
-0.017
(-1.45)
0.155
(9.46)

(4)

0.092
(4.31)
0.047
(1.77)
-0.023
(-1.23)
0.158
(8.95)
0.011
(0.48)

(5)

0.080
(3.78)
0.037
(1.38)
-0.025
(-1.44)
0.115
(5.35)
0.031
(1.48)
0.041
(2.08)
0.010
(0.57)
0.056
(1.97)

16.5
2,605

17.3
2,605

27.5
1,716

27.5
1,716

28.9
1,716

(6)

0.088
(3.76)
0.092
(3.60)
-0.044
(-2.62)
0.119
(3.98)
0.066
(4.08)
0.044
(1.54)
0.012
(0.65)
0.051
(1.20)
0.008
(0.46)
33.5
1,074

Table 5: Sovereign Rating Downgrades and News-implied Sovereign Risk Index. The table shows
results for the panel regression of an indicator variable of rating downgrade (Downgradej,t) on the NSRI averaged
over the past 30-day window ending t − 2 before rating change on day t. Downgradej,t equals equals 1 if a
country’s rating change on t is a downgrade and 0 otherwise. The other regressors are computed over the same
30-day window ending t − 2 as NSRI. Sentiment is the average of country j’s news sentiment. Ret is country j’s
cumulative stock market return. Vol is country j’s stock market volatility. World Ret is the cumulative return
of the MSCI World stock market index. EPU is the average US economic policy uncertainty index. ADS is the
average US economic activity index. VIX is the average implied volatility index. CDS Spread is the average of
country j’s CDS Spread. We normalize continuous regressors to unit variance, and all regressions include country
ﬁxed-eﬀects. Shown in parentheses are t-statistics based on standard errors clustered by country. The sample
period is January 2006 - February 2021.

Columns (1) – (5) of Table 5 show once again that NSRI does not simply reﬂect news

sentiment, given that we directly control for news sentiment, which turns out to be insigniﬁcant

in most speciﬁcations. Furthermore, Column (6) of Table 5 shows that conditional on NSRI, CDS

spreads do not provide additional signiﬁcant information about future sovereign downgrades.

Conversely, NSRI remains strongly signiﬁcant with a sizeable eﬀect size after controlling for

CDS spreads. This result indicates that NSRI captures incremental default risk information

not fully reﬂected in either CDS spreads or the other macro-ﬁnancial variables.19 A potential

explanation for this is that the news text on which the NSRI is based reﬂects agents’ default

19We conduct additional robustness analyses presented in Tables A6 and A7, and discussed in Section 5, by
averaging NSRI over the past 60-days ending t−2 before downgrade announcement on t and by estimating Eq. (4)
using logistic regression. The results reinforce the main ﬁndings.

23

concerns more timely before those concerns are fully incorporated in CDS spreads, ultimately

leading to downgrades.

To summarize, the analyses in this section and several robustness analyses discussed in Sec-

tion 5 provide strong evidence that NSRI is a reliable proxy for sovereign default risk. It captures

CDS dynamics across countries and is informative about future rating downgrades. Importantly,

NSRI is not redundant in the face of CDS spreads and other macro-ﬁnancial variables. Hence,

NSRI provides complementary default risk information for countries with liquid market-based

measures, such as CDS contracts, and can directly serve as a high-frequency default risk proxy

for those countries lacking liquid market-based default risk measures.

4.2 Sovereign Default Concerns and Equity Returns

We have shown that NSRI is a reliably proxy for sovereign default risk. In this section, we use

NSRI to examine the role of sovereign default concerns in equity markets around the world.

The theoretical literature has proposed diﬀerent channels through which sovereign default (e.g.,

Gennaioli et al., 2014; Cole and Kehoe, 1998) and heightened default risk (Jeanneret, 2017) inﬂu-

ence output, investments, and equity valuations. Jeanneret (2017) suggests a risk-based channel

where a rise in sovereign default risk translates into a threat of future economic slowdown, which

in turn impacts equity prices across countries. However, a diﬀerent channel supported by be-

havioural explanations (e.g., Dremen, 1983; De Bondt and Thaler, 1985; Gennaioli et al., 2015)

is investors’ overreaction to news of sovereign default concerns that may not be strongly asso-

ciated with economic fundamentals. Overreaction occurs, for example, when investors become

excessively pessimistic about a country’s economic fundamentals following a string of default

concern news, hence depressing equity prices. However, as more news emerges, which turn out

to be better than the previous forecasts now identiﬁed as too pessimistic, prices adjust leading

to a reversal of the initial decline.

Since NSRI is a news-based measure of sovereign risk covering a sizeable cross-section of

countries, it is well suited to determine whether sovereign default risk concerns aﬀect equity

returns through the risk-based or overreaction channel. If sovereign default risk aﬀects equity

markets by primarily reﬂecting a higher likelihood of future economic slowdown, we expect

24

its impact on equity valuations to be more persistent, lasting several months. Conversely, if

investors mainly overreact to news of sovereign default concerns, we expect a reversal of NSRI’s

inﬂuence on equity returns over a short time frame. We use the following regression speciﬁcation

to examine these hypotheses:

Retj,t = β0 + β1∆NSRIj,t−τ + γX + ξj + λt + (cid:15)j,t,

(5)

where Retj,t is country j’s stock market index return on week t and ∆NSRIj,t−τ is the percentage

change in the country’s NSRI in week t − τ relative to its median over the past three-month

period, τ = 0 for contemporaneous regression and τ = 1 for one-week predictive regression. X

is a vector of control variables that include news sentiment, world stock market return, and one-

period lag of the dependent variable. Given the lack of high-frequency macroeconomic variables

for most countries, we follow Edmans et al. (2021) and Gao et al. (2020) to use US variables to

capture economic conditions. Although US macro-ﬁnancial variables does not fully capture local

economic conditions, they likely reﬂect sizeable local macro-ﬁnancial dynamics, given the ﬁnding

of Brusa et al. (2020) that US macroeconomic policy has a stronger impact on foreign country

stock markets than local macroeconomic policy. Hence, we control for percentage changes in the

same US macro-ﬁnancial variables in Eq. (3), namely VIX, EPU, and ADS. ξj captures other

time-invariant country characteristics that may inﬂuence the equity market, and λt captures

year-month ﬁxed eﬀects to control for time-varying global drivers of the equity market. We

normalize continuous regressors to unit variance and cluster standard errors by country. We

restrict the analysis to country-week observations with at least 60 news articles in a calendar

month to reduce noise and ensure suﬃcient information to compute NSRI.20

We use weekly returns for the analysis because, as earlier discussed, the news data used to

compute NSRI are from various sources with diﬀerent time zones. Hence, by aggregating weekly,

we side-step potential complications surrounding whether the news was published before or after

market trading hours. Furthermore, in this section and the remainder of the paper, we use NSRI

growth relative to its three-month median for three reasons: ﬁrst, to control for country-level

diﬀerences in the average level of NSRI; second, to capture more persistent news shocks compared

20In the robustness analysis, Section 5, we show that this threshold does not materially inﬂuence our results.

25

to week-on-week changes; third, because results from Table 4 shows that, compared to week-on-

week percentage change, the NSRI percentage change relative to the past three-month median

is more informative about future default risk dynamics captured by CDS spreads.

Table 6 shows the results of estimating Eq. 5. Panel A shows results for the dollar-denominated

stock market index returns. Focusing on Column (2), we ﬁnd a negative contemporaneous asso-

ciation between stock market returns and sovereign default risk concerns reﬂected in NSRI. A

standard deviation increase in the NSRI percentage change is associated with a 7.1 bps (3.7%

annualized) decline in same-week market returns. Columns (3) and (4) report results for the

one-week lagged NSRI percentage change, indicating a reversal of the initial decline in equity

returns following an increase in sovereign default risk concerns. Precisely, Column (4) shows

that a standard deviation increase in the NSRI percentage change predicts an increase in mar-

ket returns by 6.4 bps (3.4% annualized) over the next week, indicating a price-reversal pattern

consistent with investors’ overreaction to news of sovereign default concerns.

For robustness, Panel B of Table 6 presents results for estimations using local-currency

denominated stock market index returns. The results are similar and comparable to those

in Panel A. Once more, we ﬁnd that stock market returns have a negative contemporaneous

association with an increase in sovereign default concerns, which reverses over the next week.

Further robustness analyses—dropping one country at a time from the panel regression to ensure

that an outlier country does not drive the results, using only observations with at least 90 news

articles in the calendar month to potentially reduce noise in NSRI, and stopping the sample in

2019 to exclude the Covid-19 period—presented in Tables A8 and A9 and discussed in Section 5

support the ﬁnding that equity investors tend to overreact to news of sovereign default concerns.

