CentralBankCommunicationandSocialMedia#FED

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1 CentralBankCommunicationandSocialMedia#FED Marc-André Luik Helmut Schmidt University, Hamburg Dennis Wesselbaum University of Otago July 2, 216 Abstract Central bank communication has changed dramatically over the last decades. This paper develops a new approach to identify and quantify the effects of central bank communication on asset prices and the real economy. We use Twitter data to identify monetary policy announcements. Using tweets by the Federal Reserve Board we show that announcements significantly affect asset prices: they flattened the yield curve over the time span from 212 to 216. We then estimate a mixed-frequency VAR to identify real effects of central bank communication. We find that the FED s communication had the desired real effects: higher output and inflation and lower unemployment. Keywords: Asset Prices, Communication, Mixed-Frequency VAR, Twitter. JEL codes: C32, E43, E52, E58. We would like to thank David Fielding, Alfred Haug, and Bernd Hayo for very useful comments and suggestions. Helmut Schmidt University, Chair of Political Economy and Empirical Economics, Holstenhofweg 85, 2243 Hamburg, Germany. luikma@hsu-hh.de. University of Otago, Department of Economics. P.O. Box 56, Dunedin 954, New Zealand. dennis.wesselbaum@otago.ac.nz. 1

2 1 Introduction Central bank communication has changed dramatically over the last decades. In 1975 the Federal Open Market Committee (FOMC, for short) was sued under the Freedom of Information Act for not making the Domestic Policy Directive available within the month in which it was effective. In 1981, after a hearing before the U.S. Supreme Court, a verdict was rendered that allowed the public to have access to details about monetary policy for the first time in U.S. monetary policy history. 1 Changes in the policy rate were announced from February 1994 on. In 2 the FOMC announced its"immediate Announcement" policy, issuing a brief(usually about three pages) statement regarding its policy decision, important impact factors, and the voting result. During the financial crisis, when the interest channel was muted, the FED relied on forward-guidance: announcements about the future path of the police rate. 2 What is the rationale for this change in the FED s communication strategy? Generally speaking, communication has two purposes: creating news about monetary policy and reducing market uncertainty. Blinder (1996) argues that increased communication enhances the effectiveness of monetary policy. More communication, he argues, will provide markets with more information, increasing the predictability of the central bank and market reactions. Woodford (21) pursues this thought even further arguing that the essence of monetary policy is managing expectations through communication. The theoretical transmission channel of central bank communication works along the impact of announcements on short-term interest-rate expectations influencing long-term rates and other financial market rates. Those asset prices then influence macroeconomic variables. As we will discuss later, the effects of central bank communication on financial markets is studied extensively in the literature. Usually, the findings indicate a significant effect of FOMC statements and speeches on financial markets. Our paper adds to this literature on central bank communication and has two novel contributions distinguishing our paper from the existing literature. First, we are the first to use Twitter data to measure central bank communication. Second, we are the first to estimate a mixed-frequency VAR to identify the real effects of central bank communication. Our paper develops a new approach to identify and quantify the effects of central bank communication. Precisely, we focus on "short-run" communication in contrast to forward guidance. To do so, we combine different streams of the literature. First, we use Twitter data to identify monetary policy announcements. Second, we use this unprecedented announcement series to estimate the effects along the theoretical transmission channel. To do so, we proceed in two steps. In a first step, we estimate the effects of monetary policy announcements on the yield curve. This first step is necessary, because if announcements would not move the yield curve they could only have real affects via uncertainty. In a second step, we use a mixed-frequency VAR model to quantify the real effects of monetary policy announcements. To be precise, our first contribution is to construct a novel time series of announcements made by the Federal Reserve Board using Twitter. Since its launch in 26, Twitter accumulated approx. 33 million user world-wide tweeting about 5 million tweets per day. Therefore, it is a source of an enormous amount of information that can be used to address economic questions. 3 While other papers in the literature use Twitter to capture sentiment (Azar and Lo (216)) we directly use the 1 See U.S. Supreme Court: Federal Open Market Comm. of the Federal Reserve System vs. Merrill, No , 443 U.S. 34, Forward guidance started in 28. Woodford (212) highlights the importance of this signalling channel. 3 Norges Bank was the first central bank to announce its decisions via Twitter (see Qvigstad (29)), while the FOMC announces its policy decisions since March 212 via Twitter. 2

