metadata: authors: 0: author: Fraiberger,Samuel Paul
1: author: Lee,Do
2: author: Puy,Damien
3: author: Rancier,Romain
count: World
docna: 0: docna: Media Sentiment and International Asset Prices
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Commodity Risk Management,International Trade and Trade Rules,Non Bank Financial Institutions,Mutual Funds,Securities Markets Policy & Regulation,Capital Markets and Capital Flows,Capital Flows
teratopic: Macroeconomics and Economic Growth,Agriculture,Finance and Financial Sector Development,International Economics and Trade
abstracts: cdata!: This paper assesses the impact of media
            sentiment on international equity prices using a dataset of
            more than 4.5 million Reuters articles published across the
            globe between 1991 and 2015. Media sentiment robustly
            predicts daily returns in both advanced and emerging
            markets, even after controlling for known determinants of
            stock prices. But not all news sentiment is alike. A local
            (country-specific) increase in news optimism (pessimism)
            predicts a small and transitory increase (decrease) in local
            returns. By contrast, changes in global news sentiment have
            a larger impact on equity returns around the world, which
            does not reverse in the short run. Media sentiment affects
            mainly foreign -- rather than local -- investors: although
            local news optimism attracts international equity flows for
            a few days, global news optimism generates a permanent
            foreign equity inflow. Our results confirm the value of
            media content in capturing investor sentiment.
repnme: repnme: Media Sentiment and International Asset Prices
display_title: Media Sentiment and International
            Asset Prices
listing_relative_url: /research/2018/11/30623658/media-sentiment-international-asset-prices
topicv3: Trade m1326924 698,Mutual Funds m1326970 2180,Equity Finance m1327686 334,Commodity and Resource Prices m1503849 3034,Investment Entry m1504647 2544
publishtoextweb_dt: 2018-12-04T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Agriculture,Finance and Financial Sector Development,International Economics and Trade
no_of_pages: 34
author_keys: 0: author_key: 1596352
model_extractions: WPS8649 Policy Research Working Paper 8649 Media Sentiment and International Asset Prices _Samuel P. Fraiberger Do Lee Damien Puy Romain Ranciere_ Development Economics Development Data Group November 2018
Policy Research Working Paper 8649

# **Abstract**

This paper assesses the impact of media sentiment on international equity prices using a dataset of more than 4.5 million Reuters articles published across the globe between 1991 and 2015. Media sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. Media sentiment affects mainly foreign— rather than local—investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at sfraiberger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Media Sentiment and International Asset Prices**

Samuel P. Fraiberger (World Bank & NYU), Do Lee (IMF), Damien Puy (IMF), Romain Ranciere (USC, NBER & CEPR)<sup>1</sup> <u>Keywords: Asset Pricing, Capital Flows, Behavioral Finance, Investor Sentiment, News</u> Media, Natural Language Processing.

<u>JEL Codes : F32; G12; G15; G41</u> 1 We are grateful to the IMF Big data initiative for financial support, as well as participants in the CEPR International Macroeconomic and Finance meeting and the Macro-Financial Research Seminar of the IMF for helpful comments and suggestions. Authors’ email: <u>sfraiberger@worldbank.org, Dlee2@imf.org, Dpuy@imf.org, Ranciere@usc.edu. Samuel Fraiberger is also visiting scholar at Northeastern and Harvard</u> University. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, the World Bank, their Executive Boards or their management.
2

# **I.** **<u>Introduction</u>**

The amount financial and economic news has increased dramatically over the past 20 years. The rise of real time news-wire such as Reuters and Bloomberg has also greatly increased the speed at which news travel across the world. Beyond providing information, journalists also choose to portray events related to asset market variations using tonal words that could intrinsically affect investors’ investment decisions. This paper explores the impact of such media sentiment on equity prices.

