Media sentiment on monetary policy: determinants and
relevance for inﬂation expectations
Matthieu Picault1, Julien Pinter∗2, and Thomas Renault3
1Univ. Orléans, LEO
2University of Minho
3Université Paris 1 Panthéon-Sorbonne
accepted at Journal of International Money and Finance
We construct a new indicator to capture media sentiment about the European Central
Bank monetary policy and its relevant environment by analyzing 25,000 articles from ﬁve
major international newspapers. Using named entity recognition and part-of-speech tagging,
we propose a methodology to dissociate the dissemination of oﬃcial communications of the
central bank from the media comments. The resulting (daily) index correlates with some
(monthly) standard measures of economic sentiment but reveals idiosyncratic information on
monetary policy. Analyzing the determinants of our index, we ﬁnd that both press conference
and inter-meeting communications of the President signiﬁcantly aﬀect media sentiment. We
then show that, controlling for a large range of factors, daily changes in media sentiment have
predictive power for ﬁnancial market inﬂation expectations.
Keywords: central bank communication, European Central Bank, textual analysis, inﬂation
expectations, media sentiment.
JEL Classiﬁcation : E43, E52, G12
∗Corresponding author: email@example.com.
The authors are particularly grateful to the participants of the IMF Research Department internal seminar, the
participants of the GDRE and AFSE conferences, and the participants of internal seminars at Université Paris 1
Panthéon-Sorbonne and Poitiers University. We are also particularily grateful to Roman Horvath, Pavel Gertler,
Paul Hubert, Bernd Hayo and Guntehr Capelle-Blancard for their useful remarks, and to Pavel Gertler for kindly
sharing his data with us. We also thank Paul Tetlock, Diego Garcia, and Hamza Benani for sharing their thoughts
at diﬀerent stages of this paper.
CRediT author statement : Picault Matthieu: conceptualization, software, formal analysis, investigation (data
collection, coding), writing - review and editing, visualization (presentations), project administration; Pinter Julien:
conceptualization, formal analysis, investigation (data collection, econometrics), writing - original draft, writing
- review and editing, visualization (presentations), project administration; Thomas Renault: conceptualization,
software, formal analysis, writing - review and editing.
The media coverage of central bank actions is of central importance for monetary policy ef-
fectiveness (Berger et al., 2011). As ﬁnancial market participants rely heavily on media reports
to get information on central banks (Hayo and Neuenkirch, 2015), media coverage can facilitate
the transmission of the central bank policy while simultaneously enhancing the perception of its
actions. However, media are not just a mere relay of the information released by central banks.
Through their analysis of the central bank actions and communications, the media could also
inﬂuence the perception of the central bank policies and their relevant economic environment by
market participants and, consequently, have an impact on the economic outcome. While such a
media channel is acknowledged in research, such as Hayo and Neuenkirch (2015) or Blinder et al.
(2008), its existence has not been, to our knowledge, empirically documented.
In this paper, we address the question of whether central banks do aﬀect media sentiment
through their actions and regular communications and whether media sentiment does in turn mat-
ter for ﬁnancial markets’ inﬂation expectations. We thus adopt a framework consistent with the
"more realistic view" of the eﬀect of central bank communication of Hayo and Neuenkirch (2015),
where it is noted that media coverage is a potential key channel between central bank communi-
cation and ﬁnancial market participants’ perception (see Figure 1). Doing so, we depart from a
large strand of research focusing solely on the direct link between central banks’ communications
and ﬁnancial variables.1
Media sentiment refers to the net degree of positivity (optimism) or negativity (pessimism) of
the media content, in line with the connected literature (Kearney and Liu, 2014, Tetlock, 2007). We
quantify it using advanced text-mining methods on a corpus of about 25,000 newspapers articles
related to the European Central Bank (ECB henceforth), published between 2006 and 2017. When
doing so, we do not consider each sentence of the media content. We use an innovative approach
to disentangle the media dissemination of the central bank’s communications from the media
comments on the monetary policy and its environment. In fact, newspaper articles following
a central bank communication (press conference, interviews, speeches, etc.) often contain several
direct quotes of the oﬃcial communication. If those quotes are considered to capture the sentiment
of a given article, media sentiment will not only relate to the opinions of the journalists and experts
but will also reﬂect the tone of the oﬃcial communications. To avoid this, we use text-mining
1Important contributions include, among others, Jansen and De Haan (2005), Ehrmann and Fratzscher (2007a),
Ehrmann and Fratzscher (2007b), Jansen and de Haan (2007), Rosa and Verga (2008), Schmeling and Wagner
(2015), Gertler and Horvath (2018), Hubert and Labondance (2020), Beaupain and Girard (2020), and Tillmann
(2020). See Blinder et al. (2008) for an inﬂuential survey on the topic.
Figure 1: "Standard view" versus "more realistic view" of the transmission of the central bank
actions and communications, adapted from Hayo and Neuenkirch (2015)
Central bank actions
and communications Economic outcome
More realistic view
Central bank actions
and communications Economic outcome
Note: This Figure presents the "standard view" in which central bank actions and communications are assumed
to directly aﬀect the economic outcome, and the "more realistic view" (adapted from Hayo and Neuenkirch
(2015)) in which the perception by ﬁnancial markets is presented as a potential key element between the central
bank action / communication and the economic outcome.
methods to remove all sentences and quotes where the subject of the sentence is one of the ECB
Governing Council members or the ECB.
Once non-relevant sentences are ﬁltered out, we use standard lexicon approaches (Loughran
and McDonald, 2011) to capture the sentiment of each article related to the ECB and construct a
daily media sentiment indicator. The resulting index intends to capture media sentiment on the
ECB monetary policy and its relevant environment. Empirical investigations suggest that it does
so relatively well. The monthly average of our index indeed correlates with some monthly standard
measures of economic sentiment, but it also reveals idiosyncratic information on monetary policy.
When, in a second stage, we investigate the determinants of our index, we ﬁnd that monetary policy
decisions, press conferences, and inter-meeting communications of the Presidents all signiﬁcantly
aﬀect our media sentiment index, even when controlling for other factors. Consistent with some
central bankers’ comments, we ﬁnd a straightforward linear eﬀect: hawkish communications on
monetary policy decrease media sentiment, while dovish communications increase media sentiment.
Communications related to the economic outlook are also found to matter in some cases, but to
a lower extent than communications related to monetary policy inclination. The same applies for
communications of other Governing Council members related to monetary policy inclination. In a
last stage, we analyze the relevance of our index for the inﬂation expectations of ﬁnancial markets.
We focus on the 10-years and the 5-years-to-5-years inﬂation swap, of well known relevance for
policy makers (Draghi, 2014).2We ﬁnd that our media sentiment index has predictive power for
the next-day change in the 5-year 5-year and 10-year inﬂation swap, with more positive sentiment
increasing inﬂation expectations. This holds true even after controlling for a large set of macroeco-
nomic surprises, central bank decisions, the textual content of press conferences, as well as central
bank inter-meeting communications, where the latter are taken from a rich external hand-coded
dataset. The eﬀect we ﬁnd is of low magnitude, suggesting a negligible economic relevance.
The paper ﬁrst contributes to the literature on monetary policy and the media by providing
new evidence showing the impact of various types of communication from central bankers on media
sentiment. In particular we complement the ﬁndings of Berger et al. (2011) and Böhm et al. (2012),
who analyzed the determinants of media favorableness toward, respectively, the monetary policy
decisions of the ECB and the Czech Central Bank. Although our focus is on media sentiment
rather than favorableness, we ﬁnd, as in Berger et al. (2011), that only the surprising component
of an interest rate decision matters for media. Tobback et al. (2017) build an index of perceived
hawkisness / dovishness in the media for the days following the ECB press conference and ﬁnd that
the tone of the ECB communication (hawkish versus dovish) is reﬂected in the media perception
of the ECB actions. We diﬀer from Tobback et al. (2017) and from the two previously mentioned
works insofar as we do not focus solely on what media say a few days after the conference: our
index is a continuous measure. Consequently, we also contribute to the literature by showing that
inter-meeting communications, especially those related to monetary policy inclination, do aﬀect
We also contribute to the literature on the impact of media sentiment on ﬁnancial variables.
Tetlock (2007) and Garcia (2013), among others, apply textual analysis methods to newspaper
content and show that the resulting sentiment index helps forecast stock returns. Fraiberger et al.
(2021) use articles from Reuters to show that general news sentiment predicts daily returns in
both advanced and emerging markets and drives worldwide capital ﬂows. Instead, the novelty of
2"The 5year/5year swap rate (...) is the metric that we usually use for deﬁning medium term inﬂation"
3Another strand of the literature has focused on uncertainty in media. Husted et al. (2019) build an index of
monetary policy uncertainty from three US newspapers and show that shocks to monetary policy uncertainty raise
credit spreads and reduce output. Bennani (2018) measures media uncertainty related to the ECB’s monetary
policy decisions and ﬁnds it to be related to future monetary policy decisions.
this paper is that we focus on ﬁnancial markets’ inﬂation expectations. This variable is of special
interest for central bankers and policymakers, as it shows whether markets are convinced that a
central bank will keep the inﬂation rate within its set target. We ﬁnd that sentiment also plays a
role in that context though of limited economic relevance.
We add to the literature on inﬂation expectations, a key component of the response of long-
term nominal rates to news (Bauer, 2015), by providing evidence that media sentiment has an
impact them. Several studies noted that ﬁnancial markets’ long-term4inﬂation expectations were
not responsive to monetary policy or macroeconomic news surprises when the central bank was
perceived as credible in achieving an explicit inﬂation target (see Garcia and Werner (2018) for a
comprehensive review). For example, Beechey et al. (2011) ﬁnd that long-term inﬂation expecta-
tions were unresponsive to monetary policy or macroeconomic surprises in the Euro area before
the global ﬁnancial crisis, while they were in the US. In contrast, Garcia and Werner (2018) ﬁnd
that long-term ﬁnancial markets’ expectations have become more responsive to macroeconomic
news surprises after 2013 in the Euro area, while Ambler and Rumler (2019) ﬁnd that some recent
unconventional monetary policy announcements moved ﬁnancial markets’ inﬂation expectations.5
Lastly, we contribute to the overall literature on monetary policy transmission by providing a
new index related to media sentiment on monetary policy, which can be used for further research.
Our Media Sentiment Indicator is freely available online.6
The rest of the paper is organized as follows. Section 2 details the construction of our index and
discusses its interpretation. Section 3 describes the methodology and the empirical results on the
determinants of media sentiment. Section 4 presents the methodology and the empirical results
on the relevance of media sentiment for inﬂation expectations. Section 5 concludes the paper.
4By "long-term" we refer in this paper to inﬂation expectations where the relevant horizon is 10 years. Some
papers we survey later also refer to such horizons as the "medium term".
5Other important contributions on the anchoring of ﬁnancial markets’ expectations in the Euro area include,
among others, Bundick and Smith (2018) and Galati et al. (2011).
6http://www.cbcomindex.com/data.php or https://sites.google.com/site/julienpinter/data-1
2 Measuring media sentiment on monetary policy
We ﬁrst extract from the Factiva database all articles containing the keywords “ECB” or “Eu-
ropean Central Bank” published by ﬁve major international newspapers (The Financial Times,
The Wall Street Journal, The New-York Times, Barron’s, and The Times) from January 2006
to December 2016. These newspapers are selected because they have a large audience of profes-
sional investors and they are known to include journalists’ views and analysis on the information
compared to other outlets, like newswire services.7
Within these, we consider only articles of which the main topic is the ECB by imposing a
minimum of three8mentions of either "European Central Bank" or the name of a member of the
Governing Council.9Our ﬁnal database contains 24,931 articles.