3 FED s tweets to measure monetary policy communication. When we consider central bank communication, the question is what "good" communication is. Winkler (2) suggests three criteria: communication should be clear, effective, and honest. A clear communication is a communication free of imprecisions not allowing for misinterpretation. An effective communication is a communication that is time-saving while revealing all important information. Finally, an honest communication implies that external communications reflect internal deliberations. Twitter data fulfils all three criteria and even outperforms "standard" FOMC statements made available on the FED s website or by speeches of FOMC members. Tweets are clear, as they are limited to only 14-character messages which reduces the signal extraction problem. The psychology of reading (see Cohen (1972)) established reading and comprehending involves a complex set of skills. Reading longer and more difficult material, therefore, will take more time and more cognitive resources to be processed. Along this line, research (e.g. Cowan (21)) has shown that our working memory is limited. Hence, the content of shorter texts will be more easily remembered. Further, tweets are published by the FOMC such that there is no intermediary involved that could select or interpret the announcements. For the same reasons, they are effective. Probably, reading tweets is even more effective than reading a three page statement or listening to a speech. For example, Hayo and Neuenkirch (216) show that central bank predictability is not increased by reporting detailed information. Moreover, tweets are as honest as other statements by the FOMC. In addition, Twitter also allows a much faster diffusion of announcements compared to "standard" ways of communication. Tweets by the Board are frequently re-tweeted. For example, the FOMCmeeting injanuary 216 was re-tweeted 15 times. We argue that this starts a fast diffusion process. As a matter of fact, the hashtag FOMC was trending in the United Kingdom and various major cities in the United States on the January meeting date. This fast diffusion of monetary policy announcements is important as Shiller (1984) argues that investments are a social activity, therefore, influenced by social movements. This theory is supported by various empirical studies: Kelly and Ó Gráda (2), for example, show that the banking crises in 1854 and 1857 were mainly influenced by bad news communicated through a social network. 4 Our second contribution combines the unprecedented announcement series with macroeconomic variables. Data used in the estimation of monetary policy effects is usually available on a monthly (mainly financial data) or quarterly (macroeconomic) frequency. Using monthly data is beneficial, as more observations allow tracking the economy more closely, while the use of quarterly frequency is beneficial, as much more time series are available. Twitter data, in contrast, is available on a much higher frequency. Research in recent years has developed tools to use high- and low-frequency time series jointly. 5 The advantage of combining data of different frequency is that high-frequency information are used to estimate lower-frequency variables. For example, Francis et al. (211) use a MIDAS regression showing that FOMC policy shocks have small real effects. Several results stand out. We find that announcements by the Federal Reserve Board have significant effects on the yield curve. We observe a flattening of the yield curve as implied by our estimation results. Short-term rates (less than a year) stayed roughly constant over the observed time period, being already close to the zero-lower bound, while the rates between one and 3 years 4 Hong et al. (24, 25) and Ivkovic and Weisbenner (27) show that social interactions play an important role in investment decisions, trading volume, and participation decisions. Tetlock (27) highlights the role of media in investor sentiment. Finally, herd behaviour, the effect of sentiment on agents behaviour, is found to be a significant factor by various studies (e.g. Dorn et al. (28)). 5 Ghysels et al. (24, 27) develop the mixed-data sampling (MIDAS) approach, while Ghysels (212) and Schorfheide and Song (212) develop mixed-frequency VARs. 3

4 increased. Most importantly, long-run rates (more than three years), as desired by the FED, decreased sizably. Interestingly, the effects on the volatility of those yields is not always as desired as, for example, the volatility of the 1-year yield increases. We then estimate a mixed-frequency VAR showing that FED announcements made via Twitter significantly affect the real economy. We find a positive effect on output, industry production, and the inflation rate. The effect on unemployment, although significant, is small. Our results are robust to including several other variables such as the FED s balance sheet, the exchange rate, or policy uncertainty. We find that there is a positive effect on the exchange rate and a negative effect on policy uncertainty. Further, we show that controlling for the content of the announcements supports our findings. Then, given the high-frequency of Twitter data we also estimate a mixedfrequency VAR on a weekly-to-monthly frequency which supports our previous findings obtained on a monthly-to-quarterly frequency. Finally, we show that the effects of speeches are different to the effects of written communication. This finding can be explained by a signal extraction problem due to a "cacophony of voices". Overall, with the emergence of social media and its implications for the consumption of news, central banks need to adjust their communication strategies. With closely connected individuals who develop a taste for precise and condensed information in contrast to speeches and detailed statements, central banks should adjust their communication strategies taking into account the role played by social media. 2 Literature Review Central Bank Communication There is a large literature on the effects of surprise changes in the policy rate; either relying on VARs (Christiano et al. (1999)) or DSGE models (Smets and Wouters (27)). In reality, anticipated changes might be as important as surprise changes. A small literature emerged estimating monetary news shocks. 6 A larger literature focuses on the effects of central bank communication on financial markets and, in particular, interest rates. The effects of central bank communication on financial markets is studied extensively in the literature. Guthrie and Wright (2) analyze the communication of the Reserve Bank of New Zealand showing that their communications have large and persistent effects on interest rates across different maturities. Kohn and Sack (24), Reeves and Sawicki (27) respectively show that FOMC, Bank of England statements respectively had effects on financial markets. In particular, Kohn and Sack (24) show that FOMC statements affect the volatility of various asset prices. Ehrmann and Fratzscher (27) report that interviews and speeches by committee members of the FED, Bank of England, and the ECB had significant interest-rate effects. Along this line, Connolly and Kohler (24) find that monetary policy reports in four different economies affect expectations and, therefore, interest rate futures. Further, Gürkaynak et al. (25) use a principal component approach and show that FOMC statements are important drivers of financial markets. Most recently, Jegadeesh and Wu (215) analyze the informational content of FOMC meeting minutes using an automated content algorithm. They show that the content has significant incremental informational value. Similarly, Lucca and Trebbi (29) focus on statements issued by the FOMC. They find a significant effect of those statements on short-term treasury yields. Then, they 6 There is a larger literature estimating news shocks to technology. See, for example, Beaudry and Portier (26) and Forni et al. (213). 4

