Selected Presentation at the 2020 Agricultural & Applied Economics

Selected Presentation at the 2020 Agricultural & Applied Economics Association Annual Meeting, Kansas City, Missouri, July 26-28

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EXPLORING THE EFFECT OF CHIEF EXECUTIVE SOCIAL MEDIA ENGAGEMENT ON AGRICULTURAL COMMODITY PRICES

Justin R Benavidez, Luis A Ribera, Anastasia W Thayer

Abstract: The trade war between the US and China had far-reaching impacts within the agricultural markets. First seen in this administration, tweets by President Trump provide information to markets ahead of official policy decisions. Following research from other financial market literature that shows the impact of tweets by President Trump on markets, this paper seeks to determine the impact of tweets by President Trump regarding the trade war on agricultural commodity prices. We show that days with high counts of tweets with keywords associated with the trade war lead to statistically significant structural breaks in the price series for hogs, corn, cotton, and soybeans. Future research will explore the magnitude of the effect and Granger causality. Keywords: China, trade war, structural break, agriculture, future contracts.

1. Introduction

Months of rising tensions, increasing United States (US) tariffs on Chinese imports, and public commentary on US/China trade relations came to bear on agricultural markets on April 2,

2018 when China proposed a 25 percent tariff to pork imports from the US and a smaller tariff on other agricultural commodities (Marchant and Wang, 2018). Over the following days, in response to a $46.2 billion import tariff for Chinese products, an additional tariff of 25 percent was added on soybeans, beef, sorghum, and other agricultural commodities of US origin

(Marchant and Wang, 2018). These actions ignited the so-called trade war. Through 2018 and

2019, the trade dispute between the US and China, respectively the world’s largest exporter and importer of agricultural goods, occupied a significant amount of agricultural media through official and unofficial announcements. During that period, President Trump provided information on the trade war through engagement with the public via his Twitter feed. Given that it is US policy not to make any formal announcements during negotiations, this represents a deviation from previous US foreign policy and it is possible that information flowing from the

executive branch regarding the resolution or further complications of the trade war impacted the agricultural markets in day to day trading.

Researchers speculated throughout the trade war that tweets from President Trump were impacting financial markets (Benton and Phillips, 2018; Burggraf et al., 2019). Even captured by popular media, JP Morgan created an index, called the Volfefe index, that showed Trump tweets had an impact on market behavior, specifically the stock market (Kollmeyer, 2019). While no study exists to show the impact of Trump tweets on agricultural markets, other analysis has emerged to show that even more unrelated tweets such as those related to fake news or the border wall have an impact on stock market behavior (Mao, 2019). These findings are comparable to other work showing the impact of news announcements on other commodities, such as crude oil (Mensi et al., 2014).

The purpose of this research is to determine the economic impact of social media engagement from the executive branch of the US government, specifically Twitter engagement from President Trump, on agricultural market prices. As shown, introduction of additional information into the market, not dependent on credibility, has impacted the broader stock market.

In this analysis, we will conduct time series analysis of agricultural commodity price contracts, including soybeans, pork sorghum, and corn to show the impact of tweets not just to commodities impacted by tariffs but boarder US agricultural markets. Wald tests for structural breaks were used as an initial attempt to determine an impact. Findings indicate that days of high tweet from President Trump on related keywords have a different impact on each price series.

2. US-China Trade Relations

China is a significant US trading partner in agricultural goods. From 2009 to 2019 US agricultural exports to China totaled $209 billion (Figure 1). From 2015 to 2019, US agricultural exports to China accounted for 12.4% of the total value of agricultural exports.

(Billion Dollars)

160 140 120 100 80 60 40 20 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Thru China World Less China April

Figure 1: Foreign Agricultural Service agricultural exports from the US to China and the rest of the world. Source: FAS GATS Database https://apps.fas.usda.gov/gats/default.aspx

In 2017, prior to the outset of the trade war, China was the top value destination for oilseeds and their products. At that time, exports of oilseeds (primarily soybeans) from the US to

China totaled over $12 billion in 2017 alone. In the same year, the US exported in excess of $2 billion in grains and feeds, and in excess of $1 billion each of livestock and meats, and horticultural products to China. At the time, China ranked second in US agricultural export value. Only Canada, a then-NAFTA now-USMCA partner purchased more agricultural goods from the US than China in 2017.