4.2.1 Fiscal Constraint, Sovereign Default Concerns and Equity Returns

While Table 6 indicates that, on average, equity markets overreact to an increase in sovereign

default concerns in the news media, there might be heterogeneity in the response to countries’

default concerns. For instance, consistent with theories on the economic costs of high debt

levels and default risk, the equity markets of countries with tight ﬁscal constraints, and hence

26

Panel A: Dollar index return

Panel B: Local-currency index return

Contemporaneous

Lagged

Contemporaneous

Lagged

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-9.316
(-4.25)

∆NSRI

Ret(t − 1)

Sentiment

World Ret

EPU

ADS

VIX

R2 (%)
Obs.

11.74
24,101

-7.120
(-3.50)
-14.100
(-2.99)
-11.684
(-3.00)
179.999
(14.00)
-5.446
(-4.19)
-8.164
(-4.76)
-14.838
(-3.27)
36.78
24,101

7.560
(2.75)

11.72
24,101

6.443
(2.39)
-26.247
(-4.16)
10.474
(2.93)
-18.791
(-3.13)
0.363
(0.23)
6.681
(4.27)
13.745
(4.26)
13.40
24,101

-6.111
(-3.14)

10.03
24,004

-3.978
(-2.31)
-13.159
(-2.80)
-8.677
(-2.35)
139.780
(13.07)
-2.468
(-1.86)
-1.797
(-1.32)
-24.143
(-5.97)
32.50
24,004

6.294
(2.70)

10.03
24,004

5.466
(2.37)
-21.231
(-3.77)
9.305
(2.77)
-16.724
(-3.35)
2.408
(2.08)
5.108
(3.59)
11.853
(4.66)
11.61
24,004

Table 6: Equity Returns and Sovereign Risk Concerns. The table shows results for the panel regression
of countries’ weekly stock market returns (Ret), in basis points, on the percentage change of the weekly-averaged
NSRI relative to its past three-month median. The regressors are either contemporaneous or one-period lagged,
as indicated in the column headers. Panel A shows results for US dollar stock market indices, and Panel B
shows results for local-currency stock market indices. Ret(t − 1) is the one period lag of country j’s weekly stock
market return. Sentiment is the weekly average of country j’s news sentiment. World Ret is the weekly return of
the MSCI World stock market index, EPU is the percentage change in the weekly-averaged US economic policy
uncertainty index. ADS is the percentage change in the weekly-averaged US economic activity index. VIX is the
percentage change in the weekly-averaged implied volatility index. We normalize continuous regressors to unit
variance and include and year-month ﬁxed eﬀects in all regressions. Shown in parentheses are t-statistics based
on standard errors clustered by country. The sample period is January 2006 – February 2021.

limited ability to accommodate adverse ﬁscal shocks, may be impacted more by elevated default

concerns. We explore this conjecture to further tease out the risk versus overreaction hypotheses.

To examine the heterogeneity in the equity markets’ response to news of default concerns,

we use a similar speciﬁcation as Eq. (5):

Retj,t = β0 + β1∆NSRIj,t−τ + β2∆NSRIj,t−τ × HighFiscalConstraintj,t

+ HighFiscalConstraintj,t + γX + ξj + λt + (cid:15)j,t,

(6)

where, HighFiscalConstraintj,t is a dummy variable that equals 1 if the measure of a country’s

ﬁscal constraint is higher than the sample median and 0 otherwise. The measurement of coun-

27

tries’ ﬁscal constraints follows a similar approach as Augustin et al. (2021) and is described in

Section 2. The rest of the variables are as described under Eq. (5).

In Eq. (6), we are interested in the estimates of β1 and β2, which are reported in Table 7. We

ﬁnd that the coeﬃcient of the interaction term is sizable and signiﬁcant, indicating heterogeneity

in the equity markets’ reaction to sovereign default concerns. For the contemporaneous regres-

sion, Columns (1) and (2) of Table 7 show that much of the equity markets’ response to sovereign

default concern news is driven by countries with high ﬁscal constraints for which elevated default

concerns are more likely to result in actual default, with potentially more economic costs.21 For

instance, the contemporaneous decline in equity market returns for a standard deviation increase

in the NSRI percentage change is 9.1 bps higher for the ﬁscally constrained countries. At the

same time, the reversal in equity returns over the next week is signiﬁcantly lower for the ﬁscally

constrained countries by roughly 10 bps. The results indicate that for countries experiencing

high ﬁscal constraints, the inﬂuence of sovereign default risk news on equity returns is more

persistent and more in line with a risk-based story. Conversely, for the rest of the countries,

investors mainly overreact to sovereign default concerns.

Contemporaneous

Lagged

∆NSRI

∆NSRI × High Fiscal Constr.

High Fiscal Constr.

Controls
R2 (%)
Obs.

(1)

-3.078
(-1.11)
-10.005
(-2.48)
8.221
(1.81)
NO
11.74
23,862

(2)

-1.651
(-0.68)
-9.098
(-2.44)
6.912
(1.55)
YES
36.73
23,862

(3)

12.023
(2.61)
-8.586
(-1.74)
9.242
(2.05)
NO
11.72
23,862

(4)

11.863
(2.61)
-9.958
(-2.04)
10.431
(2.13)
YES
13.41
23,862

Table 7: Equity Returns, Sovereign Risk Concerns and Fiscal Constraint. The table shows results for
the panel regression of countries’ weekly stock market returns (Ret), in basis points, on the percentage change of
the weekly-averaged NSRI relative to its past three-month median and its interaction with a dummy variable for
high ﬁscal constraint (High Fiscal Constr.). The regressors are either contemporaneous (Columns 1 and 2) or one-
period lagged (Columns and 3, 4). Ret(t-1) is the one-period lag of country j’s weekly stock market return. The
control variables included in Columns (2) and (4) are deﬁned under Table 6. We normalize continuous regressors
to unit variance and include country and year-month ﬁxed eﬀects in all regressions. Shown in parentheses are
t-statistics based on standard errors clustered by country. The sample period is January 2006 - February 2021.

21Romer and Romer (2018), Battaglini and Coate (2016) and Yared (2019), among others, note that a high
ﬁscal constraint can limit governments’ responses to future catastrophes, such as ﬁnancial crises, natural disasters,
or wars, and can result is default through debt repudiation or inﬂation.

28

4.2.2 Global versus Local Sovereign Default Concerns and Equity Returns

Having seen how stock markets react to country-speciﬁc default risk concerns measured by

NSRI, we now study the common component in NSRI and how equity markets react to global

versus country-speciﬁc sovereign risk concerns. The motivation for this analysis is the extensive

literature documenting a strong factor structure in countries’ default risk dynamics, reﬂected

in CDS spreads and its common drivers (e.g., Pan and Singleton, 2008; Longstaﬀ et al., 2011;

Augustin et al., 2014).

We start by examining whether, like CDS spreads, NSRI has a strong factor structure. Using

weekly averaged NSRI and a panel of 74 countries with suﬃcient news coverage to compute the

time-series of NSRI, we ﬁnd that the ﬁrst ﬁve principal components account for roughly 51%

of the variation in the countries’ NSRI.22 This indicates that NSRI has a strong common com-

ponent, corroborating the existing literature that observes a strong co-movement in countries’

sovereign default risk. A possible explanation for this co-movement proposed by Benzoni et al.

(2015) is as follows: a negative country-speciﬁc shock induces agents to revise their expectations

about the default risk of other countries, resulting in greater credit spread correlations across

countries compared to if spreads depended only on macroeconomic fundamentals. A similar

intuition suggests that a surge in one country’s default concern can prompt a higher discussion

of other countries’ default risks in the news media, resulting in the co-movement we observe in

NSRI. Alternatively, comovement in countries’ default concerns reﬂected in CDS spreads and

NSRI could be jointly driven by global factors.

To analyze equity market’s response to the average perception of default risk globally com-

pared to country-speciﬁc default risk perception, we compute Global News-implied Sovereign

Default Risk Index (GNSRI) as the cross-sectional value-weighted average of the weekly-averaged

NSRI following the description in Footnote 15. We then augment Eq. (5) with ∆GNSRIt com-

puted in the same way as ∆NSRIj,t, i.e., the weekly percentage change in GNSRI relative to

its three-month median. We exclude the US macroeconomic variables from the regression be-

22We use country-week observations with at least three news articles available to compute NSRI, which reduces
noise in the data. Furthermore, we start the sample from 2007 as several countries had little coverage from
PressReader in 2006. Finally, we drop countries with missing values for more than one-third of the sample period,
then replace missing values with the cross-sectional median to obtain a complete time series for each country.

29

cause, as shown in Pan and Singleton (2008), the common component in sovereign default risk is

strongly related to global macro-ﬁnancial variables such as VIX. Hence, including such variables

absorbs much of the information in GNSRI.23

Table 8 shows the estimation results. Focusing on Panel A, which shows results for the

dollar-denominated stock market index return, Columns (1) and (2) show that the magnitude

of the contemporaneous association between global sovereign default concerns and equity re-

turns is more than twice the association between country-speciﬁc NSRI and equity returns. In

particular, a standard deviation increase in the percentage change of the country-speciﬁc NSRI

is contemporaneously associated with a decline in the stock market returns by roughly 10 bps,

with t-statistic of 4.2. In comparison, for the global NSRI (GNSRI), the magnitude is 22 bps,

with a much larger t-statistic of 7.9. We observe a similar result for the predictive regression

(Columns (3) and (4)), where the regressors are one-week lagged. Here the initial declines in

market returns following the increases in the NSRI and GNSRI percentage changes reverse in

the following week, with the strength of the reversal again more pronounced by more than twice

for GNSRI compared to NSRI. Precisely, a standard deviation increase in the percentage change

of country-speciﬁc NSRI predicts an increase in market returns by 5.1 bps (with t-statistic of

1.80) over the next week compared to 16.7 bps (with t-statistic of 5.7) for the global NSRI.