Using more than 4.5 million Reuters articles published across the globe between 1991 and 2015, we find that news tone – our measure of news sentiment - robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. However, not all news has the same impact. Local (countryspecific) news optimism (or pessimism) only affects equity returns temporarily, i.e. for a few days. By contrast, changes in global news sentiment have a larger impact on equity returns around the world that does not reverse in the short run, suggesting a novel and arguably stronger source of sentiment-driven asset price fluctuations. Combining our news sentiment indices with daily equity flows from mutual funds investing in sixteen EMs between 2007 and 2015, we find an effect strikingly close to that of stock returns: although local news optimism attracts equity flows for a few days only, global sentiment optimism attracts them permanently. However, this effect is driven by the (net) asset demand of foreign funds domiciled outside of the country rather than local funds domiciled in the country, suggesting that news tone affects prices mostly through its effect on foreign investors’ sentiment.

Our paper is constructed as follows. Using the fraction of positive and negative words in each article to capture the tone of news published, we first construct a daily news-based sentiment index for 25 advanced and emerging countries between 1991 and 2015. We then estimate the response of equity prices to sentiment shocks using Jordà’s (2005) local projection method. We first estimate the effect of US news sentiment on US equity returns and compare our results to existing studies on the US such as Tetlock (2007). We then test whether these results extend to all countries, after controlling for known sources of predictability in international equity returns, both locally and globally (e.g. commodity prices, VIX etc.). Overall, we find panel estimates to be close to the US benchmark. Positive sentiment shocks also imply a significant impact on equity returns of 10 basis points that partially reverses after a few days, confirming that the effect of news sentiment on asset prices is a pervasive phenomenon that is not limited to the US.

Since a large fraction of news articles in our corpus refer to multiple countries, we then explore whether sentiment shocks are mainly driven by local (country-specific) news, or by global news having an impact on a country. To isolate the effect of local news from that of global news, we start by recomputing the daily sentiment index for each country after having excluded articles that mention any other country than the country of interest, allowing us to
3 capture the sentiment in genuinely local news. Second, we augment our specifications with a daily “global news sentiment index” extracted from the original cross-country sentiment indices, allowing us to control for the tone of news published in the world every day. We find that the effect of local sentiment shocks is still significant and transitory. However, its magnitude is roughly cut in half, peaking around 5 basis points before vanishing after a week. By contrast, global news sentiment shocks have a stronger impact on returns around the world and do not reverse in the short run (i.e. at least 3 weeks). In our sample, sudden changes in optimism (or pessimism) in global news generate a permanent increase (decrease) of 25 basis points - roughly five times the size of the local sentiment shock – that reaches its peak slowly after ten to fifteen days, both in Advanced Economies (AEs) and in Emerging Markets (EMs).

Finally, we extend our analysis to international equity flows. Using daily equity flows from a very large number of international equity mutual funds investing in sixteen EMs between 2007 and 2015, we explore the response of capital flows to both local and global news sentiment shocks. Overall, we find a very similar response to that of stock returns: although local news optimism attracts equity fund flows for a few days only, global sentiment optimism generates a permanent equity inflow peaking after two weeks. Using the official domicile of each fund as a proxy for the location of investors, we then contrast the response of local and foreign equity funds. Although funds located outside the country respond strongly to news sentiment, the response from local funds is muted, suggesting that news tone affects prices mostly through its impact on foreign investors’ sentiment.