Growing Chinese dependence on US agricultural exports during the 2010s, while a positive market force for farmers in the US, set the stage for their use as a tool in the trade war of

2018-2019.

3. Twitter Research

Twitter is a popular microblogging platform that allows users to compose ‘tweets’ that are

280 characters or less, with the option to attach a short video or image. Tweets are posted to a public profile or they can be sent in direct messages to other users. Of US adults surveyed in

2019, 24% indicated that have used Twitter (Pew Research Center, 2019). Twitter is also being used as a source of news. Approximately, 12% of Americans get their news from Twitter, suggesting approximately 71% of Americans on Twitter are using it to consume news (Pew

Research Center, 2019). Further, governmental and political use of Twitter is not limited to

President Trump, nor the government of the United States. Other domestic politicians utilize

Twitter as a means of public discourse and engagement. As early as the 2008 Democratic

Primary campaign both John Edwards and then-Senator Barack Obama used the platform to keep their followers informed of upcoming appearances (Diaz, 2007).

The reach and frequency of tweets from President Trump’s Twitter account cannot be understated. President Trump’s @realDonaldTrump account is the most followed account of any world leader as of October 2017 (CITE) and had 82.1 million followers as of June, 2020, the most among world leaders. In 2018, President Trump’s tweets averaged more than 20 thousand retweets, and the President has posted the most retweeted message of any world leader at 475 thousand retweets (Matthias, 2018).

A large body of work is dedicated to the study of Twitter’s ability to predict and influence movement in financial markets. Mood, as reflected in text analysis of Twitter posts, is a commonly studied driver for the direction of large financial markets such as the Dow Jones

Industrial Average index (Bollen et al., 2011; Mittal and Goel, 2012; Ranco et al., 2015) Similar

studies by Oliveira et al. (2017) find that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market cap, and some industries, and that Twitter sentiment had more significant impacts on smaller cap stocks. Suggestive that the financial market and Twitter agent might matter, other studies find that Twitter does not predict pricing and trading of renewable energy stocks (Reboredo and Ugolini, 2018). Further, the ability of Twitter to influence short-term decision making has also been observed through the impact of Twitter engagement and first adoption purchase activity by reducing the information asymmetry between producers of entertainment and consumers of entertainment (Hennig-Thurau et al., 2012).

4. Data and Results

4.1 Tweet data

Tweets and associated data from President Trump’s Twitter account (@realDonaldTrump) have been compiled from August 1, 2011 until January 1, 2020 from a publicly available database (Brown, 2019). Only original tweets from January 1, 2017 were considered, no retweets were included. For the purposes of this study, tweets containing the following words were included: tariffs, trade, farmers, pork, soybean, agriculture, steel, aluminum, China. These words were selected based on their relevance to the trade war.

Of particular interest to this study are tweets highlighting unofficial policy and opinions such as the following from July 20, 2018: “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!”. Keywords were selected to identify tweets such as the one

above although no additional editing was done to determine the nature of the tweet or what impact it would have on trade relationship.

The keywords “China”, “tariffs” and “trade” were the keywords used most frequently over the entire study period (See Table 1). Other keywords referencing specific commodities and traded goods are referenced less frequently. Some keywords such as “China”, “farmers” and

“tariffs” were used frequently in a single day.

Table 1: Summary statistics for number of tweets containing the keywords of interest. Keyword Mean St. Deviation Min Max Tariffs .158 .67 0 14 Trade .304 .697 0 7 Farmers .117 .528 0 12 Pork .002 .043 0 1 Soybean .004 .060 0 1 Agriculture .013 .135 0 3 Steel .089 .323 0 3 Aluminum .016 .138 0 2 China .327 1.05 0 17

All of the keywords of interest were used in tweets at least once during the study period although some words such as “China” were used more frequently (see Figure 2). The words

“farmer” and “tariffs” were not used early on in the study period but became used more frequently at the end of the study period. It can also be seen that days with high tweet activity around some keywords correspond closely with official announcements from the government of either nation during the trade war. For example, tweets including the keywords “China”

“farmers” and “tariffs” reached their peak on May 10, 2019. This corresponded with an announcement from President Trump that the United States government would raise the tariffs on $200 billion of Chinese goods to 25%. During that period, the CME November ’19 Soybeans contract dipped approximately 3.4% from $8.65/bu. to $8.35/bu.