For robustness, Panel B of Table 8 presents results using returns on local-currency denomi-

nated stock market indices. The estimates are quantitatively and qualitatively similar to those

in Panel A, indicating that the choice of the stock market indices does not drive the results.

Overall, the results once more support the view that equity markets around the world overreact

to sovereign default risk concerns reﬂected in the news media. However, compared to country-

speciﬁc concerns, the overreaction to global default concerns is even more pronounced. The

results, therefore, underline the importance of global (or push) factors for local asset prices, in

line with the existing literature (e.g., Colacito et al., 2018; Fraiberger et al., 2021).

23For example, we ﬁnd that only the levels of VIX, EPU, ADS and the world stock market index explain roughly

40% of the time-series variation in GNSRI.

30

Panel A: Dollar index return

Panel B: Local-currency index return

Contemporaneous

Lagged

Contemporaneous

Lagged

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-7.093
(-3.07)
-18.900
(-6.53)

∆NSRI

∆GNSRI

Ret(t − 1)

Sentiment

R2 (%)
Obs.

11.88
24,101

-9.908
(-4.23)
-21.554
(-7.93)
-41.845
(-6.22)
-18.085
(-3.94)
13.27
24,101

5.287
(1.86)
19.640
(6.45)

11.87
24,101

5.138
(1.80)
16.683
(5.71)
-39.172
(-5.96)
10.578
(2.85)
13.06
24,101

-3.770
(-1.90)
-19.914
(-7.41)

10.23
24,004

-5.765
(-2.89)
-21.741
(-8.24)
-33.777
(-5.36)
-13.709
(-3.25)
11.45
24,004

4.129
(1.72)
18.551
(6.21)

10.20
24,004

4.350
(1.80)
15.976
(5.66)
-31.676
(-5.15)
9.520
(2.70)
11.26
24,004

Table 8: Impact of Local Vs. Global Sovereign Risk Concerns on Equity Returns. The table shows
results for the panel regression of countries’ weekly stock market returns (Ret), in basis points, on the percentage
change of the weekly-averaged NSRI relative to its past three-month median and its global equivalent (GNSRI).
GNSRI is computed as the time t weighted average of the country-speciﬁc NSRIs, with country weights based on
the preceding year’s dollar stock market capitalizations. The regressors are either contemporaneous (Columns 1,
2, 5, and 6) or one-period lagged (Columns 3, 4, 7 and 8). Ret(t-1) is the one period lag of country j’s weekly
stock market return. Sentiment is weekly average of country j’s news sentiment. All right-hand-side variables are
normalized to unit variance, and all regressions include country and year-month ﬁxed eﬀects. t-statistics based
on standard errors clustered by country are in parentheses. Sample period is January 2006 - February 2021.

5 Additional Analysis and Robustness Tests

Equity Market Volatility and Sovereign Default Concerns. The theoretical model of

Jeanneret (2017) suggests that an increase in sovereign default risk translates into an increase

in equity market volatility. We examine if this prediction holds in our sample using our news-

based measure of sovereign default risk (NSRI). Using the regression speciﬁcation of Eq. (5)

and weekly stock market realized volatility as the left-hand-side variable, we ﬁnd that NSRI is

positively and signiﬁcantly associated with the same-week stock market volatility and predicts

an increase in volatility over the next week. Table A10 presents the results, indicating that a

standard deviation increase in the NSRI percentage change is associated with a contemporaneous

increase in the realized stock market volatility by 0.08 – 0.10 pp. Furthermore, it predicts an

increase in next week’s volatility by 0.02 – 0.05 pp. Hence, sovereign default concerns reﬂected

in NSRI is informative about both stock market returns and risk measured by realized volatility.

CDS Spreads and NSRI. We conduct additional tests to ensure that the observed infor-

mativeness of NSRI about CDS spread dynamics is robust.

In the main analysis, we used

country-week observations for which at least 60 news articles were available in a given calendar

31

month to compute NSRI, which reduces noise. We verify that this choice does not inﬂuence our

results by requiring a minimum of 90 news articles in a calendar month, i.e., an average of three

articles per day. Table A3 shows the results of estimating Eq. (3) with the sample that meet

the requirement. We ﬁnd that requiring more news articles does not inﬂuence our results. In

particular, the coeﬃcient estimates for the NSRI percentage change for the weekly contempora-

neous regressions, shown in Panel A of Table A3, range between 0.21 and 0.40, are signiﬁcant

and similar to those in the main analysis (Table 4). The same picture obtains for the one-week

predictive regressions. Similarly, for the monthly contemporaneous (Columns (5) and (6)) and

predictive (Columns (7) and (8)) regressions shown in Panel B of Table A3, the percentage

change of NSRI is signiﬁcantly and positively associated with CDS spreads percentage change

(returns). A standard deviation increase in the NSRI percentage change is associated with an

increase in CDS spread returns by 2.4% in the same month and predicts an increase of 1.1% in

the next month.

We further check whether some speciﬁc countries drive our results. We re-estimate Eq. (3)

dropping one country at a time from the panel regression. Table A4 collects the coeﬃcient esti-

mates for the NSRI percentage change for the weekly frequency panel regressions and Table A5

collects those for the monthly frequency. In both cases, we ﬁnd that dropping any of the coun-

tries for which we have CDS spreads data and suﬃcient news coverage from the analysis does

not materially aﬀect the association between NSRI and CDS spreads. For instance, Column (2)

of Table A4 shows that for the contemporaneous weekly regressions, the coeﬃcient of the NSRI

percentage change across all estimations is around 0.20 and signiﬁcant in all cases. For the one-

week predictive regressions, the coeﬃcients are also signiﬁcant in all cases, ranging between -0.14

and -0.19. These coeﬃcients are quantitatively and qualitatively similar to those in the main

analysis. We ﬁnd a similar picture for the monthly panel regressions. In all estimations, the

NSRI percentage change is positively associated with CDS spreads returns contemporaneously

and predicts an increase in CDS spreads returns over the next month.

Rating Downgrades and NSRI.

In the main analysis, we examined the informativeness

of NSRI about future sovereign rating downgrades by predicting future downgrades on day t

by NSRI averaged over the preceding 30 days ending t − 2. We check whether NSRI remains

32

informative when older information is used by computing the average over the preceding 60 days

ending t − 2 and then re-estimate Eq. (4) using OLS. Table A6 shows the estimation results,

indicating that averaging NSRI over a longer period does not wither away NSRI’s informativeness

about future sovereign rating downgrades.

In particular, we ﬁnd that a standard deviation

increase in the average NSRI over the 60 days ending t−2 predicts an increase in the probability of

a rating downgrade by between 9 – 12 pp. Again, we observe that CDS spread has no predictive

power about future rating downgrade after conditioning on NSRI. This result underlines the

incremental sovereign default information NSRI provides on top of CDS spread. Our main

analysis and the initial robustness test relies on OLS estimations. We show that the results hold

when we estimate Eq. (4) using conditional logistic regression. Table A7 collects the results for

NSRI averaged over the past 30-day and 60-day periods ending t − 2. Across board, we ﬁnd the

NSRI signiﬁcantly predicts sovereign rating downgrade even after controlling for CDS spread.

Equity Returns and NSRI. We conduct additional tests on the association between NSRI

and stock market returns to ensure that our results in the main analysis are robust. First, we

check that the observed equity market overreaction to news of sovereign default concerns is not

driven by our requirement of at least 60 news articles per country in a calendar month to reduce

noise in NSRI. We re-estimate Eq. (5) using country-week observations with at least 90 news

articles in a calendar month. Furthermore, we check that the wild equity market ﬂuctuations

during the Covid-19 pandemic do not drive our results by stopping our sample in December

2019.

Table A8 shows the result of the two tests. Panel A shows that requiring more news per

country in a calendar month to compute NSRI does not inﬂuence our main results. Panel B

yields the same conclusion: our results are robust to excluding the Covid-19 period. For example,

focusing on results for the dollar stock index returns (Columns (1) – (4) of Table A8), we ﬁnd

that, as in the main analysis, a standard deviation increase in the NSRI percentage change is

contemporaneously associated with around 9 bps decline in the weekly stock market return,

which signiﬁcantly reverses over the next week. This result conﬁrms our initial evidence that

equity investors overreact to news of sovereign default concerns. Turning to the local-currency

stock market index returns (Columns (5) – (8) of Table A8) we obtain a similar pattern: the

33

NSRI percentage change is signiﬁcantly and negatively associated with same-week stock market

returns and predicts an increase in stock market returns over the next week.