To dissociate the mere dissemination of central bank oﬃcial communication by the media from
the sentiment of the media, we remove from each article all quotes where the subject of the sentence
is a member of the ECB or the ECB itself. To do so, we use the spaCy package in Python to
analyze the semantic structure of each sentence. We use part-of-speech (POS) tagging to classify
each word based on its context within a sentence. POS allows us to "tag" each word, that is,
to classify it as a noun, verb, determinant, or any other grammatical category and identify its
syntactic dependency in a given sentence.10
Table 1 shows an example of sentences that our methodology allows us to detect (and remove)
in an article extract from The Wall Street Journal (available entirely in Appendix A.1).
Following previous research on the quantiﬁcation of textual content, we use the Loughran and
McDonald (2011) dictionary to compute a sentiment score for each article.11 We adapt it to
7Doing so, our indicator may not capture the overall sentiment of the market and may be relatively biased. In
particlular, the sentiment from the English-speaking newspapers we collected may diﬀer from the views conveyed
in French or German newspapers for example.
8We read and manually classiﬁed 600 articles to select the threshold value to obtain an adequate balance between
type 1 errors (selecting an irrelevant article) and type 2 errors (not selecting a relevant article).
9The list thus includes "ECB", "European Central Bank", and the main members of the Governing Coun-
cil: Draghi, Coeure, Mersch, Asmussen, Constancio, Gonzales, Lautenschlager, Praet, Bini-Smaghi, Stark, Issing,
Papademos, Gonzalez Paramo, Trichet, Tumpel-Gugerell, Noyer, Orphanides, Smets, Visco, Weidmann, Wellink,
Knot, Linde, Reinesch, Lane, Villeroy de Galhau, Honohan, Weber, Ordonez, Quaden, Coene, Nowotny, Provopou-
los, and Stournaras.
10We remove not only the sentences in which the subject is explicitly the ECB or a member of the Governing
Council but also sentences where the subject is a pronoun indirectly referring to the member of the ECB or the
ECB itself (as identiﬁed by the subject of the previous sentence(s) in that case).
11This approach is probably the most widely used in the connected literature, and it has been shown to capture
relevant information in a central banking context (e.g., Schmeling and Wagner, 2015 or Hubert and Labondance,
Table 1: Media sentiment computation: example based on an article (extract)
Text format Meaning
in blue Positive words from LM dictionary
in red negative words from LM dictionary
crossed out Exact re-transciptions of ECB members’ words removed with part-of-speech tagging
The Wall Street Journal, September 9, 2011 :
The ECB’s sudden shift opens it to renewed criticism that -as in July 2008 when it increased rates
just weeks before the collapse of Lehman Brothers- oﬃcials didn’t recognize early warning signs
on the economy and overestimated inﬂation risks, exacerbating the slowdown. The ECB made the
right call, Mr. Trichet said Thursday. "We think what we did was appropriate" he said, referring
to this year’s rate hikes. (...) "I would very much like to hear the congratulations for an institution
that has delivered price stability in Germany...which is better than has ever been achieved in this
country," he said, responding to a question about Mr. Gabriel’s criticism.
Full article in Appendix A.1
our monetary policy context: to avoid a straightforward bias in the sentiment measure, we discard
expressions that undoubtedly refer directly to ECB monetary policy measures while containing the
following words from the Loughran and McDonald (2011) dictionary: "negative rate", "negative
interest/deposit rate", and "quantitative easing".12 Then, we deﬁne Sentimentias the diﬀerence
between the number of positive and negative words in the article idivided by the total number of
words in the article:
Sentimenti=P(P ositiv e wordsi)−P(N egative W ordsi)
N umber of W ordsi
By construction, each article has a sentiment score between -1 and +1. Examples of very
negative and very positive articles are given in Appendix A.1. In the article extract of Table 1, we
also show the words captured by the Loughran and McDonald (2011) dictionary (the methodology
is illustrated in Appendix A.1 on the full article). The 20 positive and negative words with the
highest number of occurrences are detailed in Appendix A.2. Finally, at a daily frequency, we
compute an aggregate media sentiment indicator equal to the average sentiment of all articles
published during the period, Sentimentt, so that:
Sentimentt= 100 ×Pnt
2020). However, it has the well-known drawback of ignoring qualitative diﬀerences between words.
12This happens to be particularly important for "negative". By applying our ﬁlter, we remove about one-third
of its mentions.
where ntis the number of all our relevant articles published during day tin all of our ﬁve major
Built as such, our index intends to capture the media sentiment regarding the ECB monetary
policy and its relevant economic environment. A more negative media sentiment indicates that
the ECB monetary policy and its underlying environment are viewed with more pessimism, while
a more positive media sentiment indicates more optimism. Figure 2 shows the Media Sentiment
Index (MSI) as well as the evolution of the number of articles during our sample period, with a
2.2 Discussion and comparison with other indexes
As Figure 2 highlights, many highs and lows of the MSI are related to important monetary
policy events. For example, the second allotment of the ECB Targeted Longer-Term Reﬁnancing
Operations (TLTROs) in February 2012 and the beginning of the ECB Quantitative Easing (QE)
in January 2015 are both associated with an unusually sharp increase in media optimism. No other
policy after 2013 appears to be associated with a higher media sentiment than the one associated
with the launch of QE. The index also soars when the media convey high optimism on the economic
outlook, indicating that our index captures well information on economic developments which are
relevant to the ECB. Conversely, the media sentiment decreased sharply during the Great Recession
and during the Eurozone crisis. It is interesting to observe that not all unconventional monetary
policies are associated with a sharp increase in media sentiment. For example, the ﬁrst TLTRO
allotment, at the end of 2011, is not associated with an increase in media sentiment while the second
one is. One potential explanation is that the allotment ﬁgures were better than expected for the
second TLTRO, while they were more in line with expectations for the ﬁrst TLTRO, according to
some newspaper articles. We also do not observe any sharp increase in media sentiment the week
of the implementation of the Security Market Program (second week of May 2010).
The MSI signiﬁcantly diﬀers from other well-known measures related to monetary policy per-
ception, such as the newspaper-based Monetary Policy Uncertainty (MPU) index of Husted et al.
(2019). The MSI has a correlation of only -0.05 with the MPU index for the ECB, considering
monthly aggregate data (given that the MPU index is only available at a monthly frequency).
13In addition, we also corrected for any potential newspaper-speciﬁc political bias by subtracting the mean senti-
ment for each newspaper (over the whole time period) from each sentiment score of the articles of the corresponding
newspaper. The resulting index maintained a correlation of 99.5% with the original one. We still used this version
in our empirical analyses, although the transformation does not aﬀect the results. Note also that the values of the
MSI are mostly negative because the Loughran and McDonald (2011) dictionary, as the English dictionary, contains
more negative words than positive words (2,355 negative words for 354 positive ones).
Figure 2: Weekly aggregated sentiment and number of media articles
Notes: Figure A (top) presents the media sentiment indicator (weekly frequency for readability). Positive
(negative) events are shown in green (red). Figure B (bottom) shows the number of articles per week.
This suggests that the two indexes capture very diﬀerent aspects of monetary policy perception.
The MSI has a higher correlation (0.49) with the widely used market-based measure of investors’
sentiment of Baker and Wurgler (2006),14 a correlation of 0.51 with the Economic Sentiment Indi-
cator (ESI) from Eurostat, and 0.61 with the investor economic conﬁdence index for the Eurozone
developed by Sentix, all available only at a monthly frequency. This suggests that the "relevant
economic environment" information component of our index is relatively important at a monthly
14Investor sentiment, as the authors deﬁne it (belief about future cash ﬂows and investment risks that is not
justiﬁed by the facts at hand), is not conceptually equivalent to the sentiment we measure. Media sentiment, i.e.,
the positivity or negativity of newspapers’ tone, can reﬂect both fundamentals and autonomous beliefs. When we
take their measure not orthogonal to macroeconomic fundamentals measures, we get a correlation of -0.53. This
may suggest that our index is correlated to investor sentiment as "autonomous beliefs on the future state of the
economy", assuming that the comparison with US data is relevant.
frequency, while about half of the variations of our index still cannot be attributed to the variations
in the sentiment on the economic environment relevant for monetary policy. To better understand
the idiosyncratic information of our index relative to economic sentiment indexes, in Appendix
B, we plotted the residual component of the MSI index after having regressed it (its monthly
average) on the Sentix investor conﬁdence index. We observed that the three highest points after
2010 corresponded in turn to the month when the "whatever it takes" speech was pronounced, the
month when forward guidance was introduced, and the month when QE was introduced. Overall,
this further conﬁrms that our index captures relevant and idiosyncratic information on monetary
We also compared our index with a commonly used index capturing the overall tone of the
speech of the President during the ECB press conferences, measured also by the Loughran and
McDonald (2011) dictionary (LM press conference tone henceforth). We mean-standardized the
index, as well as our index, taking only its values on average over the day of the conference and
two days after it to ensure a meaningful comparison. The correlation with the LM press conference
and our index for these days is quite large, about 46%. It suggests that our index captures relevant
information on monetary policy. Investigating which idiosyncratic information is present in our
index for these particular days, we looked at the events with the largest diﬀerences between the
value of our index and the value of the LM press conference tone. The largest diﬀerence arises
in the meeting of February 2010 during which Jean-Claude Trichet "hinted strongly" that the
ECB emergency measures to support ﬁnancial markets would further unwind, according to The
Financial Times, in a context of fears related to sovereign debts. That day, an analyst summarized
in The Financial Times that "Mr Trichet’s ﬁrm attitude has fuelled fears of horror scenarios".
The second largest diﬀerence arose during the meeting of January 2009. At that meeting, the
ECB decreased its key interest rate, but several newspapers report a substantial "disappointment"
among market actors, related to fears over growth prospects. Many other large diﬀerences arise
in similar contexts, thereby tending to conﬁrm that our index captures idiosynchratic information
on the media perception on monetary policy that are not available in standard indexes based on
the oﬃcial ECB communications.
3 The determinants of the media sentiment
In investigating the determinants of media sentiment, we are particularly interested in analyz-
ing whether the central bank can aﬀect it. We conjecture that the central bank can inﬂuence media
sentiment through two channels. First, its monetary policy decisions per se may impact media
sentiment. In this case, media sentiment might primarily reﬂect the favorableness/unfavorableness
of central bank news coverage, as studied by Berger et al. (2011). Second, monetary policy com-
munications of the President and other members of the Governing Council could, by themselves,
inﬂuence media sentiment on a regular basis. Taking into account relevant control variables, the
model we intend to estimate is as follows:
αiMSIt−i+β ECB_decisionst+φEC B_commt+τControlst+t(3)
where MSItcorresponds to our Media Sentiment Index on the ECB monetary policy at day t,
EC B_decisionstis a vector of variables related to ECB monetary policy decisions, ECB_commt
is a vector of variables related to the communication of ECB Governing Council members, and
Controlstis a set of economic and ﬁnancial variables related to the economic environment. α=
(α1, α2, ...),β,φ, and τare the associated vectors of coeﬃcients, and Jcorresponds to the number
of lags for the dependent variable accounting for the persistence of sentiment.