Figure 2: Frequency of Tweets from @RealDonaldTrump including specified keywords from January 1, 2017- December 31, 2019. Source: http://www.trumptwitterarchive.com/ 7

Of particular interest are days with tweets containing a high number of keywords. Initial evaluation of tweets to identify days where tweets might have had an effect on agricultural commodity price series is based on identifying the days with the greatest number of tweets for a given keyword. Of particular interest is a day such as May 10, 2019 which is a top tweet day for multiple keywords (see Table 2).

Table 2: Days from January 1, 2017 – December 31, 2019 with the most tweets from President Trump using the given keywords. Keyword Top 3 Days with Most Tweets and Keyword China 5/10/2019 8/23/2019 12/5/2018 Tariff 5/10/2019 6/29/2019 8/4/2018 Trade 12/10/2019 6/11/2018 7/25/2018 Farmer 5/10/2019 7/11/2018 12/2/2019

4.2 Agricultural market data

Agricultural market price data was gathered from Barchart.com and include 2019 future contracts for corn, soybeans, cotton, and lean hogs. Contracts were selected based on their importance in the production schedule and that included the most time during the trade war.

Table 3 provides additional information on the contracts used including the type of contract, characterization of price, and contract time period. Most contracts begin in January 2017 and expire at the end of 2019.

Table 3: Details for agricultural commodities contracts used in this study.

Commodity Type of contract Price Trading platform Contract used Period Corn- Yellow #2 1 futures U.S. cents per Chicago Mercantile January 3, corn contract for bushel Exchange 2017 – 5,000 bushels December 13, 2019 Soybeans 1 futures U.S. cents per Chicago Mercantile January 3, contract for bushel Exchange 2017 – 5,000 bushels November 14, 2019 Cotton- No. 2 1 futures U.S. cents per Intercontinental January 10, contract 50,000 pound Exchange 2017 – pounds net December 6, weight 2019 Lean hogs 1 futures U.S. cents per Chicago Board of May 15, 2018 contract for pound Trade – October 14, 40,000 pounds 2019

Despite spanning a similar time horizon, the contract price series selected for this analysis exhibit different variability over time (See Figure 3, Table 4). The contract prices for corn and soybeans appear to display a generally downward sloping trend over time and are highly variable. Conversely, the contract prices for lean hogs and cotton appear relatively stable.

Statically, this relationship can be seen with the mean price of corn and soybeans (403.72 and

945.40, respectively) being much higher than hogs and cotton (69.87 and 71.27, respectively) with higher variability (st. dev of 16.89 and 39.78) versus (st. dev of 8.37 and 5.42).

Table 4: Summary statistics for agricultural commodity price series. Price series Variable No. Mean St. Dev Min Max Observations Corn Corn 984 403.72 16.89 342.88 463 Hogs Hogs 518 69.87 8.37 58.63 94.63 Soybean Soy 1046 945.40 39.78 828.5 1009 Cotton Cotton 1061 71.26 5.42 57.59 84.02

Visually, there are a number of clear jumps or periods of greater variability in each price series (See Figure 3). For example, the soybean price series shows substantial drops in prices in

May 2018 and April 2019.

Figure 3: Plots of prices for corn, soybeans, cotton, and lean hogs over contract period. Source: Data collected by barchart.com from the CME

4.3 Other Variables

Efforts were made to ensure that tweets included for analysis did not correspond to other factors which might move markets such as official trade negotiations or United States

Department of Agriculture (USDA) agricultural market reports. These other variables that have the potential to influence the market were also included in the analysis as binary variables either occurring or not occurring on each day. Other studies have shown the importance of USDA agricultural market report to agricultural commodity prices (Ying, Chen & Dorfman, 2019).

USDA reports included in this analysis were chosen because of their relevance to the markets for corn, cotton, soybeans, and hogs. In particular, the World Agricultural Supply and

Demand Estimates (wasde), Acreage (acre), Prospective Plantings (prosplant), Grain Stocks

(grainstock), Crop Production (cropprog), Cattle on Feed (cattlefeed), and Quarterly Hogs and

Pigs (HogPic) reporting days were indicated as binary variables in this analysis. From Figure 4, it is clear that USDA reports are consistently released during the duration of the study period. In particular, reports are generally released on a set, predictable time schedule. Some days, multiple reports are released.

