Do words hurt more than actions? : the impact of trade tensions on financial markets.
Date: 2020-12-08
Institutions: European Central Bank
Subjects: economic analysis, economic consequence, exchange rate, financial market, international market, international trade, trade dispute

Massimo Minesso Ferrari, Frederik Kurcz,  
Maria Sole Pagliari 

Working Paper Series 

Do words hurt more than actions?  
The impact of trade tensions  
on financial markets 

No 2490 / November 2020 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract

In this paper, we apply textual analysis and machine learning algorithms to construct an
index capturing trade tensions between US and China. Our indicator matches well-known
events in the US-China trade dispute and is exogenous to the developments on global ﬁnan-
cial markets. By means of local projection methods, we show that US markets are largely
unaﬀected by rising trade tensions, with the exception of those ﬁrms that are more exposed
to China, while the same shock negatively aﬀects stock market indices in EMEs and China.
Higher trade tensions also entail: i) an appreciation of the US dollar; ii) a depreciation of
EMEs currencies; iii) muted changes in safe haven currencies; iv) portfolio re-balancing be-
tween stocks and bonds in the EMEs. We also show that trade tensions account for around
15% of the variance of Chinese stocks while their contribution is muted for US markets.
These ﬁndings suggest that the US-China trade tensions are interpreted as a negative de-
mand shock for the Chinese economy rather than as a global risk shock.

Keywords: Trade Shocks; Machine Learning; Stock indexes; Exchange rates

JEL Codes: D53; E44; F13; F14; C55

ECB Working Paper Series No 2490 / November 20201Non-technical summary

Since the beginning of the 2016 US electoral campaign, trade deﬁcits, trade agreements, ex-

change rate management and tariﬀs imposition have been at the core of the economic policy

debate.

In the subsequent years there has been an escalation in trade tensions between US

and China, a period that is often referred to as the Sino-American “trade war”, whereby both

countries have implemented bilateral tariﬀs aimed at harming key sectors of the opponent’s

economy, in an attempt to force it into a more favourable trade treaty.

As to the consequences of trade tensions for the global economy, academics and policy

makers are still struggling to quantify the extent to which such tensions have spoken to the

global economic slowdown of 2019. In this regard, the dominant view argues that “the weak-

ness in growth is driven by a sharp deterioration in manufacturing activity and global trade,

with higher tariﬀs and prolonged trade policy uncertainty damaging investment and demand for

capital goods” (Gopinath (2019)). There are several channels through which tariﬀs can hamper

global economic activity. For example, higher tariﬀs increase the price of imported goods, shift-

ing demand towards domestic production and reducing revenues for foreign producers. Such

losses may be further ampliﬁed by second-round eﬀects through the global value chain (i.e.

production-reducing exporters in turn decrease their demand for intermediate goods) which

can eventually feed back into the tax-imposing country. Another channel is related to risk:

tariﬀ threats might increase uncertainty around the proﬁtability of investment projects, thus

making them less appealing and leading to a delaying of investments with detrimental on the

economic outlook. There is not however a strong consensus on which of these channel prevails

or on the quantiﬁcation of the eﬀects of rising trade tensions. For example, general equilibrium

models based on trade elasticities produce sizable eﬀects of trade barriers only when assum-

ing large shocks. Recent contributions have also suggested that these results might be driven

by signiﬁcant asymmetries in the response to tariﬀs (Furceri et al. (2018)), or by changes in

exchange rate adjustments mechanism over the last decade (Eichengreen (2017)).

Against this backdrop, our paper proposes three innovative contributions to the existing

literature. First, we construct a proxy for trade tensions shocks by adopting a novel approach,

which is based on textual analysis. Speciﬁcally, we use a supervised machine learning algo-

rithm to assess the relative “protectionism content” of a large set of announcements in the

Sino-American “trade war”. The time series of such scores provides a trade tension measure,

which can be used to quantify their impact on the global economy. Second, we show that the

constructed measure is exogenous to ﬁnancial market developments (both domestic and global),

ECB Working Paper Series No 2490 / November 20202thus supporting the main assumption that announcements have been largely unanticipated by

markets. This result is indeed relevant as allows to use the indicator as a measure of unexpected

trade-policy surprises. Finally, we use the index to assess the reaction of ﬁnancial agents to

an increase in trade tensions. We show that a trade tension shock leaves the US stock market

almost unaﬀected, except for those companies heavily exposed to China, while also inducing

a contraction of stock market indexes both in China and other emerging market economies

(EMEs). Moreover, the same shock entails an appreciation in the US dollar, a broad deprecia-

tion in EMEs currencies, while safe heaven currencies remain almost unaﬀected. The absence

of safe-haven eﬀects seems to suggest that trade tension shocks are interpreted by international

investors as a negative demand to Chinese production rather than as a global risk aversion

shock, with the US dollar appreciation simply reﬂecting a trade re-balancing. This intuition is

further reinforced by the results for ﬁxed income markets. While, indeed, US long term yields

do not react signiﬁcantly to the shock, market participants in EMEs tend to substitute stocks

with bonds, thus eﬀectively re-balancing their portfolios.

ECB Working Paper Series No 2490 / November 202031 Introduction

Since the beginning of the 2016 US electoral campaign, trade deﬁcits, trade agreements, ex-

change rate management and tariﬀs imposition have been at the core of the economic policy

debate1. In the subsequent years there has been an escalation in trade tensions between US

and China, a period that is often referred to as the Sino-American “trade war”, whereby both

countries have implemented bilateral tariﬀs aimed at harming key sectors of the opponent’s

economy, in an attempt to force it into a more favourable trade treaty. The barriers eﬀectively

implemented, however, are minor compared to those each contender threatened to raise (Fig-

ure 1). The US administration got to the point of suggesting it would withdraw from the WTO,

should the organization not “shape up” to tackle alleged unfair trade practices by China2.

Figure 1: Current and proposed US tariﬀs against China by good category.
Source: Congressional Research Service (2019).

The “trade war” seems to have then entered a phase of armed truce, with the US and China

involved in the deﬁnition of a “phase one deal”, which is still ongoing at the time of redaction

of this paper. As to the consequences of trade tensions for the global economy, academics and

policy makers have in particular tried to quantify the extent to which such tensions have spoken

to the global economic slowdown of 2019. In this regard, the dominant view argues that “the

weakness in growth is driven by a sharp deterioration in manufacturing activity and global trade,

with higher tariﬀs and prolonged trade policy uncertainty damaging investment and demand for

capital goods” (Gopinath (2019)). Speciﬁcally, tariﬀs can hamper global economic activity via

1See for example the 2018 Charlevoix G7 Summit Communique.
2See “Donald Trump threatens to pull US out of the WTO”, The Financial Times, 31 August 2018.

ECB Working Paper Series No 2490 / November 20204two main channels. First, higher tariﬀs trigger direct eﬀects through trade, as higher tariﬀs

interfere with the optimal price equilibrium, thus giving rise to deadweight losses as per the

Harberger triangle. Such losses are then ampliﬁed by second-round eﬀects through the global

value chain (i.e. production-reducing exporters in turn decrease their demand for intermediate

goods) which can eventually feed back into the tax-imposing country (Meinen (2019)). The

second channel is mainly related to risk: the mutual barrages of tariﬀ threats between the US

and China might have raised market participants’ concerns about the proﬁtability of investment

projects, thus making them less appealing3. This in turn leads to further delay investments,

with detrimental eﬀects on both aggregate demand and (possibly) future output contractions

stemming from productivity losses.

Our paper proposes three innovative contributions to the existing literature on trade tensions

between the US and China. First, we construct a proxy for trade tensions shocks by adopting a

novel approach, which makes use of the announcements regarding the US-China trade dispute
on the US President Twitter© account to measure innovations in the history of Sino-American

trade tensions that are exogenous to macroeconomic and ﬁnancial developments. The new

indicator is based on the “protectionism content” of each tweet. Speciﬁcally, we download all
tweets from President Trump’s Twitter© account and we deploy textual analysis and machine

learning techniques to assign a score quantifying the degree of protectionism to each tweet. The

time series of such scores provides us with the trade tension measure, which we name Trade

Tension Tweet Index (3T-Index).

Second, we show that the 3T-Index is exogenous to ﬁnancial market developments (both do-

mestic and global), which indicates that announcements have not been anticipated by markets.

Finally, we use the 3T-Index to assess the reaction of ﬁnancial agents to an increase in trade

tensions. We show that a trade tension shock leaves the US stock market almost unaﬀected,

except for those companies heavily exposed to China, while also inducing a contraction of stock

market indexes both in China and other emerging market economies (EMEs). Moreover, the

same shock entails an appreciation in the US dollar, a broad depreciation in EMEs currencies,

while safe heaven currencies remain almost unaﬀected. The absence of safe-haven eﬀects seems

to suggest that trade tension shocks are interpreted by international investors as a negative

demand to Chinese production rather than as a global risk aversion shock, with the US dollar

appreciation simply reﬂecting a trade re-balancing. This intuition is further reinforced by the

results for ﬁxed income markets. While, indeed, US long term yields do not react signiﬁcantly

to the shock, market participants in EMEs tend to substitute stocks with bonds, thus eﬀectively

3This is often referred to as the “conﬁdence channel” (Gloe Dizioli and van Roye (2018)).

ECB Working Paper Series No 2490 / November 20205re-balancing their portfolios. These results are reinforced by the analysis of the contribution of

the 3T-Index to the forecast error variance decomposition (FEVD) of ﬁnancial variables. We

show that the index explains 10% to 15% of the volatility of EMEs ﬁnancial markets while its

contribution is muted for US stocks; the index also does not explain movements of safe have

assets such as US 10-year yields or the Japanese yen.

Our results suggest that market participants have read trade-related surprises as real shocks

to Chinese demand, rather than an increase in global risk.

1.1 Related literature

This paper is related to and builds on three main strands of literature: i) research assessing the

macroeconomic implications of trade shocks; ii) research focusing on the role of uncertainty;

iii) research applying machine learning and textual analysis techniques to economics.

The macroeconomic implications of trade shocks

The beginning of the so-called “trade war” between the US and China has brought about

several contributions investigating the eﬀects of rising protectionism. One strand of literature

uses multi-country general equilibrium models to assess the implications of rising trade barriers.

Berthou et al. (2018), for example, ﬁnds that a general increase of tariﬀs by 10% would contract

global GDP by 2% on impact. Gloe Dizioli and van Roye (2018) reach similar conclusions

under slightly diﬀerent assumptions. Moving to empirical approaches, Barattieri et al. (2018)

use country-level VARs to show that tariﬀs act as a supply shock for the imposing country with

limited eﬀects on the trade balance. Other papers, such as Feenstra et al. (2019), Autor et al.

(2016), Feenstra and Sasahara (2018) and Acemoglu et al. (2016), analyse sector-level data and

ﬁnd that reducing import barriers have positive spillovers to domestic sectors, which in turn

leads to an expansion of domestic output and employment. Another stream of research assesses

the impact of trade-induced uncertainty on the business cycle. Among others, Caldara et al.

(2019) derive a text-based measure of trade policy uncertainty.

One of the main drawbacks of these contributions, however, is that sizable movements in real

variables can be induced only by very severe shocks. For instance, the framework proposed by

Berthou et al. (2018) is able to generate an economic contraction by 2% of GDP by assuming

that all countries worldwide rise bilateral tariﬀs on all goods by 10% at once. In Barattieri

et al. (2018), a one standard deviation increase in trade barriers can shrink GDP by 0.2%. An

explanation to these results is provided by Furceri et al. (2018), who ﬁnd that the eﬀects of

changes in protectionism are highly non-linear, with the imposition of trade barriers being much

ECB Working Paper Series No 2490 / November 20206more impactful than trade liberalization. Therefore, trade models estimated with historical data

might produce downward biased results, since episodes of trade liberalization strongly dominate

the sample. Alternatively, as highlighted by Eichengreen (2017), the exchange rate adjustment

to tariﬀs might have changed in the last decade.

Measures of uncertainty

Fern´andez-Villaverde et al. (2011) show in a closed economy model that increasing volatility of

macro variables have signiﬁcant real eﬀects. One of the reasons why volatility can increase is

indeed higher uncertainty related to trade policy developments.

In this regard, since the seminal paper by Bloom (2009), many researchers have tried to

quantify uncertainty. A stream of this research makes use of high-frequency stock data to

construct uncertainty measures. Caggiano et al. (2014), for instance, use the VIX to estimate

the eﬀect of uncertainty on US employment4, while Baker and Bloom (2013) uses the stock

market reaction to natural disasters as a natural experiment to measure uncertainty shocks.

The drawback of this approach is that asset valuation in the short run is mostly driven by

factors other than uncertainty, in spite of the quick reaction on the part of ﬁnancial markets

to new information. Hence the constructed measure might relate to economic activity only

indirectly.

An alternative method consists of deﬁning uncertainty as the unexplained component of

economic forecasts. Jurado et al. (2015) measure uncertainty as the error term of a forecast

model based on a large time-varying VAR and show that the constructed measure of uncertainty

spikes around well-known uncertainty episodes. In their approach, however, also common large

shocks might be identiﬁed as uncertainty, since they inﬂate the residual of the forecast model

by construction.

Finally, the strand of literature which is closest to our work includes several papers that

extract measures of uncertainty from text data. Baker et al. (2016) and Caldara and Iacoviello

(2018) deﬁne uncertainty as the share of newspaper articles discussing US or global geopolitical

tensions. Caldara et al. (2019), on the other hand, count the number of occurrences of tariﬀs-

related words in CEO earning calls to construct an index of trade uncertainty. Diﬀerently from

these contributions, however, our purpose is not to construct a measure of trade uncertainty,

but rather to measure the intensity of trade shocks. In addition, we go beyond simple word

counting in that we deploy a sophisticated learning model to derive a trade tension index.