The variables are summarized and described in Table 2. The variable selection regarding the
monetary policy decisions is inspired by Böhm et al. (2012) and Berger et al. (2011). We also
include a variable aimed at capturing the unconventional monetary policy measures adopted by
the ECB by building a dummy variable, taking the value of 1 for each unconventional monetary
policy announcement and 0 otherwise, as well as a variable related to negative rates adoption.15
The sample period for which we estimate the model is January 1, 2010 until May 31, 2016. It
is limited by data availability on Governing Council members’ communications, which we discuss
Regarding monetary policy communications, we study communications during both the press
conference and the inter-meeting period. The ﬁrst refers to the traditional press conference of the
ECB President, previously given once a month until January 2015 (once every six weeks afterward),
during which the ECB President gives the Governing Council’s assessment of the economic and
15Arguably, negative rates adoption had an important signal eﬀect, which we intend to quantitatively unveil with
the inclusion of this variable.
ﬁnancial conditions and further develops its monetary policy decisions. The second refers to
the various communications (e.g., media interviews, testimonies, conference talks) of the ECB
Governing Council members, during which they usually express their views on the future stance of
the ECB monetary policy and on the economic outlook. For press conference communications, we
gather an exhaustive database containing all the textual content of the ECB press conferences and
build quantitative measures for the policy inclination (hawkish versus dovish) and for the economic
outlook content inclination (positive economic outlook versus negative) of each communication
using the central bank-speciﬁc dictionary of Picault and Renault (2017) developed speciﬁcally for
that purpose. Appendix C provides further details. For inter-meeting communications, we use
a daily database kindly provided by Gertler and Horvath (2018). The authors manually coded
each communication from each Governing Council member for the above-mentioned period.16 We
obtained these daily detailed data from 2010 to May 2016, which in turn sets our sample size.17
The daily data give us the average policy inclination and economic outlook content inclination for
the day, both for the President and all other members of the Governing Council. We also include
a dummy variable which takes a value of 1 on the day of the "whatever it takes" speech, which is
known for being a particularly inﬂuential communication.
Control variables are included to limit the likelihood of any omitted variable bias in the esti-
mated coeﬃcients for our variables of interest. We include a set of inﬂation and macro-economic
news surprises for the Eurozone and for major European countries. All of the 17 variables included
for that purpose are deﬁned as the diﬀerence between the announced value and the median expec-
tation, as surveyed by Bloomberg, and detailed in Appendix D. We include variables to capture
ﬁnancial uncertainty (the level of the VSTOXX index) and ﬁscal stress (change in sovereign spread
of peripheral economies relative to Germany). We also consider the changes in the EUR/USD ex-
change rate and in the EuroStoxx 50 to control for changes in general ﬁnancial market conditions.
Descriptive statistics of the main variables are provided in Appendix F.
In addition to the above stated variables, we add four lags of the dependent variable (J= 4 ).
We do so both to limit potential auto-correlation in the residuals and to inform on the persistence
of media sentiment.18 The estimates are performed considering a daily frequency.19 In our baseline
16They follow the standard of the related literature and use Reuters news to get the raw communications. See
Gertler and Horvath (2018) for more details.
17Note that all the results related to the variables available for 2006–2010 are unchanged when we consider the
whole period (2006–2016).
18The fourth ﬁrst lags only appeared signiﬁcant in most of our regressions. Without the lags, the Breusch-Godfrey
LM and the Durbin’s alternative tests for autocorrelation both reject the null hypothesis of no autocorrelation at
the 1% level, when we consider autocorrelation of order 1 or higher (up to an order of 4).
19The sentiment value on Mondays is set at the average value over Saturday-Sunday-Monday. For days without
Table 2: Variables description
∆iAnnounced changes in the ECB key interest rate.
Positive interest rate surprise Diﬀerence between the actual interest rate decided during the press con-
ference and the market expected policy rate (Bloomberg survey median)
(0 if the diﬀerence should be negative).
Negative interest rate surprise Diﬀerence between the actual interest rate decided during the press con-
ference and the market expected policy rate (Bloomberg survey median)
(0 if the diﬀerence should be positive).
APP Dummy variable equal to 1 if an unconventional monetary policy is
announced. Detailed list in Appendix D.
Negative rate Dummy variable equal to 1 the day a negative rate on the deposit facility
Whatever it takes Dummy variable equal to 1 the day of Draghi’s "whatever it takes"
speech at the Global Investment Conference (July 26, 2012).
President MP - Press Conferences Net monetary policy (MP) inclination of the ECB President speech
during the Press Conference (PC), measured using Picault and Renault
President EC - Press Conferences Net economic outlook view (EC) inclination of the ECB President press
conference speech, measured using Picault and Renault (2017).
President MP - Inter Meeting Net monetary policy (MP) inclination of the ECB President communi-
cations in the inter-meeting period, manually coded, based on Gertler
and Horvath (2018).
President EC - Inter Meeting Net economic view (EC) inclination of the ECB President communi-
cations in the inter-meeting, manually coded, based on Gertler and
Other members MP - Inter Meeting Net monetary policy (MP) inclination of the members of the Govern-
ing Council inter-meeting (IM) communications (excluding President),
manually coded, based on Gertler and Horvath (2018).
Other members EC - Inter Meeting Net economic view (EC) inclination of the members of the Govern-
ing Council inter-meeting (IM) communications (excluding President),
manually coded, based on Gertler and Horvath (2018).
Macro-economic surprises Diﬀerence between the announced macroeconomic data and its median
expectation (from Bloomberg’s surveys). Detailed list in Appendix D.
Financial uncertainty Level of the VSTOXX index
Fiscal stress Changes in the following: the average of the 10-years bond yield for
Portugal, Spain, Italy, and Greece minus the 10-years bond yield for
∆EURUSD Change in the log of the average EUR / USD exchange rate (USDs per
∆EUROSTOX Change in the log of the average EUROSTOXX 50 level.
model, we include the contemporaneous values for all our variables except for the variables related
to the inter-meeting communications, for which we consider the lag of the variables. First, this
allows us to take into account the potential delay of the treatment of the information of these
communications by journalists. These communications are indeed likely to be treated with less
importance than a regular press conference and thus be read and processed with less priority;
the inﬂuence they may have on journalists’ writings cannot be expected to always be immediately
apparent.20 Second, it allows us to avoid potential simultaneity issues. Governing Council members
arguably have some ﬂexibility over their speeches, and they might also react to media sentiment
(or to the market developments of the day), with Governing Council members attempting to oﬀset
a more negative media sentiment (or an adverse market development) by communicating more
We perform our baseline estimates using standard OLS21 with Newey-West standard errors to
account for heteroskedasticity and potential remaining auto-correlation in the residuals.22
We perform our estimates by ﬁrst including only the variables related to the ECB communi-
cation, disentangling between communications of the President versus those of the other members
of the Governing Council (Model 1). We then add variables related to the ECB monetary policy
decisions (Model 2) and the diﬀerent controls for the economic and ﬁnancial environment (Models
3 and 4).23 Table 3 presents the results, where column ishows the estimates of Model i.
The ﬁrst observation from Table 3 is that media sentiment exhibits some persistence. Higher
media optimism on a given day increases media sentiment for the following four days. This is
any sentiment observation (which represents about 1.6%of our sample), we simply linearly interpolate the missing
value. The results are not dependent on these choices.
20Blinder et al. (2008) acknowledge a potential media reaction delay to central banker communications and
present it as a challenge to identify what represents the timing of a monetary policy "event". We will more deeply
investigate this aspect in what follows.
21Standard unit root tests lead us to infer that our data are stationary.
22In the ﬁnal model (including lags for the dependent variable), the results from both the Breusch-Godfrey LM
and the Durbin’s alternative tests for autocorrelation do not allow us to conclude that autocorrelation is absent
in the residuals. We chose to deal with this concern by using Newey-West standard errors, and we will consider
alternative approaches in the robustness section. Newey-West tests are performed using ﬁxed-b critical values
and the truncation parameter rule S = 1.3 T1/2(S = 52 in our case), as recommended in Lazarus et al. (2018).
Standard Newey-West tests lead to very similar conclusions. Regarding heteroscedasticity, the Breusch-Pagan test
for heteroscedasticity rejects the null hypothesis of homoscedasticity at the 5% level (but not at the 1%), while the
White test for heteroscedasticity cannot reject the null hypothesis of homoscedasticity at the 10% level.
23Only macro-economic surprises that appeared close to signiﬁcance (p-value less than 0.20) were kept in the ﬁnal
estimations displayed here, which leads us to consider four surprises out of the seventeen.
Table 3: Results - Media Sentiment determinants
   
MSIt−10.295*** 0.295*** 0.293*** 0.266***
(0.033) (0.033) (0.033) (0.031)
MSIt−20.070*** 0.070*** 0.071*** 0.050*
(0.027) (0.027) (0.027) (0.026)
MSIt−30.044 0.044 0.045 0.030
(0.031) (0.031) (0.032) (0.031)
MSIt−40.087*** 0.087*** 0.086*** 0.062***
(0.024) (0.024) (0.024) (0.024)
President MP - Press Conferencest-0.445*** -0.483*** -0.479*** -0.401***
(0.130) (0.136) (0.137) (0.122)
President EC - Press Conferencest0.542* 0.603* 0.583* 0.554*
(0.320) (0.325) (0.326) (0.306)
Other members MP - Inter Meetingt−1-0.074*** -0.072*** -0.077*** -0.066**
(0.027) (0.028) (0.027) (0.029)
Other members EC - Inter Meetingt−10.023 0.022 0.025 0.012
(0.044) (0.044) (0.043) (0.045)
President MP - Inter Meetingt−1-0.174*** -0.174*** -0.166*** -0.166***
(0.042) (0.042) (0.043) (0.041)
President EC - Inter Meetingt−10.224** 0.224** 0.224** 0.217**
(0.088) (0.088) (0.088) (0.091)
∆i-0.405 -0.391 -0.736
(0.529) (0.528) (0.631)
Negative Interest Surprise 1.591*** 1.589*** 1.733***
(0.491) (0.490) (0.609)
Positive Interest Surprise 1.319** 1.265** 0.829
(0.529) (0.532) (0.706)
APP -0.126* -0.120 -0.151
(0.074) (0.075) (0.142)
Negative Rate 0.196*** 0.195*** 0.166***
(0.036) (0.036) (0.037)
Whatever it takes 0.373*** 0.368*** 0.163***
(0.042) (0.043) (0.054)
Macro-economic surprises No No Yes Yes
Financial uncertainty -0.012***
Fiscal stress -0.135**
Constant -0.044** -0.044** -0.044** 0.241***
(0.018) (0.018) (0.018) (0.049)
Adjusted R20.152 0.150 0.152 0.188
Obs. 1630 1630 1630 1630
Note: Newey-West Standard Errors in parenthesis. ***, **, and * represent statistical signiﬁcance at respectively
1%, 5%, and 10%. M SI is the Media Sentiment Index. All control variables are deﬁned in Table 2.
important in that it implies that any factor that aﬀects media sentiment on a given day can be
expected to aﬀect media sentiment with a certain degree of persistence.