Figure 4: Plot of days with release of USDA reports and official trade announcements from January 1, 2017- December 31, 2019.

Official announcements from the US government on trade are also of interest.

Announcements occur sporadically over the study period (See Figure 4). Days of official announcements are likely to impact the price series of agricultural commodities and to be tweeted by President Trump. In order to capture the effect of tweets unrelated to official announcements, a date of official announcements is included and controlled for as a binary variable. A full list of official announcements and dates can be seen in Appendix Table 1.

4.4 Statistical Framework for Analysis

Structural break tests were used to analyze the impact of tweets on agricultural commodity market prices. Structural break tests have been used to identify changes to a time series due to

policy changes or other shocks (Gutierrez, L., Westerlund, J., & Erickson, K., 2007; Tiffin, R., &

Dawson, P. J., 2000). Structural break tests are appropriate here because of the exogenous information speculated to be added as a result of tweets from President Trump. Analysis of structural breaks in price time series is important to ensure stable estimates of coefficients

(Tomek and Mount, 2009). Alternative specifications such as an ARDL models are an appropriate multivariable time-series modeling framework to determine the impact of tweets from President Trump. At this time, a generalized autoregressive conditional heteroskedasticity

(GARCH) model and explorations into heteroskedasticity of the price series are not considered.

The methodology behind modern structural break testing can be attributed to Chow (1960) and Bai and Perron (1998, 2003). Here, a basic test for structural break is constructed as an additional variable inserted into an established regression model as shown in Equation 1

(StataCorp, 2015).

푦푡 = 푥푡 + (푏 ≤ 푡 )푥푡훿 + 휖푡 (1)

Under this specification the null hypothesis of no structural break is captured by 훿=0. Based on the available software, a Wald statistic ~휒2. At this time, multiple breaks in a series are not jointly estimated.

4.5 Correlations

One potential concern in the specification of testing for structural breaks of the agricultural price series based on dates with high count of Tweets with specified keywords is that those dates might also be correlated with other variables that would influence price movement. A basic correlation matrix of all other variables considered and Tweet keywords reveals that generally,

days with a large number of Tweets using the keywords of interest are not correlated with days when other USDA reports are released or official announcements from the administration regarding the trade war (See Appendix Table 2). In particular, days with official announcements show low correlation to days when keywords of interest are tweeted such as China, tariffs, trade, and farmers (.1313, .0481, 01369, .0518, respectively). Correlation between reports and tweet keywords can be high; however, should not influence the tests for structural breaks.

4.6 Structural Break Testing

Vector autoregression (VAR) equation models for each agricultural commodity price series were constructed before structural break tests were conducted (See equations 2-5). Optimal lag lengths were determined based on minimization of Akaike Information Criterion.

퐶표푟푛푡 = 퐵0 + 퐵1퐶표푟푛푡−1 + 퐵2퐶표푟푛푡−2 + 휀푡 (2)

퐶표푡푡표푛푡 = 퐵0 + 퐵1퐶표푡푡표푛푡−1 + 퐵2퐶표푡푡표푛푡−2 + 퐵3퐶표푡푡표푛푡−3 + 퐵4퐶표푡푡표푛푡−4 + 휀푡 (3)

푆표푦푡 = 퐵0 + 퐵1푆표푦푡−1 + 휀푡 (4)

퐻표푔푡 = 퐵0 + 퐵1퐻표푔푡−1 + 퐵2퐻표푔푡−2 + 퐵3퐻표푔푡−3 + 퐵4퐻표푔푡−4 + 휀푡 (5)

Wald structural break tests reveal that all of the days with high tweet-counts including the keywords of interest were associated with a structural break in at least one of the agricultural commodity price series used in this analysis (see Table 5). Generally, all keywords tested were statistically significant and caused structural breaks. Based on the dates selected, the price series for corn exhibited the greatest variability and influence from the tweets as shown by all structural breaks being statistically significant at the 10% level or greater. This indicates that this price series is the most influenced by tweets from President Trump using the keywords analyzed here.

The hog price series was least influenced by tweets with the specified keywords as shown by only one of the dates of interest were associated with a statistically significant structural break.

The cotton and soybean price series which were selected based on the volume of trade with

China both had many days with high tweet counts containing keywords of interest and structural breaks. The following dates were statistically significant in the corn, cotton, and soybean markets: May 10, 2019, August 4, 2018, June 11, 2018. Further research is required to determine the magnitude of these impacts and specific nature of the tweets.