4Bekaert et al. (2013) further decompose the VIX into a “risk aversion” and an “uncertainty” component.

ECB Working Paper Series No 2490 / November 20207Textual analysis and machine learning in economics

There is a growing literature that applies machine learning and textual analysis tools to economic

problems5. Gholampour and van Wincoop (2019), Bianchi et al. (2019), Hansen et al. (2018) and

Ke et al. (2019) are the most recent contributions that are relevant for our paper. Speciﬁcally,

Gholampour and van Wincoop (2019) extract information from the tweets of professional traders

which is found to improve the forecast of exchange rate movements at high frequency. Bianchi
et al. (2019), on the other hand, use Twitter© announcements as proxy for shocks to the FED

independence by assessing their impact on fed fund futures. Hansen et al. (2018) as well analyse

monetary policy decision-making by analysing the FOMC minutes via computation linguistic

algorithms. Finally, Ke et al. (2019) construct a time series of relevant information by applying

textual analysis methods to the Dow Jones Newswires. Such series is found to signiﬁcantly

improve the forecast of stock prices6.

These analyses mainly leverage on the existing time lag between the availability of new

information in text data, their pricing-in on the part of markets and their ﬁnal eﬀect on slower-

moving macroeconomic variables. Textual analysis methods can summarize these information

in a quantitative indicator, in a manner which is not diﬀerent from the synthesis across diﬀerent

time series performed by a principal component analysis. In particular, the index is assumed to

include information that have not been already priced in by market participants, thus providing

additional insights as to future movements of stock indices. In this paper we exploit similar

techniques to quantify the “degree of protectionism” of diﬀerent announcements that have not

been anticipated by markets. The resulting index can be used as a proxy for the severity of the

surprise of each event.

The remainder of the paper is structured as follows: Section 2 presents the main data,

explains how the index is constructed and, in Section 2.5, performs the validation of the exo-

geneity assumption; Section 3 assesses the reaction of ﬁnancial markets to rising trade tensions;

Section 4 concludes.

2 Data and the 3T-Index

This section describes how we construct the 3T-Index, starting with the analysis of the tweets

by President Trump.

Important events in the Sino-American trade dispute have been often

pre-announced by the US president on social medial. The most striking example are the Steel

5See Currie et al. (2019) and Gentzkow et al. (2019) for an extensive review.
6Other contributions include Werner and Murray (2004), Bollen et al. (2011), Da et al. (2011), Leung and

Ton (2015), Loughran and McDonald (2011).

ECB Working Paper Series No 2490 / November 20208and Aluminium tariﬀs, which have been discussed on Twitter© posts since March 1 2018, well

in advance of the oﬃcial decision by the US administration on March 9 2018 (Figure 2). This

pattern is consistent across all major actions undertaken by the US administration during the

trade dispute which have strongly impacted global markets7. On top of that, not only have

large policy changes been communicated by social media, but also information on the US trade

stance have been leaked almost daily, thus signalling small and large developments in the Sino-

American negotiations. Our objective is to construct a quantitative indicator of the relevance of

each of those communications by selecting the ones related to trade tensions, in order to assess

their medium-term eﬀect on ﬁnancial variables.

Figure 2: Reaction of the S&P500 Index to the Steel and Aluminium tariﬀ announcement.
Notes: the S&P500 Index is standardized to 100 on February 28 2018.

Speciﬁcally, we exploit Twitter© posts to assess the stance of the US communication. The

baseline assumption of this paper is that such tweets, though unexpected by markets, have been

nonetheless used after their release to extrapolate information on the likely stance of the US

administration on a trade deal with China. We then use these tweets to track developments in

the trade dispute.

A potential caveat to our analysis stems from the possible endogenous nature of the index,

especially vis-`a-vis ﬁnancial markets developments. For example, the language used by President

Trump might have changed in response to movements in the US stock market or in the US

dollar exchange rate. This would ultimately make the whole analysis biased. Therefore, in

Section 2.5 we ﬁrst test for the presence of endogeneity by regressing the 3T-Index on lagged and

contemporaneous ﬁnancial market developments. Results show that our constructed indicator

7It is widely accepted by economic commentators that Trump’s tweets have moved markets (“Trump’s Tweets

Drive The Market”, Forbes, Jan 8, 2020). In this regard, additional evidence is provided in Section 2.1.

ECB Working Paper Series No 2490 / November 20209cannot be systematically predicted by either US or Chinese ﬁnancial data. In Section 2.4 we

also report the summary statistics for the other main variables of interest.

2.1 Preliminary evidence

In this section we show that announcements on social media had indeed eﬀects on ﬁnancial

markets. We ﬁrst select days where the US President published tweets on (1) China, (2) China

& Trade and (3) China & Tariﬀs. Figure 3 depicts the the average absolute daily change on those

days against all trading days for the following variables: the S&P 500 Index, the Shanghai Stock

Market Index, the JP Morgan EMEs stock market index and the nominal eﬀective exchange

rate of the US dollar, the renminbi and emerging market economies8. All in all, the average

absolute change of the variables of interest is higher on days featuring releases of tweets related

to China and tariﬀs (green bars), compared to other days (blue bars), the only exception to this

trend being the change in the US dollar exchange rate. This stylized fact already suggests that

markets were responsive to social media communications related to the developments of trade

negotiations between the US and China.

Figure 3: Average absolute daily changes in days with tweets related to (1) China and tariﬀs;
(2) China and trade; (3) China; (4) all other days.

We check more formally for the signiﬁcance of such changes by setting up an event study as

follows:

8We use absolute changes to avoid that positive and negative changes net out in the averaging.

∆yt−1,t+k = αk + βkEventt + εt+k

(2.1)

ECB Working Paper Series No 2490 / November 202010where ∆yt−1,t+k is the log change in the variable of interest between t−1 and t+k for k = 0, 1, 2.9

Eventt is a dummy taking value of 1 (-1) for days with positive (negative) tweets as identiﬁed
by the Bloomberg “trade war” timeline.10 The coeﬃcient βk, therefore, captures the impact

of such events relative to the average change of the dependent variable at horizon k. If βk is

positive (negative) a positive event leads to a larger positive (negative) change of the dependent

variable relative to all other days.

We consider four key ﬁnancial variables: the S&P500, the Shangahai stock market index,

the US dollar NEER and the bilateral Chinese renminbi/US dollar exchange rate. Results

from the estimation of Equation (2.1) are reported in Table 1. Large events in the trade war,

as captured by the timeline, had indeed a signiﬁcant eﬀect on ﬁnancial markets. Notably,

positive developments in the trade war (release in trade tensions) lead to an increase in the

stock valuations in the US and China and a depreciation of the US dollar, both in nominal

eﬀective terms and bilaterally against China. On the contrary, rising trade tensions negatively

impact stock markets and trigger an appreciation of the US dollar.

Table 1: Daily impact of trade war events

S&P 500 index
0
0.104*
(0.062)
0.070
(0.064)
212
0.011

1
0.181**
(0.084)
0.169*
(0.098)
161
0.020
USD NEER

-0.027
(0.019)
-0.005
(0.020)
212
0.008

-0.083***
(0.020)
-0.008
(0.030)
161
0.047

k
βk

αk

Obs.
R2

βk

αk

Obs.
R2

2
0.228**
(0.111)
0.234**
(0.104)
155
0.024

-0.086**
(0.038)
0.006
(0.035)
155
0.031

βk

αk

Obs.
R2

βk

αk

Obs
R2

0
0.106*
(0.059)
0.037
(0.065)
212
0.012

Shanghai stock narket index
2
1
0.326***
0.335***
(0.118)
(0.078)
-0.040
-0.040
(0.118)
(0.103)
155
161
0.057
0.035
CHN/USD
-0.134***
(0.028)
-0.025
(0.025)
161
0.140

-0.151***
(0.046)
0.008
(0.031)
155
0.109

-0.040
(0.026)
-0.006
(0.016)
212
0.022

Notes: estimates of Equation (2.1) on daily data for the S&P500 index, the Shanghai stock market
index, the USD nominal eﬀective exchange rate and the CHN/USD exchange rate. The depen-
dent variable is expressed in log changes ×100 and Eventt is a dummy of value 1 (-1) for positive
(negative) tweets as identiﬁed by the Bloomberg “trade war” timeline. We consider the contempo-
raneous daily change (k = 0) and the change 1 and 2 days after the event. Robust standard errors
are reported in parentheses with *** p < 0.01, ** p < 0.05, * p < 0.1 conﬁdence intervals.

The event study outcome seems to validate the preliminary evidence depicted in Figure 3:

large events in the trade dispute between the US and China had an impact on ﬁnancial markets.

9Notice that for k = 0 the dependent variable is the daily change.
10See: https://www.bloomberg.com/news/articles/2019-08-28/u-s-china-trade-war-timeline-what-s-happened-

since-may-2019. Table B.1 reports the events and the related tweets used to construct the dummies.

ECB Working Paper Series No 2490 / November 202011However, the framework provided by Equation (2.1) has several limitations which make it less

appealing for an analysis over the medium-term.

First, the events considered are only the largest and most signiﬁcant in the trade dispute

history, which means that conclusions from the event study cannot be generalized to any event

not explicitly included in the sample. Second, the sampled events are considered as all equally

important, while we know that some of them have been more relevant than others. Third,

announcements on the trade dispute have often been contradictory, with positive statements

being closely followed (even within the same day) by negative ones. This complicates the

evaluation of single events as it is hard, with simple dummies, to disentangle which of them have

been dominated. Consider for example the sequence of tweets started on May 13 2018. At 15:03

a tweet was posted, suggesting that the US administration would commit to reduce barriers on

the imports of ZTE, a Chinese tech giant. This is clearly a positive and relevant development.

However, already at 19:22 another tweet was published on the the US President account stating:

“negotiations have been so one sided in favor of China for so many years that it is hard for

them to make a deal that beneﬁts both countries”, a clear setback in negotiations. A careful

evaluation of the trade stance needs then to take into account these intra-day communication

changes. Fourth, events in the trade war have escalated over time, involving more than a

single tweet. A throughout indicator of the trade stance would therefore need to combine the

information from several tweets and to weight them by relevance.

The index we derive in the next sections addresses each of these issues, in that it quantiﬁes

the relative strength (positive or negative) of each tweet, thus allowing to weight and compare

diﬀerent events. In addition, it also automatically disentangles trade-related tweets from all

others. Finally, by constructing a complete time-series for the development of the trade dispute,

we are able to evaluate the impact on stock markets of each event assessing the medium-term

implications of escalating trade tensions.

2.2 A textual analysis approach to protectionism

To go beyond a simple event study approach and evaluate the impact of rising trade tensions

over longer horizons, we develop a quantitative indicator for the degree of protectionism of each

communication by the US President. The index allows us to evaluate each tweet, distinguishing

which part of it (if any) is linked to trade tensions and which is not. We aggregate the index

at a weekly frequency taking into account all communications related to trade tensions and not

limiting only to the largest and most signiﬁcant events. The weekly aggregation is preferable

for our analysis as several events occur outside trading hours or during weekends. It is worth

ECB Working Paper Series No 2490 / November 202012underlining that only tweets discussing trade tensions are used to compute the indicator and

that the proposed methodology automatically selects them based on historical regularities.

There are few important caveats that need to be considered in this analysis. First, tweets

concerning trade tensions need to be systematically teased out from all other tweets. This

is even more complicated, given that a single tweet can touch upon diﬀerent topics at once.

Second, not all communications are the same. Although the most relevant of them are included

in the Bloomberg’s Trade War history, there are however many more that are not included in

the list, but contributed to increase tensions on a daily basis. These tweets contain important

information for our analysis, as they capture the vast majority of developments in trade tensions,

which are not as extreme as the events in the Timeline. Finally, all communications need to be

systematically evaluated and converted into a quantitative indicator for aggregation.

The literature on computational linguistic has developed several alternative methods to

conduct this type of analyses11. Historically the ﬁrst approach, and by far the most straight-

forward to implement, is the so-called word count:

in each text sample the words associated

to a speciﬁc sentiment are counted and the indicator is the ratio between them and all words

in text12. This methodology is simple, but suﬀers from an important drawback: the researcher

needs to set a priori a dictionary of sentiment words which outlines the list of words associated

to each sentiment. There exist some pre-compiled dictionaries, e.g., the Harvard-IV13, which

provide a list of “positive” and “negative” words. These classiﬁcations are constructed based

on standardized language patterns used in texts discussing general topics. For this reason, they

often perform poorly when applied to speciﬁc language or specialized topics. Gholampour and

van Wincoop (2019), for example, show that such dictionaries do not capture well economic

assessments. Alternatively, some authors have came up with their own dictionaries, tailored to

the text and context of interest. This solution, unfortunately, could not suit our sample, as all

tweets come from the same source and use a very speciﬁc language. Moreover, most tweets are

unrelated to trade tensions and should not be used to inform the classiﬁcation of words for the

subsample of posts we are focusing on.

As an alternative to these problems, we apply an algorithm that jointly identiﬁes the words

used in tweets about trade tensions and their relative “sentiment”14. This approach has several

advantages. First, we do not need to make any a priori choice about the relevance of speciﬁc

words, but we extract information from the available text so that the resulting indicator is

11Refer to Gentzkow et al. (2019) for applications to economics.
12This approach is used, among others, by Bloom (2009) and Caldara et al. (2019).
13Other examples are Wilson et al. (2005), Hutto and Gilbert (2014) and Chung and Pennebaker (2004).
14In other terms the algorithm distinguishes words that contribute to ease tensions (e.g., the expression “great

trade deal”) or increase them (e.g., the expression “increase tariﬀs”).