5 estimate a VAR model showing that treasury shocks generate real effects while sentiment shocks do not gemerate real effects. In addition to this strand of the literature, there are some papers about forward guidance and monetary policy news shocks. Woodford (213) and Del Negro et al. (214) show that forward guidance lowers the long-term interest rate. Papers dealing with monetary policy news shocks include Matsumoto et al. (28), Milani and Treadwell (29), and Gomes et al. (213) using DSGE models and Ben Zeev et al. (215) using a SVAR model. Twitter Data in Research Most closely related to our paper is the paper by Azar and Lo (216). They use Twitter to track sentiment about the FED s policy in real time. They show that tweets can be used to predict future returns. In contrast to their paper we focus directly on the tweets by the Federal Reserve Board, whereas Azar and Lo (216) consider all tweets with certain keywords. Similarly, Arias et al. (212) use total tweets on four technology companies combined with stock market data to account for market sentiment. They show that the prediction of the implied volatility (VXO) of the S&P1 index is increased when including Twitter data. Meinusch and Tillmann (215) quantify market participants beliefs about the exit time of quantitative easing using Twitter data. They estimate a standard VAR model with sign restrictions in order to identify belief shocks. They find that those shocks have significant effects on financial markets. The first paper to use Twitter data in economics is Antenucci et al. (214) who use Twitter data to measure labor market flows. They use job-related tweets to construct time series for job loss, job search, and job posting. Their social media index co-moves with the official data but features independent movements and, therefore, offers incremental information. 3 Data Our main FED announcement time series is constructed from the Twitter account of the Federal Reserve Board. We download tweets from this account starting on the 14th of March 212 (its first, ever tweet) and end with the last tweet on January, 29th 216. Overall, we have 243 tweets, which gives us two tweets per day on average. There are three fundamental problems with this time series. First, we observe it by the second. This frequency obviously is too high to link those announcements to macroeconomic variables. Therefore, we aggregate tweets to a weekly and monthly frequency and use those time series for our mixed-frequency VAR analysis. For our estimation of the yield curve (section 4), we employ daily aggregated tweets. Second, not all of them are actually relevant for the conduct of monetary policy. The literature usually employs algorithms that filter according to some keywords. 7 However, this approach is not feasible for us, as tweets about new working papers, job offers, or conference announcements will contain at least a subset of those keywords. Further, it is questionable whether the automated text search algorithms are able to process and properly understand the context of the statement. Therefore, we manually inspect all tweets and classify them as relevant if they are potentially interesting for market participations. This approach is also used by Ehrmann and Fratzscher(27). To be precise, non-relevant tweets are tweets about: new working papers, job offers, conference announcements, learning experiences, exhibitions, or tweets about the history of the FED. 7 Lucca and Trebbi (29) use a text heuristic on FED statements. 5

6 Tweets per Day Figure 1: Histogram of relevant Tweets per Day from the Federal Reserve Board. Third, given the relevant tweets do they represent good or bad news? In a robustness check we address this issue and quantify the effects of good announcements. We classify a tweet as good news, if the difference between the S&P5 daily closening and opening price is positive. Finally, we end up with 966 relevant tweets for the Board (4% of total tweets). Figure 1 presents a histogram of the daily, relevant tweets of the FED Board and plots the estimated normal density. We find that on most days no relevant announcements were made. This is important for the following reason: agents receiving announcements need to be able to identify those announcements as new and relevant. If there would be relevant news on every day, agents would face a much more difficult signal extraction problem. In our case, we find that if there is a relevant tweet on a day, it is most likely that there is only one relevant tweet. Receiving more than two relevant tweets per day is very unlikely and occurs rarely. 8 Some works in the literature on central bank communications use speeches to measure announcements (e.g. Ehrmann and Fratzscher (27)). Although our data set is much more general as it, for example, includes updates on forecasts, we also construct a subset of all relevant tweets that only relate to speeches by the Chairman of the Board (Bernanke until January, 31st 214 and Yellen from February, 1st 214 onwards). We find 174 (7% of all tweets) relevant speeches by the FED Board. This also higlights that our announcements series does not just relate to FOMC announcements but can best be described by "FED talk". In addition to the Twitter time series, we use the following set of time series: inflation rate, GDP, industry production, private consumption, unemployment, S&P5 index, the exchange rate, policy uncertainty, central bank balances, VIX, and yields on different Treasury bills. All time series are downloaded from the St. Louis FED online system FRED. Again, the sample starts in March 212 and ends in February 216. All variables are measured on a monthly frequency except GDP. All time series, if not stationary, are detrended using the Hodrick-Prescott filter. Inflation is measured by the consumer price index (CPI) and GDP is the real gross domestic product (GDPC1). Industry production is measured by the industrial production index (IND- 8 On February, 27th 215 the Fed Board tweeted five relevant tweets about newly available data, two successful applications by banks to acquire shares of other banks, and Chairman Yellen s Monetary Policy Report to the Senate s Banking, Housing, and Urban Affairs Committee. Four relevant tweets per day (12 times in our sample) occur if many data updates are announced or on FOMC dates. 6