We close the paper by investigating further the properties of global news sentiment. We report three interesting findings. First, the effect of global news sentiment is state-dependent: the impact of global news sentiment is four times stronger in global “bear” markets than in global “bull” markets, suggesting that investors are more sensitive to news tone during global market downturns. Second, we find that the global news sentiment index explains more of the variance in equity returns across the world than other typical measures of global risk aversion, such as the VIX. Although VIX shocks have a stronger impact on equity markets around the world, they are also much less frequent than shocks captured by the global news sentiment index, which arguably captures a much broader set of events than the VIX, especially when it comes to periods of global market optimism. Finally, we investigate the kind of news that drive sudden changes in global news sentiment. In general, global news sentiment is driven by multi-country news, which are broader, longer and with a stronger tonality than local news articles. However, the content of those news varies significantly with the direction of the global news sentiment index: when the global news sentiment index is strongly positive, the corpus of news in the world tilts towards positive financial and corporate news in advanced AEs, especially in the US. By contrast, the coverage tilts strongly towards economic and political news in EMs when the global news sentiment index goes into negative territory, suggesting that the drivers of global market rallies differ from those of global sell-offs.
4 Overall, our findings are robust to a variety of tests and extensions. Importantly, we show that results are stable over time and across countries (EMs and AEs), are not driven by extreme values or crisis events (such as the Global Financial Crisis) or by the presence of key countries – such as the US – in the sample. They are also robust to an alternative measure of sentiment based on a “Term Frequency - Inverse Document Frequency” (“TF-IDF”) weighting of words. Since we control for the number of articles published every day, the effect we capture is also unlikely to capture the volume of news, as opposed to its tone.

Taken together, our results are consistent with shocks to investors sentiment, affecting both advanced and emerging countries, during which local news and their tone affect investor sentiment and prices momentarily before returning to their fundamental values. From a theoretical perspective, this finding is consistent with the presence of noise (or liquidity) traders (DeLong et al (1990), Campbell (1993)). Although we are not able to identify which theory prevails, using capital flows data reveals that such traders are more likely to be foreigners than locals. The response of equity prices to global news sentiment shocks is, however, more ambiguous. The longer and more permanent impact on world stock markets is consistent with global news containing genuinely new (good or bad) information on global fundamentals that is only slowly incorporated into stock prices. An alternative explanation is that sudden changes in global news sentiment are strong enough to cause drifts in investors’ sentiment that do not reverse in the short run, even in the absence of new “hard” information about fundamentals. The fact that global news shocks have a stronger impact in troubled times, when investors are more anxious, support this interpretation (Garcia, 2013).

This paper relates to the growing body of research investigating the link between the news media, investor sentiment, and asset prices, in particular Tetlock (2007) and Garcia (2013). We contribute to this literature in several ways. To our knowledge, we are the first to assess the link between news sentiment and equity returns in a large sample of advanced countries and emerging countries using a large dataset of news.<sup>2</sup> Extending the analysis to other countries allows us to isolate the contribution of global sentiment and highlight its differential effect. Finally, we are the first to assess the effect of sentiment shocks on high frequency capital flows data. Beyond validating the results obtained on prices, our results cast a light on _who_ are the investors most sensitive to sudden changes in sentiment. More generally, we are closely connected to the vast empirical literature on investor sentiment that has focused on how to measure investor sentiment and to quantify its effects on a variety of financial market outcomes (See Baker and Wurgler (2007) for a review). With respect to that literature, we confirm the value of media text (and tone) in in capturing investors’ sentiment.

> 2 For instance, Garcia (2013) and Tetlock (2007) use one column in one newspaper per day to capture US news sentiment, representing roughly 30.000 and 3.000 articles respectively. For the US only, we use 1.8 million articles.
5 Our results also contribute to the vast literature documenting the strength – and rise – of comovement in asset prices and capital flows (Fratzscher, (2012), Raddatz and Schmukler (2012), Jotikasthira et al. (2012), Ghosh et al., (2014), Broner et al. (2013), Rey, (2013), Puy (2016), Claessens, Cerutti and Puy (2015)). Most of the debate has focused on the importance of global (or external) factors for (local) asset price movements, and the role of foreign investors in propagating shocks across countries. Broadly speaking, our results support the view that external events matter and are in general stronger than local factors. In particular, global news sentiment contributes significantly to global asset prices comovement, through its impact on international investors’ behavior. However, we do not find evidence that this phenomenon has increased over time or affects more EMs than AEs.