ECB Working Paper Series No 2490 / November 202013really tailored to the speciﬁc analysis. Second, selected words do not have a binary connotation

(“positive” vs “negative”), but receive a speciﬁc score so that words can be ranked in an ordinal

manner and compared to others15. This is a key and important characteristic of the model,

which allows to automatically select the text used to compile the indicator. Text samples where

none of the selected words is present receive zero score and are automatically excluded from the

computation of the indicator. Third, the set of words is deﬁned by a statistical model exploiting

the correlation across diﬀerent words. In other terms, the choice is based on the occurrence

of words. For example, those used in a text sample with a negative connotation are picked as

negative and the more often they are used in such context, the higher is the score associated to

them.

Practically, the algorithm works in three steps. First, a training sample (generally a subset

of all available data) of relevant text needs to be identiﬁed. This is the only choice that the

researcher needs to take a priori. The algorithm then uses that sample to select and score

words associated to the desired topics. In this application we use the tweets included in the

Bloomberg’s Trade War history before 2018, as we have shown how those tweets coincided with

events that had a signiﬁcant impact on markets (Table 1).

The training sample provides us with the writing patterns featured by trade-related an-

nouncements, thus ensuring that our indicator captures only communications pertaining trade.

Concretely, and this is the second step, a statistical model is estimated on the training sam-

ple. Each word enters the regression as a dummy variable and is used to explain the text used

for the training16. Intuitively, dummies associated to non-relevant words should have a zero

coeﬃcient, while positive or negative coeﬃcients capture the relative importance of selected

words. In many applications, including ours, it is impossible to consider all the dummies at the

same time as the number of words exceeds the number of text in the training sample (in our

case, after standardization, the training sample includes more than 600 unique words). For this

reason, the model is estimated using shrinking regression methods that score only the relevant

dummies. This is a common choice in the textual analysis literature (Gentzkow et al. (2019),

Hansen et al. (2018), Ke et al. (2019)).

The ﬁnal step consists of constructing an indicator that is simply the “ﬁtted value” of the

model for all texts in the sample (including the “observations” not used for the training). Each

text, in fact, receives a score given by the sum of the scores of all words contained therein. For

example, if a tweet is completely unrelated to trade tensions, all the coeﬃcients would be zero

15As an example, the algorithm puts more weight on the use of the word “barriers” rather than “deﬁcit” despite

both of them are associated with an increase of tensions.

16Each text sample is scored so that the dummies for words are used to explain the score. In our application

we rely on the Bloomberg’s “trade war” timeline for scoring, all scored tweets are reported in the Appendix.

ECB Working Paper Series No 2490 / November 202014and, hence, the overall score would be zero. As such, the text is identiﬁed as non-related to

trade announcements and becomes irrelevant for the later part of the analysis.

This approach is particularly useful to identify text samples that discuss mixed topics as

the part of the text unrelated to trade tensions would get zero score, while the part discussing

them would contribute positively or negatively. The next section presents more in detail the

methodology used, which is an example of a so-called “supervised machine learning” algorithm,

in that the model needs to be provided with a set of relevant observations for the training

sample. Once this is done, however, the selection and scoring of words based on the sample is

completely automatized.

2.3 Construction of the 3T-Index

In this section, we formally explain the algorithm used to quantify the change in the protectionist

stance of each tweet by President Trump related to the US-China trade negotiations. We name

the resulting indicator the Trade Tensions Tweet Index (3T-Index), which is scaled so that

higher values are associated to a more accommodating trade stance towards China, whereas

harsher threats of trade barriers and retaliation correspond to lower values of the index.

The ﬁrst step consists of collecting all tweets by President Trump from January 2016 to

November 201917. These tweets cover the period since the beginning of President’s Trump

(a) All tweets since January 2016

(b) Tweets in Bloomberg’s Trade War timeline

Figure 4: Word clouds

presidential campaign, when he started tweeting about (what was wrong with) the trade deal

with China.

Figure 4 compares word clouds for all tweets since 2016 (Figure 4a) and only for those related

to the Bloomberg’s Trade War timeline (Figure 4b). In each cloud, words are thicker depending

on the frequency of appearance. Hence, they provide an indication of the topics covered in the

17These tweets can be downloaded directly from Twitter or from the Trump Twitter Archive. The tweets
are cleaned using standard methods. For instance, we remove all stop-words, singular and plural endings and
include certain bigrams (i.e., two subsequent words are treated as one unit; for example, “good deal” is treated
diﬀerently from “bad deal”), as commonly done in textual analysis applications (Gholampour and van Wincoop
(2019), Werner and Murray (2004)). Appendix A reports the full list of standardization techniques.

ECB Working Paper Series No 2490 / November 202015underlying text. At a ﬁrst glance, it seems that tweets in the “trade war” sample use a diﬀerent

language compared to the others. Figure 4b indeed shows that “China”, “tariﬀ”, “trade” and

“deal” are the most used words compared to “great”, “Trump” and “new”, which on the other

hand prevail in Figure 4a. Comparing the two word clouds also suggests that tweets in the

training sample discuss very speciﬁc topics like trade deals and the US dollar and are closely

related to developments in trade, which make them very well-suited to train the model.

In this regard, the algorithm actually goes beyond the simple word counting, as it quantiﬁes

to what extent each tweet is tilted towards “protectionism”. Notably, tweets of the Bloomberg’s

Trade War timeline are analysed using a supervised machine learning algorithm, since standard

dictionaries (even economic ones) are not suitable to construct a sentiment indicator based on

tweets’ text. Constructing an ad-hoc dictionary, as done by Gholampour and van Wincoop

(2019) on the basis of the language used by traders in trading rooms, is not an alternative

either for three main reasons:

i) the tweets we focus on do not use a standard language set;

ii) second, they are all taken from the same source, which does not allow to identify common

patterns; iii) third, some tweets use idiomatic words or sentences that are missing in pre-com-

piled dictionaries. Given this, the most viable approach consists of adopting an algorithm to

identify the sets of words that are more frequently deployed in episodes of heightened trade

tensions with China and, then, construct the sentiment indicator.

With this aim, we ﬁrst retrieve the single tweets corresponding to the events listed in the

Bloomberg’s Trade War timeline before January 201918 and score them between 1 and -1 de-

pending on whether the tweet is associated to a relaxation of trade tensions or, on the contrary,

to a tightening. These tweets and their relative scores, provides us with the training sample for

the algorithm19.

We then model tweets’ sentiment by ﬁtting an elastic net framework on the training sample.

This statistical method allows to select relevant regressors among a large pool of variables which

cannot be used simultaneously in the estimation. The elastic net combines also the beneﬁts of

the lasso and the ridge regressions, in that the framework:

i) includes a penalty term in the

score function that constrains the number of estimated coeﬃcients (in this way models with

redundant explanatory variables are penalized); ii) shrinks the number of coeﬃcients to zero

the higher the penalty term becomes20. Therefore, the elastic net selects by construction more

parsimonious models, a feature which is particularly useful when dealing with a large set of

18See https://www.bloomberg.com/news/articles/2019-08-28/u-s-china-trade-war-timeline-what-s-happened-

since-may-2019.

19The complete list of tweets and related scores that make the training sample up are reported by Table B.2

in Appendix B.1

20Refer to Tibshirani (1996), Zou and Hastie (2005) and Hastie et al. (2009) for further details.

ECB Working Paper Series No 2490 / November 202016potential explanatory variables that are also highly correlated21. Speciﬁcally, the model solves:

with

(cid:34)

1
2N

N
(cid:88)

i=1

min
β0,β

(cid:0)Si − β0 − xT

i β(cid:1)2

(cid:35)

+ λPα (β)

,

Pα (β) =

(1 − α)
2

||β||2 + α||β||.

(2.2)

(2.3)

In Equation (2.2), Si is the score attached to tweet i, xi is a matrix of dummy variables equal

to 1 if a word is present in tweet i and zero otherwise, β0 and β are the estimated parameters,

λ and α ∈ [0, 1] are tuning parameters. In particular, β0 is the loading of the constant, β is a

vector of loadings for each dummy variable in xi and λ is the penalization parameter. Hence,

the higher is λ, the fewer words (dummies) are included as explanatory variables. Finally, α

is a scaling parameter that sets the penalty function in Equation (2.3) as a weighted average

of the penalty under the lasso (α = 1) and the ridge (α = 0). When λ is zero, Equation (2.2)

simply coincides with the OLS estimator.

The elastic net approach presents a distinct advantage, in that it allows to select the most

powerful predictors in xt while maintaining (feasible) degrees of freedom even with very large

sets of potential explanatory variables. In our speciﬁc case, the model selects only 27 out of

more than 600 unique words in the training sample to construct the ﬁnal sentiment indicator.

Figure 5 reports the trace plot for the estimated coeﬃcients and the optimal value of λ, i.e.

the joint path of the estimated coeﬃcients and λ from the initial condition to the optimum

(λ∗)22. The latter is detected by using a ﬁve fold cross-validation approach, whereby λ is

chosen based on the mean squared prediction error. In practice, the algorithm draws a value of

λ and α, estimates Equation (2.2) for diﬀerent combinations of explanatory variables (dummies

in xT

i ) and selects the model with the lowest score. It then moves on to another draw of λ

and α until convergence (i.e. the score of Equation (2.2) stops improving). Figure 5 plots the

loadings β associated to the 27 selected dummies at each iteration from the starting point of

the optimization (λ = 0.25) to the optimum (λ = 0.04). The ﬁnal values of β for λ = 0.04 are

the loadings selected by the model.

We use the estimated coeﬃcients to ﬁt the model on the remaining tweets after 2018. The

model’s predicted values are the implied sentiment from each tweet, the 3T-Index. Figure 6

shows the 3T-Index at weekly frequency against the most relevant Bloomberg’s Trade War

timeline events23. The index tracks relatively well negative events in the sample, which are also

21In this exercise each word is treated as a separate independent variable.
22In Table B.3 of the Online Appendix we report the full list of the selected words with the related loadings.
23When there are more than one tweet in each day or week we sum the index in that day or week. Refer to

Figure B.1 in the Appendix for the daily version of the index.

ECB Working Paper Series No 2490 / November 202017Figure 5: Trace plot of elastic net coeﬃcient estimates

largely dominant. The only relevant positive event, the tweet on May 13 2018 in which President

Trump committed to remove barriers to the imports of the Chinese tech ﬁrm ZTE, is missed. As

also explained in Section 2.1 above, this is mainly due to the peculiar communication strategy

adopted by the US President on that occasion: at 15:03 President Trump tweeted in support of

ZTE but, in a later tweet on the same day (19:22), he largely scaled that commitment down24.

The index averages out the events, thus damping the overall daily eﬀect. Communication in

the previous week, on the contrary, was mainly positive and this is the reason why the index

spikes before this event.

2.4 Other data

Other relevant weekly data are taken from Haver Analytics and cover the period between Jan-

uary 2016 and November 2019. Table 2 reports the list of the variables included in our dataset,

together with their summary statistics. The 3T-Index is aggregated at weekly frequency to

Table 2: Summary statistics

Mean
Std dev.
Min
Max
Start
End

Mean
Std dev.
Min
Max
Start
End

SP500
2534.46
323.56
1850.27
3142.19
01/01/2016
29/11/2019
VIX
14.80
4.05
9.34
31.51
01/01/2016
29/11/2019

Dow Jones
22634.12
3384.30
15918.04
28100.89
01/01/2016
29/11/2019

EME Stock
703.73
76.63
508.50
878.92
01/01/2016
29/11/2019

Shanghai Stock
3031.20
231.09
2481.51
3552.40
01/01/2016
29/11/2019
US 2-year yield US 10-year yield Citi US Surprise
2.32
0.47
1.38
3.21
01/01/2016
29/11/2019

1.68
0.69
0.58
2.94
01/01/2016
29/11/2019

-1.52
33.94
-76.38
78.94
01/01/2016
29/11/2019

EME NEER USD/EUR

95.18
1.37
91.90
97.97
01/01/2016
29/11/2019

1.13
0.04
1.04
1.24
01/01/2016
29/11/2019

USD NEER
119.95
3.03
113.09
125.84
01/01/2016
29/11/2019
EMBI+
807.03
39.52
698.33
887.26
01/01/2016
29/11/2019

CHN NEER
117.22
2.46
112.84
124.21
01/01/2016
29/11/2019
Shanghai bond price
164.38
6.36
154.58
177.45
01/01/2016
29/11/2019

24Refer to Table B.2 in the Appendix.

ECB Working Paper Series No 2490 / November 202018Figure 6: Weekly 3T-Index (blue solid line) and Bloomberg’s trade war events (vertical bars)

smooth out volatility in the daily sentiment indicator and account for weeks where several

trade-related tweets were posted.

2.5 Endogeneity checks

A major caveat to our approach might derive from the potential endogeneity of the US Pres-

ident’s communication strategy in the context of the “trade war” vis-`a-vis the developments

on ﬁnancial markets. For example, the President’s stance towards China might have become

harsher if the US stock market underperformed or the US dollar appreciated. If this was the

case, the tweets would be endogenous to ﬁnancial variables and the 3T-Index could not be used

as instrument of the harshness of the US administration’s trade stance.