7 PRO). Further, private consumption is the time series for real personal consumption expenditures (PCEC96). The unemployment rate (UNRATE) is the civilian unemployment rate for people 16 years and over. Stock market dynamics are measured by the S&P5 index and foreign trade dynamics are accounted for by using the trade weighted U.S. Dollar index (TWEXMMTH). Stock market uncertainty, policy uncertainty respectively is measured by the VIX (VIXCLS) index, the economic policy uncertainty index for United States (USEPUINDXM) respectively. The central bank balance sheet, used to account for the non-standard monetary policy tools, is measured by total assets of all Federal Reserve Banks (WALCL). Finally, we use seven Treasury bills of different, constant maturities (from one month to ten years): DGS1M, DGS3M, DGS1, DGS3, DGS5, DGS7, DGS1. 4 Announcements and the Yield Curve The transmission channel of central bank communication works through manipulating the yield curve by lowering the risk premium. In theory, announcements by a central bank affect short-term interest-rate expectations which influence long-term rates. In a second step, those changes in various rates, i.e. the yield curve, and the effect on uncertainty will generate effects on macroeconomic variables. During the financial crisis, with the interest rate channel muted, the FED engaged in nonconventional monetary policy. Using balance sheet tools purchasing mortgage-backed securities, for example, the FED intended to lower the yield on those securities and allow investors to rebalance their portfolios. We would like to emphasize that monetary policy over our sample period was non-standard as the short-run rate was constrained by the zero-lower bound. This implies that we do not have to disentangle the effects of interest-rate monetary policy changes from announcements as other papers in the literature have to. Again, in the second step, this will stimulate economic activity directly through lower yields and, indirectly, by easing financial conditions and through forward-guidance. In 28 the FED initiated the LSAP 1, the large-scale asset purchase program or quantitative easing, in 21 it started LSAP 2, and in September 211 it announced the MEP, the maturity extension program. Those programs substantially expanded the balance sheet up to about 4.5 Trillion $ in February Both programs, mainly purchased assets with a maturity between 4 and 1 years: LSAP 1 with 55%, LSAP 2 with 66%, and MEP with 64% of total asset purchases. Did those asset purchases had the desired effects? Evidence is mixed. Gagnon et al. (211) find a range of 58 and 91 basis points for the reduction in the 1-year Treasury yield. Hamilton and Wu (21) find smaller values while Neely (211) finds a reduction of 17 basis points. In a first step, in order to show that central bank communication has significant effects on the real economy, we establish the result that FED announcements (made via Twitter) do affect yields on different maturities. This result is necessary for announcements to have real effects. If announcements would not move yields they could only have real affects via reducing uncertainty. We follow the approach by Ehrmann and Fratzscher (27) and estimate a GARCH model. The choice of the GARCH model is motivated by the theoretical effects of central bank announcements as they might affect mean and volatility. Therefore, in order to capture those two effects and the interaction between mean and volatility, we choose the GARCH model. This approach allows us to estimate whether announcements by the FED (made via Twitter) affect financial markets. Following Jansen and De Haan (25), assuming that financial markets are efficient, only unexpected 9 Over our data period, the balance sheet increased from 3 Trillion $ to about 4.5 Trillion $. 7