Finally, from a technical perspective, we are connected to the recent and fast-growing literature that links textual information to both economic and financial outcomes (see Gentzkow, Kelly, and Taddy (2017) for a review). Among many others, Baker, Bloom, and Davis (2016) develop an index of economic policy uncertainty from US newspaper articles, showing that it forecasts declines in investment, output, and employment.<sup>3</sup> Using daily Internet search volume from millions of households in the US, Da, Engelberg and Gao (2015) found that the volume of queries related to household concerns (e.g., “recession,” “unemployment,” and “bankruptcy”) could predict short-term return reversals, temporary increases in volatility, and mutual fund flows out of equity funds and into bond funds.

The rest of the paper is constructed as follows. Section II presents the data used in this paper, and the text analysis we use to construct sentiment measures. Section III presents the empirical framework and key results. Section IV provides further results on the properties of the global news sentiment index. Section V reports extensions and robustness. The last section concludes.

> 3 Our results are orthogonal to the various EPU indexes constructed by Baker, Bloom and Davis (2016). Results available on request.
6

# **II.** **<u>Data description</u>**

The paper uses three main data sources: (i) articles to construct country-specific newssentiment indexes (ii) asset prices and related high-frequency financial variables (such as trading volumes or liquidity measures) and (iii) capital flows data. We detail them in turn.

# **A.** **<u>News articles and Sentiment measures</u>**

# **1.** **<u>News articles</u>**

Our dataset of news articles was acquired from Factiva, a leading provider of news archives. We focused on articles published by Reuters in English, as it offers the most consistent coverage over time and across countries. Each article contains a timestamp, a title, a main text, as well as topics and locations tags generated by Factiva using a proprietary algorithm. We selected all the articles tagged with either “Economic News” or “Financial Market News”, as well as with at least one of the countries included in our sample. Our dataset contains close to 4.5 million daily articles covering 25 countries - 9 Advanced Economies (AEs) and 16 Emerging Markets (EMs) - from 1991 to 2015.

Summary tables and figures reporting (i) the start (and end) of the news coverage by country (ii) the number of articles by country and (iii) the main topics covered in our corpus are provided in Appendix. Overall, the articles we use cover a wide range of economic news (economic policy, government finance etc..), financial news (commodity markets, equity markets, forex etc..), corporate news and political news. The distribution of topics is similar in AEs and in EMs. Quantitatively, US-related articles represent one fourth of our sample (200 articles per day).<sup>4</sup> The distribution of non-US country tags is more balanced, with an average of 97,000 articles per country over the whole sample (20 articles per day).

# **2.** **<u>News-Sentiment measures</u>**

To measure sentiment, we use pre-existing dictionaries of tonal words. Given the range of topics covered in our corpus of news, we first combine dictionaries of positive and negative words compiled by Loughran and McDonald (2011) for financial texts and by Young and Soroka (2012) for political and economic texts. Next, for each word in a dictionary (e.g. “lose”), we collect all of its inflections (“losing”, “loser”, “lost”, “loss”, etc.) that are present in our corpus and include them in the dictionary, obtaining a list of 7,217 negative words and 3,250 positive words. Table 1 reports the most frequent tonal words in our corpus, illustrating that the most frequent positive and negative words we selected reflect the sentiment typically associated with economic or financial outcomes.

