Food Security Media Analysis System
Maximo ToreroDivision Director Markets, Trade and Institutions
Factors Behind Food Price Trends
Media?
Cartoon by Don Addis / Quick Take
Misperceptions/Lack of accurateinformation
Harmful policies
Foodprice
changes
Can perceptions of food prices portrayed in themedia impact real prices?
Evidence: Russian Wildfires of 2010 and Wheat Prices
Analysis of Articles Referencing Wheat Prices
From August-October 2010, every media article covering the price of wheat was accompanied by a spike in price volatility; 57 percent of these articles forecast an increase in global wheat prices (GHI 2011).
CBOT Wheat Prices
Source: Chicago Board of Trade (CBOT)
Global Wheat Stocks
Source: World Agricultural Outlook Board (August 12, 2010)
CBOT wheat prices – IFPRI model to detect abnormal spikes
Drought inRussia began
+Locus inAustralia
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0.02
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12/1
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5/7/
2002
9/27
/200
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2/24
/200
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7/17
/200
3
12/8
/200
3
5/3/
2004
9/24
/200
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2/17
/200
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7/13
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12/2
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4/28
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9/20
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2/14
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7/10
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11/2
9/20
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4/24
/200
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9/16
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2/9/
2009
7/2/
2009
11/2
3/20
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4/20
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0
Realized Return
95th percentile
Source, Martins Filho, Torero, Yao (2010)
Why?
Image source: Flickr, Willi Heidelbach
Filling the Information Gaps
Perception: Media Reports on Current and Foreseeable Supply, Demand, Stocks, Trade, and Prices
Food Security Media Analysis System
• Scans daily international news articles for keywords regarding food security, food prices, and food supplies
• Searchable database going back to 2009 shows relationship between keywords (eg. Russia and wheat prices)
Food Security Media Analysis System created by IFPRI in collaboration with Sophic Systems Alliance Inc.® (Sophic Intelligence TM)
Heatmap and table views show number of co-occurences of selected search words.
What can FOMA help researchers do?
Accurately measure marketexpectations
Compare food price news toother variables to determineaccuracy of media portrayal
Analyze cause and effect offood security news in global
press
Our analytical approach
• Influence of media on price levels because thisis what the poor consumers of thesecommodities will feel
• We proceeded to analyze the returns becausethe behavior of investors and speculators areconditional on them
• Finally look at effects on price volatility
What we do
Data
• Prices:– daily futures price data from the Chicago Board of Trade for futures of Maize,Soft, Soybean, Rice and Oil and from Kansas City Board of Trade for Hard Wheat.
– We augment these price data with market variables such as the SP index, thedaily exchange rates between the US dollar and the currencies of majorparticipant countries in the agricultural commodity markets, for exampleCanada, Thailand, China, Australia, and The European Union.
• Measures of media coverage:– every day, we monitor a comprehensive set of RSS (Really Symple Syndication)feeds drawn from global media outlets via Google news. A total of 31 feedsrelated to global food prices and food security are monitored
– Each media article is analyzed using linguistic and semantic object networkmapping algorithms to analyze the relationships between key terms found ineach article.
– On a daily basis, the system provides reports analyzing movement (increasesups or decreases downs) in commodities prices. These reports provide a countof the number of articles each day with “up” or “down” movements for eachcommodity by analyzing the text within the articles.
– The period spans from the 3rd of August of 2009 to the 11th of June of 2012. In“market time” we obtain 707 periods (days) for a total of 4,242 observations
How we do it
Empirical implementation
1. For Price levels:
Where:i=Hard Wheat, Maize, Oil, Rice, Soft Wheat, Soybeanst= 1…T (1 is 08/03/2009 and T is 06/12/2012 in ‘market time’)
is the log price levelis a commodity specific intercept (fixed effect)is the number of ‘increase in price of i news for day t
is the number of ‘decrease in price’ of i news for day tis a matrix of market variables at date tis a random error term, which depending of the specification will have a different structure
K is the number of lags
We assume that the news variables are predetermined or sequentially exogenous, that is thatwhich allow us to use moment restriction to obtain
a GMM IV estimator
How we do it
Empirical implementation
How we do it
2. For Price returns:
Alternative we use the following specification of the returns, whichaccounts for the possible persistent correlation for each commodityand exploits better the variation in the media coverage variables.
We note that the are different parameters than the parameters.These can be related by . We cluster the standard errors bydate and allow for auto correlated (AR1) common disturbances andarbitrary heteroskedasticity, using a truncated kernel as recommendedin Thompson (2009).
Empirical implementation
How we do it
3. For Price volatility:
We estimate the following model (in addition to simpledifference in variance tests); this is informed by the estimationsin Ohlson and Penman (1985) and Dubofsky (1991):
where
`
Summarizing Effect Size of Media Influence
Key results
Results on Volatility
• We present a graphical analysis of the residuals, giventhat this simple test might not reflect theheterogeneity in volatility due to the intensity of mediacoverage (we don’t differentiate the intensity ofmedia).
• We found that for days with fewer than 5 articles of upor down news, the residuals are very spread out incomparison to ones in day with more than 5 articles.
• This evidence points to lower volatility when mediacoverage is more intense.
Key results
Results on Volatility: : Squared Residual vs. Intensity of Media Coverage
0.0
05.0
1.0
15.0
2S
q. R
esid
uals
0 5 10 15 20 25Increase in price news
Volatility : Residual Squared vs. Number of Up News
Key results
Results on Volatility: : Squared Residual vs. Intensity of Media Coverage
0.0
05.0
1.0
15.0
2S
q. R
esid
uals
0 5 10 15Decrease in price news
Volatility : Residual Squared vs. Number of Down News
Key results
Conclusion
Media reports are one of many variables Important for food prices.
FOMA can help researchers determine accuracy of media portrayals and identify potential information gaps, helping to calm markets and prevent knee-jerk policy responses.
• There are interestingcorrelations between theprice dynamics and themedia coverage intensity
• Increased media attentioncan exacerbate the increasein price (more than 8% ofthe change in prices)
• The variability ofcommodities return andprices tends to decrease asmore attention is paid bythe media to the situationin those commoditiesmarkets
• The major policy implication is the crucial roleof providing appropriate information as fast aspossible so media reacts in the correctdirection
Conclusions
Conclusions
“In the real world, the right thing never happens in theright place and the right time. It is the job of journalists
and historians to make it appear that it has”Mark Twain