8 information (or news) can affect returns. Technically, we estimate the following NGARCH(1,1)-ARMA(1,1) model r t = γ+ρ(r t 1 x t 1 β 1 )+x t β +ε t +θε t 1, (1) Var(ε t ) = δ+ψ(ε t 1 κ) 2 +φσ 2 t 1+x t α, (2) where r t are yields at different maturities and x t contains the Twitter announcements and our control variable the VIX. Further, ρ is the autocorrelation parameter while θ is the moving-average component in our ARMA(1,1) estimation. The intercept for the mean isγ andδ for the variance. The non-linear ARCH components areψandκ, while the GARCH component isφ. The model is estimated using the Davidon-Fletcher-Powell algorithm with robust standard errors. The estimation results are presented in table 1. Table 1: GARCH estimation results for different maturities. Significance levels: :1 %, : 5%, : 1%. 1 month 1 year 5 year 7year γ Twitter VIX HET δ Twitter t Twitter t VIX t VIX t We estimate the effects of communication on four different interest rates for short- and longmaturities. We choose those four rates because (i) they represent short-, medium-, and long-run maturities and (ii) most of the FED s asset purchases happened on those maturities. For the 1-month Treasury bill we find that central bank announcements have no effect on the mean but affect the variance, i.e. the uncertainty in the time series. While period t announcements increase the variance, past announcements (here, a one period lag) tend to reduce uncertainty substantially. The effect on the variance is larger for announcements than for our control variable, the overall market volatility measured by the VIX index. In contrast, uncertainty measured by the VIX does lower the yield on this short-rate significantly. When we consider the one-year Treasury bill we find that announcements created upward pressure on the yield. Again, increases in the VIX lowered the yield on this rate. The effect of central bank announcements and the market uncertainty on the variance of the 1-year bond are similar to the short-rate. We find that announcements increase, decrease the variance of this rate with a contemporaneous, lagged relation respectively. Again, the effect of central bank announcements on the variance is larger compared to market uncertainty. When we focus on long-rates we find that central bank communication significantly lowers the yields on those rates. Similarly, higher market uncertainty also lowers the yield. The effects of those variables on the variance again is similar to our results for the short-rate. Interestingly, the impact of market uncertainty is less pronounced on longer-term yields. 8

9 month 3 month 6 month 1 year 2 year 3 year 5 year 7 year 1 year Figure 2: Yield curves at the first trading day in January of the years 211 to 216. Our results are in line with the dynamic behavior of the yieldcurve in the United States. Figure 2 presents the yield curves from 211 to 216 for the first trading day in January of the respective year. We observe a flattening of the yield curve as implied by our estimation results. Short-term rates (less than a year) stayed roughly constant over the observed time period, being already close to the zero-lower bound, while the rates between one and 3 years increased. Most importantly, long-run rates (more than three years), as desired by the FED, decreased sizably. Most asset price purchases occurred on the 4- to 1-year bonds. In our estimation, we observe the largest effects for the rates on those maturities. We can draw the conclusion that central bank communication has significant effects on asset prices. Further, our results on the FED s communication support other findings that the FED s policy (communication and quantiative easing) was successful in bringing down long-rates, flatten the yield curve, and, therefore, stimulate economic activity and ease financial conditions. One should notice that the short-term yields over our sample period have been very low, such that they hardly could fall any further. We find the largest effect for the rates with a maturity longer than one year. Although the size of the estimated parameters appears to be small, our estimates are in line with the related empirical literature. Kohn and Sack (24) also find FOMC communication to increase the unconditional variance of interest rates between 1989 and 23. Correcting for macroeconomic and monetary policy surprises, testimonies affected the entire yield curve, while FOMC statements had a positive and significant impact up to a maturity of two years. Interestingly, speeches do not seem to have any significant impact in their setting. The estimated significant increases range from 1 to 7 basis points. They thereby conclude in favor of FOMC communication moving financial markets and shaping expectation. Gürkaynak et al. (25) extract a principal component capturing the forward-looking communication of the FOMC between 199 and 24. Using day and intraday data they find that the "path" factor affects the entire yield curve and is particularly dominant for longer-term maturities. Between 75 and 9% of the variation in five- and ten-year treasury yields following a monetary announcement can be explained by the path factor. Also using GARCH techniques, Ehrmann and Fratzscher(27) analyze the impact of interviews and speeches by committee members of the FED, 9

10 Bank of England, and the ECB. Covering years from 1999 to 24, they find FED communication to significantly increase interest rates up to a maturity of five years by roughly one basis point. In line with our results they report communication to have the highest impact on five-year interest rates (1.14). As their communication indicator is coded according to inclining either tightening (+1) or easing (-1) their results also indicate that the effect of communication has its intended sign. Finally, they also find communication to increase volatility significantly across the yield spectrum. Lucca and Trebbi (29), using automated content analysis of FOMC statements between 1999 and 28, find mainly longer-dated treasuries to react to changes in policy communication content. The effect of an index increase of one standard deviation peaks at maturities between two and five years (roughly 1.6 to 2 basis points). Hence, our findings that communication increases mean and variance of yields with different maturities, in particular at the longer end, are in line with the related empirical literature. At the end of this section we want to provide a robustness check controlling for the content of the announcement. This approach follows the work by Jansen and De Haan (25), while we use the EGARCH framework along the lines of Ehrmann and Fratzscher (27): 1 r t = γ+ρ(r t 1 x t 1 β 1 )+x t β +ε t +θε t 1, (3) ln[var(ε t )] = δ+ξ 1 z t 1 +ξ 3 z t 3 +ξ 4 z t 4 (4) ϑ 1 z t 1 +ϑ 3 z t 3 +ϑ 4 z t 4 π π π +λ 1 ln σ 2 t 1 +λ3 ln σ 2 t 3 +λ4 ln σ 2 t 4 +xt α, wherez t =ε t /σ t. For this robustness check we use the announcement series that classifies tweets according to their effect on the stock market. Table 2 presents the estimation results, where we choose the given specification as it results in the best fit with the data. Further, we only present those estimations where the Twitter announcements had a significant effect on the mean, while usually (except for the seven year T-bill) we also find a significant effect on the variance. 11 In contrast to our baseline results we find that the Table 2: EGARCH estimation results for different maturities. Significance levels: :1 %, : 5%, : 1%. 3 months 1 year 1 years Twitter t VIX t HET Twitter t Twitter t VIX t VIX t announcements had a positive, significant effect on the yield of the short-term bond. This result 1 We choose a GARCH(1,1) structure to make the results comparable to the previous results. However, including lags, we also find a significant, negative effect of announcements on the five year T-bill yield and its variance. 11 The results for the omitted variables are available upon request. 1