It is possible to test for the presence of endogeneity by regressing the 3T-Index on contem-

poraneous and lagged stock indices and the (log-change of) US dollar NEER25:

∆Indext = α +

L
(cid:88)

i=0

βi∆SP 500t−i +

L
(cid:88)

i=0

γi∆N EERU SD

t−i +

L
(cid:88)

i=0

δi∆StockCHN

t−i + εt

(2.4)

If the relevant coeﬃcients in Equation (2.4) were found to be statistically signiﬁcant, this

would mean that ﬁnancial variables can somewhat drive the 3-T Index, thus hindering its validity

25As a robustness check, Table B.4 in the Appendix shows that results are robust to using the 3-T Index in

levels instead of ﬁrst-diﬀerence.

ECB Working Paper Series No 2490 / November 202019as exogenous instrument to capture trade tension shocks. The set of regressors in Equation (2.4)

also includes the contemporaneous changes in ﬁnancial market variables to test for the existence

of common shocks that might move both ﬁnancial markets and the 3-T Index. Results reported

in Table 3 show that changes in the 3T-Index are not systematically predicted by developments

in ﬁnancial markets as measured by changes in the US stock market, the Chinese stock market

and the US dollar NEER, the latter being also a measure of global risk. All speciﬁcations

reported show non-signiﬁcant coeﬃcients and explain a very limited share of the volatility of

the index. Moreover, results of the F − test show that coeﬃcients are not jointly signiﬁcant.

The Index is found to be exogenous to both domestic and foreign ﬁnancial market developments

also when estimating Equation (2.4) at the daily frequency (Table 4)26.

In the following sections we use the index at weekly frequency, which is better suited to

analyse the medium-term impact of trade tensions on ﬁnancial markets for several reasons:

i) many relevant events have occurred outside trading hours. Daily ﬁnancial variables would

therefore miss the contemporaneous eﬀects and would be biased by the information priced-in

by markets before the next opening (this can indeed be signiﬁcant in the case of weekends);

ii) it has often been the case that negative (positive) communications have closely followed

positive (negative) ones within the same day or week. In this context, ﬁnancial markets receive

opposite signals that could oﬀ-set each other, thus adding noise to the estimation; iii) not all

communications have the same relevance and some of them might be more relevant than others.

Aggregating at weekly frequency then allows to smooth part of that volatility by netting

positive and negative communication within the same week and computing the prevailing stance

in that time frame. The aggregation is made possible by the speciﬁc characteristics of our index,

that allow to directly compare and cumulate diﬀerent events using their implied score.

3

Impact on ﬁnancial variables

In this section, we assess the eﬀect of an increase in the US-China trade tensions by means of

local projections `a l`a Jord`a (2005). Notably, we estimate:

yt+k = α + βk ˆSt + δyt−1 + Γ(cid:48)Xt + εt

(3.1)

where yt is the (logged) variable of interest, Xt collects a set of control variables and ˆSt is
the 3T-Index, given by the ﬁtted values from Equation (2.2). As ˆSt is exogenous to ﬁnancial
market movements27, the coeﬃcient βk can be interpreted as the impact of changes in ˆSt on

26Table B.5 reports the same regression with the index in levels.
27See Section 2.5.

ECB Working Paper Series No 2490 / November 202020Table 3: Estimates from equation Equation (2.4) at weekly frequency

(3)

(6)

(2)

(1)
-0.096
(0.588)
0.147
(0.984)
0.706
(0.839)

(5)
-0.203
(0.583)
0.128
(1.027)
0.691
(0.819)

Model Model Model Model Model Model Model
(4)
-0.090
(0.620)
0.296
(0.999)
0.414
(0.849)
0.035
(1.653)
1.709
(2.030)
-2.694
(1.658)

0.129
(1.574)
1.581
(1.900)
-3.018*
(1.591)

0.382
(0.483)
-0.555
(0.477)
0.816
(0.549)
-0.001
(0.006)
190
1.017
0.386
1.22

-0.002
(0.006)
190
0.879
0.511
1.22

0.474
(0.493)
-0.707
(0.514)
0.645
(0.521)
-0.002
(0.006)
190
0.684
0.663
1.23

-0.002
(0.006)
190
0.757
0.519
1.20

-0.001
(0.006)
190
1.208
0.308
1.21

(7)
-0.165
(0.609)
0.231
(1.031)
0.478
(0.832)
0.373
(1.680)
1.318
(2.138)
-2.368
(1.756)
0.482
(0.514)
-0.672
(0.532)
0.562
(0.548)
-0.002
(0.006)
190
0.999
0.442
1.24

0.493
(1.629)
1.334
(2.056)
-2.587
(1.711)
0.445
(0.505)
-0.544
(0.495)
0.679
(0.580)
-0.000
(0.006)
190
1.374
0.227
1.24

∆SP 500t

∆SP 500t−1

∆SP 500t−2

∆N EERU SD

t

∆N EERU SD
t−1

∆N EERU SD
t−2

∆StockCHN

t

∆StockCHN

t−1

∆StockCHN

t−2

Constant

Observations
F test
F prob
R2

Notes: explanatory variables are in log-diﬀerences; the China stock index is the Shanghai stock
market index. T-stats reported in parenthesis below coeﬃcients and computed based on HAC
standard errors. *** p < 0.01, ** p < 0.05, * p < 0.1

the dependent variable at any future horizon k going from 0 (i.e. the impact eﬀect) to 12 weeks

in the future. The sequence {βk}12
k=0 is then the (non-linear) impulse response of variable y to
an innovation in ˆS. βk can be estimated with linear regressions using HAC standard errors, as

residuals of Equation (3.1) are autocorrelated by construction (see Newey and West (1987) and

Jord`a (2005)). It has been shown that local projection estimates suﬀer from larger uncertainty

than VARs (Kilian and Kim (2011)). In addition, developments not directly captured by the

3-T Index might impact the dependent variable. For these reasons, we include the lag of the US

2-year yield, the VIX and the US Citigroup macroeconomic surprise index among the controls.

These variables should indeed capture the eﬀect that shocks in the US and world economy other

than those to the -implied- US trade stance might exert onto ﬁnancial markets.

One of the advantages of local projections is that they are piece-wise linear and, hence, easily

scalable. Therefore, we scale the responses by the value of the index on the announcement of

ECB Working Paper Series No 2490 / November 202021Table 4: Estimates from equation Equation (2.4) at daily frequency

Model Model Model Model Model Model

∆SP 500t

∆SP 500t−1

∆SP 500t−2

∆SP 500t−3

∆N EERU SD

t

∆N EERU SD
t−1

∆N EERU SD
t−2

∆N EERU SD
t−3

∆StockCHN

t

∆StockCHN

t−1

∆StockCHN

t−2

∆StockCHN

t−3

Constant

Observations
F test
F prob
R2

(2)

(3)

(1)
1.164
(4.944)
0.821
(4.010)
3.729
(3.535)
1.176
(6.761)

1.460
(13.543)
-9.874
(14.237)
-9.648
(11.374)
6.568
(9.214)

-11.013
(8.607)
-1.079
(4.126)
5.715
(4.092)
2.007
(2.820)
-0.027
(0.042)
158
0.974
0.407
3.32

-0.041
(0.043)
158
0.463
0.708
0.00

-0.037
(0.041)
158
0.458
0.712
0.00

(4)
-0.080
(5.477)
-0.447
(3.882)
3.420
(3.718)
2.181
(7.656)
0.501
(14.194)
-10.421
(14.987)
-6.532
(13.061)
9.603
(8.027)

-0.041
(0.040)
158
0.442
0.849
0.00

(5)
3.250
(4.671)
1.432
(4.482)
2.266
(3.164)
1.333
(8.136)

-11.816
(9.880)
-1.606
(4.221)
4.845
(3.686)
1.476
(3.330)
-0.029
(0.037)
158
0.989
0.434
1.24

(6)

-7.380
(9.642)
-9.079
(11.496)
-6.998
(12.593)
4.550
(13.186)
-11.598
(8.704)
-1.718
(4.016)
4.636
(4.114)
2.111
(3.011)
-0.027
(0.039)
158
0.921
0.482
1.26

Model
(7)
1.856
(5.096)
0.292
(4.671)
2.338
(3.275)
2.258
(8.560)
-6.627
(10.375)
-8.193
(12.482)
-4.436
(14.156)
6.332
(10.237)
-12.101
(9.971)
-1.997
(4.027)
3.901
(3.437)
1.606
(3.544)
-0.029
(0.035)
158
0.741
0.671
0.00

Notes: explanatory variables are in log-diﬀerences; the China stock index is the Shanghai stock market
index. T-stats reported in parenthesis below coeﬃcients and computed based on HAC standard errors.
*** p < 0.01, ** p < 0.05, * p < 0.1

the 2018 Steel and Aluminum tariﬀ, so that our results can be interpreted as the reaction of

ﬁnancial markets to that speciﬁc announcement. The data sample spans from the US President

election in November 2016 to November 201928.

Stock indices

A trade tension shock comparable to the 2018 Steel and Aluminum tariﬀ announcement exerts a

non signiﬁcant impact on the US stock market as proxied by the S&P 500 (Figure 7). However,

28Our results are robust to a number of additional checks that are presented in Section 3.1.

ECB Working Paper Series No 2490 / November 202022Figure 7: Response of stock market indices to the 2018 steel and aluminium tariﬀ announcement.
Source: Haver Analytics and authors’ calculations.
Notes: We use the Shanghai stock market index as proxy for the stock index of China. The subindex of S&P 500
exposed to China is computed including in the S&P 500 only those ﬁrms that generate at least 10% of revenues
from China.

the shock has a sizeable eﬀect on stocks of US companies exposed to Chinese demand29. Those

stocks indeed depreciate by around 5% in a four-week window. The Chinese stock market reacts

in a very similar way, with the Shanghai stock index depreciating by 6% four weeks after the

shock. Figure 7 also suggests that US-China tariﬀs announcements have signiﬁcant spillovers

to other EMEs, as the aggregate stock market index of emerging market depreciates by about

4% within four weeks and grows weaker in the following months.

Exchange rates

As to exchange rates, Figure 8 shows two main results. First, the US dollar appreciates six

to twelve weeks after the trade tension shock, both in bilateral terms against the euro and in

nominal eﬀective terms30. Second, the Chinese renminbi and the synthetic EME exchange rate31

tend to depreciate at shorter horizons, while the reaction is muted over the longer-term. This

latter evidence might depend on the fact that many EME currencies (including the renminbi)

are either oﬃcially or de facto pegged to the US dollar.

29We use here a sub-index of the S&P 500 including only shares of companies that have at least 10% of their

revenues originating from China.

30We use the bilateral USD/EUR exchange rate as this is the most liquid exchange rate market in the world

(Bank for International Settlements (2019)).

31Speciﬁcally, this is the J.P. Morgan EME nominal eﬀective exchange rate index.

ECB Working Paper Series No 2490 / November 202023Figure 8: Response of exchange rates to the 2018 steel and aluminium tariﬀ announcement.
Source: Haver Analytics and authors’ calculations.

Safe haven currencies, on the other hand, show a muted reaction to an increase in trade

tensions (Figure 9). This result seems to suggest that ﬁnancial market participants do not

consider a tariﬀ shock as a pure risk-oﬀ scenario, with investors selling risky asset to acquire

safe securities. On the contrary, the reaction of exchange rates suggests a change in expectations

over the international economic outlook and trade. Investors anticipate the slowdown in EMEs

economic activity, with a subsequent reduction in trade ﬂows, and consequently rebalance their

portfolios. These dynamics trigger an appreciation of the US dollar, in that most of these

currencies are actively traded against the USD32.

Bond markets

The estimated impact on bond markets suggests that ﬁnancial agents do not read trade shocks

as global risk shocks, but rather as changes to expectations on future economic performance

(Figure 10). Results indeed highlight a very muted reaction of both US 10-year yields, which

even increase at longer horizons, and the Chinese 10-year yield. This is inconsistent with a

global risk shock that should trigger a portfolio rebalancing towards safe assets (i.e. US bonds),

and consequently decrease their yields. Meanwhile, both the Shanghai government bond total

return index and the EMBI+ index contract, which might be due to an increase in demand of

these assets on the part of international investors. Coupled with the reaction of stock indices

32See Bank for International Settlements (2019).

ECB Working Paper Series No 2490 / November 202024Figure 9: Response of safe haven currencies to the 2018 steel and aluminium tariﬀ announce-
ment.
Source: Haver Analytics and authors’ calculations.

(Figure 10), these ﬁndings show that trade tension shocks provoked a portfolio rebalancing

eﬀect within China and, more generally, emerging markets, with investors selling stocks and

buying bonds. This behaviour is more consistent with a shift in expectations towards lower

proﬁtability of EME companies due to a contraction of their foreign trade. Finally, as several

emerging market bonds are denominated in US dollar, the contraction in the EMBI+ index is

compatible with the US dollar appreciation.

Discussion

Market reactions to a heightening of trade tensions is diﬀerent from the reaction to rising global

risk. Global risk shocks have negative eﬀects on the US stock market, contract US yields in

the wake of capital ﬂows to the United States and lead to a dollar appreciation (Caballero

and Kamber (2019)). At the same time, emerging market bond spreads increase, due to a

devaluation of EMEs ﬁxed-income assets (Akıncı (2013)).