Regarding the ECB press conference communications, the tonality of President speeches during
the press conference signiﬁcantly aﬀects media sentiment. Both the economic outlook tone and
monetary policy inclination are found to have an impact on media sentiment, though the coeﬃcient
associated to the former variable is statistically signiﬁcant only at the 10% level. A more hawkish
tone during the press conference is signiﬁcantly associated with a decrease in media sentiment,
while a more positive communication on the economic outlook during the press conference is
associated with an increase in media sentiment. The eﬀect actually appears stronger when we add
controls for monetary policy decisions (column 2 with respect to column 1).
Regarding the inter-meeting communications, we ﬁnd that communications of the President,
both on the economic outlook and the future stance of monetary policy, signiﬁcantly aﬀect media
sentiment. More dovish communications are associated with a decrease in media sentiment, and
positive communications on the economic outlook increase media sentiment. Communications of
other Governing Council members on the future monetary policy stance also appear to matter for
media sentiment. The eﬀect consistently goes in the same direction as the eﬀect of the communi-
cations of the President (but it appears to be about three times lower) in our last estimate (Model
4). Inter-meeting communications about the economic outlook of the members of the Governing
Council other than the President are not signiﬁcantly linked to media sentiment on the next day in
our regressions. The fact that we ﬁnd a lower eﬀect for the communications of Governing Council
members other than the President on the monetary policy stance and no eﬀect for their com-
munications on the economic outlook may indicate that communications from Governing Council
members other than the President are perceived as less important than communications from the
President. It could be also that these diﬀerences reﬂect a diﬀerence in media attention. These two
interpretations are naturally diﬃcult to isolate from each other. They should also be taken with
caution, insofar as standard Wald tests reject the null hypothesis of equality of the coeﬃcients only
when we consider those associated with the variables related to communications on the economic
Regarding the monetary policy decisions, we ﬁnd that interest rate surprises aﬀect media
sentiment. This is in line with the results of Berger et al. (2011) who found that the tone of news
reports is generally more negative when the policy decision surprises the ﬁnancial market analysts.
We robustly detect this eﬀect only for negative interest rate surprises; when the decided policy
24For the coeﬃcients of the variables related to communications on the monetary policy stance, the p-value of
the standard Wald test is about 0.12.
rate is lower than the market expectation, media sentiment decreases. We do not ﬁnd an eﬀect
for positive interest rate surprises when we control for ﬁnancial variables (Model 4), although this
could arguably be due to the imperfect nature of such controls in this case (which may also react
to the interest rate surprise). As in Berger et al. (2011), interest rates moves per se do not appear
to matter. The announcements of asset purchase programs also do not appear to systematically
aﬀect media sentiment in our baseline estimates. However, we ﬁnd that the adoption of a negative
rate in June 2014 and the "whatever it takes" speech of Mario Draghi are both associated with a
signiﬁcantly important increase in media sentiment.
In terms of economic magnitude, further analysis reveals that events related to the press confer-
ence usually have more impact on media sentiment than standard inter-meeting communications.
For example, based on column (4), a one standard deviation increase in the tone of the press
conference in terms of hawkishness decreases media sentiment by about 0.13. This is about a
fourth of the MSI standard deviation on the analyzed period, and it could make the MSI move
from one decile to another. The eﬀect is of comparable magnitude to the eﬀect of a one standard
deviation decrease in the tone of the press conference regarding the economic outlook (0.09 de-
crease in MSI). A negative interest rate surprise of 25 basis points is associated with a decrease
in media sentiment of about 0.44. As a comparison, a one standard deviation increase in the VS-
TOXX leads "only" to a 0.08 decrease in media sentiment. Direct and contemporaneous eﬀects of
events linked to the press conference on the MSI thus appear of particularly important magnitude
when compared to the eﬀect that alternative broader variables have on the MSI. Standard moves
in the tone of inter-meeting communications impact media sentiment with less magnitude; a one
standard deviation increase in the tone of President communications related to monetary policy
inclination decreases media sentiment from about 0.03, an eﬀect almost similar to a one standard
deviation decrease in the tone of President communications related to economic outlook (0.02 de-
crease in MSI). Exceptional communications can, however, have very important impacts on media
sentiment. Considering column (3), the "whatever it takes" speech contemporaneously moved the
MSI index from about 0.37, about ﬁve times more than a one standard deviation increase in the
While such an analysis sheds light on the direct and contemporaneous eﬀect (or next-day eﬀect
for inter-meeting communications) of our variables on media sentiment, in Appendix E, Table 7,
we analyzed and took into account the potential eﬀect of ECB communications on media sentiment
up to 4 days after the communication by including more lags for our variables of interest in the
regression. The tone of the press conference in terms of monetary policy inclination appears to
signiﬁcantly move media sentiment several days after the press conference. A much larger eﬀect
of the President speech on media sentiment is unveiled through this analysis. On average, a one
standard deviation increase in the tone of the press conference in terms of hawkishness will have
decreased media sentiment by about 0.64 after ﬁve days,25 and thus it will have decreased media
sentiment by more than a standard deviation. Other newly included lags are usually not found to
aﬀect media sentiment, suggesting that the ECB communications move media sentiment only at
the timing we initially set.26
3.3 Robustness and interpretation of the results
The most important result that arose in the previous section is that both press conference
and inter-meeting communications signiﬁcantly aﬀect media sentiment. This is especially true for
those related to the monetary policy inclination, with hawkish (dovish) communications decreasing
(increasing) media sentiment. In this section, we test for its robustness and further discuss its
To test the robustness of our results, we performed a set of alternative regressions, shown in
Table 8 of Appendix E. We ﬁrst performed the same estimates with a standard Huber-White ma-
trix for the residuals. All the coeﬃcients (including the ones related to monetary policy decisions)
either maintained the same signiﬁcance level or saw it increase when we did so (column (1)). We
then performed the same estimates but included the lag of all the control variables instead of
their contemporaneous value.27 The coeﬃcients for the variables related to inter-meeting commu-
nications as well as their associated statistical signiﬁcance were barely aﬀected (column (2)). We
then dealt with the potential remaining autocorrelation in the residuals with alternative methods.
25The ﬁgure is computed based on the estimates of column (4) of Table 7 and takes into account the persistence
of the MSI.
26An exception is the second lag of the variable capturing the tonality of the inter-meeting communications of the
President regarding the economic outlook, which is statistically signiﬁcant at the 5% level, while being negative.
This suggests that the positive eﬀect of these communications on sentiment reverses over the next day. A standard
Wald test cannot reject the hypothesis that this coeﬃcient is equal to the opposite of the coeﬃcient related to
the ﬁrst lag of the same variable, at conventional statistical signiﬁcance levels. In addition, the second lag of the
variable capturing the tonality of the inter-meeting communications of the Governing Council members other than
the President regarding the economic outlook appears statistically signiﬁcant at the 5% level in all regressions
(columns (1) to (4)). This suggests that these also aﬀect media sentiment, but with more delay than those of the
President, possibly owing to a diﬀerence in media attention. Regarding policy decisions, the "whatever it takes
speech" is also found to positively aﬀect media sentiment the day after it arouse, and the asset purchase programs
are found to positively impact media sentiment the day after as well as three days after their announcements
(coeﬃcient statistically signiﬁcant at the 1% level). The MSI is also found to decrease three days after a positive
interest rate surprise, reinforcing the interpretation that interest rate surprises negatively aﬀect media sentiment.
These latter results are all available on request.
27Compared to the previous speciﬁcation including several lags for all variables, such a speciﬁcation has the
advantage of being much more parsimonious.
First, we kept three lags for each variable related to ECB communications and ECB decisions,
adding only the lags of the control variables which appeared statistically signiﬁcant (up to three
lags). Such a speciﬁcation has the advantage of being more parsimonious than the ones previously
tested on Table 7, while autocorrelation tests cannot here reject the null hypothesis that there is
no autocorrelation in the residuals at a 10% signiﬁcance level.28 Column (5) of Table 7 presents
the results with a Huber-White matrix for the residuals, and column (6) presents the results with a
standard covariance matrix. The previous key results are unaﬀected with the former speciﬁcation,
while with the second speciﬁcation the statistical signiﬁcance of the coeﬃcients of the variables
related to Governing Council communications and the variable capturing the tone of the President
on the economic outlook during the press conference now slightly overpasses the conventional 10%
level. In fact, the variance of most coeﬃcients increases with the second speciﬁcation, and some
variables for which the impact on media sentiment may seem self-evident to some observers (such
as the dummy for the "whatever it takes" speech or the ﬁscal stress variable) also lose statistical
signiﬁcance at the 10% level. Second, we repeated the same estimates without the lags for the de-
pendent variable. While these were included to limit serial correlation in the residuals, remaining
serial correlation together with the presence of the lags could bias downward the other coeﬃ-
cients (Wilkins, 2018). We ran the baseline regression without the lagged dependent variables,
using respectively OLS with Newey-West standard errors, OLS with a Huber-White matrix for
the residuals, the Prais–Winsten estimator, and an ARMAX model estimated through maximum
likelihood; the conclusions remained broadly unchanged.29
To assess the robustness of our results to alternative sentiment measures, we built our index
with other dictionary approaches and then performed the same regressions. We ﬁrst included only
the negative words when building the sentiment score. In a second speciﬁcation, we selected the
positive and negative words used to build our sentiment measure in equation 1 from a generic
well-known dictionary, the Harvard-IV dictionary. In a third speciﬁcation, we used the Vader
sentence-level classiﬁer.30 Though the Vader approach and Harvard-IV dictionary are not built
speciﬁcally for a ﬁnancial context, using them here allows us to have an idea of the extent to
28Durbin’s alternative tests for autocorrelation (Breusch-Godfrey LM tests for autocorrelation) of the order 1 till
5 all reject the null hypothesis at the 20% threshold (at the 18% threshold).
29In all speciﬁcations, the coeﬃcients of the variables related to ECB communications that were statistically sig-
niﬁcant at the 5% level remained signiﬁcant at this level, with the exception of the communications of the Governing
Council members (other than President) related to monetary policy inclination with the ARMAX speciﬁcation. The
coeﬃcient of the variable related to the tone of the President on the economic outlook during the press conference
was statistically signiﬁcant at the 5 or 10% level in all speciﬁcations but the Prais-Winsten speciﬁcation. The results
are available on request.
30See Shapiro et al. (2020) for a general discussion on Vader and on the Harvard-IV dictionary.
which our results are dependent on the dictionary we used. Results are displayed in columns
(3), (4), and (5) of Table 8. Most key ﬁndings remain irrespective of the approach chosen. The
press conference and inter-meeting communications of the President related to monetary policy
inclination are still found to aﬀect media sentiment, with coeﬃcients statistically signiﬁcant at
1% levels except with the Harvard IV-4 dictionnary. The same applies for the inter-meeting
communications of other Governing Council members, related to monetary policy inclination. The
eﬀect of all communications related to the economic outlook cannot however be seen as robust
when using the Harvard-IV and Vader dictionary.31 In a ﬁnal robustness check, we gave a diﬀerent
weight to each newspaper article based on a proxy for an audience of professional investors.32 The
last column of Table 8 shows that the results are mostly unaﬀected by this change.
Focusing on the interpretation of our results, we then test for potential non-linearities in the
eﬀect of communications on sentiment. One might expect that hawkish communications decrease
media sentiment only when a restrictive monetary policy seems unwarranted (e.g, when inﬂation
is below its target), and that they should be positively associated with sentiment only when such a
policy seems warranted. Evidence of such eﬀects would aﬀect the interpretation of our main result.