11 is in line with the observed flattening of the yield curve (cf. figure 2). However, we only find a significant mean effect, there is no effect on the variance of the short-term rate. Moving on to the one-year yield, we find that our results are robust to controlling for the content of the announcement. We find that the effect on the mean is smaller compared to the baseline scenario. In addition, there is an unambiguously negative effect on the variance of the one year yield. For the ten year yield, we find that, again, the announcements had a negative effect on its mean and a positive effect on its variance. Overall, our baseline results also hold when we control for the content of the announcement: the effects of the monetary policy announcements are in line with observed flattening of the yield curve. Our classification approach for good vs. bad news, being constrained by data availability, could be improved by using the change in the stock price the minute after the Twitter announcement. Since a key advantage of Twitter is the fast diffusion of news, this could improve identification. Moreover, our results in both GARCH models are robust to including the gold and the oil price, day-of-the-week effects, and FOMC meeting dates. Again monetary policy was non-standard over our sample. Hence, we do not have to control for changes in the FED Funuds rate but we may need to control for changes in the balance sheet. Given that balance sheet data is only available on a weekly frequency, we choose to include an QE announcements dummy and a dummy over the time the balance sheet expands. Given that efficient markets should only be moved by news, this approach should control for the effects of the asset purchase program by the FED on financial markets. Our results are robust to both checks. 5 Real Effects of Announcements In the previous section we established that announcements made by the Federal Reserve Bank via Twitter significantly affect the yield curve. This result is necessary for announcements to have real effects. In this section, we want to show that announcements have real effects. To do so, we estimate a mixed-frequency VAR making use of the high frequency of the Twitter announcement series. We begin by establishing our main, baseline results. Then, we perform several robustness checks controlling for various potential driving forces. Further, we check for the effects of speeches, good vs. bad announcements, and robustness to using a higher frequency. 5.1 Baseline Results In this section we present our main, baseline results. We estimate a mixed-frequency VAR with constant in the following variables: Twitter, S&P5, Inflation rate, Unemployment, Industry Production, and GDP. Given the small number of quarterly observation we choose to select a parsimonious model with seven variables. We choose those variables as they capture key macroeconomic processes as in Schorfheide and Song (212). Further, we use monthly-to-quarterly frequency unless otherwise indicated. Figure 3 presents the estimated impulse response functions to a FED announcement made via Twitter. Our results show that a FED announcement significantly increases the S&P5 index by 12 points (or.63 percent) on impact. The positive effect lasts for roughly three months. The finding that announcements increases the S&P5 on impact is in line with Ehrmann and Fratzscher (27). The announcement increases output by.12 percent on impact lasting for only one month before there is another statistically significant effect after four months. The increase in GDP can be explained by the effects of announcements on the yield curve. Announcements reduced the yield on long-term bonds which creates incentives for firms to invest and households to purchase more houses. Hence, the increase in GDP is generated by an increase in investment which, after 11

12 SP5 Inflation.4 Unemployment Consumption Industry Production GDP Figure 3: Estimated impulse response functions to a FED announcement in the baseline model. The horizontal axes measure months. four months, also increases industry production (by 2.81 percent). This occurs at the same time firms labor demand increases and unemployment falls. This result is supported by the initial decrease in consumption (.2 percent). Standard macroeconomic models would imply that there is a substitution between consumption and savings (investment). This is what our results show in the given closed-economy setting. Further, we find that this monetary policy announcement creates inflation for four months. The announcement of the FED made via Twitter had the desired effects between 212 and 216. The announcements increased, at least temporarily, inflation and output. The effect on unemployment are significant but small. Finally, although our effects appear to be small, for example output increases by.12 percent, however, one has to recall that it is the effect generated by one tweet by the FED. Our results are in line with other findings in the literature on real effects of central bank communication. Hansen and McMahon (215) use a FAVAR model and estimate the effect of changes in the tone of sentences in FOMC statements. They find that those "sentiment" shocks generate real effects. As in our MF-VAR they find a positive effect on industrial production after roughly three months and a positive effect on employment after eight months. Their effect is five times as large as our effect. In contrast, they do not find any significant effect on inflation. Also, they find only a very limited effect on the stock market. The second paper estimating real effects of central bank communication is Lucca and Trebbi (29). They identify the content of FOMC statements and estimate a VAR model. They find a significant effect on Treasury yields at different maturities but do not find any significant real effects of a change in the FED s communication about future policy changes. Both papers use monthly data and do not take mixed-frequency effects into account. 5.2 Robustness In this section we want to extend our baseline model and control for various other potentially important variables. First, we control for the content of the announcement. Then, we include the 12