Next, we examine if our results are driven by speciﬁc outlier countries in the sample and ﬁnd

that this is not the case. Table A9 collects the coeﬃcient estimates for the NSRI percentage

change from re-estimating Eq. (5) using the dollar-denominated stock market index returns and

dropping one country at a time from the panel regression. We ﬁnd that for the contemporaneous

regressions (Columns (1) and (2) of Table A9), the coeﬃcient of the NSRI percentage change

across all estimations is statistically signiﬁcant and sizeable, ranging between -6 bps to -10 bps.

Similarly, for the predictive regressions (Columns (3) and (4) of Table A9), we ﬁnd a reversal of

the initial decline in returns following an increase in NSRI. Precisely, across all estimations, the

coeﬃcient of the lagged NSRI percentage change is positive, statistically signiﬁcant, and ranges

between 5 bps and 9 bps. These results provide convincing evidence that NSRI is informative

about equity market movements. In particular, equity investors tend to overreact to news of

sovereign default concerns, resulting in a decline in returns in the same week news broke and a

reversal over the following week.

6 Conclusion

This paper develops a novel framework for quantifying sovereign default risk in real-time using

machine learning and natural language processing techniques. Our framework relies on two

building blocks: topic modelling for dimensionality and noise reduction and text similarity

for quantifying default concerns in the news media. Applying our framework to a corpus of 10

million news articles, we obtain the News-implied Sovereign Risk Index (NSRI) for a broad cross-

section of countries. We conduct several validation exercises that showcase the intuitiveness and

robustness of our methodology.

We analyze the informativeness of NSRI about sovereign CDS spread dynamics and credit

rating downgrades. Our analyses provide convincing evidence that NSRI is strongly associated

with CDS spreads contemporaneously and predicts future CDS spread dynamics and rating

downgrades. Importantly, we ﬁnd that NSRI contains additional sovereign default risk infor-

34

mation not fully reﬂected in CDS spreads. For example, controlling for CDS spreads, NSRI

signiﬁcantly predicts rating downgrades. Conversely, CDS spreads have no incremental infor-

mation on top of NSRI about future rating downgrades. These results imply that NSRI can

complement CDS spreads in tracking sovereign default risk at a high frequency for countries

with liquid CDS contracts. More importantly, for countries without liquid CDS contracts and

other daily market-based proxies of default risk, NSRI can serve as a reliable stand-alone high-

frequency proxy for sovereign default risk.

We apply NSRI to examine how equity markets respond to news of sovereign default con-

cerns and ﬁnd that NSRI is informative about equity returns. For example, a standard deviation

increase in the NSRI percentage change is contemporaneously associated with a signiﬁcant de-

cline in the weekly stock market returns by 7.1 bps (3.7% annualized) and predicts an increase

in returns by 6.4 bps (3.4% annualized) over the next week. The reversal in returns suggests

that equity investors tend to overreact to news of sovereign default concerns. Furthermore, we

ﬁnd that compared to country-speciﬁc default concerns, global default concerns have a much

stronger inﬂuence on stock market returns, underlining the relevance of global “push” factors

for local asset prices.

There are plausible extensions of our framework which may yield even stronger sovereign

default risk signals from news text: (i) supervised machine learning methods that use news text

and an alternative high-frequency indicator of default risk for model training; (ii) use of language

translation algorithms to avoid reliance on only news in the English language, thereby providing

even more signals for many countries; (iii) use of alternative news API providers to achieve more

local news coverage for certain countries. We leave these extensions for future research.

In sum, textual data from the news media contains valuable information about countries’

default risks useful for monitoring default concerns in real-time. The NSRI can serve as an input

to credit risk early warning systems, credit portfolio management, and can be applied to test

theories on the economic costs and ﬁnancial market impacts of sovereign default risk.

35

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38

A Appendix

A.1 Text Preprocessing

The goal of text preprocessing is to reduce noise and dimensionality in text data to improve

signal extraction. We use the following steps:

1. Screen retrieved news articles for country relevance: We use regular expression to check

that each news article retrieved for a country suﬃciently relates to that country. To do

so, we search for the number of times variants of a country’s name is mentioned in a news

article and its ﬁrst ten sentences. Then, we keep only articles that mention the country’s

name at least once in the ﬁrst ten sentences or at least twice in the entire article.

2. Remove entity names: To further reduce noise and improve signal extraction, we use

Named Entity Recognition (NER) algorithms to identify and remove country names, names

of persons, geographical locations, political entities, and places from the news corpus.

3. Remove stopwords: We ﬁlter out common English language stopwords using standard

stopword lists found in NLP libraries.

4. Remove non-English words: We ﬁlter out non-English words using the English language

word list from the NLTK python library. We retain common economics and ﬁnancial

acronyms such as “gdp”, “imf”, “cds”, “fx”, “cpi”.

5. Normalize words: Finally, we lemmatize words in our news corpus to reduce diﬀerent

forms of a word to their root word while avoiding ambiguities that could arise from word

stemming.

39

A.2 Additional Tables and Figures

Name

# Articles

Country

# Articles

Country

# Articles

Country

# Articles

Afghanistan
Albania
Algeria
Angola
Argentina
Armenia
Aruba
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bermuda
Bhutan
Bolivia
Botswana
Brazil
Brunei Darussalam
Bulgaria
Burkina Faso
Burundi
Cabo Verde
Cambodia
Cameroon
Canada
Central African Rep.
Chad
Chile
China
Colombia
Comoros
Costa Rica
Cote d’Ivoire
Croatia
Cuba
Cyprus
Czechia
Denmark
Djibouti
Dominica

162,041
4,238
9,686
5,087
30,441
3,007
999
150,633
6,426
9,618
7,542
15,634
17,901
3,269
8,560
32,098
2,184
1,372
4,629
4,540
8,514
5,538
73,248
224
9,968
2,738
2,564
105
26,669
6,091
527,203
4,850
18,404
20,689
577,578
25,791
370
6,181
189
8,153
54,944
18,135
37
8,336
2,538
1,101

Dominican Rep.
DR Congo
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Eswatini
Ethiopia
Fiji
Finland
France
Gabon
Gambia
Georgia
Germany
Ghana
Greece
Grenada
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong
Hungary
Iceland
India
Indonesia
Iran (Islamic Rep.)
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Kazakhstan
Kenya
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia

7,786
39,595
10,950
78,413
13,153
1,214
4,187
6,720
69
15,588
20,589
5,261
85,164
1,373
1,717
114,482
95,886
7,397
95,438
1,693
15,897
14,443
500
36,033
17,134
14,812
109,717
19,038
17,630
1,148,893
81,286
183,588
232,631
246,719
201,846
124,875
42,792
85,857
9,568
36,767
3,829
6,465
6,371
53,035
4,169
7,937

Libya
Liechtenstein
Lithuania
Luxembourg
Macao
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mauritius
Mexico
Micronesia
Moldova
Monaco
Mongolia
Montenegro
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
New Caledonia
New Zealand
Nicaragua
Niger
Nigeria
North Korea
Norway
Oman
Palestine
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Rep. of Congo
Romania
Russia
Rwanda

66,892
1,733
7,169
11,910
10,515
2,324
3,919
195,572
2,794
9,082
1,841
2,260
226,711
486
2,306
6,397
12,483
2,867
10,920
4,234
30,557
5,357
9,783
11,402
1,025
114,858
9,274
6,602
250,843
70,456
10,373
41,298
15,513
15,760
2,761
3,103
14,994
416,841
39,310
31,436
22,524
41,747
6,980
13,181
258,694
9,192

Samoa
Sao Tome & Principe
Saudi Arabia
Senegal
Serbia
Seychelles
Sierra Leone
Singapore
Slovakia
Slovenia
Somalia
South Africa
South Korea
South Sudan
Spain
Sri Lanka
Sudan
Suriname
Sweden
Switzerland
Syria
Taiwan, Province of China
Tajikistan
Tanzania
Thailand
Timor-Leste
Togo
Tonga
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Tuvalu
UAE
Uganda
Ukraine
United Kingdom
Uruguay
USA
Uzbekistan
Venezuela
Vietnam
Western Sahara
Yemen
Zambia
Zimbabwe

2,444
159
90,194
5,405
11,591
1,756
8,107
117,999
6,969
5,637
25,961
382,812
98,657
12,129
117,483
71,847
27,914
680
10,724
14,056
173,367
32,715
2,434
8,920
101,392
145
1,775
1,190
2,335
18,235
113,541
1,599
463
87,892
11,199
77,985
347,792
6,156
347,792
4,522
43,693
63,682
1,215
43,505
12,769
67,750

Table A1: News API Country Coverage. The table shows the countries, dependent territories, and special
areas of geographical interest for which we have news articles from the PressReader News API, as well as the
number of news articles we obtained. To save space we use abbreviated names in some cases instead of the oﬃcial
names.