Estimates of Equation (3.1) deliver quite diﬀerent conclusions. While an increase in trade

tension still triggers an appreciation of the US dollar, US sovereign bond yields do not move

signiﬁcantly and aggregate stock market indices remain broadly stable. However, the stocks

of US companies exposed to China face a strong devaluation in the short-to-medium-run. As

concerns the foreign exchange rate markets, currencies of countries that are more exposed to

trade tensions depreciate, whereas safe haven currencies do not react. Moreover, EMEs do not

ECB Working Paper Series No 2490 / November 202025Figure 10: Response of bond indices to the 2018 steel and aluminium tariﬀ announcement.
Source: Haver Analytics and authors’ calculations.

seem to experience a net capital outﬂow, but rather a rebalancing between portfolio equity

and debt, as indicated by the contraction in bond yields. This collection of results show that

ﬁnancial markets do not react to an increase in trade tension as they would when facing a global

risk shock33.

Our results are indeed more aligned with the ﬁndings of standard economic models analyzing

the impact of protectionism. When trade tensions rise, output contracts as trade is jeopardized.

This is anticipated by investors, which then dis-invest from companies that might be hit harder

by the shock, i.e. those companies operating in the countries and regions potentially targeted

by the restrictive trade measures.

3.1 Robustness checks

We additionally perform several robustness checks on our baseline speciﬁcation. Notably, we

augment Equation (3.1):

yt+k = α + βk ˆSt + δyt−1 + Γ(cid:48)Xt + Ξ(cid:48)Gt + εt

(3.2)

where G is a matrix of additional control variables. First, we include a time-trend to account

for an unobserved component that might aﬀect both the dependent variable and the 3T-Index.

33See Ioannou et al. (2020) for estimates of global risk aversion shocks.

ECB Working Paper Series No 2490 / November 202026Figure B.2 reports the impulse responses for this extended model, which are broadly in line

with the baseline estimates: following a positive trade tension shock, stock indices contract in

EMEs, the US dollar appreciates and EMEs currencies depreciate. There are not signiﬁcant

eﬀects on safe haven currencies and the EMBI+ total return index goes down, thus signalling the

presence of portfolio re-balancing within EMEs. However, diﬀerently from the baseline results,

the S&P500 signiﬁcantly increases over the longer-term, which is in line with expectations for

improvements in US real activity triggered by trade diversion from EMEs.

In a further check, we alternatively include two lags of controls and one lag of the 3-T Index

in G, in order to account for persistence in the data. Figure B.3 and Figure B.4 present the

impulse response functions under the two alternative speciﬁcations: results are not signiﬁcantly

diﬀerent from the baseline.

Finally, we check for the presence of some systematic factors in the 3T-Index that are

orthogonal to market data, which would potentially generate an omitted-variable bias in the

estimates of {βi}K

i=0 in Equation (2.4). We can ﬁlter-out these components by estimating an

auxiliary AR(1) process34:

ˆSt = α + (cid:37) ˆSt−1 + ηt

(3.3)

where ˆSt is the 3-T Index. We then take the estimated residuals ˆηt as the proxy for the trade

tension shock in Equation (3.1). Figure B.5 reports the results, which are again broadly in line

with our baseline estimates.

3.2 Contribution to ﬁnancial market developments

Section 3 shows that trade tension shocks have a sizeable impact on ﬁnancial markets, which

interpret them as a negative demand shock for the Chinese economy. However, those shocks

might be rare events which do not systematically contribute to the volatility of stock prices and

bonds.

In other words, ﬁnancial markets might have reacted to large trade announcements,

but, on a daily basis, changes in the communication stance of the US government might have

been largely ignored by agents.

We shed some light on this particular issue by estimating the contribution of the 3-T Index to the

volatility of ﬁnancial variables. Notably, we adopt the methodology proposed by Gorodnichenko

and Lee (2019) to perform a forecast error variance decomposition (FEVD) of the variables of

34Assume that the variable Y depends on a latent factor S which has a persistent functional form. Formally:
t and ε2
t are two error terms. The equation can be rewritten
t ≡ δ + ψYt−1 + ηt where δ = α + βγ − α
β and
t−1. Fitting Equation (3.3) allows to extract ηt. When used in local projections ηt delivers a
t are uncorrelated and

Yt = α + βSt + ε1
as: Yt = α + β[γ + ρ( Yt−1
ηt = βε2
clean (but ineﬃcient) estimate of the impact of ε1
the Newey-West corrections control for the autocorrelation of ηt.

t and St = γ + ρSt−1 + ε2
β − α

i=0 of Equation (3.1)) as ε1

t , where ε1
t ] + ε1

β , ψ = β ρ

t −1
β ) + ε2

β − ε1

t ({βi}K

t and ε2

t + ε1

t − (cid:15)1

ECB Working Paper Series No 2490 / November 202027interest at diﬀerent horizons35. For each dependent variable, the contribution of the 3-T Index

quantiﬁes the share of the variance of that variable with is explained by our index at each

horizon k = 0, . . . , K. Following Gorodnichenko and Lee (2019), we also apply a small sample

bias correction to our estimates.

Figure 11 shows the FEVD for stock market indices at diﬀerent horizons.36 As suggested by

the impulse responses presented in Section 3, rising trade tensions do not contribute signiﬁcantly

to the volatility of US stocks. This is also true for the subsample of stocks exposed to Chinese

demand, suggesting that US stock market reacts only to large changes in the trade stance of

the US administration. EMEs stocks, on the contrary, seem to have been more aﬀected by

the increase in trade tensions between 2017 and 2019. The 3-T index indeed explains about

10% of monthly returns on the Chinese stock market, with a peak contribution of 15% over

three months. More generally, the escalation of trade tensions between the US and China has

contributed for around 10% to the volatility of EME markets in a 1 to 4-month horizon.

Figure 11: Contribution of trade tensions shocks to the FEVD of stock markets between 2017
and 2019.
Notes: White bars indicate contributions that are not statistically diﬀerent from 0 at the 68% conﬁdence level.
Source: Haver Analytics and authors’ calculations.

Meanwhile, exchange rates of advanced economies are not signiﬁcantly driven by rising

tensions (Figure 12 and Figure B.6), a result that directly derives from the interpretation of

35Gorodnichenko and Lee (2019) show that the contribution to the FEVD decomposition of a shock x at horizon
k can be computed with local projections as the R2 of an auxiliary regression of the local projection residuals at
horizon k on the sequence of shocks {xt+k, ..., xt}.

36The FEVD is computed on the full sample 2017-2019.

ECB Working Paper Series No 2490 / November 202028trade tensions as demand shocks for China rather than as global risk shocks. In addition, the

3-T Index does not contribute signiﬁcantly to the volatility Chinese renminbi either, most likely

because the Chinese currency is de facto pegged to the US dollar. On the contrary, the FEVD

of the EMEs exchange rates index, which includes several fully ﬂoating exchange rates, shows

a signiﬁcant contribution of trade tensions over a 3-month horizon (Figure 12).

Finally, the 3-T Index contributes little to the FEVD of 10-year yields, both in the US

and Asia, which is in line with the absence of safe haven ﬂows (Figure B.7). Rising trade

tensions, instead, contribute to the volatility of the Shanghai government bond index, which

is a composite of Chinese government securities of diﬀerent maturities. This suggests that the

escalation between the US and China have aﬀected the short-end of the yield curve in emerging

markets.

Figure 12: Contribution of trade tensions shocks to the FEVD of exchange rates between 2017
and 2019.
Notes: White bars indicate contributions that are not statistically diﬀerent from 0 at the 68% conﬁdence level.
Source: Haver Analytics and authors’ calculations.

4 Conclusion

This paper proposes a novel identiﬁcation approach to assess the impact that rising trade

tensions have on ﬁnancial markets. Notably, we take into consideration those announcements

on the US-China trade dispute that were made on social media and we shoe that they were

largely unanticipated by markets. Given this, we use machine learning tools to quantify the

ECB Working Paper Series No 2490 / November 202029degree of “protectionism” of Twitter© posts related to the Sino-America trade tensions. We

show that the constructed measures (3-T Index) is exogenous to ﬁnancial markets developments

and we use it in local-projection regressions to assess how global markets react to changes in

trade tensions.

Our results show that rising trade tensions lead to a contraction of stock valuation in China

and EMEs. That contraction is economically signiﬁcant and explains 10 to 15% of the volatility

of those stock indices. US stocks instead are largely unaﬀected, except for those companies

whose revenues heavily depend on trade with China. Moreover, the US dollar appreciates,

EMEs exchange rates depreciate, while safe have currencies do not react. Finally, on bond

markets, there are no signs of safe have ﬂows to the US, while in EMEs there is evidence of a

portfolio re-balancing between stocks and bonds.

These ﬁndings, especially the lack of safe-haven eﬀects, challenge the interpretation of trade

tensions as global risk aversion shocks. On the contrary, results suggest that ﬁnancial markets

rather interpret trade tensions as a more standard negative demand shock for China, which, in

turn, might help explain the contraction in global output observed in 2019.

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ECB Working Paper Series No 2490 / November 202033Appendix

A Standardization and cleaning of the tweets

Tweets (and retweets) by the US President are downloaded from the Trump Twitter Archive

(http://www.trumptwitterarchive.com/) for the period January 2016 to November 2019. All

special characters and numbers are removed. The remaining letters are converted to lowercase,

common stop words are removed, and word endings standardized (stemmed) according to (44).

Finally, words occurring only once in the entire sample, as well as single character words, are

deleted. Tweets are then merged with identical timestamps (occurring in the same second).

This sample of cleaned tweets is narrowed down by keeping only those that feature one or

several of the keywords China, trade, or tariﬀ. From the training sample (Section 2), we include

frequently occurring bigrams (two consecutive words forming a unit to retain their meaning).

After these steps, the training sample features 662 unique words or bigrams which are used as

regressors in the elastic net estimation.

B Figures

Figure B.1: 3T-Index at daily frequency

ECB Working Paper Series No 2490 / November 202034Figure B.2: IRFs to the 2018 steel and aluminium tariﬀ announcement - full sample with time
trend
Source: Haver Analytics and authors’ calculations.

Figure B.3: IRFs to the 2018 steel and aluminium tariﬀ announcement - full sample with 2 lags
of controls and endogenous variable.
Source: Haver Analytics and authors’ calculations.

ECB Working Paper Series No 2490 / November 202035Figure B.4: IRFs to the 2018 steel and aluminium tariﬀ announcement - full sample with 1 lag
of the Index.
Source: Haver Analytics and authors’ calculations.

Figure B.5: IRFs of bond indices to the 2018 steel and aluminium tariﬀ announcement - full
sample, shocks are computed as residuals from an AR(1) estimated on the index.
Source: Haver Analytics and authors’ calculations.

ECB Working Paper Series No 2490 / November 202036Figure B.6: Contribution of trade tensions shocks to the FEVD of safe haven exchange rates
between 2017 and 2019.
Notes: White bars indicate contributions that are not statistically diﬀerent from 0 at the 68% conﬁdence level.
Source: Haver Analytics and authors’ calculations.

Figure B.7: Contribution of trade tensions shocks to the FEVD of bond yields between 2017
and 2019.
Notes: White bars indicate contributions that are not statistically diﬀerent from 0 at the 68% conﬁdence level.
Source: Haver Analytics and authors’ calculations.

ECB Working Paper Series No 2490 / November 202037B.1 Tables

Table B.1: Trade war tweets included in the event study

Assessment
Negative

Original Tweet

Date & Time
13/05/2018 15:01 President Xi of China and I are working together to give
massive Chinese phone company ZTE a way to get back
into business fast. Too many jobs in China lost. Commerce
Department has been instructed to get it done!

Assessment
Positive

Date & Time
08/06/2018 11:22

Original Tweet
I am heading for Canada and the G-7 for talks that will
mostly center on the long time unfair trade practiced against
the United States. From there I go to Singapore and talks
with North Korea on Denuclearization. Won’t be talking
about the Russian Witch Hunt Hoax for a while!

Assessment
Negative

Original Tweet

Date & Time
01/03/2018 12:12 Our Steel and Aluminum industries (and many others) have
been decimated by decades of unfair trade and bad policy
with countries from around the world. We must not let our
country companies and workers be taken advantage of any
longer. We want free fair and SMART TRADE!

02/03/2018 10:50 When a country (USA) is losing many billions of dollars
on trade with virtually every country it does business with
trade wars are good and easy to win. Example when we are
down $100 billion with a certain country and they get cute
don’t trade anymore-we win big. It’s easy!

08/03/2018 12:38

02/03/2018 13:57 When a country Taxes our products coming in at say 50%
and we Tax the same product coming into our country at
ZERO not fair or smart. We will soon be starting RECIP-
ROCAL TAXES so that we will charge the same thing as
they charge us. $800 Billion Trade Deﬁcit-have no choice!
Looking forward to 3:30 P.M. meeting today at the White
House. We have to protect &amp; build our Steel and Alu-
minum Industries while at the same time showing great ﬂexi-
bility and cooperation toward those that are real friends and
treat us fairly on both trade and the military.
Spoke to PM @TurnbullMalcolm of Australia. He is commit-
ted to having a very fair and reciprocal military and trade
relationship. Working very quickly on a security agreement
so we don’t have to impose steel or aluminum tariﬀs on our
ally the great nation of Australia!

09/03/2018 22:48

10/03/2018 15:22 Chinese President XI XINPING and I spoke at length about
the meeting with KIM JONG UN of North Korea. Presi-
dent XI told me he appreciates that the U.S. is working to
solve the problem diplomatically rather than going with the
ominous alternative. China continues to be helpful!