13 SP5.1.5 Inflation.2 Unemployment Consumption 1 Industry Production 2 GDP Figure 4: Estimated impulse response functions to a good announcement. Horizontal axes measure months. FED s balance sheet, the exchange rate, and policy uncertainty. Finally, we estimate the effects of speeches and higher frequencies Good vs. Bad News Up to this point, we treated all announcements, good and bad, alike. However, the effects of good announcements might be systematically different. For this purpose, we categorize announcements and use the resulting time series of good announcements in our baseline VAR. Figure 4 presents the estimated impulse response functions for good announcements via Twitter. We find that our baseline results are largely confirmed. The only two noticeable differences are the significant effect on consumption and the non-significant effect on inflation. Again, we find a positive effect on the stock market(s&p5) as well as positive effects on consumption, industry production, and output. We find the largest decrease in unemployment across all our model variants Additional Variables In this section we want to expand our baseline model and control for various other potentially important variables. We do not include a measure of the nominal interest rate set by the Federal Reserve because we argue that this is not the relevant policy instrument after the financial crisis. With the interest rate constrained by the zero-lower bound the FED engaged in unconventional monetary policy expanding its balance sheet. This fact also increases the credibility of our estimation approach as the usual monetary policy instrument, the short-term interest rate, does not change over our sample. Therefore, we do not have to disentangle the effects of monetary policy changes from announcements. In order to control for the effects of quantitative easing, figure 3 presents the estimated impulse 13

14 Figure 5: Robustness: MF-VAR including the FED Balance Sheet. Horizontal axes measure months. response functions to an announcement shock including the FED s balance sheet. The results are largely in line with the baseline results. First, non surprisingly, we find that the announcement has no effect on the FED s balances. GDP is increased with a one month lag. Again, the decrease in the yields on long-term bonds stimulates investment activity leading to a delayed increase in industry production and GDP. Controlling for the balance sheet gives a now significant, positive effect on private consumption. The increase in inflation is only significant for the fourth month after the announcement. In this model variant, we do not find significant effects on unemployment or the stock market, measured by the S&P5. Further, although exchange rate policy in the United States is under control of the Treasury, exchange rate effects of monetary policy might have significant effects on the real economy. Therefore, we include a measure of the exchange rate and re-estimate our baseline VAR. Figure 4 show the estimated impulse response function when we control for exchange rate movements. This robustness check confirms our baseline results. GDP and industry production increase almost twice as much as in the baseline model. In contrast to the baseline scenario, we find a positive, significant effect on private consumption. The effect on the inflation rate is slightly larger compared to the baseline model which is mainly due to the larger, significant effect of consumption and higher output. We again find a positive effect on the S&P5 index roughly the same size as in the baseline model. Interestingly, we find that an announcement shock increases the exchange rate for several quarters. The exchange rate is measured as the foreign exchange value of the U.S. Dollar versus major currencies. Our findings imply an increase in the value of the Dollar against other major currencies. From the U.S. s point of view this appreciation should increase imports and reduce exports. Assuming that the Marshall-Lerner condition holds, we expect an inverted J-curve effect with an increase in net exports on impact. These findings are in line with the results by Jansen and De Haan (25) for the Euro Area and Ehrmann and Fratzscher (27) for the United States. In contrast, to Ehrmann and Fratzscher (27), who report the coefficient of FOMC announcements on the exchange rate, we also show that this positive effect is highly persistent. So far, we have not controlled for the effect of uncertainty in the economy. Bloom (29), for example, showsthat higherriskmay leadahouseholdto defer investmentsbecause of the fearto hit 14