Table 5: Results of structural break tests showing Corn Cotton Soybean Hog China May 10, 2019 14.7377*** 24.7338*** 7.878** 4.795 August 23, 2019 4.5704 8.9850 .9237 19.32*** December 5, 2018 15.2081*** 6.3462 4.2856 5.304 Tariff May 10, 2019 14.7377*** 24.7338*** 7.878** 4.795 June 29, 2019 17.7441*** 11.0892** 4.139 6.862 August 4, 2018 21.676*** 9.4044* 6.13** 4.727 Trade December 10, ------2019 June 11, 2018 17.3584*** 10.1105* 12.918*** 1.276 July 25, 2018 17.9201*** 8.2434 5.729* 3.54 Farmer May 10, 2019 14.7377*** 24.7338*** 7.878** 4.795 July 11, 2018 18.7838*** 4.9418 6.01** 1.530 December 2, 2019 ------Note: the following notation is used in Table 5, * statistically significant at the 10%, ** statistically significant at the 5%, *** statistically significant at the 1% level

The results in Table 5 indicate that tweets by President Trump had a statistically significant impact on the agricultural future markets for corn, cotton, soybeans, and hogs. As described, the price series for corn exhibited more statistically significant structural breaks than the other commodities which are traded in greater volume and economic significance with China.

This effect is likely indicating the importance of substitute feedstocks in the global agricultural

supply chain. For example, soybeans and corn are typically considered substitutes for animal feed and thus any anticipated variability in one commodity will lead to speculation in the other series. Given that tests for structural breaks do not determine the magnitude of impact and do not indicate any directionality for Granger causality, further research is required.

5. Conclusion

The struggle between the governments of the US and China, utilizing tariffs and retaliatory tariffs, induced a significant loss in farm incomes nationwide. It is clear from our work, that

President Trump’s engagement through Twitter, outside of the official channels of government, induced price changes in certain contracts. Of the selected days in which the President’s tweets included high-keyword volumes, there were significant moves, and in certain cases directional changes in soybeans, cotton, hogs, and even corn, which has not historically been a crop exported to China in high volume. It is important to remember, these days did not necessarily correspond with official government announcements, nor did they necessarily occur the same day as any major USDA report.

These findings are financially significant for producers, and politically significant.

Additional work is necessary to ascertain the magnitude of price change attributed to Tweets from the President, and as a result their total market impact. Future work to extend this analysis will include a formal GARCH analysis to determine the magnitude and direction of the effect.

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7. Appendix Appendix Table 1: Table of official announcements from the US government on trade war. Date Official Announcement 31-Mar- Trump, now president, calls for tighter tariff enforcement in anti-subsidy and anti- 2017 dumping cases and a review of U.S. trade deficits. 7-Apr-2017 At their first meeting, Trump and Chinese President Xi Jinping agree to a 100-day plan for trade talks 19-Jul-2017 The two sides fail to agree on new steps to reduce the U.S. trade deficit with China within 100 days. 14-Aug- Trump orders here a "Section 301" probe into alleged Chinese intellectual