> 4 Note that articles can tag multiple location and topics at the same time. See Appendix for details and next section for an example.
<!-- Start of picture text --> Fraction Of Positive Words Fraction Of Articles IDF Fraction Of Negative Words Fraction Of Articles IDF<br>Positive Word Negative Word<br>strong 0.107 0.118 2.135 crisis 0.088 0.069 2.675<br>gains 0.099 0.104 2.265 losses 0.072 0.069 2.677<br>well 0.082 0.103 2.271 deficit 0.071 0.044 3.132<br>good 0.065 0.077 2.561 weak 0.070 0.070 2.656<br>help 0.061 0.074 2.603 limited 0.063 0.062 2.774<br>recovery 0.056 0.058 2.850 concerns 0.063 0.067 2.705<br>highest 0.044 0.053 2.935 decline 0.050 0.052 2.960<br>agreement 0.043 0.042 3.179 weaker 0.048 0.049 3.007<br>assets 0.042 0.042 3.159 poor 0.047 0.049 3.017<br>positive 0.041 0.051 2.973 unemployment 0.045 0.030 3.493<br>better 0.041 0.053 2.932 lost. 0.045 0.048 3.034<br>gained 0.041 0.049 3.007 fears 0.041 0.045 3.109<br>boost 0.040 0.054 2.914 |} dropped 0.040 0.045 3.095<br>leading 0.039 0.052 2.957 slow 0.039 0.042 3.162<br>confidence 0.036 0.039 3.255 negative 0.039 0.040 3.225<br>gain 0.035 0.042 3.159 problems 0.037 0.039 3.233<br>agreed 0.034 0.042 3.179 worries 0.037 0.040 3.210<br>stronger 0.032 0.042 3.172 hard 0.036 0.039 3.234<br>worth 0.032 0.039 3.239 recession 0.035 0.032 3.457<br>opening 0.032 0.041 3.199 loss 0.033 0.032 3.441<br><!-- End of picture text -->

_ ~~ij if ~ LW ij~~ ij ij ij ij ij ij iy=1 ij = log ~~(—)~~ U ,
8 where N is the number of articles in the corpus and N� is the number of articles in which word _i_ is present. Hence, this weighting gives more weight to words that appear more rarely across documents.

To illustrate our dictionary-based approach to measuring sentiment, here is an example of an article in our corpus in which the tonal words are underlined.<sup>6</sup> It suggests that although our sentiment measure s� does not capture all the nuances in the text, it provides a good indication of its overall tone.

<u>Title: Argentina’s Peronists defend Menem’s labor reforms.</u> <u>Timestamp: 1996-09-02</u> Text: BUENOS AIRES, Sept 2 (Reuters) - The Argentine government Monday tried to counter **criticisms** of President Carlos Menem’s proposals for more **flexible** labor laws, **arguing** that not just workers would contribute to new **unemployment** insurance. Menem **angered** trade unions, already in **disagreement** over his fiscal **austerity** programs, by announcing a labor reform package Friday including **suspending** collective wage deals and replacing **redundancy** payouts with unemployment insurance.

<u>Topics: Labor/Personnel Issues, Corporate/Industrial News, Economic/Monetary Policy,</u> Economic News, Political/General News, Labor Issues, Domestic Politics <u>Locations: Argentina, Latin America, South America</u> After computing the sentiment of each article s�, we obtain a daily sentiment index for each country _c_ by taking the average of s� across articles published in any given day that contain country _c_ in its location tags.

# **B.** **<u>Asset prices and related variables</u>**

We explore the impact of news-sentiment measures on stock market returns. Equity returns are computed using the country’s main stock market index. Returns are computed in local currency to isolate the effect of sentiment on the local stock market from its effect on the exchange rate. Daily world returns, which are used as a global control, are computed using the Dow Jones World Index. The final coverage, along with the list of indexes and sources used to compute equity returns is reported in the Appendix.

To proxy for market liquidity, we also collect daily equity trading volumes reported by local stock exchanges. Following Campbell, Grossman, and Wang (1993) and Tetlock (2007), we compute the detrended log of daily trading volume, using the rolling average of the past 60

> 6 This article’s main location tag is Argentina and one of the topic tags is “Economic News”.
9 days of log volume as the trend. Similarly, we measure stock market volatility by first demeaning each daily stock return, squaring this residual and then subtracting the past 60-day moving average of the squared residual.

Finally, we use (i) the S&P Goldman Sachs Commodity index to measure daily changes in commodity prices (in %) and (ii) the CBOE VIX to proxy for global volatility.