40

Name

MSCI Index (USD) MSCI Index (local)
MSARGT$
Argentina
MSAUST$
Australia
MSASTR$
Austria
MSBAHR$
Bahrain
MSBNGS$
Bangladesh
MSBELG$
Belgium
MSBTSW$
Botswana
MSBRAZ$
Brazil
MSBLGN$
Bulgaria
MSCNDA$
Canada
MSCHIL$
Chile
MSCHIN$
China
MSCOLM$
Colombia
IFFMCI$
Cote d’Ivoire
MSCROA$
Croatia
MSCZCH$
Czechia
MSDNMK$
Denmark
IFFMEC$
Ecuador
MSEGYT$
Egypt
MSESTN$
Estonia
MSFIND$
Finland
MSFRNC$
France
IFGEGR$
Georgia
MSGERM$
Germany
MSGHAN$
Ghana
MSGREE$
Greece
MSHGKG$
Hong Kong
MSHUNG$
Hungary
ICEXALL
Iceland
MSINDI$
India
MSINDF$
Indonesia
MSEIRE$
Ireland
MSISRL$
Israel
MSITAL$
Italy
MSJMCA$
Jamaica
MSJPAN$
Japan
MSJORD$
Jordan
Kazakhstan MSKZKT$
MSKNYA$
Kenya
MSKUWA$
Kuwait
LAOLSXI
Laos
RIGSEIN
Latvia
MSLITH$
Lithuania
SBBLUX$
Luxembourg
MSMALF$
Malaysia

MSARGTL
MSAUSTL
MSASTRL
MSBAHRL
MSBNGSL
MSBELGL
MSBTSWL
MSBRAZL
MSBLGNL
MSCNDAL
MSCHILL
MSCHINL
MSCOLML
IFFMCIL
MSCROAL
MSCZCHL
MSDNMKL
IFFMECL
MSEGYTL
MSESTNL
MSFINDL
MSFRNCL
IFGEGR$
MSGERML
MSGHANL
MSGREEL
MSHGKGL
MSHUNGL
ICEXALL
MSINDIL
MSINDFL
MSEIREL
MSISRLL
MSITALL
MSJMCAL
MSJPANL
MSJORDL
MSKZKTL
MSKNYAL
MSKUWAL
LAOLSXI
RIGSEIN
MSLITHL
SBBLUXL
MSMALFL

Country

MSCI Index (USD) MSCI Index (local)
MSIMLT$
Malta
MSMAUR$
Mauritius
MSMEXF$
Mexico
MSMORC$
Morocco
IFFMNA$
Namibia
MSNETH$
Netherlands
MSNZEA$
New Zealand
MSNGRA$
Nigeria
MSNWAY$
Norway
MSOMAN$
Oman
MSPAKI$
Pakistan
MSIPLE$
Palestine
IFFPNM$
Panama
MSPERU$
Peru
MSPHLF$
Philippines
MSPLND$
Poland
MSPORD$
Portugal
MSQATA$
Qatar
MSROMN$
Romania
MSRUSS$
Russia
MSSAUD$
Saudi Arabia
MSSERB$
Serbia
MSSING$
Singapore
MSSLVN$
Slovenia
MSSARF$
South Africa
MSKORE$
South Korea
MSSPAN$
Spain
MSSRIL$
Sri Lanka
MSSWDN$
Sweden
MSSWIT$
Switzerland
Taiwan, Province of China MSTAIW$
MSTHAF$
Thailand
MSTRTG$
Trinidad and Tobago
MSTNSA$
Tunisia
MSTURK$
Turkey
ALSIUGI
Uganda
MSUKRN$
Ukraine
MSUAEI$
UAE
MSUTDK$
United Kingdom
MSUSAM$
USA
MSVIET$
Vietnam
IFFMZB$
Zambia
MSZMBW$
Zimbabwe
MSWRLD$
World

MSIMLTL
MSMAURL
MSMEXFL
MSMORCL
IFFMNAL
MSNETHL
MSNZEAL
MSNGRAL
MSNWAYL
MSOMANL
MSPAKIL
MSIPLEL
IFFMPAL
MSPERU$
MSPHLFL
MSPLNDL
MSPORDL
MSQATAL
MSROMNL
MSRUSSL
MSSAUDL
MSSERBL
MSSINGL
MSSLVNL
MSSARFL
MSKOREL
MSSPANL
MSSRILL
MSSWDNL
MSSWITL
MSTAIWL
MSTHAFL
MSTRTGL
MSTNSAL
MSTURKL
ALSIUGI
MSUKRNL
MSUAEIL
MSUTDKL
MSUSAML
MSVIETL
IFFMZBL
MSZMBWL
MSWRLD$

Table A2: Stock Market Indices per Country. The table shows the MSCI dollar-denominated and local-
currency denominated stock market indices used per country, dependent territory, or special areas of geographical
interest. To save space we use abbreviated names in some cases instead of the oﬃcial names.

41

Panel A: Weekly frequency

Panel B: Monthly frequency

Contemporaneous

Lagged

Contemporaneous

Lagged

NSRI

CDS Spread (t − 1)

(1)

0.397
(4.54)

Sentiment

Ret

Vol

World Ret

EPU

ADS

VIX

R2 (%)
Obs.

23.39
12,673

(2)

(3)

(4)

0.210
(3.20)
1.226
(11.33)
0.402
(4.20)
-1.689
(-10.74)
0.727
(4.57)
-0.082
(-0.56)
-0.010
(-0.16)
0.111
(2.79)
1.517
(12.17)
36.60
12,673

-0.124
(-1.71)

23.15
12,673

-0.165
(-2.55)
0.699
(3.79)
-0.050
(-0.74)
-0.832
(-5.43)
-0.102
(-0.99)
-0.903
(-4.36)
-0.092
(-1.21)
0.086
(2.39)
-0.843
(-12.53)
27.04
12,673

(5)

4.647
(9.27)

13.90
2,838

(6)

(7)

(8)

2.286
(7.52)
0.999
(2.54)
1.228
(1.80)
-2.442
(-7.38)
5.507
(6.06)
-4.708
(-10.05)
2.297
(7.85)
0.140
(0.90)
0.858
(1.83)
47.32
2,838

1.667
(3.71)

8.43
2,838

1.134
(3.13)
-0.854
(-1.52)
0.744
(1.55)
-6.119
(-10.99)
-1.569
(-2.46)
2.186
(4.56)
0.262
(0.54)
2.267
(6.77)
2.508
(4.82)
19.09
2,838

Table A3: Panel Regression of CDS Spreads on NSRI Filtering for Min 90 News Articles. The
table shows results for the panel regression of percentage changes in countries’ CDS Spreads (in %) on percentage
changes in NSRI relative to its three-month median. We drop observations with less than 90 news articles for a
country in a calendar month. The analysis uses only observations for which Time is either at the weekly (Panel A)
and monthly (Panel B) frequency. The regressors are either contemporaneous or one-period lagged, as indicated
in the column headers. CDS Spread (t − 1) is the one-period lag of country j’s CDS Spread (the dependent
variable). Sentiment is the weekly average of country j’s news sentiment. Ret and Vol are country j’s weekly
stock market return and realized volatility, respectively. World Ret is the weekly return on the MSCI World stock
market index. EPU is the weekly percentage change of the US economic policy uncertainty index. ADS is the
weekly percentage change of US economic activity index, and VIX is the weekly percentage change of the implied
volatility index. We normalize continuous regressors to unit variance and include country and time ﬁxed eﬀects
(year-month ﬁxed eﬀects in Panel A and year ﬁxed eﬀects in Panel B) in all regressions. Shown in parentheses are
t-statistics based on standard errors clustered by country. The sample period is January 2006 – February 2021.

42

Contemporaneous

Lagged

Excluded country Without controls With controls

Without controls With controls

(1)

(2)

(3)

(4)

0.419
Brazil
0.392
Chile
0.404
China
0.394
Colombia
0.391
Croatia
0.391
Denmark
0.412
Egypt
0.377
France
0.365
Germany
0.390
Greece
0.391
Hungary
0.390
Indonesia
0.394
Israel
0.360
Italy
0.403
Japan
0.398
Malaysia
0.406
Mexico
0.394
Netherlands
0.390
Panama
0.393
Peru
0.397
Philippines
0.393
Poland
0.384
Portugal
0.393
Romania
0.359
Russia
0.406
Saudi Arabia
0.391
Slovakia
0.393
South Africa
0.378
Spain
0.391
Sweden
0.422
Thailand
0.360
Turkey
United Kingdom 0.386
0.387
USA
0.391
Venezuela

(6.10)
(5.41)
(5.50)
(5.29)
(5.39)
(5.39)
(5.69)
(5.02)
(5.09)
(4.99)
(5.36)
(5.26)
(5.33)
(4.52)
(5.35)
(5.37)
(5.44)
(5.43)
(5.38)
(5.41)
(5.37)
(5.23)
(5.23)
(5.40)
(5.28)
(5.60)
(5.39)
(5.26)
(4.81)
(5.39)
(5.96)
(4.85)
(5.19)
(5.24)
(5.39)