10/03/2018 16:15 Chinese President XI JINPING and I spoke at length about
the meeting with KIM JONG UN of North Korea. Presi-
dent XI told me he appreciates that the U.S. is working to
solve the problem diplomatically rather than going with the
ominous alternative. China continues to be helpful!
Spoke to Prime Minister Abe of Japan who is very enthusi-
astic about talks with North Korea. Also discussing opening
up Japan to much better trade with the U.S. Currently have
a massive $100 Billion Trade Deﬁcit. Not fair or sustainable.
It will all work out!

10/03/2018 17:23

10/03/2018 21:29 The European Union wonderful countries who treat the U.S.
very badly on trade are complaining about the tariﬀs on
Steel &amp; Aluminum. If they drop their horriﬁc barriers
&amp; tariﬀs on U.S. products going in we will likewise drop
ours. Big Deﬁcit. If not we Tax Cars etc. FAIR!

22/03/2018 18:40 As a candidate I pledged that if elected I would use
every lawful tool to combat unfair trade protect Amer-
ican workers and defend our national security. Today
we took another critical step to fulﬁll that commitment.
https://t.co/7NBI0Dibmx https://t.co/nmzqos3BUA

27/03/2018 00:44 Trade talks going on with numerous countries that for many
years have not treated the United States fairly. In the end
all will be happy!

28/03/2018 10:16 Received message last night from XI JINPING of China that
his meeting with KIM JONG UN went very well and that
KIM looks forward to his meeting with me. In the meantime
and unfortunately maximum sanctions and pressure must be
maintained at all cost!
.@USTradeRep just announced an agreement in principle
with South Korea on KORUS! A great deal for American and
Korean workers. Let’s now focus on our important security
relationship.

28/03/2018 16:14

04/04/2018 11:22 We are not in a trade war with China that war was lost
many years ago by the foolish or incompetent people who
represented the U.S. Now we have a Trade Deﬁcit of $500
Billion a year with Intellectual Property Theft of another
$300 Billion. We cannot let this continue!
05/04/2018 13:10 The Fake News Washington Post Amazon’s “chief lobby-
ist” has another (of many) phony headlines “Trump Deﬁ-
ant As China Adds Trade Penalties.” WRONG! Should read
“Trump Deﬁant as U.S. Adds Trade Penalties Will End Bar-
riers And Massive I.P. Theft.” Typically bad reporting!

06/04/2018 11:11 Despite the Aluminum Tariﬀs Aluminum prices are DOWN
4%. People are surprised I’m not! Lots of money coming
into U.S. coﬀers and Jobs Jobs Jobs!

06/04/2018 14:32 China which is a great economic power is considered a Devel-
oping Nation within the World Trade Organization. They
therefore get tremendous perks and advantages especially
over the U.S. Does anybody think this is fair. We were
badly represented. The WTO is unfair to U.S.

16/04/2018 12:31 Russia and China are playing the Currency Devaluation
game as the U.S. keeps raising interest rates. Not accept-
able!

Negative

Negative

17/04/2018 12:24

I am in Florida and looking forward to my meeting with
Prime Minister Abe of Japan. Working on Trade and Mili-
tary Security.

Negative

25/04/2018 14:11

Looking forward to my meeting with Tim Cook of Apple.
We will be talking about many things including how the U.S.
has been treated unfairly for many years by many countries
on trade.

Negative

27/04/2018 11:50 Please do not forget the great help that my good friend Pres-
ident Xi of China has given to the United States particularly
at the Border of North Korea. Without him it would have
been a much longer tougher process!

Positive

02/05/2018 11:45 There was no Collusion (it is a Hoax) and there is no Ob-
struction of Justice (that is a setup &amp; trap). What
there is is Negotiations going on with North Korea over
Nuclear War Negotiations going on with China over Trade
Deﬁcits Negotiations on NAFTA and much more. Witch
Hunt!

03/05/2018 03:45 Our great ﬁnancial team is in China trying to negotiate a
level playing ﬁeld on trade!
I look forward to being with
President Xi in the not too distant future. We will always
have a good (great) relationship!

Negative

Negative

Negative

Negative

Positive

Positive

13/05/2018 19:22 China and the United States are working well together on
trade but past negotiations have been so one sided in favor
of China for so many years that it is hard for them to make
a deal that beneﬁts both countries. But be cool it will all
work out!
ZTE the large Chinese phone company buys a big percent-
age of individual parts from U.S. companies. This is also
reﬂective of the larger trade deal we are negotiating with
China and my personal relationship with President Xi.

14/05/2018 20:06

Negative

Positive

15/05/2018 12:35 Trade negotiations are continuing with China. They have
been making hundreds of billions of dollars a year from the
U.S. for many years. Stay tuned!

Negative

16/05/2018 13:09 The Washington Post and CNN have typically written false
stories about our trade negotiations with China. Nothing
has happened with ZTE except as it pertains to the larger
trade deal. Our country has been losing hundreds of billions
of dollars a year with China...
...haven’t even started yet! The U.S. has very little to give
because it has given so much over the years. China has much
to give!

16/05/2018 13:09

Negative

Negative

Negative

Positive

16/05/2018 13:09

...We have not seen China’s demands yet which should be
few in that previous U.S. Administrations have done so
poorly in negotiating. China has seen our demands. There
has been no folding as the media would love people to believe
the meetings...

Negative

17/05/2018 21:27 Talking trade with the Vice Premier of the People’s Republic

Positive

of China Liu He. https://t.co/9T7Iq6F3Xe

Negative

Negative

21/05/2018 11:21

I ask Senator Chuck Schumer why didn’t President Obama
&amp; the Democrats do something about Trade with China
including Theft of Intellectual Property etc.? They did
NOTHING! With that being said Chuck &amp; I have long
agreed on this issue! Fair Trade plus with China will happen!
21/05/2018 11:27 China has agreed to buy massive amounts of ADDITIONAL
Farm/Agricultural Products - would be one of the best
things to happen to our farmers in many years!

Negative

Positive

Negative

21/05/2018 11:31 On China Barriers and Tariﬀs to come down for ﬁrst time. Positive

Negative

Negative

Negative

Negative

21/05/2018 13:16 Under our potential deal with China they will purchase from
our Great American Farmers practically as much as our
Farmers can produce.

Positive

23/05/2018 11:55 Our Trade Deal with China is moving along nicely but in
the end we will probably have to use a diﬀerent structure in
that this will be too hard to get done and to verify results
after completion.

Negative

25/05/2018 22:45 Funny to watch the Democrats criticize Trade Deals being
negotiated by me when they don’t even know what the deals
are and when for 8 years the Obama Administration did
NOTHING on trade except let other countries rip oﬀ the
United States. Lost almost $800 Billion/year under “O”
...but complain and obstruct. They made only bad deals
(Iran) and their so-called Trade Deals are the laughing stock
of the world!

25/05/2018 23:13

Negative

29/05/2018 11:27

Sorry I’ve got to start focusing my energy on North Ko-
rea Nuclear bad Trade Deals VA Choice the Economy re-
building the Military and so much more and not on the
Rigged Russia Witch Hunt that should be investigating Clin-
ton/Russia/FBI/Justice/Obama/Comey/Lynch etc.

Negative

Negative

Negative

Negative

Negative

04/06/2018 13:47 Farmers have not been doing well for 15 years. Mexico
Canada China and others have treated them unfairly. By
the time I ﬁnish trade talks that will change. Big trade bar-
riers against U.S. farmers and other businesses will ﬁnally
be broken. Massive trade deﬁcits no longer!
Isn’t it Ironic? Getting ready to go to the G-7 in Canada
to ﬁght for our country on Trade (we have the worst trade
deals ever made) then oﬀ to Singapore to meet with North
Korea &amp; the Nuclear Problem...But back home we still
have the 13 Angry Democrats pushing the Witch Hunt!

07/06/2018 11:57

07/06/2018 19:55 PM Abe and I are also working to improve the trad-
ing relationship between the U.S. and Japan something
we have to do. The U.S. seeks a bilateral deal with
Japan that is based on the principle of fairness and reci-
procity. We’re working hard to reduce our trade imbalance...
https://t.co/pnqEHoplk0

07/06/2018 22:04 Please tell Prime Minister Trudeau and President Macron
that they are charging the U.S. massive tariﬀs and create
non-monetary barriers. The EU trade surplus with the U.S.
is $151 Billion and Canada keeps our farmers and others
out. Look forward to seeing them tomorrow.

08/06/2018 02:15 Why isn’t the European Union and Canada informing the
public that for years they have used massive Trade Tariﬀs
and non-monetary Trade Barriers against the U.S. Totally
unfair to our farmers workers &amp; companies. Take down
your tariﬀs &amp; barriers or we will more than match you!
08/06/2018 10:16 Canada charges the U.S. a 270% tariﬀ on Dairy Products!
They didn’t tell you that did they? Not fair to our farmers!

Positive

08/06/2018 10:25

Looking forward to straightening out unfair Trade Deals
with the G-7 countries. If it doesn’t happen we come out
even better!

Negative

06/04/2018 11:29 RT @realDonaldTrump: We are not in a trade war with
China that war was lost many years ago by the foolish or
incompetent people who repr. . .

Negative

04/06/2018 12:41 China already charges a tax of 16% on soybeans. Canada
has all sorts of trade barriers on our Agricultural products.
Not acceptable!

Negative

26/06/2018 11:25

26/06/2018 11:16 Early this year Harley-Davidson said they would move much
of their plant operations in Kansas City to Thailand. That
was long before Tariﬀs were announced. Hence they were
just using Tariﬀs/Trade War as an excuse. Shows how un-
balanced &amp; unfair trade is but we will ﬁx it.....
....We are getting other countries to reduce and eliminate
tariﬀs and trade barriers that have been unfairly used for
years against our farmers workers and companies. We are
opening up closed markets and expanding our footprint.
They must play fair or they will pay tariﬀs!
....When I had Harley-Davidson oﬃcials over to the White
House I chided them about tariﬀs in other countries like
India being too high. Companies are now coming back to
America. Harley must know that they won’t be able to sell
back into U.S. without paying a big tax!
....We are ﬁnishing our study of Tariﬀs on cars from the E.U.
in that they have long taken advantage of the U.S. in the
form of Trade Barriers and Tariﬀs. In the end it will all even
out - and it won’t take very long!

26/06/2018 11:37

26/06/2018 11:49

10/07/2018 09:35 Getting ready to leave for Europe. First meeting - NATO.
The U.S. is spending many times more than any other coun-
try in order to protect them. Not fair to the U.S. taxpayer.
On top of that we lose $151 Billion on Trade with the Eu-
ropean Union. Charge us big Tariﬀs (&amp; Barriers)!

10/07/2018 10:59 Thank you to all of my great supporters really big progress
being made. Other countries wanting to ﬁx crazy trade
deals. Economy is ROARING. Supreme Court pick getting
GREAT REVIEWS. New Poll says Trump at over 90% is
the most popular Republican in history of the Party. Wow!
10/07/2018 18:52 The European Union makes it impossible for our farmers
and workers and companies to do business in Europe (U.S.
has a $151 Billion trade deﬁcit) and then they want us to
happily defend them through NATO and nicely pay for it.
Just doesn’t work!
I am in Brussels but always thinking about our farmers.
Soy beans fell 50% from 2012 to my election. Farmers have
done poorly for 15 years. Other countries’ trade barriers and
tariﬀs have been destroying their businesses. I will open...

11/07/2018 12:40

11/07/2018 16:50 What good is NATO if Germany is paying Russia billions
of dollars for gas and energy? Why are their only 5 out of
29 countries that have met their commitment? The U.S. is
paying for Europe’s protection then loses billions on Trade.
Must pay 2% of GDP IMMEDIATELY not by 2025.

11/07/2018 17:07 What good is NATO if Germany is paying Russia billions
of dollars for gas and energy? Why are there only 5 out of
29 countries that have met their commitment? The U.S. is
paying for Europe’s protection then loses billions on Trade.
Must pay 2% of GDP IMMEDIATELY not by 2025.

12/07/2018 06:03 Presidents have been trying unsuccessfully for years to get
Germany and other rich NATO Nations to pay more toward
their protection from Russia. They pay only a fraction of
their cost. The U.S. pays tens of Billions of Dollars too much
to subsidize Europe and loses Big on Trade!

20/07/2018 12:43 China the European Union and others have been manipulat-
ing their currencies and interest rates lower while the U.S.
is raising rates while the dollars gets stronger and stronger
with each passing day - taking away our big competitive
edge. As usual not a level playing ﬁeld...
....The United States should not be penalized because we are
doing so well. Tightening now hurts all that we have done.
The U.S. should be allowed to recapture what was lost due to
illegal currency manipulation and BAD Trade Deals. Debt
coming due &amp; we are raising rates - Really?

20/07/2018 12:51

20/07/2018 13:04 Farmers have been on a downward trend for 15 years. The
price of soybeans has fallen 50% since 5 years before the
Election. A big reason is bad (terrible) Trade Deals with
other countries. They put on massive Tariﬀs and Barriers.
Canada charges 275% on Dairy. Farmers will WIN!

17/09/2018 10:11 Tariﬀs have put the U.S. in a very strong bargaining position
with Billions of Dollars and Jobs ﬂowing into our Country -
and yet cost increases have thus far been almost unnotice-
able. If countries will not make fair deals with us they will
be “Tariﬀed!”

18/09/2018 12:55

18/09/2018 12:50 China has openly stated that they are actively trying to
impact and change our election by attacking our farmers
ranchers and industrial workers because of their loyalty to
me. What China does not understand is that these people
are great patriots and fully understand that.....
.....China has been taking advantage of the United States
on Trade for many years. They also know that I am the
one that knows how to stop it. There will be great and fast
economic retaliation against China if our farmers ranchers
and/or industrial workers are targeted!
23/09/2018 20:52 Going to New York. Will be with Prime Minister Abe of
Japan tonight talking Military and Trade. We have done
much to help Japan would like to see more of a reciprocal
relationship. It will all work out!