# **C.** **<u>Capital flows</u>**

We assess the impact of news sentiment shocks on capital flows using daily equity fund flows from EPFR. The EPFR global dataset, which tracks the asset allocation of equity and debt funds domiciled in developed countries and key offshore financial centers, has become an important source of high-frequency capital flows.<sup>7</sup> Because of its extensive industry coverage and quality, the EPFR global has been widely used recent academic contributions on funds behavior (e.g., Raddatz and Schmukler (2012), Jotikasthira et al. (2012), Fratzscher (2012), and other references therein).<sup>8</sup> In policy circles, fund flows reported by EPFR have been increasingly used as a (high frequency) proxy for foreign capital inflows, especially in Emerging Markets.<sup>9</sup> In practice, we use the “equity country flows” data set, which reports, every day, the estimated amount of equity funding in US dollars that came (or left) each country, each day, because of international funds’ portfolio reallocation. Overall, our dataset of equity flows covers 16 emerging markets between 2005 and 2015.<sup>10</sup> After estimating the effect of sentiment shocks on the total equity inflow (or outflow) into (or out of) the country, we then 7 Its coverage has increased significantly over time, reaching currently a wide industry and geographic coverage. As of 2013, the EPFR global was collecting information from more than 29,000 equity funds and 18,000 fixedincome funds representing US$20 trillion of assets invested in over 80 advanced economies and EMs.

8 The EPFR dataset has been found to be a reliable data source. Comparing TNAs (Total Net Assets) and monthly returns of a subsample of EPFR funds to CRSP mutual fund data, Jotikasthira et al. (2012) found only minor differences between EPFR and CRSP datasets.

9 Most funds followed by the EPFR global dataset (i) are located in advanced economies and (ii) account for a significant share of the external funding received by EMs. As a result, country flows dataset has proved to be a good (high frequency) proxy of total gross inflows in (or out) of emerging countries. For instance, Miao and Pant (2012) showed that EPFR fund flows correlate well with BOP recorded capital flows into EMs.

10 We focus on EMs for two reasons. First the EPFR data coverage is generally much higher for EMs than for AEs, so the correlation between EPFR equity flows and equity flows measured by the IMF Balance of Payments is higher for EMs. Using the fund’s domicile in the EPFR database to distinguish foreign vs. local funds is also more accurate when focusing on EMs. A high number of funds investing in AEs are domiciled in regional tax heavens (e.g. Luxembourg for European funds) which makes them foreign from the point of you of many AEs, even though they are local funds. This problem is much less present for EMs.
h h h Ritt+h _—— ht Hin t DaJ jo it-j DonJ j it-j 7 DonJ jo iti J J J + h h h h t > i tty t > i itt > ftp bit + Ein j=1 J=1 J=1 it,t+h ih it it it it i,t t it j
11 returns control for market microstructure phenomena that can generate auto-correlation in observed daily returns (e.g. bid-ask bounce, nonsynchronous trading, and transactions costs). Trading volumes capture the effects of changes in market liquidity, whereas measures of (detrended) volatility proxy for the influence of several other market frictions that can affect prices in the short run. Finally, dummies in the vector _D_ make sure our results are not driven by outliers (e.g. crisis) or predictable spikes in returns, which typically occur at the beginning or the end of the week.

This specification deviates from Tetlock (2007) in several ways, however. First, we control for the number of articles published each day, allowing us to differentiate between the volume of news and its (average) tone.<sup>12</sup> Second, we include global proxies - global returns or yields, VIX and change in commodity prices - to capture global co-movement, ensuring that our sentiment index is not only capturing shocks that are known to affect asset prices around the world. Third, we estimate the cumulative response of returns up to 20 days (as opposed to 5 trading days for Tetlock (2007)).