15 Figure 6: Robustness: MF-VAR including the exchange rate. Horizontal axes measure months. a liquidity or credit constraint that may impact the optimal consumption allocation in the future. Figure 5 presents the estimated impulse response functions controlling for the policy uncertainty index. Again, our baseline results are largely confirmed. We find that the announcements reduce policy uncertainty. This is a desired finding, especially for the FED using forward guidance. Along this line, communication should reduce the uncertainty about future policy. Interestingly, we find that industry production is reduced. This finding might be explained by the larger effects on consumption. In this model version, agents appear to consume more and save less. Reduced uncertainty seems to boost consumption over investment. Finally, our VAR includes the time series for inflation. As we discussed, a sizable part of central bank policy is the ability to affect expectations. Therefore, it might very well be the case that inflation expectations are differently affected than the observed inflation rate. Figure 8 presents our baseline VAR model with inflation expectations instead of the observed inflation rates. First, we do not find a significant effect of announcements on inflation expectations. This finding is in line with the results by Ehrmann and Fratzscher (27). There also is no effect on the stock market and the unemployment rate. In contrast, we find that there is an initial positive effect on consumption and industry production. The most noticeable difference is that we observe a drop in industry production and GDP after two month, before the previously observed, positive effects on both variables materialize Speeches Other papers on central bank communication extensively use speeches to identify announcements. Hence, we construct a Twitter speeches series and re-estimate our baseline VAR model. Figure 9 shows that the effects of speeches via Twitter are hardly significant. There is no significant effect on inflation or consumption. The other effects are reversed to our baseline findings. Unemployment marginally increases, industry production decreases so do the S&P5 and GDP. What is the intuition for this finding? There is the anecdotal evidence that market participants get lost in the "cacophony of voices". While the written announcements, especially the ones made via Twitter, are clear, speeches are subject to interpretation. Therefore, this result might be explained by the 15

16 Figure 7: Robustness: MF-VAR including policy uncertainty. Horizontal axes measure months. SP5 Inflation Expectations Unemployment Consumption Industry Production GDP Figure 8: Robustness: MF-VAR with inflation expectations instead of inflation. Horizontal axes measure months. 16

17 SP Consumption 5 1 Inflation Industry Production Unemployment GDP Figure 9: Estimated impulse response functions to a speech announced via Twitter. Horizontal axes measure months. difficulty of interpreting speeches correctly. Ehrmann and Fratzscher (27), for example, find that there is a highly individualistic communication strategy amongst FOMC members with a high dispersion in what they say. Along this line, Hayo and Neuenkirch (21) show that speeches by FOMC Board members increase the predictability of federal funds rate target decisions. While there is some research on the effects of speeches on asset prices (e.g. Ehrmann and Fratzscher (27)) there is no research that quantifies the real effects of speeches. Our findings, besides showing that speeches have real effects, show that there is a difference between written and spoken communication. The financial market (here measured by the S&P5) and the real economy appear to react stronger to the written communication than to speeches. This can be explained with the findings by Hayo and Neuenkirch (216) who show that individuals do not demand very detailed information. Finally, a cautionary note is in order. Given the much smaller sample size of speeches versus overall tweets, the insignifcant effects might just be a technical consequence of this small number of observations Higher Frequency At the end of this section we want to provide a robustness check that accounts for high frequency movements. One potential problem in aggregating the twitter announcement series is that announcements get averaged-out. In order to address this potential problem. we estimate a mixedfrequency VAR with the time series for twitter announcements, VIX, industry production, and consumption spending. Figure 1 presents the estimated impulse responses, now plotting the effects over weeks not months. We find that our lower frequency results hold. There is a positive, on-impact effect on the stock market, a positive effect on industry production, a delayed positive effect on uncertainty, and a non-significant response of consumption. Those findings support our baseline findings. 17

18 12 SP5 15 VIX Industry Production 2 4 Consumption Figure 1: Estimated impulse response functions for the high frequency VAR. Horizontal axes measure weeks. 6 Conclusion Central bank communication changed dramatically over the past decades. Communication as a monetary policy tool has two purposes: creating news about monetary policy and reducing market uncertainty. The theoretical transmission channel works along the impact of news on short-term interest-rate expectations influencing long-term rates and other financial market rates. Those asset prices then influence macroeconomic variables. Our paper has two contributions. First, we construct an unprecedented time series of central bank announcements using Twitter. Second, we estimate the effects of those announcements on the real economy. We proceed in two steps along the theoretical transmission channel. In a first step, we estimate the effect of monetary policy announcements on the yield curve. This first step is necessary, because if announcements would not move the yield curve they could only have real affects via reducing uncertainty. In a second step, we use a mixed-frequency VAR model in order to make use of the high-frequency of this time series. We quantify the response of key macroeconomic variables to monetary policy announcements. We find that announcements by the Federal Reserve Board had significant effects on the yield curve. We observe a flattening of the yield curve as implied by our estimation results. Short-term rates (less than a year) stayed roughly constant over the observed time period, being already close to the zero-lower bound, while the rates between one and 3 years increased. Most importantly, long-run rates (more than three years), as desired by the FED, decreased sizably. Most asset price purchases occurred on the 4- to 1-year bonds. In our estimation, we observe the largest effects for the rates on those maturities. We proceed and estimate a mixed-frequency VAR showing that FED announcements made via Twitter significantly affect the real economy. We find a positive effect on output, industry production, and the inflation rate. The effect on unemployment, although significant, is small. Our results are robust to including several other variables such as the FED s balance sheet, the exchange rate, or policy uncertainty. We find that there is a positive effect on the exchange rate and a negative effect on policy uncertainty. Further, we show that controlling for the content of 18