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Positive

Positive

24/09/2018 20:44 US-Korea Free Trade Agreement Signing Ceremony!

Positive

https://t.co/yLFkAZgagG

Negative

24/09/2018 21:46

Joint Statement
Trade
Agreement:
https://t.co/lcrhsJtv00

on the United States-Korea Free
https://t.co/m0jW8nqdQW

Positive

Negative

Negative

04/12/2018 14:30 The negotiations with China have already started. Unless
extended they will end 90 days from the date of our wonder-
ful and very warm dinner with President Xi in Argentina.
Bob Lighthizer will be working closely with Steve Mnuchin
Larry Kudlow Wilbur Ross and Peter Navarro.....

Positive

ECB Working Paper Series No 2490 / November 202038Table B.2: List of tweets used in the training sample

Original Tweet

Date & Time
01/03/2018 12:12 Our Steel and Aluminum industries (and many others) have
been decimated by decades of unfair trade and bad policy
with countries from around the world. We must not let our
country companies and workers be taken advantage of any
longer. We want free fair and SMART TRADE!

02/03/2018 10:50 When a country (USA) is losing many billions of dollars
on trade with virtually every country it does business with
trade wars are good and easy to win. Example when we are
down $100 billion with a certain country and they get cute
don’t trade anymore-we win big. It’s easy!

08/03/2018 12:38

02/03/2018 13:57 When a country Taxes our products coming in at say 50%
and we Tax the same product coming into our country at
ZERO not fair or smart. We will soon be starting RECIP-
ROCAL TAXES so that we will charge the same thing as
they charge us. $800 Billion Trade Deﬁcit-have no choice!
Looking forward to 3:30 P.M. meeting today at the White
House. We have to protect &amp; build our Steel and Alu-
minum Industries while at the same time showing great ﬂexi-
bility and cooperation toward those that are real friends and
treat us fairly on both trade and the military.
Spoke to PM @TurnbullMalcolm of Australia. He is commit-
ted to having a very fair and reciprocal military and trade
relationship. Working very quickly on a security agreement
so we don’t have to impose steel or aluminum tariﬀs on our
ally the great nation of Australia!

09/03/2018 22:48

10/03/2018 15:22 Chinese President XI XINPING and I spoke at length about
the meeting with KIM JONG UN of North Korea. Presi-
dent XI told me he appreciates that the U.S. is working to
solve the problem diplomatically rather than going with the
ominous alternative. China continues to be helpful!

10/03/2018 16:15 Chinese President XI JINPING and I spoke at length about
the meeting with KIM JONG UN of North Korea. Presi-
dent XI told me he appreciates that the U.S. is working to
solve the problem diplomatically rather than going with the
ominous alternative. China continues to be helpful!
Spoke to Prime Minister Abe of Japan who is very enthusi-
astic about talks with North Korea. Also discussing opening
up Japan to much better trade with the U.S. Currently have
a massive $100 Billion Trade Deﬁcit. Not fair or sustainable.
It will all work out!

10/03/2018 17:23

10/03/2018 21:29 The European Union wonderful countries who treat the U.S.
very badly on trade are complaining about the tariﬀs on
Steel &amp; Aluminum. If they drop their horriﬁc barriers
&amp; tariﬀs on U.S. products going in we will likewise drop
ours. Big Deﬁcit. If not we Tax Cars etc. FAIR!

22/03/2018 18:40 As a candidate I pledged that if elected I would use
every lawful tool to combat unfair trade protect Amer-
ican workers and defend our national security. Today
we took another critical step to fulﬁll that commitment.
https://t.co/7NBI0Dibmx https://t.co/nmzqos3BUA

27/03/2018 00:44 Trade talks going on with numerous countries that for many
years have not treated the United States fairly. In the end
all will be happy!

-0.8

28/03/2018 10:16 Received message last night from XI JINPING of China that
his meeting with KIM JONG UN went very well and that
KIM looks forward to his meeting with me. In the meantime
and unfortunately maximum sanctions and pressure must be
maintained at all cost!
.@USTradeRep just announced an agreement in principle
with South Korea on KORUS! A great deal for American and
Korean workers. Let’s now focus on our important security
relationship.

28/03/2018 16:14

04/04/2018 11:22 We are not in a trade war with China that war was lost
many years ago by the foolish or incompetent people who
represented the U.S. Now we have a Trade Deﬁcit of $500
Billion a year with Intellectual Property Theft of another
$300 Billion. We cannot let this continue!
05/04/2018 13:10 The Fake News Washington Post Amazon’s “chief lobby-
ist” has another (of many) phony headlines “Trump Deﬁ-
ant As China Adds Trade Penalties.” WRONG! Should read
“Trump Deﬁant as U.S. Adds Trade Penalties Will End Bar-
riers And Massive I.P. Theft.” Typically bad reporting!

Score Date & Time
-1

16/05/2018 13:09

Original Tweet
...haven’t even started yet! The U.S. has very little to give
because it has given so much over the years. China has much
to give!

-1

16/05/2018 13:09

...We have not seen China’s demands yet which should be
few in that previous U.S. Administrations have done so
poorly in negotiating. China has seen our demands. There
has been no folding as the media would love people to believe
the meetings...

-0.6

-1

17/05/2018 21:27 Talking trade with the Vice Premier of the People’s Republic

0.1

of China Liu He. https://t.co/9T7Iq6F3Xe

-0.4

0.2

21/05/2018 11:21

I ask Senator Chuck Schumer why didn’t President Obama
&amp; the Democrats do something about Trade with China
including Theft of Intellectual Property etc.? They did
NOTHING! With that being said Chuck &amp; I have long
agreed on this issue! Fair Trade plus with China will happen!
21/05/2018 11:27 China has agreed to buy massive amounts of ADDITIONAL
Farm/Agricultural Products - would be one of the best
things to happen to our farmers in many years!

-0.3

0.5

0.1

21/05/2018 11:31 On China Barriers and Tariﬀs to come down for ﬁrst time.

0.3

0.1

-0.3

-0.8

-1

21/05/2018 11:40 China must continue to be strong &amp; tight on the Bor-
der of North Korea until a deal is made. The word is that
recently the Border has become much more porous and more
has been ﬁltering in. I want this to happen and North Korea
to be VERY successful but only after signing!

0

21/05/2018 13:16 Under our potential deal with China they will purchase from
our Great American Farmers practically as much as our
Farmers can produce.

0.2

23/05/2018 11:55 Our Trade Deal with China is moving along nicely but in
the end we will probably have to use a diﬀerent structure in
that this will be too hard to get done and to verify results
after completion.

25/05/2018 22:45 Funny to watch the Democrats criticize Trade Deals being
negotiated by me when they don’t even know what the deals
are and when for 8 years the Obama Administration did
NOTHING on trade except let other countries rip oﬀ the
United States. Lost almost $800 Billion/year under “O”
...but complain and obstruct. They made only bad deals
(Iran) and their so-called Trade Deals are the laughing stock
of the world!

25/05/2018 23:13

-0.2

29/05/2018 11:27

Sorry I’ve got to start focusing my energy on North Ko-
rea Nuclear bad Trade Deals VA Choice the Economy re-
building the Military and so much more and not on the
Rigged Russia Witch Hunt that should be investigating Clin-
ton/Russia/FBI/Justice/Obama/Comey/Lynch etc.

Score Date & Time
-0.6

Original Tweet

10/07/2018 18:52 The European Union makes it impossible for our farmers
and workers and companies to do business in Europe (U.S.
has a $151 Billion trade deﬁcit) and then they want us to
happily defend them through NATO and nicely pay for it.
Just doesn’t work!
I am in Brussels but always thinking about our farmers.
Soy beans fell 50% from 2012 to my election. Farmers have
done poorly for 15 years. Other countries’ trade barriers and
tariﬀs have been destroying their businesses. I will open...

11/07/2018 12:40

11/07/2018 16:50 What good is NATO if Germany is paying Russia billions
of dollars for gas and energy? Why are their only 5 out of
29 countries that have met their commitment? The U.S. is
paying for Europe’s protection then loses billions on Trade.
Must pay 2% of GDP IMMEDIATELY not by 2025.

11/07/2018 17:07 What good is NATO if Germany is paying Russia billions
of dollars for gas and energy? Why are there only 5 out of
29 countries that have met their commitment? The U.S. is
paying for Europe’s protection then loses billions on Trade.
Must pay 2% of GDP IMMEDIATELY not by 2025.

12/07/2018 06:03 Presidents have been trying unsuccessfully for years to get
Germany and other rich NATO Nations to pay more toward
their protection from Russia. They pay only a fraction of
their cost. The U.S. pays tens of Billions of Dollars too much
to subsidize Europe and loses Big on Trade!
....proliferation cyber attacks trade Ukraine Middle East
peace North Korea and more. There are many answers some
easy and some hard to these problems...but they can ALL
be solved!

19/07/2018 13:30

20/07/2018 12:43 China the European Union and others have been manipulat-
ing their currencies and interest rates lower while the U.S.
is raising rates while the dollars gets stronger and stronger
with each passing day - taking away our big competitive
edge. As usual not a level playing ﬁeld...
....The United States should not be penalized because we are
doing so well. Tightening now hurts all that we have done.
The U.S. should be allowed to recapture what was lost due to
illegal currency manipulation and BAD Trade Deals. Debt
coming due &amp; we are raising rates - Really?

20/07/2018 12:51

20/07/2018 13:04 Farmers have been on a downward trend for 15 years. The
price of soybeans has fallen 50% since 5 years before the
Election. A big reason is bad (terrible) Trade Deals with
other countries. They put on massive Tariﬀs and Barriers.
Canada charges 275% on Dairy. Farmers will WIN!

31/07/2018 10:14 The globalist Koch Brothers who have become a total joke
in real Republican circles are against Strong Borders and
Powerful Trade. I never sought their support because I don’t
need their money or bad ideas. They love my Tax &amp;
Regulation Cuts Judicial picks &amp; more. I made.....

24/08/2018 09:57 Target CEO raves about the Economy. “This is the best con-
sumer environment I’ve seen in my career.” A big statement
from a top executive. But virtually everybody is saying this
&amp; when our Trade Deals are made &amp; cost cutting
done you haven’t seen anything yet! @DRUDGEREP ORT
...Additionally because of our much tougher Trading stance
with China I do not believe they are helping with the pro-
cess of denuclearization as they once were (despite the UN
Sanctions which are in place)...

24/08/2018 17:36

-0.3

04/06/2018 12:41 China already charges a tax of 16% on soybeans. Canada
has all sorts of trade barriers on our Agricultural products.
Not acceptable!

-0.5

24/08/2018 17:36

...Secretary Pompeo looks forward to going to North Korea
in the near future most likely after our Trading relationship
with China is resolved. In the meantime I would like to send
my warmest regards and respect to Chairman Kim. I look
forward to seeing him soon!

-0.6

04/06/2018 12:43 The U.S. has made such bad trade deals over so many years

0

that we can only WIN!

-0.8

-0.6

-0.2

-0.1

-0.2

0

-0.1

Score
-0.4

-0.5

-0.2

-0.2

-0.1

0

-0.1

-0.2

-0.5

0

0

0

0

-0.7

-0.2

-0.6

0.2

-0.4

04/06/2018 13:47 Farmers have not been doing well for 15 years. Mexico
Canada China and others have treated them unfairly. By
the time I ﬁnish trade talks that will change. Big trade bar-
riers against U.S. farmers and other businesses will ﬁnally
be broken. Massive trade deﬁcits no longer!

06/04/2018 11:11 Despite the Aluminum Tariﬀs Aluminum prices are DOWN
4%. People are surprised I’m not! Lots of money coming
into U.S. coﬀers and Jobs Jobs Jobs!

-0.1

06/04/2018 11:29 RT @realDonaldTrump: We are not in a trade war with
China that war was lost many years ago by the foolish or
incompetent people who repr. . .

-0.1

06/04/2018 14:32 China which is a great economic power is considered a Devel-
oping Nation within the World Trade Organization. They
therefore get tremendous perks and advantages especially
over the U.S. Does anybody think this is fair. We were
badly represented. The WTO is unfair to U.S.

-0.7

16/04/2018 12:31 Russia and China are playing the Currency Devaluation
game as the U.S. keeps raising interest rates. Not accept-
able!

-0.3

17/04/2018 12:24

I am in Florida and looking forward to my meeting with
Prime Minister Abe of Japan. Working on Trade and Mili-
tary Security.

-0.1

07/06/2018 11:57

06/06/2018 01:23 Chris Farrell Judicial Watch. “They were running an op-
eration to undermine a candidate for President of the U.S.
These are all violations of law. This is intelligence trade-
craft to steer an election. There’s nothing more grave when
it comes to abuse of our intelligence system...
Isn’t it Ironic? Getting ready to go to the G-7 in Canada
to ﬁght for our country on Trade (we have the worst trade
deals ever made) then oﬀ to Singapore to meet with North
Korea &amp; the Nuclear Problem...But back home we still
have the 13 Angry Democrats pushing the Witch Hunt!
Looking forward to seeing my friend Prime Minister
@AbeShinzo of Japan at noon. Will be discussing North
Korea and Trade.