To test this hypothesis, we create a dummy variable equal to 1 when CPI inﬂation is above the ECB
2% inﬂation target (0 otherwise) and we interact each central bank communication variable with
this dummy variable. We add both the dummy variable and the interaction terms to equation
3. Detailed results are presented in Table 9 in Appendix E. The results do not strongly point
out the presence of non-linearity in the eﬀect of communications; none of the interactive terms
are signiﬁcant at the 5% level. This conﬁrms the interpretation of the results of the previous
section: more hawkish communications make media content more pessimist, and more dovish
communications make media content more optimist, unconditional on inﬂation being above or
below the target.33 While such an interpretation may appear puzzling to some observers, it is
also consistent with some recent central banker comments. Powell (2018), for example, recognized
that "looser policy leading to more positive sentiment in markets and tighter policy depressing
sentiment" was a possible simple link between monetary policy and risk sentiment: our results
31The fact that the adjusted R2is lower for the estimates performed with these three alternative measures (relative
to our initial measure) can be seen as a factor justifying the relevance of our initial measure choice.
32A key issue here is that there is no standardized data on the worldwide audience for newspapers. We used data
from Erdos &Morgan’s Professional Investment Community Study of 2010. We computed the daily sentiment score
for each newspaper and then built a daily weighted average, using a weight of 0.59 for The Wall Street Journal,
0.35 for The Financial Times, 0.28 for Barron’s, 0.24 for The New York Times, 0.12 for The Times.
33Note that, when performing the same estimates with the available data for 2006-2016, we get the same results:
the tone of press conference communications related to monetary policy inclination is linearly and signiﬁcantly
associated to media sentiment, with no evidence of non-linearity.
suggest that a similar direct link applies for media sentiment on monetary policy.
4 Media sentiment and inﬂation expectations
We now focus on the relevance of media sentiment for ﬁnancial markets’ long-term inﬂation
4.1 Empirical strategy
It is usually considered that any factor aﬀecting ﬁnancial market long-term inﬂation expecta-
tions does so because the central bank is not perceived either as completely willing or as completely
able to keep inﬂation in line with its target in the future.34 The fact that ﬁnancial market ex-
pectations for Euro area inﬂation in ﬁve years for the next ﬁve years have been falling below the
ECB inﬂation target since 2014 (Draghi, 2014) can be interpreted as a sign that investors see the
ECB as less able to keep inﬂation in line with its 2 percent target. If media sentiment is, as one
may expect, correlated with investor perceptions, the MSI gives a unique opportunity to directly
test this hypothesis that long-term inﬂation expectations do respond to investor perceptions on
monetary policy and its relevant environment. Following this intuition, we test whether daily vari-
ations in the MSI aﬀect ﬁnancial markets’ long-term inﬂation expectations. We adopt a framework
close to Tetlock (2007) and Garcia (2013) who analyze the eﬀect of investor sentiment proxied by
media content on the stock returns of the next day, but we focus our attention on ﬁnancial market
We measure inﬂation expectations using daily data on inﬂation-linked forward swap rates in the
Euro area from Thomson Reuters (see e.g, Garcia and Werner, 2018, Ambler and Rumler, 2019, or
Beechey et al., 2011 for a similar measure). We focus our analysis on the 5-year forward inﬂation
compensation ﬁve years ahead (5-y to 5-y forward rate henceforth) and 10-year forward inﬂation-
linked swap rate (10-y forward rate henceforth), insofar as they represent inﬂation expectations for
the long-term and as such are of primary relevance to policymakers. The 5-y to 5-year forward rate
is often seen as the most relevant measure of ﬁnancial market inﬂation expectations in the Euro
area and it receives a considerable attention from policymakers, central bankers (Draghi, 2014),
34For example, in Beechey et al. (2011), the fact that long-term inﬂation expectations respond to inﬂation surprises
in the US while they do not in the Eurozone before 2008 can be interpreted as evidence that the ECB is perceived
as more willing to keep inﬂation at a target than the FeD. Garcia and Werner (2018) note that the lack of evidence
that long-term inﬂation expectations responded to macroeconomic news before the global ﬁnancial crisis for the
ECB was interpreted as a sign that the central bank was perceived as credible in achieving its inﬂation target (thus
willing or able to achieve it).
and market participants (Rennison, 2019).35 Inﬂation expectations daily changes in basis points
t−1) for inﬂation expected in ﬁve years for the next ﬁve years (m= 5/5) or for the average
inﬂation expected for the next 10 years (m= 10/0) are assumed to be linked to media sentiment
through the following model:
where cis a constant, Xta set of exogenous variables deﬁned thereafter, dowtare days-of-the-
week dummy variables, tis the error term. We follow Natoli and Sigalotti (2018) to deal with
persistence and heteroskedasticity common with such ﬁnancial variables and model the squared
tusing a GARCH(1,1) model given by:
Xt= (EC B_decisionst,EC B_commt,Macro_surprisest,F inancialst,N ews_Sent) is the
set of exogenous variables considered, where ECB_decisionst,ECB_commt,M acro_surprisest
are, respectively, the ECB monetary policy decisions, the ECB communications, and the macroe-
conomic surprises. In our context, it is particularly important to control for central bank commu-
nications, central bank decisions, and macroeconomic surprises. Without doing so, an apparent
eﬀect of sentiment on inﬂation expectations could be a reﬂection of these factors. To better purge
the eﬀect of economic news broadly deﬁned, we also include two sentiment indexes of (domestic
and international) economic news through the vector News_Sentt: the index calculated by Bor-
toli et al. (2018) from the content of the French newspaper Le Monde, and the index of Shapiro
et al. (2020) focusing on the US. For a similar purpose, we will also consider the ﬁnancial variables
previously deﬁned F inancialst.
The coeﬃcients in each regression are estimated by Maximum Likelihood with the same sample
period as in the previous sections (January 1, 2010 until May 31, 2016), taking only trading days
into consideration. We consider n= 5 lags for the dependent variable and the MSI. The model
is ﬁrst estimated without the Xvector of controls, and these are then added with lags (m) chosen
with parsimony considerations so that they match any potential statistically signiﬁcant lag of our
variable of interest.
Results are displayed in Table 4.
35The ﬁve- and ten-year maturities are also considered to concentrate a signiﬁcant amount of liquidity relative
Table 4: 5Y-5Y and 10Y Inﬂation Expectations and Media Sentiment
5Y-5Y 5Y-5Y 5Y-5Y 10Y 10Y 10Y
     
t−10.094*** 0.089*** 0.081*** 0.152*** 0.145*** 0.145***
(0.026) (0.026) (0.027) (0.027) (0.028) (0.029)
t−20.074*** 0.076*** 0.082*** 0.030 0.038 0.038
(0.026) (0.027) (0.027) (0.028) (0.030) (0.030)
t−3-0.019 -0.031 -0.029 0.010 -0.001 0.001
(0.028) (0.028) (0.028) (0.029) (0.030) (0.030)
t−4-0.015 -0.010 -0.015 0.013 0.014 0.007
(0.026) (0.026) (0.026) (0.026) (0.027) (0.027)
t−5-0.031 -0.024 -0.025 -0.041 -0.034 -0.036
(0.026) (0.026) (0.026) (0.028) (0.028) (0.028)
MSIt−10.202** 0.216*** 0.186** 0.209*** 0.234*** 0.231***
(0.083) (0.082) (0.087) (0.074) (0.075) (0.084)
MSIt−2-0.225** -0.217** -0.217** -0.129 -0.133* -0.161**
(0.088) (0.089) (0.090) (0.079) (0.078) (0.079)
MSIt−3-0.038 -0.023 -0.010 -0.109 -0.108 -0.080
(0.092) (0.094) (0.093) (0.082) (0.083) (0.085)
MSIt−4-0.013 -0.005 -0.006 -0.005 0.005 0.008
(0.090) (0.093) (0.094) (0.071) (0.075) (0.077)
MSIt−50.029 -0.011 0.003 0.040 -0.007 0.008
(0.082) (0.083) (0.085) (0.083) (0.082) (0.083)
Constant -0.058 0.145 0.211 -0.060 0.225* 0.119
(0.042) (0.136) (0.240) (0.043) (0.129) (0.235)
ARCH(1) 0.058*** 0.056*** 0.058*** 0.098*** 0.107*** 0.094***
(0.009) (0.008) (0.009) (0.010) (0.011) (0.011)
GARCH(1) 0.941*** 0.944*** 0.942*** 0.898*** 0.891*** 0.904***
(0.008) (0.008) (0.008) (0.010) (0.010) (0.010)
Constant 0.013* 0.010 0.011 0.041*** 0.038*** 0.029***
(0.007) (0.006) (0.007) (0.010) (0.010) (0.009)
Day-of-the-week Yes Yes Yes Yes Yes Yes
ECB Press conference communications No Yes Yes No Yes Yes
ECB Inter-meeting communications No Yes Yes No Yes Yes
Monetary Policy Decisions No Yes Yes No Yes Yes
Macro. Surprises No Yes Yes No Yes Yes
News-sent No Yes Yes No Yes Yes
Financial Variables No No Yes No No Yes
Log −likelihood -3244.551 -3221.753 -3209.509 -3176.578 -3149.496 -3138.629
Obs. 1572 1572 1572 1572 1572 1572
Note: Standard Errors in parenthesis. ***, **, and * represent statistical signiﬁcance at respectively 1%, 5%,
and 10%. 5Y-5Y (10Y) is the 5-y to 5-y (10 years) forward rate from inﬂation-linked swaps. Day-of-the-week are
dummy variables for each day of the week, News-sent are the economic news indexes of Shapiro et al. (2020) and
Bortoli et al. (2018). M SI is the Media Sentiment Index. All other control variables are deﬁned in Table 2.
to all other inﬂation swap instruments. See Garcia and Werner (2018) for a thorough discussion.
Columns (1) and (4) correspond to the estimates of equation 4 for our two inﬂation expectations
measures, without the vector of controls X. We ﬁnd in both cases that the ﬁrst lag of our sentiment
measure has a positive and signiﬁcant eﬀect on next day inﬂation expectations. An exogenous
increase in our measure of media sentiment on monetary policy at day twill thus increase the 5-y
to 5-y and the 10-y inﬂation expectations in t+ 1. We also observe in both cases that the positive
eﬀect at day t+ 1 seems to at least partly reverse at day t+ 2: the second lag of the MSI is
negative for both measures and statistically signiﬁcant at the 1% level for the 5-y to 5-y inﬂation
swap measure. We also ﬁnd that changes in inﬂation expectations exhibit some persistence with a
statistically signiﬁcant coeﬃcient for the ﬁrst lag for both measures, while the second lag appears
to be statistically signiﬁcant for the 5-y to 5-y inﬂation swap. This is not surprising in itself, as
we are dealing with inﬂation swaps, which are over-the-counter instruments, where daily changes
are relatively small and arbitrage opportunities limited. In columns (2) and (5), we added the ﬁrst
two lags for our control variables (m= 2) except ﬁnancial variables. Doing so only slightly aﬀects
the magnitude of our coeﬃcient estimates but renders the second lag of the MSI now statistically
signiﬁcant at the 10%conﬁdence level when the dependent variable is the change in the 10-year
inﬂation expectations (column (5)). Lastly, in columns (3) and (6), we included ﬁnancial variables
in the regressions to make sure our results are not driven by any other market relevant information
that would matter for next-days inﬂation expectations. The results remained unchanged.