0.243
0.219
0.230
0.217
0.216
0.216
0.219
0.212
0.198
0.200
0.215
0.216
0.214
0.200
0.224
0.224
0.228
0.218
0.215
0.216
0.221
0.218
0.209
0.216
0.186
0.224
0.216
0.216
0.213
0.216
0.239
0.190
0.205
0.227
0.216

(4.46)
(3.68)
(3.86)
(3.57)
(3.62)
(3.62)
(3.52)
(3.41)
(3.29)
(3.18)
(3.59)
(3.54)
(3.48)
(3.00)
(3.56)
(3.69)
(3.76)
(3.66)
(3.61)
(3.61)
(3.64)
(3.53)
(3.46)
(3.62)
(3.41)
(3.71)
(3.62)
(3.52)
(3.29)
(3.62)
(4.13)
(3.13)
(3.38)
(3.71)
(3.62)

-0.103
-0.122
-0.143
-0.121
-0.126
-0.126
-0.125
-0.120
-0.105
-0.148
-0.128
-0.125
-0.136
-0.161
-0.114
-0.125
-0.103
-0.123
-0.127
-0.126
-0.135
-0.125
-0.134
-0.127
-0.124
-0.114
-0.126
-0.130
-0.158
-0.126
-0.114
-0.137
-0.130
-0.123
-0.127

(-1.52)
(-1.77)
(-2.08)
(-1.72)
(-1.82)
(-1.82)
(-1.71)
(-1.63)
(-1.50)
(-2.03)
(-1.84)
(-1.76)
(-1.92)
(-2.31)
(-1.66)
(-1.76)
(-1.57)
(-1.78)
(-1.83)
(-1.81)
(-1.91)
(-1.71)
(-1.90)
(-1.82)
(-1.75)
(-1.66)
(-1.82)
(-1.84)
(-2.24)
(-1.82)
(-1.66)
(-1.82)
(-1.80)
(-1.76)
(-1.83)

-0.135
-0.148
-0.172
-0.141
-0.152
-0.152
-0.149
-0.143
-0.132
-0.171
-0.153
-0.150
-0.161
-0.185
-0.139
-0.149
-0.134
-0.150
-0.153
-0.152
-0.160
-0.158
-0.157
-0.153
-0.154
-0.139
-0.152
-0.155
-0.181
-0.152
-0.145
-0.161
-0.157
-0.142
-0.153

(-2.08)
(-2.28)
(-2.67)
(-2.19)
(-2.33)
(-2.33)
(-2.20)
(-2.08)
(-1.99)
(-2.44)
(-2.34)
(-2.26)
(-2.41)
(-2.78)
(-2.15)
(-2.25)
(-2.14)
(-2.29)
(-2.34)
(-2.32)
(-2.41)
(-2.31)
(-2.35)
(-2.34)
(-2.31)
(-2.18)
(-2.33)
(-2.33)
(-2.70)
(-2.33)
(-2.23)
(-2.29)
(-2.30)
(-2.20)
(-2.34)

Table A4: Panel Regression of CDS Spreads on NSRI Dropping One Country at a Time I. The
table shows results for the panel regression of weekly percentage changes in countries’ CDS Spreads (in %) on
the weekly percentage changes in NSRI relative to its three-month median (∆NSRIj,t) based on Eq. (3). We
drop one country at a time from the regression and report only the resulting coeﬃcient of ∆NSRIj,t from each
regression. The column “Excluded country” indicates the country dropped from the analysis. The regressors are
either contemporaneous or one-period lagged, as indicated in the column headers. Columns (2) and (4) include
the control variables described under Table 4. We normalize continuous regressors to unit variance and include
country and year-month ﬁxed eﬀects in all regressions. Shown in parentheses are t-statistics based on standard
errors clustered by country. The sample period is January 2006 – February 2021 and includes observations with
at least 60 news articles in a calendar month to compute NSRI.

43

Contemporaneous

Lagged

Excluded country Without controls With controls

Without controls With controls

(1)

(2)

(3)

(4)

4.702
Brazil
4.773
Chile
4.758
China
4.805
Colombia
4.772
Croatia
4.942
Egypt
4.544
France
4.551
Germany
4.910
Greece
4.780
Hungary
4.709
Indonesia
4.887
Israel
4.669
Italy
4.736
Japan
4.795
Malaysia
4.782
Mexico
4.772
Netherlands
4.773
Panama
4.774
Peru
4.840
Philippines
4.869
Poland
4.772
Portugal
4.772
Romania
4.687
Russia
4.672
Saudi Arabia
4.832
South Africa
4.602
Spain
4.901
Thailand
Turkey
4.948
United Kingdom 4.837
4.819
USA
4.777
Venezuela

(8.32)
(8.97)
(8.81)
(8.94)
(8.97)
(9.10)
(9.08)
(9.01)
(9.08)
(8.97)
(8.91)
(8.93)
(8.20)
(8.70)
(8.83)
(8.77)
(8.97)
(8.97)
(8.97)
(8.87)
(8.96)
(8.90)
(8.97)
(8.66)
(8.85)
(8.79)
(7.91)
(8.88)
(9.42)
(8.83)
(8.70)
(8.98)

2.363
2.392
2.488
2.408
2.392
2.448
2.285
2.246
2.258
2.392
2.414
2.408
2.384
2.437
2.436
2.424
2.391
2.390
2.393
2.468
2.444
2.346
2.393
2.372
2.341
2.432
2.255
2.519
2.341
2.448
2.439
2.392

(8.00)
(8.65)
(8.76)
(8.65)
(8.66)
(8.51)
(8.39)
(8.82)
(8.18)
(8.64)
(8.57)
(8.49)
(8.66)
(8.39)
(8.66)
(8.62)
(8.65)
(8.63)
(8.65)
(8.83)
(8.64)
(8.49)
(8.66)
(8.40)
(8.44)
(8.49)
(7.42)
(9.31)
(7.94)
(8.59)
(8.39)
(8.62)

1.782
1.657
1.583
1.726
1.656
1.710
1.478
1.528
1.675
1.657
1.664
1.699
1.363
1.665
1.706
1.741
1.656
1.656
1.652
1.655
1.641
1.654
1.657
1.739
1.627
1.688
1.484
1.713
1.875
1.644
1.699
1.658

(4.18)
(3.86)
(3.62)
(4.07)
(3.86)
(3.88)
(3.42)
(3.43)
(3.64)
(3.86)
(3.79)
(3.88)
(3.04)
(3.77)
(3.88)
(3.99)
(3.86)
(3.86)
(3.86)
(3.77)
(3.70)
(3.79)
(3.86)
(3.96)
(3.70)
(3.82)
(3.20)
(3.90)
(5.15)
(3.72)
(3.85)
(3.85)

1.194
1.090
1.045
1.120
1.089
1.098
0.977
0.998
1.022
1.088
1.115
1.103
0.892
1.119
1.141
1.137
1.090
1.089
1.086
1.036
1.051
1.087
1.090
1.165
1.066
1.124
0.969
1.130
1.269
1.123
1.181
1.091

(3.65)
(3.28)
(3.10)
(3.35)
(3.28)
(3.22)
(2.82)
(2.84)
(2.87)
(3.28)
(3.29)
(3.26)
(2.49)
(3.25)
(3.39)
(3.30)
(3.28)
(3.28)
(3.28)
(3.11)
(3.09)
(3.22)
(3.29)
(3.46)
(3.15)
(3.30)
(2.69)
(3.30)
(4.65)
(3.28)
(3.60)
(3.28)

Table A5: Panel Regression of CDS Spreads on NSRI Dropping One Country at a Time II. The
table shows results for the panel regression of monthly percentage changes in countries’ CDS Spreads (in %) on
the monthly percentage changes in NSRI relative to its three-month median (∆NSRIj,t) based on Eq. (3). We
drop one country at a time from the regression and report only the resulting coeﬃcient of ∆NSRIj,t from each
regression. The column “Excluded country” indicates the country dropped from the analysis. The regressors are
either contemporaneous or one-period lagged, as indicated in the column headers. Columns (2) and (4) include
the control variables described under Table 4. We normalize continuous regressors to unit variance and include
country and year ﬁxed eﬀects in all regressions. Shown in parentheses are t-statistics based on standard errors
clustered by country. The sample period is January 2006 – February 2021 and includes observations with at least
60 news articles in a calendar month to compute NSRI.

44

NSRI

Sentiment

Ret

Vol

World Ret

EPU

ADS

VIX

CDS Spread

R2 (%)
Obs.