07/06/2018 14:01

07/06/2018 19:55 PM Abe and I are also working to improve the trad-
ing relationship between the U.S. and Japan something
we have to do. The U.S. seeks a bilateral deal with
Japan that is based on the principle of fairness and reci-
procity. We’re working hard to reduce our trade imbalance...
https://t.co/pnqEHoplk0

07/06/2018 22:04 Please tell Prime Minister Trudeau and President Macron
that they are charging the U.S. massive tariﬀs and create
non-monetary barriers. The EU trade surplus with the U.S.
is $151 Billion and Canada keeps our farmers and others
out. Look forward to seeing them tomorrow.

25/04/2018 14:11

Looking forward to my meeting with Tim Cook of Apple.
We will be talking about many things including how the U.S.
has been treated unfairly for many years by many countries
on trade.

27/04/2018 11:50 Please do not forget the great help that my good friend Pres-
ident Xi of China has given to the United States particularly
at the Border of North Korea. Without him it would have
been a much longer tougher process!

02/05/2018 22:40

02/05/2018 11:45 There was no Collusion (it is a Hoax) and there is no Ob-
struction of Justice (that is a setup &amp; trap). What
there is is Negotiations going on with North Korea over
Nuclear War Negotiations going on with China over Trade
Deﬁcits Negotiations on NAFTA and much more. Witch
Hunt!
“This isn’t some game. You are screwing with the work of
the president of the United States.” John Dowd March 2018.
With North Korea China the Middle East and so much more
there is not much time to be thinking about this especially
since there was no Russian “Collusion.”
03/05/2018 03:45 Our great ﬁnancial team is in China trying to negotiate a
level playing ﬁeld on trade!
I look forward to being with
President Xi in the not too distant future. We will always
have a good (great) relationship!

13/05/2018 15:01 President Xi of China and I are working together to give
massive Chinese phone company ZTE a way to get back
into business fast. Too many jobs in China lost. Commerce
Department has been instructed to get it done!

13/05/2018 19:22 China and the United States are working well together on
trade but past negotiations have been so one sided in favor
of China for so many years that it is hard for them to make
a deal that beneﬁts both countries. But be cool it will all
work out!
ZTE the large Chinese phone company buys a big percent-
age of individual parts from U.S. companies. This is also
reﬂective of the larger trade deal we are negotiating with
China and my personal relationship with President Xi.

14/05/2018 20:06

15/05/2018 12:35 Trade negotiations are continuing with China. They have
been making hundreds of billions of dollars a year from the
U.S. for many years. Stay tuned!

16/05/2018 13:09 The Washington Post and CNN have typically written false
stories about our trade negotiations with China. Nothing
has happened with ZTE except as it pertains to the larger
trade deal. Our country has been losing hundreds of billions
of dollars a year with China...

-0.3

0.2

08/06/2018 02:15 Why isn’t the European Union and Canada informing the
public that for years they have used massive Trade Tariﬀs
and non-monetary Trade Barriers against the U.S. Totally
unfair to our farmers workers &amp; companies. Take down
your tariﬀs &amp; barriers or we will more than match you!
08/06/2018 10:16 Canada charges the U.S. a 270% tariﬀ on Dairy Products!
They didn’t tell you that did they? Not fair to our farmers!

-0.1

08/06/2018 10:25

Looking forward to straightening out unfair Trade Deals
with the G-7 countries. If it doesn’t happen we come out
even better!

0

08/06/2018 11:22

I am heading for Canada and the G-7 for talks that will
mostly center on the long time unfair trade practiced against
the United States. From there I go to Singapore and talks
with North Korea on Denuclearization. Won’t be talking
about the Russian Witch Hunt Hoax for a while!

0.1

0.7

-0.5

0.2

-0.4

-0.3

26/06/2018 11:25

26/06/2018 11:16 Early this year Harley-Davidson said they would move much
of their plant operations in Kansas City to Thailand. That
was long before Tariﬀs were announced. Hence they were
just using Tariﬀs/Trade War as an excuse. Shows how un-
balanced &amp; unfair trade is but we will ﬁx it.....
....We are getting other countries to reduce and eliminate
tariﬀs and trade barriers that have been unfairly used for
years against our farmers workers and companies. We are
opening up closed markets and expanding our footprint.
They must play fair or they will pay tariﬀs!
....When I had Harley-Davidson oﬃcials over to the White
House I chided them about tariﬀs in other countries like
India being too high. Companies are now coming back to
America. Harley must know that they won’t be able to sell
back into U.S. without paying a big tax!
....We are ﬁnishing our study of Tariﬀs on cars from the E.U.
in that they have long taken advantage of the U.S. in the
form of Trade Barriers and Tariﬀs. In the end it will all even
out - and it won’t take very long!

26/06/2018 11:37

26/06/2018 11:49

10/07/2018 09:35 Getting ready to leave for Europe. First meeting - NATO.
The U.S. is spending many times more than any other coun-
try in order to protect them. Not fair to the U.S. taxpayer.
On top of that we lose $151 Billion on Trade with the Eu-
ropean Union. Charge us big Tariﬀs (&amp; Barriers)!

10/07/2018 10:59 Thank you to all of my great supporters really big progress
being made. Other countries wanting to ﬁx crazy trade
deals. Economy is ROARING. Supreme Court pick getting
GREAT REVIEWS. New Poll says Trump at over 90% is
the most popular Republican in history of the Party. Wow!

17/09/2018 10:11 Tariﬀs have put the U.S. in a very strong bargaining position
with Billions of Dollars and Jobs ﬂowing into our Country -
and yet cost increases have thus far been almost unnotice-
able. If countries will not make fair deals with us they will
be “Tariﬀed!”

18/09/2018 12:55

18/09/2018 12:50 China has openly stated that they are actively trying to
impact and change our election by attacking our farmers
ranchers and industrial workers because of their loyalty to
me. What China does not understand is that these people
are great patriots and fully understand that.....
.....China has been taking advantage of the United States
on Trade for many years. They also know that I am the
one that knows how to stop it. There will be great and fast
economic retaliation against China if our farmers ranchers
and/or industrial workers are targeted!
23/09/2018 20:52 Going to New York. Will be with Prime Minister Abe of
Japan tonight talking Military and Trade. We have done
much to help Japan would like to see more of a reciprocal
relationship. It will all work out!

0

24/09/2018 20:44 US-Korea Free Trade Agreement Signing Ceremony!

0.4

https://t.co/yLFkAZgagG

0.4

0

0.2

0.3

24/09/2018 21:46

Joint Statement
Trade
Agreement:
https://t.co/lcrhsJtv00

on the United States-Korea Free
https://t.co/m0jW8nqdQW

25/10/2018 13:57 The New York Times has a new Fake Story that now the
Russians and Chinese (glad they ﬁnally added China) are
listening to all of my calls on cellphones. Except that I
rarely use a cellphone &amp; when I do it’s government
authorized.
I like Hard Lines. Just more made up Fake
News!

04/12/2018 14:30 The negotiations with China have already started. Unless
extended they will end 90 days from the date of our wonder-
ful and very warm dinner with President Xi in Argentina.
Bob Lighthizer will be working closely with Steve Mnuchin
Larry Kudlow Wilbur Ross and Peter Navarro.....

-0.3

-0.8

-0.5

-0.5

-0.4

-0.2

-0.3

-0.2

-0.7

-0.5

-0.1

ECB Working Paper Series No 2490 / November 202039Table B.3: Selected words and loadings by elastic net algorithm, Equation (2.2)

Loading
Word
-0.06667
advantag
0.049195
agreement
anoth
-0.00874
-0.01356
barrier
-0.09218
billion
-0.04538
charg
-0.15007
countri
-0.03529
deﬁcit
-0.09196
end
-0.0222
even
-0.03681
fair
0.073555
korea
0.027548
made
0.00242
much
-0.06014
not
0.012072
pai
presid
0.07043
relationship 0.059126
-0.05362
trade
-0.1656
unfair
-0.01003
us
-0.02898
veri
0.090961
work
-0.13911
worker
0.069126
xi
-0.00401
year
0.015077
zte

ECB Working Paper Series No 2490 / November 202040Table B.4: Estimates from equation Equation (2.4) at weekly frequency

Dep. variable
3-T Index (lev.)
∆SP 500t

∆SP 500t−1

∆SP 500t−2

∆N EERU SD

t

∆N EERU SD
t−1

∆N EERU SD
t−2

∆StockCHN

t

∆StockCHN

t−1

∆StockCHN

t−2

Constant

Observations
F test
F prob
R2

Model
(2)

Model
(3)

Model
(1)
0.381
(0.673)
0.208
(0.738)
1.055
(0.659)

-0.764
(1.485)
0.959
(1.416)
-2.181
(1.695)

Model
(4)
0.326
(0.707)
0.313
(0.749)
0.871
(0.665)
-0.595
(1.558)
1.019
(1.415)
-1.575
(1.664)

0.496
(0.475)
-0.144
(0.488)
0.684
(0.590)
-0.131***
(0.011)
190
0.882
0.451
0.02

-0.135***
(0.012)
190
0.675
0.670
0.03

-0.135***
(0.012)
190
0.984
0.401
0.02

-0.131***
(0.011)
190
0.891
0.447
0.01

Model
(5)
0.201
(0.714)
0.073
(0.768)
1.021
(0.675)

0.541
(0.510)
-0.382
(0.548)
0.442
(0.615)
-0.134***
(0.011)
190
0.827
0.551
0.03

Model
(6)

-0.355
(1.532)
1.010
(1.495)
-1.679
(1.586)
0.503
(0.479)
-0.132
(0.509)
0.592
(0.590)
-0.131***
(0.011)
190
0.917
0.483
0.03

Model
(7)
0.186
(0.733)
0.156
(0.786)
0.889
(0.693)
-0.260
(1.575)
0.793
(1.472)
-1.321
(1.612)
0.524
(0.516)
-0.362
(0.560)
0.394
(0.616)
-0.134***
(0.011)
190
0.751
0.662
0.04

Notes: explanatory variables are in log-diﬀerences; the China stock index is the Shanghai stock market
index. T-stats reported in parenthesis below coeﬃcients and computed based on HAC standard errors.
*** p < 0.01, ** p < 0.05, * p < 0.1

ECB Working Paper Series No 2490 / November 202041Table B.5: Estimates from equation Equation (2.4) at daily frequency at daily frequency

Dep. variable
3-T Index (lev.)
∆SP 500t

∆SP 500t−1

∆SP 500t−2

∆SP 500t−3

∆N EERU SD

t

∆N EERU SD
t−1

∆N EERU SD
t−2

∆N EERU SD
t−3

∆StockCHN

t

∆StockCHN

t−1

∆StockCHN

t−2

∆StockCHN

t−3

Constant

Observations
F test
F prob
R2

Model
(2)

Model
(3)

Model
(1)
2.900
(4.387)
2.213
(3.076)
1.943
(3.084)
1.470
(6.894)

12.661
(12.172)
-10.890
(12.308)
3.355
(7.672)
0.014
(10.158)

-11.422
(9.092)
1.411
(3.675)
-2.260
(3.458)
-1.322
(2.180)
-0.363***
(0.038)
158
1.274
0.285
3.13

-0.368***
(0.040)
158
0.458
0.712
0.00

-0.363***
(0.038)
158
0.476
0.699
0.00

Model
(4)
4.173
(4.688)
2.217
(2.936)
2.019
(3.370)
1.906
(7.720)
17.702
(11.921)
-6.087
(13.732)
6.481
(9.856)
0.519
(9.254)

-0.364***
(0.037)
158
0.557
0.764
0.00

Model
(5)
5.763
(3.915)
3.105
(3.384)
0.976
(2.536)
4.930
(8.325)

-13.507
(10.342)
0.720
(3.766)
-4.311
(2.875)
-2.656
(2.781)
-0.357***
(0.033)
158
0.942
0.466
2.78

Model
(6)

-0.689
(8.296)
-9.268
(10.613)
3.228
(10.034)
-3.561
(14.232)
-11.453
(9.110)
0.943
(3.789)
-2.195
(4.020)
-1.577
(2.394)
-0.358***
(0.034)
158
0.655
0.686
0.88

Model
(7)
6.145
(4.179)
2.761
(3.196)
1.317
(2.699)
4.975
(8.720)
4.551
(9.182)
-3.904
(11.999)
6.965
(11.592)
-2.032
(11.313)
-13.126
(10.392)
0.487
(3.720)
-3.709
(3.301)
-2.858
(3.028)
-0.354***
(0.031)
158
0.656
0.747
0.37

Notes: explanatory variables are in log-diﬀerences; the China stock index is the Shanghai stock market
index. T-stats reported in parenthesis below coeﬃcients and computed based on HAC standard errors.
*** p < 0.01, ** p < 0.05, * p < 0.1

ECB Working Paper Series No 2490 / November 202042Acknowledgements 

We would like to thank Michele Ca' Zorzi, Georgios Georgiadis, Johannes Gräb, Maurizio Habib, Arnaud Mehl, Livio Stracca, Fabrizio 
Venditti and an anonymous referee for valuable comments. The views expressed in this paper are those of the authors only and should 
not be attributed to Banque de France, the European Central Bank, its Executive Board or the ESCB. 

Massimo Minesso Ferrari 
European Central Bank, Frankfurt am Main, Germany; email: massimo.ferrari1@ecb.europa.eu 

Frederik Kurcz 
Berlin School of Economics, Berlin, Germany; email: frederik.kurcz@gmail.com 

Maria Sole Pagliari 
Banque de France, Paris, France; email: maria-sole.pagliari@banque-france.fr 

© European Central Bank, 2020 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-4407-6 

ISSN 1725-2806 

doi:10.2866/94916 

QB-AR-20-142-EN-N