We estimate this equation using OLS and lags up to 8 days. Since the error term in the local projection framework follows a moving average process of order _h-1_ by construction however, standard errors are always corrected for serial auto-correlation and heteroskedasticity using a Newey and West (1987) estimator. In addition, since the local projections suffer an efficiency loss that increases with the horizon h, we include the residual from the estimation at horizon _h-1_ in the regression at horizon _h_ , as suggested by Jordà (2005) and Teulings and Zubanov (2014).<sup>13</sup>

# **B.** **<u>Results – Benchmark</u>**

To compare our results with the seminal work of Tetlock (2007) and help the interpretation of subsequent panel regressions, Figure 1.A reports regression results on the US only, i.e. using US news only and (daily) US Dow Jones Industrial Index returns between 1991 and 2015.<sup>14</sup> Our estimation is based on 6,260 observations, against 4,000 in the original paper. Interestingly, although our set-up varies in a few ways compared to Tetlock (2007), we find very similar results. Good news – measured by a one standard positive deviation in sentiment

> 12 The number of articles used to estimate sentiment can vary with the definition of sentiment (unweighted, TFIDF etc..) or when performing robustness checks. We adjust the number of articles accordingly, so that the number of articles always correspond to the definition of sentiment used.

> 13 Adding the residual from the regression for horizon h−1 also addresses a potential bias identified in Teulings and Zubanov (2014).

> 14 More specifically, we replicate Equation (1) in Tetlock (2007).
<!-- Start of picture text --> =,wo<br>cS)<br>oO<br>.(OSs<br>oO<br>3;<br>0 5 10 15 20<br>t<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>°<br>6<br><!-- End of picture text --> <!-- Start of picture text --> fo}<br>g2<br>i<br>r<br>0 5 10 15,<br>t<br><!-- End of picture text -->
13 Two types of articles constitute our corpus: local news and multi-country news. About 60% of our corpus consists of local news (i.e. ~2.5 million), which only mentions one country and convey rather specialized content. A typical example of local news is the article reported in section 2, discussing labor market laws in Argentina.<sup>16</sup> By contrast, the remaining 40% of our corpus contains articles discussing multiple countries. A typical multi-country news is the article about “Fears of Brazilian devaluation hit emerging markets” reported in Appendix, which mentions multiple countries and their interrelations.<sup>17</sup> The presence of multi-country news mechanically increases the co-movement of our country-level sentiment indices, suggesting that our previous estimates confound the impact of local and multi-country news.

To distinguish the sentiment conveyed in local news from that of multi-country news, we first re-compute the daily news sentiment of a country excluding any article mentioning any other country. This highly restrictive filter removed 1.5 million articles across countries (see Appendix),<sup>18</sup> allowing us to only focus on genuinely local (country-specific) news.<sup>19</sup> Second, we extract a common factor (“global news sentiment”) from our initial sentiment series using a Kalman filter. We then include the global news sentiment index in our regression, allowing us to contrast the effect of local and global news and to control for residual cross-country correlation in sentiment indices that was not properly purged by our filter based on location tags.

More specifically, we now estimate the following model: � � � � � � � � Cum��,�,��� �α� �μ�,� �∑��� θ� R�,��� �∑��� γ� Vlm�,��� �∑��� δ� Vol�,��� �∑��� ρ� Glob��� � � � � � � � � � ∑��� β�,�Global_GoodNews�,��� �∑��� β�,�Local_GoodNews�,��� �∑��� τ� Art�,��� �D�,� �ε�,� Figure 2 presents the results using these two alternatives. Figure 2.A reports results for the full sample, whereas Figure 2.C and 2.D report results for AEs and EMs, respectively.

16 Other recent headlines that would typically qualify as purely local articles are as follows: “Inflation in Philippines a Faultline for Duterte’s “Build, Build, Build” Ambition” (05/31/2018); Socialist chief Pedro Sanchez set to become Spain’s Prime minister” (05/31/2018); “Slovenia central bank forecasts steady growth despite global risks (10/22/2018)”. Their content can be consulted online.

> 17 Location tags include: Argentina, Asia, Brazil, Central America, Chile, Emerging Market Countries, Central/Eastern Europe, Europe, Indonesia, Latin America, Russia, South America, Southeast Asia, United Kingdom, CIS Countries, Western Europe.