2017 property theft 22-Jan-2018 Trump imposes tariffs on all imported washing machines and solar panels - not just those from China. 8-Mar-2018 Trump orders 25% tariffs on steel imports and 10% on aluminum from all suppliers - not just China. 2-Apr-2018 China imposes tariffs of up to 25% on 128 U.S. products including airplanes and soybeans. 3-Apr-2018 Trump unveils plans for 25% tariffs on about $50 billion of Chinese imports. 4-Apr-2018 China responds with plans for retaliatory tariffs on about $50 billion of U.S. imports 15-Jun-2018 The United States says that 25% levies on $34 billion of Chinese imports will go into effect July 6, and announces 25% tariffs on an additional $16 billion of goods. China responds with tariffs on $34 billion of U.S. goods. 10-Jul-2018 The United States unveils plans for 10% tariffs on $200 billion of Chinese imports. 1-Aug-2018 Trump orders the July 10 tariffs to increase to 25%. 7-Aug-2018 The United States releases a list of $16 billion of Chinese goods to be taxed by 25%. China retaliates with 25% duties on $16 billion of U.S. goods. 24-Sep- The 10% tariffs on $200 billion of Chinese imports kick in. The administration 2018 says the rate will increase to 25% on Jan. 1, 2019. China taxes $60 billion of U.S. goods. 1-Dec-2018 The United States and China agree on a 90-day halt to new tariffs. Trump agrees to postpone the Jan. 1 increase on $200 billion of Chinese goods; the White House says China will buy a “very substantial” amount of U.S. products. 1-May-2019 U.S. and Chinese negotiators hold mid-week trade talks in Beijing, craft a 150- page draft trade agreement. 3-May-2019 In a late-night cable to Washington, Beijing backtracks on almost all aspects of the draft trade pact. 5-May-2019 Trump tweets that he intends to raise the tariffs on $200 billion of Chinese goods to 25% on May 10. 16-May- The U.S. bans Chinese telecoms giant Huawei Technologies Co Ltd [HWT.UL] 2019 from buying parts and components from U.S. companies. 18-Jun-2019 Trump and Xi agree by phone to rekindle trade talks. 29-Jun-2019 At the G20 meeting in Osaka, Trump agrees to no new tariffs and to ease restrictions on Huawei. Xi agrees to unspecified new purchases of U.S. farm products. 1-Aug-2019 Trump announces 10% tariffs on $300 billion worth of Chinese imports, after two days of talks with no progress.

5-Aug-2019 China halts purchases of U.S. agricultural products, and the Chinese yuan weakens past the key seven per dollar level. Equity markets plummet. The U.S. Treasury says China is manipulating its currency. 13-Aug- Trump postpones some of the 10% tariffs on the $300 billion goods list until Dec. 2019 15. 23-Aug- China announces additional retaliatory tariffs on about $75 billion worth of U.S. 2019 goods. 20-Sep- After a two-day meeting of U.S. and Chinese deputies, USTR issues tariff 2019 exclusions on about 400 Chinese products. 7-Oct-2019 The U.S. Commerce Department puts 28 Chinese companies on its “entity list,” over their alleged involvement in human rights abuses against Uighur Muslims in Xinjiang. 11-Oct-2019 After two days of high-level talks, Trump announces a Phase 1 deal that includes suspension of planned tariffs and a Chinese pledge to buy more farm goods, but few details.

Wasde Acre Prospla Grainst Croppr Cattlef Hogpig Admin Tariff Trade Farmer Pork Soybea Agricul Steel Alumin China nt ock og eed Annou s n ture um nce Wasde 1.000

Acre -.01 1.000

Prosplant -.01 -0.003 1

Grainstock .122 0.478 0.478 1

Cropprog -0.044 -0.017 -0.017 -0.008 1

Cattlefeed -0.008 -0.01 -0.010 -0.021 -0.064 1

Hogpig -0.02 -0.006 0.156 0.066 -0.036 0.115 1

AdminAnn 0.003 -0.009 0.102 0.036 0.043 0.031 -0.018 1 ounce Tariff 0.117 -0.012 -0.012 -0.026 0.004 -0.001 0.025 0.048 1.000

Trade 0.103 0.002 -0.023 0.001 0.071 0.029 -0.024 0.137 0.31 1

Farmers 0.163 -0.012 -0.012 -0.024 0.037 -0.024 -0.024 0.052 0.619 0.301 1

Pork -0.008 -0.002 - - -0.014 -0.008 0.193 -0.007 0.15 0.043 0.031 1 0.0022 0.0047

Soybean -0.011 -0.003 - - 0.031 0.070 -0.007 -0.01 0.054 0.126 0.073 -0.003 1 0.0032 0.0066

Agriculture 0.173 -0.005 - - -0.031 -0.018 -0.01 -0.015 0.525 0.163 0.583 -0.004 -0.006 1 0.0050 0.0104 Steel 0.06 -0.014 - - -0.024 0.039 -0.004 0.027 0.1 0.156 0.052 -0.012 0.030 0.016 1 0.0144 0.0040 Aluminum 0.128 -0.006 - - -0.015 0.014 -0.012 0.066 0.082 0.103 0.051 -0.005 -0.007 -0.011 0.441 1 0.0059 0.0124 China 0.099 0.0003 - - 0.034 0.001 -0.002 0.131 0.649 0.298 0.504 0.068 0.125 0.442 0.079 -0.004 1 0.0163 0.0020 Appendix Table 2: Correlations of Tweets and additional variables