4.3 Robustness and interpretation
In separate estimates36, we used a GARCH(2,2) to model the variance in equation 5 and
obtained similar results. We also tried to estimate equation 4 directly through OLS with Newey-
West or Hubert-White standard errors. While these showed coeﬃcients for the MSI of slightly
higher magnitude, the key observation was not changed; the MSI was found to increase next day
inﬂation expectations while the eﬀect reversed the day after. In all cases, Wald tests could not
reject the null hypothesis that the coeﬃcient associated with the ﬁrst lag of the MSI was equal to
the opposite of the one associated with the second lag of the M SI , thereby clearly suggesting the
presence of a reversal pattern. We also repeated the estimates with Jordà (2005)’s local projections
and reached the same conclusion. We did not ﬁnd evidence of non-linearities, when considering a
diﬀerent impact depending on whether inﬂation is below or above its target, or for moves of the
MSI at press conference days.
The eﬀect we unveiled in this section is of low magnitude when compared to stocks or exchange
36All results are available upon request.
rates daily changes: a positive two-standard deviations increase in the MSI , which could make
the MSI move from 6 deciles, is found to increase the next-day 5-y to 5-y (10-y) forward rate
from less than a basis point, namely around 0.20 bps (0.24 bps). It has to be recalled, however,
that this concerns long-term inﬂation swaps, for which the daily changes are usually small, and
which, if anchored, should barely respond to news. In addition, we are analyzing an impact on the
next trading day. The magnitude we ﬁnd is in line with previous ﬁndings on the eﬀects of central
banks’ communications or macroeconomic surprises on inﬂation swaps. Beechey et al. (2011)
ﬁnd that long-term inﬂation expectations (nine to ten years ahead inﬂation compensation) were
unresponsive to macroeconomic surprises in the Euro area before the global ﬁnancial crisis, while
the eﬀects found in Garcia and Werner (2018) for macroeconomic surprises after 2013 are typically
less than a basis point, with, for example, German and Spanish surprises in ﬂash estimates leading
to a change in 5-y to 5-y inﬂation compensation of around 0.6 and 0.5 basis point in the day of
their release on average over their sample period. The observed response for the change in the 5-y
to 5-y forward rate is still about thrice its unconditional mean from our sample period (-0.082),
and is as such comparable in magnitude to the results obtained in Tetlock (2007).37 As in Tetlock
(2007) or Garcia (2013), the eﬀect of a sentiment shock reverses, but quicker in our case. The
small eﬀect we ﬁnd suggests nonetheless that media sentiment on monetary policy is of limited
economic relevance to understand ﬁnancial market’s inﬂation expectations.
Communication is a key part of monetary policy. While there is a clear consensus that com-
munications from central bankers impact ﬁnancial markets, little is known about the role of media
coverage in this transmission. In this paper, we shed light on this particular point.
We built a new measure of the media sentiment on the ECB monetary policy and its relevant
environment, using advanced natural language processing methods with a database of about 25,000
newspaper articles.The resulting index is, to our knowledge, the ﬁrst index measuring media sen-
timent on the ECB monetary policy and its relevant environment, on a continuous basis. A key
innovation we have introduced is the purging of each article from news re-transcription, to capture
journalist interpretation. We used a rich external dataset on inter-meeting communications to
analyze the determinants of our index. We found that media sentiment is aﬀected by the content
37In Tetlock (2007), the eﬀect of a two-standard deviations change in his sentiment index on the next day’s Dow
Jones returns is 16.2 basis points, that is, a bit more than thrice the unconditional mean of Dow Jones returns in
his sample period (5.4 basis points).
of the press conference and by inter-meeting interventions of the President as well as interventions
of other members of the Governing Council related to monetary policy inclination. We analyzed
the relevance of our index for inﬂation expectations and found that media sentiment on monetary
policy has predictive power for the daily changes in the 5-years to 5-years inﬂation swap rate as well
as for the 10-years inﬂation swap rate. The magnitude of the eﬀect we found do not suggest that
media sentiment on monetary policy is economically important to understand ﬁnancial markets’
Anecdotal evidence already suggests that media coverage matters for central bankers. For ex-
ample, in 2013, the ECB signed a contract to monitor print media, online media, broadcast, and
social media and to perform a media analysis/reputation tracking.38 The ECB itself announced on
its website that "the conclusions drawn from [this] analysis help shape the ECB’s communication
strategy." This paper provides an empirical analysis unveiling the eﬀect of diﬀerent kinds of com-
munications and decisions on media coverage. It stresses in particular the key importance of the
communications of the President, especially those during the press conference related to monetary
policy inclination. In addition, it provides empirical evidence suggesting that media sentiment also
matters in the context of central banking and that the economic importance of media sentiment
is negligible in this context. We ﬁnd a reversal pattern for the eﬀect of sentiment on next-days
inﬂation expectations, as observed in Tetlock (2007) or Garcia (2013) in another context.
Naturally, our analysis suﬀers from the traditional limits of text analysis methods, which could,
in turn, lead us to underestimate the magnitude of the media eﬀect. Qualitative diﬀerences between
words, sentences and articles are in particular neglected. Furthermore, although we only selected
media known for their analysis (in contrast to newswire services), removed all re-transcriptions,
controlled for several news variables and several indexes of central bank communications, and
looked at the relationship with next-day inﬂation expectations rather than contemporaneous rela-
tions, we cannot ﬁrmly eliminate the possibility that the eﬀect may be driven by news. In further
research, it would be interesting to assess with alternative methods the magnitude of the media
channel and to investigate the links between media sentiment on monetary policy and households
or business’ expectations.
38Germany-Frankfurt-on-Main: ECB - Provision of media monitoring and analysis services 2013/S 170-294141
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Appendix A - Measuring Media Sentiment
.1 Example of media sentiment measurement for two articles
We present two examples of texts from which the MSI is built, in which we indicate:
•Positive words (from LM dictionary) in blue
•Negative words (from LM dictionary) in red
•Exact re-transciptions of ECB members’ words removed with part-of-speech tagging crossed
The ﬁrst article below has a sentiment score which belongs to the last decile of the MSI
distribution. The sentiment of the original text is about -2,7 (in the 7th decile of our MSI measure)
while purged out from re-transcriptions, it amounts to about -3,6 (in the last decile).
The Wall Street Journal, September 9, 2011
FRANKFURT — The ECB opened the door to interest-rate cuts if needed to bolster a weakening economic
recovery —a dramatic U-turn from its decision to raise interest rates just two months ago. Economic risks have
"intensiﬁed" to the downside with "enormous" uncertainty, ECB President Jean-Claude Trichet told reporters
after the central bank held its main policy rate at 1.5%. He called the ECB’s reassessment of the economic
outlook "signiﬁcant" and highlighted weakening global growth, declines in equity markets and strains in euro-zone
government bond markets as trouble spots. At the same time, Mr. Trichet defended the ECB’s two rate hikes earlier
this year, saying they were needed to keep inﬂation in check. He also launched an uncharacteristically passionate
defense of the ECB’s recent decision to buy Italian and Spanish bonds, saying the moves were needed to restore
smooth transmission of the ECB’s interest-rate decisions to ﬁnancial markets and the economy. “ We stand ready
to do whatever is necessary,” Mr. Trichet said, adding that the ECB will “monitor very closely all developments.”
Until Thursday, the ECB had only said that it would closely monitor what it saw as upside risks to inﬂation. Those
risks are now balanced, Mr. Trichet said. At 2.5%, annual inﬂation is still above the ECB’s 2%target. But ECB
staﬀ economists expect it to fall to 1.7%in 2012, according to revised projections released Thursday. "This is an
ECB that’s worried" about the economy, said Nick Matthews, economist at Royal Bank of Scotland. "It’s clear
that the ECB has an easing bias and may be cutting rates by the end of the year" Mr. Matthews said. The
euro zone expanded just 0.7%, at an annualized rate, in the second quarter as the region’s two largest economies,
Germany and France posted little or no growth. A collapse in business and consumer sentiment last month raised
a grimmer prospect: The euro zone may be headed toward another recession. The risks of renewed contraction in
the world’s advanced economies "has gone up" the Organization for Economic Cooperation and Development said
Thursday. The Paris-based organization of developed and large developing economies urged central banks with the
"scope" to do so to consider easing rates. But recent data suggest some of the gloom may be overblown in Europe.
German industrial production jumped 4%in July from June, though business sentiment slid that month. Consumer
spending in the euro zone rose in July. The economy is softening but isn’t falling back into recession yet, analysts
say. Euro-zone gross domestic product "is expected to increase very moderately in the second half of this year" Mr.
Trichet said. ECB staﬀ cut their 2012 growth forecast to 1.3%from 1.7%. If the economy stabilizes even at weak
growth rates, then the ECB will likely keep interest rates steady, some economists said. "It may take contraction
for them to actually cut rates" said Howard Archer, economist at consultancy IHS Global Insight. The ECB raised
rates in April and again in July even as other major developed-country central banks such as the Federal Reserve,
the Bank of England and the Bank of Japan held rates at crisis-era lows closer to zero. The Bank of England voted
Thursday to keep rates at a record-low 0.5%, but didn’t announce any plans to resume its asset-purchase program,
known as quantitative easing. The ECB’s sudden shift opens it to renewed criticism that—as in July 2008 when
it increased rates just weeks before the collapse of Lehman Brothers—oﬃcials didn’t recognize early warning signs
on the economy and overestimated inﬂation risks, exacerbating the slowdown. The ECB made the right call, Mr.
Trichet said Thursday. "We think what we did was appropriate" he said, referring to this year’s rate hikes. "We
preserved a solid anchoring of inﬂation expectations." Mr. Trichet vigorously defended the ECB’s decision, made
one month ago, to reactivate its government bond purchase program and expand it to include Italian and Spanish
bonds. The ECB has bought more than 50 billion in government bonds since restarting the program, which had
been dormant for four months. That provoked a backlash in Germany, where government bond purchases by central
banks are seen as an inﬂationary taboo that puts central bankers in the realm of ﬁscal policy. The head of Germany’s
central bank, Jens Weidmann, voted against reactivating the purchases. German President Christian Wulﬀ, whose
position is largely ceremonial, has called the ECB’s bond purchases ¨politically and legally questionable.¨
of German’s center-left SPD party, Sigmar Gabriel, has also blasted the move. His voice rising uncharacteristically,
Mr. Trichet said the ECB has displayed an “impeccable, impeccable” adherence to price stability during its 12-year
history. “I would very much like to hear the congratulations for an institution that has delivered price stability in
Germany...which is better than has ever been achieved in this country” he said, responding to a question about Mr.
Gabriel’s criticism. Mr. Trichet repeated his insistence that euro-zone parliaments approve changes to Europe’s
440 billion rescue fund that would allow it to purchase bonds in ﬁnancial markets, alleviating the ECB of that task.
The ECB President dismissed concerns that the euro zone suﬀers from a lack of liquidity in its banking system, and
rejected claims that the region’s banks are in urgent need of more capital. International Monetary Fund managing
director Christine Lagarde caused a stir last month when she said European banks needed urgent recapitalization.
“I will not dramatize the situation as has been done by some” Mr. Trichet said, without referring speciﬁcally to Ms.