(1)

0.119
(6.11)

(2)

0.120
(6.17)
0.054
(2.94)

(3)

0.095
(4.39)
0.049
(1.58)
-0.090
(-6.28)
0.115
(6.26)

(4)

0.095
(4.38)
0.048
(1.57)
-0.092
(-4.03)
0.116
(6.00)
0.002
(0.07)

(5)

0.089
(4.37)
0.038
(1.27)
-0.092
(-4.22)
0.088
(4.28)
0.019
(0.76)
0.038
(1.94)
-0.009
(-0.54)
0.025
(0.87)

2606
0.165

2606
0.172

1721
0.294

1721
0.294

1721
0.303

(6)

0.089
(3.83)
0.095
(2.96)
-0.122
(-4.87)
0.098
(3.30)
0.067
(3.14)
0.044
(1.66)
-0.006
(-0.31)
0.022
(0.52)
-0.001
(-0.08)
1076
0.351

Table A6: Panel Regression of Sovereign Rating Downgrade on NSRI. The table shows results for the
panel OLS regression of an indicator variable of rating downgrade (Downgradej,t) on the NSRI averaged over the
past 60-day window ending t − 2 before rating change on day t. Downgradej,t equals equals 1 if a country’s rating
change on t is a downgrade and 0 otherwise. The other regressors are computed over the same 30-day window
ending t − 2 as NSRI. Sentiment is the average of country j’s news sentiment. Ret is country j’s cumulative stock
market return. Vol is country j’s stock market volatility. World Ret is the cumulative return of the MSCI World
stock market index. EPU is the average US economic policy uncertainty index. ADS is the average US economic
activity index. VIX is the average implied volatility index. CDS Spread is the average of country j’s CDS Spread.
We normalize continuous regressors to unit variance, and all regressions include country ﬁxed-eﬀects. Shown in
parentheses are t-statistics based on standard errors clustered by country. The sample period is January 2006 -
February 2021.

45

(1)

(2)

(3)

(4)

(5)

(6)

Panel A: NSRI Averaged over the past 30-days ending t − 2
NSRI

0.105
(11.03)

0.086
(10.76)
0.036
(3.92)

Sentiment

Ret

Vol

World Ret

EPU

ADS

VIX

CDS Spread

Sentiment

Ret

Vol

World Ret

EPU

ADS

VIX

CDS Spread

Obs.
Panel B: NSRI Averaged over the past 60-days ending t − 2
NSRI

1696

2582

2582

0.106
(15.90)

0.080
(8.95)
0.032
(4.09)

0.036
(4.52)
0.015
(1.74)
-0.013
(-1.94)
0.083
(4.24)

0.038
(4.30)
0.016
(1.68)
-0.039
(-2.57)
0.063
(3.10)

0.036
(4.45)
0.015
(1.73)
-0.014
(-1.58)
0.083
(4.23)
0.002
(0.15)

0.024
(3.32)
0.009
(1.21)
-0.012
(-1.79)
0.054
(2.89)
0.007
(0.95)
0.017
(2.03)
-0.009
(-0.65)
0.014
(1.48)

1696

1696

0.039
(4.30)
0.016
(1.67)
-0.038
(-2.32)
0.063
(3.09)
-0.003
(-0.30)

0.031
(3.41)
0.012
(1.27)
-0.035
(-2.25)
0.049
(2.23)
0.003
(0.29)
0.024
(1.95)
-0.021
(-1.36)
-0.003
(-0.21)

0.012
(2.64)
0.014
(2.38)
-0.009
(-1.40)
0.035
(1.84)
0.010
(1.91)
0.013
(1.81)
-0.018
(-1.56)
0.004
(0.50)
-0.001
(-0.21)
1059

0.012
(2.12)
0.016
(2.28)
-0.023
(-1.56)
0.034
(1.54)
0.009
(1.45)
0.016
(1.52)
-0.014
(-1.40)
-0.004
(-0.32)
-0.001
(-0.93)
1061

Obs.

2582

2582

1701

1701

1701

Table A7: Panel Logistic Regression of Sovereign Rating Downgrade on NSRI. The table shows results
for the panel conditional logistic regression of an indicator variable of rating downgrade (Downgradej,t) on the
NSRI averaged over the past 30-day (Panel A) and 60-day (Panel B) windows ending t − 2 before rating change
on day t. Downgradej,t equals equals 1 if a country’s rating change on t is a downgrade and 0 otherwise. The
other regressors are computed over the same 30-day window ending t − 2 as NSRI. Sentiment is the average of
country j’s news sentiment. Ret is country j’s cumulative stock market return. Vol is country j’s stock market
volatility. World Ret is the cumulative return of the MSCI World stock market index. EPU is the average US
economic policy uncertainty index. ADS is the average US economic activity index. VIX is the average implied
volatility index. CDS Spread is the average of country j’s CDS Spread. We normalize continuous regressors to
unit variance, and all regressions include country ﬁxed-eﬀects. Shown in parentheses are t-statistics based on
standard errors clustered by country. The sample period is January 2006 - February 2021.

46

Ret(t − 1)

Sentiment

World Ret

EPU

ADS

VIX

Dollar return

Local-currency return

Contemporaneous

Lagged

Contemporaneous

Lagged

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Panel A: Excluding months with less than 90 news articles per country
NSRI

9.607
(4.54)

-6.862
(-3.27)

-9.935
(-4.07)

-8.608
(-3.50)
-15.411
(-2.97)
-14.508
(-3.61)
179.083
(14.36)
-4.861
(-3.67)
-8.667
(-4.52)
-13.945
(-3.10)
37.00
21,767

-8.789
(-3.44)
-13.044
(-2.73)
-13.351
(-3.22)
166.353
(13.44)
-3.785
(-2.99)
-11.496
(-5.53)
-11.042
(-2.35)
34.63
19,296

8.382
(4.00)
-26.066
(-3.84)
10.245
(2.69)
-22.097
(-3.44)
-0.698
(-0.40)
7.256
(4.13)
12.276
(3.52)
13.77
21,767

6.354
(2.93)
-26.746
(-4.08)
12.496
(3.33)
-1.920
(-0.31)
-7.397
(-3.55)
6.650
(3.49)
20.601
(5.71)
12.92
19,296

11.96
21,767

7.205
(3.38)

11.52
19,296

-5.339
(-2.56)
-13.954
(-2.71)
-10.418
(-2.74)
137.359
(13.06)
-1.649
(-1.27)
-1.950
(-1.33)
-24.024
(-5.91)
32.38
21,680

-5.320
(-2.38)
-12.460
(-2.55)
-10.127
(-2.56)
126.385
(11.69)
-0.375
(-0.33)
-4.455
(-2.86)
-21.293
(-4.97)
29.68
19,257

7.876
(4.25)

10.21
21,680

6.057
(3.48)

9.33
19,257

6.938
(3.68)
-20.353
(-3.37)
9.507
(2.66)
-19.386
(-3.60)
1.865
(1.46)
5.576
(3.74)
11.111
(4.36)
11.88
21,680

5.472
(2.98)
-21.505
(-3.61)
10.758
(3.15)
-3.905
(-0.73)
-3.752
(-2.54)
5.562
(3.40)
16.518
(6.33)
10.64
19,257

10.19
21,680

-7.219
(-3.26)

9.35
19,257

R2 (%)
11.97
21,767
Obs.
Panel B: Excluding Covid-19 period
-10.503
NSRI
(-4.18)

Ret(t − 1)

Sentiment

World Ret

EPU

ADS

VIX

R2 (%)
Obs.

11.57
19,296

Table A8: Panel Regression of Stock Market Returns on NSRI: Filter for Article Count and Covid-
19. The table shows results for the panel regression of countries’ weekly stock market returns (Ret), in basis
points, on the percentage change of the weekly-averaged NSRI relative to its past three-month median. Panel A
shows results for analysis excluding observations with less than 90 news articles in a calendar month to compute
NSRI. Panel B shows results for analysis with data up to December 2019, hence excluding the Covid-19 period.
The regressors are either contemporaneous or one-period lagged, as indicated in the column headers. As shown
in the column headers, stock market returns are based on either the dollar or local-currency denominated stock
market indices. Ret(t − 1) is the one period lag of country j’s weekly stock market return. Sentiment is the
weekly average of country j’s news sentiment. World Ret is the weekly return of the MSCI World stock market
index, EPU is the percentage change in the weekly-averaged US economic policy uncertainty index. ADS is
the percentage change in the weekly-averaged US economic activity index. VIX is the percentage change in the
weekly-averaged implied volatility index. We normalize continuous regressors to unit variance and include and
year-month ﬁxed eﬀects in all regressions. Shown in parentheses are t-statistics based on standard errors clustered
by country. The sample period is January 2006 – February 2021.