18 The share of local news is reported on the histogram showing the distribution of topics in our corpus of news.

> 19 As expected, cross country-correlations in sentiment also decreases substantially once we purge “multicountry” news and restrict attention to local news.
14 As expected, we find that controlling for the global sentiment affects the size and shape of the response of the local news sentiment (in blue), the cumulative response being roughly twice smaller (5 basis points, as opposed to 10 in the previous estimates, see Figure 1.B). More importantly, while we did not see a full reversal after 20 days in the previous estimates, the gains now completely vanish after a week. Quantitatively, sentiment shocks are still economically significant however.<sup>20</sup> Broadly speaking, these results confirm the existence of pure “sentiment” shocks, affecting both advanced and emerging countries, during which the tone of local news affect investor sentiment and prices momentarily before returning to their fundamental values. From a theoretical perspective, this finding is consistent with the presence of noise (or liquidity) traders (DeLong et al (1990), Campbell (1993)).

In sharp contrast, we find that global news sentiment shocks have a stronger and permanent effect on returns (in green). We estimate a total permanent impact around 25 basis points - roughly 5 times the size of the local sentiment shock - which is reached after 10 to 15 days. Importantly, this result is only marginally affected by the exclusion of the GFC from our sample. This longer and permanent impact on world stock markets suggests that global news contain genuinely new information about fundamentals that is only slowly incorporated into stock prices. An alternative explanation is that sudden changes in global news sentiment are strong enough to cause drifts that do not reverse in the short run, even in the absence of new “hard” information about fundamentals.

> 20 The median absolute deviation in our sample is 70 basis points, for both AEs and EMs. This suggests a nontrivial economic impact of sentiment shocks of roughly the same magnitude for both emerging and advanced countries.
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22 global news sentiment index is strongly positive, the corpus of news tilts towards positive financial and corporate news in advanced AEs, especially in the US – with the notable exception of Greece related news, which are overrepresented in periods of low global sentiment. In contrast, the coverage tilts strongly towards economic and political news in EMs when the global news sentiment index goes into negative territory (Figure 8).

# **V.** **<u>Robustness and Extensions</u>**

Overall, our findings are robust to a variety of tests and extensions. First, they are relatively stable over time and across countries (EMs and AEs), suggesting that the effect is not driven by a single episode or by any distinct group of countries. Reflecting the rapid rise in international financial integration, recent research has pointed to a general increase in global financial synchronization over the past two decades (e.g. Bruno and Shin (2014), Obstfeld, (2015), Jordà _et al._ (2018)). Other important contributions have also emphasized the high sensitivity of emerging markets to the global financial cycle, at least compared to AEs (e.g. Rey (2013), Cerutti, Claessens and Puy (2015)). Although we find that global news sentiment has a stronger impact than local news sentiment, we do not find evidence that the effect of global news is significantly stronger now than in the 90’s, or that it affects more EMs than AEs (See Appendix).

Our results are also not driven by extreme values or crisis – such as the Global Financial Crisis (GFC), or by key countries – such as the US.<sup>26</sup> Finally, they are robust to an alternative measure of sentiment based on a Term Frequency - Inverse Document Frequency (TF-IDF) weighting of words, which is also widely used in the literature (See Appendix).

# **VI.** **<u>Conclusion</u>**

Using a new set of news articles around the world, we revisit the effect of news sentiment on asset prices around the world between 1991 and 2015. Extending the scope of the existing literature allows us to qualify several existing results on the link between the media, investor sentiment and stock returns, while deriving new ones. _Inter alia_ , we find that news sentiment has a pervasive impact on (short term) equity prices around the world. However, we also find local news have a very different impact than global news. Finally, we cast light on the role of foreign investors in transmitting sentiment shocks. Overall, our results contribute to the vibrant academic literature on the role of the media as propagator of sentiment shocks and highlight its role in driving international co-movement in asset prices.

26 Results without the US on request.
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