The second article below has a sentiment score which belongs to the ﬁrst decile of the MSI
distribution (very positive). Its score is about 0.017.
The Financial Times, September 4, 2013
Watch out for some upbeat comments - maybe even a joke? - from Mario Draghi today. Plenty has gone
right recently for the ECB president - with the economic data, that is, not with the politics in his native Italy.
Wednesday’s purchasing managers’ indices showed the eurozone’s recovery becoming ﬁrmly established. Unlike
previous, German-led upturns the return to solid expansion territory is broad-based. Much of the improvement
has been in Italy and Spain. The PMIs are watched closely by ﬁnancial markets, and the turnround explains why
European shares have outperformed US shares over the past month. Tensions over Syria have knocked Europe’s
equity rally recently. And the US Federal Reserve’s plan to scale back its asset purchases, or quantitative easing,
could become a big problem for the eurozone and the ECB if it results in tighter monetary conditions in Europe.
But the strength of equity rallies in countries such as Greece - still up almost 20 per cent year to date - highlight
the shift in investor sentiment towards Europe’s monetary union. The big exception? Italy’s shares are back down
to early January levels. That is hard to explain on economic grounds alone. True, Spain has made more progress in
improving competitiveness by reducing unit labour costs. But Italy’s PMIs are almost as strong as Spain’s. Both
have seen modest improvements in credit conditions recently amid clear signs that the eurozone ¨
- which resulted in widely diverging borrowing costs across the region - has gone into reverse. The only explanation
is that markets remain spooked by Italian politics - especially threats that Silvio Berlusconi could bring down the
administration led by Enrico Letta. As long as Italy’s government looks wobbly, investors will fret about reform
prospects, and Mr Draghi will keep the country on his worry list.
.2 Top 20 word occurrences
Word Occurrences Word Occurrences
strong 8664 crisis 17534
good 7206 cut 14255
despite 6941 bailout 8697
stability 6113 negative 8688
boost 5977 late 7888
better 5615 easing 7632
highest 4935 unemployment 6715
gains 4351 concerns 6574
positive 4278 fears 5585
able 4193 weak 5551
leading 3684 losses 5353
stronger 3640 warned 5337
best 3585 decline 4825
gained 3378 deﬁcit 4821
greater 3082 problems 4798
strength 2397 lost 4492
great 2371 recession 4291
gain 2362 dropped 4214
beneﬁt 1797 problem 4209
progress 1770 default 4143
Note: The table shows the 20 words from the Loughran and McDonald (2011) dictionary with the highest number
of occurrences in our database. Top positive (ﬁrst column) and top negative (second column) words are shown.
Appendix B - Sentix investor conﬁdence index versus MSI
We ﬁrst plot the monthly Sentix investor conﬁdence index for the Eurozone together with the
MSI average at the monthly frequency (Figure 3, upper part), and then the residual component of
our index after having regressed it on the Sentix investor conﬁdence index (Figure 3, lower part).
Figure 3: MSI (monthly average) versus Sentix investor economic conﬁdence for the Euro area
Notes: the top graph shows the media sentiment indicator aggregated at a monthly frequency versus the Sentix
investor economic conﬁdence index for the Euro area. The bottom graph shows the residuals of the regression of
the media sentiment index on the Sentix investor conﬁdence index, labelled "idiosynchratic component with
respect to Sentix index". The events corresponding to important highs and lows are commented.
The residual component shows the idiosyncratic information to our index with respect to the
Sentix investor conﬁdence index. We observe that it contains relevant information on monetary
policy perception. Focusing on the post-crisis period, the largest peak arises during the "whatever
it takes" speech of Mario Draghi in the summer of 2012. Our MSI index soars the month of
Draghi speech while investor sentiment stays at a low level. After that period, it peaks again when
forward guidance is introduced in 2013 and when QE talks start in late 2014. We also see that
in 2009 the idiosyncratic component of our index remains signiﬁcantly positive for some months,
possibly revealing that the conﬁdence in the central bank solving the ﬁnancial crisis was relatively
high (in the context of a falling investor economic sentiment). The lowest peak arises in January
2008, when markets become relatively pessimistic on the economic outlook, while Trichet remains
hawkish in his communication and the ECB does not cut rate in the January meeting, contrasting
with the accommodating policy decided by the FeD few days before.
Appendix C - Data on ECB communications
Press conference communications are extracted from the ECB website. We measure their quali-
tative content in terms of monetary policy inclination (hawkish versus dovish) and communication
vis-à-vis the economic outlook (positive versus negative). To do so, we apply the ﬁeld-speciﬁc
dictionary developed in Picault and Renault (2017), which aims at capturing these two aspects
of the ECB member’s speeches, and is exactly built from the ECB members communications’
wording. In contrast to standard methods, it uses contiguous sequence of words and a speciﬁc
term-weighting approach to quantify the content of communication. This ECB-speciﬁc dictionary
has been shown to better capture the central bankers’ communication subtleties compared to the
traditional alternatives and is thus a natural candidate to extract the qualitative content of the
ECB communications. With the monetary policy inclination dictionary, we build a variable reﬂect-
ing the hawkish/dovish inclination of the President speeches during the press conference as well as
a variable reﬂecting its communication on the economic outlook during the press conference. The
classiﬁcation of the ECB press conferences’ content is presented in Figure 4.
Figure 4: ECB Press Conference Introductory statement content
Note: The Figure presents, at a daily frequency, the monetary policy (lower value indicates a more
accommodating content) and economic outlook (a lower value indicates a more negative content) inclination of the
ECB Press conference Introductory Statement using Picault and Renault (2017) lexicon and methodology.
Regarding inter-meeting communications, we use data kindly provided by Gertler and Horvath
(2018), who manually classiﬁed ECB inter-meeting communications in function of their monetary
policy and economic outlook inclination. We obtain daily data representing the average inclina-
tion of the communications, diﬀerentiated between the communications from the President and
the communications from the other members of the Governing Council (other Executive Board
members and Heads of national central banks). We use them to construct two distinct daily time
series. The the number of communications and the resulting data are displayed in Figure 5 and 6
Figure 5: Inter-meeting communications from ECB oﬃcials
Note: The Figure presents the daily number of inter-meeting communications from ECB oﬃcials during our
sample period using the Gertler and Horvath (2018) database.
Figure 6: ECB Communications tonality regarding Monetary Policy and Economic Outlook
Note: The Figure presents, at a daily frequency, the monetary policy inclination of the ECB communications (on
the left - a lower value indicates a more accommodating content) and the economic outlook view of the ECB
communications (on the right - a lower value indicates a more negative content). We present here the
accumulated values for these data for readability purpose, in all estimates the ﬁrst diﬀerence of these was taken.
Appendix D - List of macroeconomic surprises and unconven-
tional monetary policies considered
Table 5: Macroeconomic surprises:
Country or area Variable
Inﬂation surprises, ﬂash CPI
Business climate surprises
Industrial conﬁdence surprises
France Inﬂation surprises, HCPI
Industrial production surprises
Inﬂation surprises, HCPI
Business climate surprises
Inﬂation surprises, HCPI
Manufacture conﬁdence surprises
Inﬂation surprises, HCPI
Industrial output surprises
Table 6: Unconventional monetary policies:
Press release date Unconventional monetary policy
7 May 2009 Covered bond purchase programme (ﬁrst) and 1-year LTRO
10 May 2010 Securities market programme
7 August 2011 Securities market programme, new annoucement
6 October 2011 Covered bond purchase programme (second) and new LTRO
6 September 2012 Technical features of OMT
5 June 2014 Asset-backed securities purchase programme and TLTRO
4 September 2014 Asset-backed securities purchase programme and Covered bond pur-
22 January 2015 Public sector purchase programme
9 March 2015 Public sector purchase programme, new announcement
10 March 2016 Public sector purchase programme, new announcement (increase in size)
Note: From Moessner (2018), press release date is veriﬁed on the ECB website.
Appendix E - Media Sentiment determinants, eﬀect of com-
munications - Robustness and further tests
Table 7: Media Sentiment determinants, eﬀect of communications - model with additional lags
     
MSIt−10.280*** 0.278*** 0.275*** 0.238*** 0.252*** 0.252***
(0.033) (0.034) (0.034) (0.032) (0.033) (0.025)
MSIt−20.069*** 0.067*** 0.068*** 0.048** 0.048* 0.048*
(0.025) (0.026) (0.026) (0.024) (0.026) (0.026)
MSIt−30.045 0.045 0.047 0.032 0.032 0.032
(0.033) (0.033) (0.033) (0.032) (0.027) (0.025)
MSIt−40.086*** 0.088*** 0.087*** 0.065** 0.063** 0.063**
(0.024) (0.025) (0.025) (0.026) (0.028) (0.025)
President MP - Press Conferencest-0.479*** -0.523*** -0.507*** -0.441*** -0.438*** -0.438***
(0.132) (0.142) (0.145) (0.132) (0.121) (0.162)
President MP - Press Conferencest−1-0.525*** -0.510*** -0.515*** -0.383*** -0.367*** -0.367**
(0.095) (0.116) (0.117) (0.121) (0.122) (0.163)
President MP - Press Conferencest−2-0.409*** -0.465*** -0.471*** -0.345*** -0.364*** -0.364**
(0.084) (0.110) (0.110) (0.117) (0.126) (0.164)
President MP - Press Conferencest−3-0.402*** -0.330** -0.331** -0.266* -0.270* -0.270
(0.140) (0.144) (0.144) (0.147) (0.149) (0.164)
President EC - Press Conferencest0.555* 0.627* 0.595* 0.590* 0.570** 0.570
(0.329) (0.341) (0.332) (0.322) (0.284) (0.417)
President EC - Press Conferencest−10.198 0.204 0.213 0.183 0.196 0.196
(0.205) (0.242) (0.243) (0.235) (0.222) (0.417)
President EC - Press Conferencest−20.072 0.061 0.075 0.050 0.065 0.065
(0.263) (0.299) (0.298) (0.301) (0.298) (0.421)
President EC - Press Conferencest−30.539 0.419 0.417 0.512 0.449 0.449
(0.376) (0.423) (0.429) (0.420) (0.390) (0.422)
Other members MP - Inter Meetingt−1-0.061** -0.060** -0.061** -0.052* -0.054* -0.054
(0.029) (0.029) (0.029) (0.028) (0.032) (0.034)
Other members MP - Inter Meetingt−2-0.045 -0.043 -0.042 -0.036 -0.036 -0.036
(0.028) (0.028) (0.028) (0.027) (0.034) (0.034)
Other members MP - Inter Meetingt−30.016 0.016 0.011 0.028 0.035 0.035
(0.039) (0.039) (0.039) (0.039) (0.036) (0.035)
Other members EC - Inter Meetingt−10.028 0.020 0.023 0.019 0.013 0.013
(0.047) (0.047) (0.046) (0.046) (0.053) (0.050)
Other members EC - Inter Meetingt−20.109** 0.105** 0.106** 0.083** 0.075* 0.075
(0.043) (0.044) (0.044) (0.041) (0.044) (0.050)
Other members EC - Inter Meetingt−3-0.002 -0.007 -0.008 -0.044 -0.046 -0.046
(0.050) (0.051) (0.051) (0.047) (0.046) (0.050)
President MP - Inter Meetingt−1-0.177*** -0.168*** -0.156*** -0.166*** -0.170*** -0.170**
(0.043) (0.046) (0.047) (0.045) (0.054) (0.075)
President MP - Inter Meetingt−20.058 0.058 0.056 0.047 0.039 0.039
(0.063) (0.065) (0.068) (0.070) (0.069) (0.075)
President MP - Inter Meetingt−30.010 0.011 0.004 -0.000 0.013 0.013
(0.072) (0.074) (0.074) (0.071) (0.079) (0.074)
President EC - Inter Meetingt−10.228** 0.231** 0.230** 0.216** 0.227** 0.227*
(0.091) (0.092) (0.090) (0.099) (0.093) (0.132)
President EC - Inter Meetingt−2-0.328*** -0.329** -0.327*** -0.337*** -0.340** -0.340**
(0.127) (0.128) (0.127) (0.124) (0.139) (0.132)
President EC - Inter Meetingt−3-0.140 -0.141 -0.145 -0.168 -0.162 -0.162
(0.145) (0.146) (0.149) (0.148) (0.190) (0.132)
ECB decisions, t to t-3 No Yes Yes Yes YesnYesn
Macro-economic surprises, t to t-3 No No Yes Yes YesnYesn
Financial variables, t to t-3 No No No Yes YesnYesn
Constant -0.061*** -0.063*** -0.062*** 0.202*** 0.215*** 0.215***
(0.018) (0.019) (0.019) (0.052) (0.047) (0.050)
Adjusted R20.165 0.159 0.158 0.197 0.197 0.197
Obs. 1630 1630 1630 1630 1630 1630
Note: Newey-West Standard Errors in parenthesis. ***, **, and * represent statistical signiﬁcance at respectively 1%, 5%, and 10%. n:
only the controls which appeared statistically signiﬁcant at the 10 percent level in column (4) included in the speciﬁcation. M SI is the
Media Sentiment Index. All control variables are deﬁned in Table 2.