47

Excluded country

Without control With control

Without control With control

Contemporaneous

Lagged

(1)

(2)

(3)

(4)

Argentina
Australia
Bahrain
Bangladesh
Belgium
Botswana
Brazil
Bulgaria
Canada
Chile
China
Colombia
Egypt
France
Georgia
Germany
Greece
Hong Kong
Hungary
Iceland
India
Indonesia
Ireland
Israel
Italy
Jamaica
Japan
Kenya
Malaysia
Mexico
New Zealand
Nigeria
Oman
Palestine
Philippines
Poland
Portugal
Qatar
Romania
Russia
Saudi Arabia
Singapore
South Africa
South Korea
Spain
Sri Lanka
Taiwan, Province of China
Thailand
Tunisia
Turkey
UAE
Ukraine
United Kingdom
USA
Vietnam
Zambia
Zimbabwe

-8.643
-9.128
-9.226
-9.445
-9.321
-9.343
-9.574
-9.310
-9.274
-9.312
-9.475
-9.014
-9.810
-9.255
-8.762
-9.451
-10.233
-9.392
-9.283
-9.379
-9.409
-9.214
-9.764
-9.298
-8.617
-9.522
-9.267
-9.234
-9.660
-9.641
-9.022
-9.367
-9.409
-9.316
-9.532
-9.308
-9.161
-9.591
-9.302
-9.485
-9.664
-9.386
-8.845
-8.703
-9.080
-9.193
-9.373
-9.723
-8.997
-8.679
-9.495
-9.423
-9.587
-9.004
-9.658
-8.824
-9.328

(-4.06)
(-4.11)
(-4.21)
(-4.30)
(-4.25)
(-4.26)
(-4.29)
(-4.25)
(-4.14)
(-4.25)
(-4.24)
(-4.13)
(-4.50)
(-4.10)
(-4.05)
(-4.20)
(-5.11)
(-4.18)
(-4.25)
(-4.27)
(-4.19)
(-4.14)
(-4.44)
(-4.15)
(-3.81)
(-4.32)
(-4.10)
(-4.18)
(-4.36)
(-4.29)
(-4.07)
(-4.21)
(-4.23)
(-4.25)
(-4.28)
(-4.18)
(-4.17)
(-4.34)
(-4.25)
(-4.23)
(-4.41)
(-4.18)
(-4.05)
(-3.97)
(-3.98)
(-4.12)
(-4.22)
(-4.38)
(-4.14)
(-3.91)
(-4.29)
(-4.24)
(-4.33)
(-4.07)
(-4.35)
(-4.05)
(-4.22)

-6.570
-7.156
-6.851
-7.226
-7.113
-7.110
-7.762
-7.118
-7.258
-7.119
-7.240
-6.894
-7.424
-7.375
-6.933
-7.438
-7.646
-7.109
-7.092
-7.190
-6.959
-6.795
-7.544
-6.924
-6.652
-7.065
-6.944
-7.084
-7.408
-7.337
-7.129
-7.108
-6.870
-7.122
-7.259
-7.001
-6.998
-7.606
-7.110
-7.056
-7.402
-7.304
-6.841
-7.029
-6.734
-6.872
-7.244
-7.437
-6.816
-6.332
-7.043
-7.254
-7.486
-7.208
-7.500
-6.406
-7.121

(-3.29)
(-3.45)
(-3.38)
(-3.54)
(-3.49)
(-3.49)
(-3.94)
(-3.50)
(-3.50)
(-3.50)
(-3.49)
(-3.40)
(-3.60)
(-3.56)
(-3.34)
(-3.60)
(-3.82)
(-3.41)
(-3.49)
(-3.52)
(-3.35)
(-3.33)
(-3.69)
(-3.37)
(-3.14)
(-3.43)
(-3.33)
(-3.45)
(-3.58)
(-3.51)
(-3.41)
(-3.43)
(-3.35)
(-3.50)
(-3.49)
(-3.39)
(-3.42)
(-3.77)
(-3.49)
(-3.39)
(-3.60)
(-3.51)
(-3.34)
(-3.37)
(-3.20)
(-3.32)
(-3.52)
(-3.59)
(-3.38)
(-3.18)
(-3.41)
(-3.51)
(-3.66)
(-3.46)
(-3.64)
(-3.29)
(-3.45)

8.061
7.416
7.623
7.392
7.514
7.567
7.535
7.547
7.436
7.547
7.906
7.816
7.647
7.304
7.789
7.234
7.704
7.007
7.542
7.581
7.991
7.874
7.588
7.830
8.068
7.360
7.592
7.463
7.572
7.032
7.794
7.798
7.499
7.557
7.735
7.371
7.770
7.532
7.546
6.995
7.190
7.432
6.701
6.922
8.211
7.488
8.044
7.432
7.475
6.891
6.956
7.555
7.802
7.436
9.099
7.687
7.569

(2.96)
(2.65)
(2.76)
(2.68)
(2.73)
(2.74)
(2.64)
(2.74)
(2.65)
(2.74)
(2.82)
(2.84)
(2.69)
(2.58)
(2.79)
(2.57)
(2.70)
(2.55)
(2.74)
(2.75)
(2.85)
(2.82)
(2.69)
(2.79)
(2.85)
(2.66)
(2.69)
(2.69)
(2.70)
(2.54)
(2.75)
(2.80)
(2.69)
(2.74)
(2.77)
(2.65)
(2.81)
(2.69)
(2.74)
(2.53)
(2.60)
(2.65)
(2.52)
(2.50)
(2.95)
(2.68)
(2.92)
(2.63)
(2.71)
(2.47)
(2.56)
(2.70)
(2.79)
(2.65)
(3.84)
(2.77)
(2.74)

7.066
6.338
6.487
6.255
6.402
6.436
6.306
6.432
6.307
6.431
6.758
6.714
6.384
6.225
6.643
6.158
6.438
5.932
6.431
6.468
6.874
6.816
6.472
6.647
7.086
6.199
6.433
6.308
6.432
5.966
6.774
6.475
6.356
6.439
6.586
6.317
6.666
6.477
6.432
5.959
5.992
6.303
5.651
5.911
7.193
6.287
6.843
6.359
6.403
5.833
5.775
6.385
6.700
6.435
7.900
6.646
6.378

(2.66)
(2.31)
(2.40)
(2.32)
(2.37)
(2.38)
(2.25)
(2.38)
(2.30)
(2.38)
(2.46)
(2.49)
(2.30)
(2.24)
(2.43)
(2.23)
(2.29)
(2.20)
(2.39)
(2.40)
(2.51)
(2.50)
(2.34)
(2.42)
(2.57)
(2.29)
(2.33)
(2.33)
(2.35)
(2.19)
(2.45)
(2.37)
(2.32)
(2.38)
(2.41)
(2.32)
(2.46)
(2.35)
(2.38)
(2.19)
(2.23)
(2.30)
(2.15)
(2.17)
(2.65)
(2.30)
(2.52)
(2.30)
(2.36)
(2.13)
(2.19)
(2.33)
(2.44)
(2.33)
(3.34)
(2.44)
(2.35)

Table A9: Panel Regression of Stock Market Returns on NSRI Dropping one Country at a Time.
The table shows results for the panel regression of countries’ weekly stock market returns (Ret), in basis points, on
the percentage change of the weekly-averaged NSRI relative to its past three-month median (∆NSRIj,t) based on
Eq. (5). We drop one country at a time from the regression and report only the resulting coeﬃcient of ∆NSRIj,t
from each regression. The column “Excluded country” indicates the country dropped from the analysis. The
regressors are either contemporaneous or one-period lagged, as shown in the column headers. Columns (2) and
(4) include the control variables described under Table 6. We normalize continuous regressors to unit variance
and include country and year-month ﬁxed eﬀects in all regressions. Shown in parentheses are t-statistics based
on standard errors clustered by country. The sample period is January 2006 – February 2021 and includes
observations with at least 60 news articles in a calendar month to compute NSRI.

48

Contemporaneous

Lagged

NSRI

Vol(t − 1)

Sentiment

World Ret

EPU

ADS

VIX

R2 (%)
Obs.

(1)

0.1045
(6.83)

46.74
24,101

(2)

0.0882
(7.06)
0.6694
(15.91)
0.1171
(4.23)
0.1476
(9.03)
0.0426
(4.53)
0.0054
(0.70)
0.2338
(12.73)
53.19
24,101

(3)

0.0536
(4.92)

46.57
24,101

(4)

0.0177
(2.05)
0.6490
(14.73)
0.0573
(2.62)
-0.1540
(-10.85)
0.0166
(1.67)
0.0113
(1.49)
0.0338
(2.30)
52.78
24,101

Table A10: Panel Regression of Weekly Stock Market Volatility on NSRI. The table shows results for
the panel regression of countries’ weekly stock market volatility (Vol), in percent, on the percentage change of the
weekly-averaged NSRI relative to its past three-month median. Volatility is based on the US dollar denominated
stock market indices. The regressors are either contemporaneous or one-period lagged, as indicated in the column
headers. Vol(t − 1) is the one period lag of country j’s weekly stock market volatility. Sentiment is the weekly
average of country j’s news sentiment. World Ret is the weekly return of the MSCI World stock market index, EPU
is the percentage change in the weekly-averaged US economic policy uncertainty index. ADS is the percentage
change in the weekly-averaged US economic activity index. VIX is the percentage change in the weekly-averaged
implied volatility index. We normalize continuous regressors to unit variance and include and year-month ﬁxed
eﬀects in all regressions. Shown in parentheses are t-statistics based on standard errors clustered by country.
The sample period is January 2006 – February 2021 and includes observations with at least 60 news articles in a
calendar month to compute NSRI.

49

ECONOMICS – WORKING PAPERS 2022/06

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News-implied sovereign default risk

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