Table 8: Media Sentiment determinants, eﬀect of communications - Robustness
     
H-W lag nega sent harvard sent vader sent weighted sent
t−10.266*** 0.255*** 0.146*** 0.160*** 0.136*** 0.196***
(0.033) (0.033) (0.035) (0.029) (0.025) (0.030)
t−20.050* 0.056** 0.097*** 0.036 0.041* 0.069**
(0.026) (0.026) (0.025) (0.024) (0.022) (0.029)
t−30.030 0.027 0.035 0.034 0.039* 0.015
(0.027) (0.031) (0.027) (0.029) (0.023) (0.031)
t−40.062** 0.070*** 0.102*** 0.074*** 0.065*** 0.094***
(0.028) (0.024) (0.025) (0.028) (0.020) (0.024)
President MP - Press Conferencest-0.401*** -0.428*** -0.369*** -3.840* -6.587*** -0.405***
(0.120) (0.134) (0.100) (2.147) (1.531) (0.127)
President EC - Press Conferencest0.554** 0.558 0.702*** -0.998 3.667 0.652**
(0.278) (0.341) (0.272) (3.857) (3.352) (0.330)
Other members MP - Inter Meetingt−1-0.066** -0.059** -0.044** -1.427** -1.065*** -0.062**
(0.032) (0.027) (0.022) (0.684) (0.376) (0.028)
Other members EO - Inter Meetingt−10.012 0.009 0.000 0.073 -0.118 0.021
(0.052) (0.042) (0.044) (0.727) (0.366) (0.055)
President MP - Inter Meetingt−1-0.166*** -0.172*** -0.139*** -1.695* -1.883*** -0.144***
(0.052) (0.041) (0.040) (0.889) (0.489) (0.045)
President EO - Inter Meetingt−10.217** 0.203** 0.023 -0.799 0.694 0.085
(0.087) (0.091) (0.095) (1.883) (0.778) (0.080)
∆i-0.736 -0.548 -0.852 0.637 2.477 -1.192
(0.648) (0.541) (0.572) (11.491) (6.744) (0.731)
Negative Interest Surprise 1.733*** 1.682*** 1.455*** 28.494** 6.742 2.050***
(0.631) (0.563) (0.554) (11.886) (9.501) (0.695)
Positive Interest Surprise 0.829 1.088** 0.327 2.086 -3.038 -0.468
(0.749) (0.540) (0.460) (8.990) (5.672) (0.856)
APP -0.151 -0.049 0.025 0.798 -0.602 -0.123
(0.144) (0.072) (0.079) (1.653) (1.051) (0.168)
Negative Rate 0.166*** 0.157*** 0.104*** 12.091*** 6.692*** -0.029
(0.034) (0.039) (0.029) (0.604) (0.433) (0.040)
Whatever it takes 0.163*** 0.330*** 0.004 1.765 -0.451 -0.139**
(0.056) (0.037) (0.055) (1.136) (0.866) (0.058)
Macro-economic surprisestYes No Yes Yes Yes Yes
Financial variablestYes No Yes Yes Yes Yes
Macro-economic surprisest−1No Yes No No No No
Financial variablest−1No Yes No No No No
Constant 0.241*** 0.206*** 0.165*** 4.431*** 2.766*** 0.223***
(0.045) (0.051) (0.042) (1.089) (0.773) (0.060)
Adjusted R20.188 0.171 0.123 0.101 0.108 0.142
Obs. 1630 1630 1630 1630 1630 1630
Note: Estimates of the coeﬃcients of equation 3, with alternative controls and alternative constructions of the MSI. In parenthesis,
Huber-White Standard Errors in column (1) and Newey-West Standard Errors in columns (2), (3), (4), (5) and (6). In columns (3),
(4), (5) and (6) we use alternative dependant variables so that M SI ais built with only the negative words considered in equation 1
(column 3), or with the positive and negative words from the Harvard-IV dictionary (column 4), or using Vader classiﬁer (column 5) or
by weighting the newspapers using a proxy for their audience (column 6, see footnote 32 for details). ***, **, and * represent statistical
signiﬁcance at respectively 1%, 5%, and 10%. All control variables are deﬁned in Table 2.
Table 9: Media Sentiment determinants, eﬀect of communications when inﬂation is above/below
   
MSIt−10.290*** 0.290*** 0.268*** 0.265***
(0.032) (0.032) (0.031) (0.031)
MSIt−20.064** 0.063** 0.048* 0.048*
(0.026) (0.026) (0.026) (0.026)
MSIt−30.036 0.036 0.025 0.026
(0.033) (0.033) (0.031) (0.031)
MSIt−40.079*** 0.079*** 0.060** 0.061**
(0.024) (0.024) (0.024) (0.024)
President MP - Press Conferencest-0.405*** -0.455*** -0.405*** -0.405***
(0.129) (0.139) (0.126) (0.126)
President EC - Press Conferencest0.973* 0.976* 1.052** 1.016**
(0.514) (0.503) (0.434) (0.441)
Other members MP - Inter Meetingt−1-0.088*** -0.089*** -0.073** -0.078**
(0.033) (0.034) (0.033) (0.033)
Other members EO - Inter Meetingt−10.046 0.046 0.063 0.068
(0.054) (0.054) (0.054) (0.053)
President MP - Inter Meetingt−1-0.200*** -0.200*** -0.190*** -0.186***
(0.048) (0.048) (0.045) (0.046)
President EO - Inter Meetingt−10.184* 0.184* 0.166 0.166
(0.102) (0.102) (0.104) (0.104)
T arget -0.088** -0.088** -0.046 -0.049
(0.039) (0.038) (0.035) (0.035)
President MP - Press ConferencestxT arget -0.081 -0.025 -0.051 -0.047
(0.321) (0.311) (0.285) (0.286)
President EO - Press ConferencestxT arget -0.733 -0.774 -0.847 -0.821
(0.675) (0.662) (0.579) (0.585)
Other members MP - Inter Meetingt−1xT arget 0.095* 0.102* 0.058 0.062
(0.056) (0.055) (0.059) (0.057)
Other members EO - Inter Meetingt−1xT arget -0.103 -0.106 -0.190* -0.193*
(0.093) (0.093) (0.100) (0.099)
President MP - Inter Meetingt−1xTar get 0.142 0.141 0.086 0.097
(0.096) (0.096) (0.096) (0.092)
President EO - Inter Meetingt−1xTar get 0.237* 0.234* 0.267* 0.267*
(0.142) (0.142) (0.145) (0.145)
∆i-0.136 -0.464 -0.455
(0.472) (0.581) (0.588)
Negative Interest Surprise 1.406*** 1.593*** 1.596***
(0.468) (0.525) (0.532)
Positive Interest Surprise 0.937 0.357 0.329
(0.609) (0.805) (0.809)
APP -0.116 -0.155 -0.151
(0.079) (0.146) (0.147)
Negative Rate 0.182*** 0.163*** 0.162***
(0.035) (0.036) (0.035)
Whatever it takes 0.507*** 0.240*** 0.240***
(0.054) (0.061) (0.063)
Financial variables No No Yes Yes
Macro-economic surprises No No No Yes
Constant -0.021 -0.021 -0.020 0.194***
(0.017) (0.017) (0.017) (0.044)
Adjusted R20.155 0.154 0.186 0.188
Obs. 1630 1630 1630 1630
Note: T arget is a dummy variable equal to 1 if inﬂation is above the 2% target, 0 otherwise. Newey-West Standard Errors in parenthesis.
***, **, and * represent statistical signiﬁcance at respectively 1%, 5%, and 10%. MS I is the Media Sentiment Index. All control variables
are deﬁned in Table 2.
Appendix F - Descriptive statistics
Table 10: Descriptive Statistics (2010-2016)
Mean S. D. Max. Min. Obs.
MSIgross -1.508 0.558 0.902 -3.945 1630
MSI -0.071 0.543 2.338 -2.562 1630
MSIN egativeW ords -0.065 0.452 1.574 -2.103 1630
MSIHarvardI V −4-0.319 9.250 32.008 -41.962 1630
MSIV ader -1.700 6.889 77.591 -63.855 1630
MSIW eighted -0.126 0.583 2.292 -3.139 1630
President MP - Press Conferences -0.013 0.088 0.545 -0.761 1630
President EC - Press Conferences -0.003 0.035 0.296 -0.473 1630
President MP - Inter Meeting -0.015 0.167 1.000 -1.000 1630
President EC - Inter Meeting -0.001 0.093 1.000 -1.000 1630
Other members MP - Inter Meeting -0.034 0.376 1.000 -1.000 1630
Other members EC - Inter Meeting -0.003 0.249 1.000 -1.000 1630
Monetary Policy Decisions
∆i-0.001 0.017 0.250 -0.250 1630
Negative Interest Surprise -0.000 0.009 0.000 -0.250 1630
Positive Interest Surprise 0.000 0.006 0.250 0.000 1630
APP 0.005 0.070 1.000 0.000 1630
Negative Rate 0.001 0.025 1.000 0.000 1630
Whatever it takes 0.001 0.025 1.000 0.000 1630
Fiscal stress 0.001 0.178 1.376 -3.608 1630
∆EURUSD -0.000 0.006 0.030 -0.024 1630
VSTOXX 23.810 6.789 53.547 12.713 1630
∆EUROSTOX 0.000 0.014 0.098 -0.063 1630
∆5Y-5Y Inﬂation Expectations (swap) -0.082 2.092 10.507 -8.741 1608
∆10Y Inﬂation Expectations (swap) -0.089 2.033 10.000 -10.500 1608