nowcasting payrolls employment with traditional media content · ieseg school of management...
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Nowcasting payrolls employment with traditional mediacontent
Bortoli, C.∗, Combes, S.∗ & Renault, T.∗∗
Institut National de la Statistique et des Etudes Economiques∗
Universite Paris 1 Pantheon-Sorbonne∗∗
IESEG School of Management∗∗
March 16, 2016
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Non-Farm Payrolls Employment
Quarterly data - Flash estimate (45 days) - First estimate (70 days) -Revised estimate (160 days)
Synthesis of administrative sources (URSSAF, Pole emploi, DADS,etc.) and company surveys (ACEMO) on employment
Estimation of employment levels and changes in different businesssectors
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Non-Farm Payrolls Employment
Figure: Change in Non-Farm Payrolls Employment (Flash Estimate)
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Methodology
Textual Analysis [Loughran & McDonald, JoF, 2011]
Quantification of Media Content [Baker & al., NBER (2013)]
Media Pessimism and Unemployment Expectations [Garz, JEP(2013)]
Alternative Data and Unemployment [Fondeur & Karame, EM (2013)]
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Methodology
1 Derive quantitative indicators from textual content published bytraditional media
2 Include textual indicators into benchmarks model used to nowcastFrench employment
3 Compare Root Mean Square Forecast Error (RMSFE)
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Baker & al. [2013] methodology
Search Le Monde (from 1987) using Lexis Nexis and Le Figaro (from2002) using Factiva
Count the number of articles containing the following triple :
(E) ”economie”, ”economique”, ”economiques”(P) ”taxe”, ”impot”, ”depense”, ”deficit”, ”politique”, ”banque centrale”,”budget”... (20 keywords)(U) ”incertitude”, ”incertain”, ”incertitudes”, ”incertains”
Scale by the total number of articles in the same newspaper andmonth. Standardize and normalize to create an aggregate indicator
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Baker & al. [2013] methodology
Figure: Economic Policy Uncertainty Index - France
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Methodology
Extract all articles published on Le Monde website between 1990 and2016
Remove articles related to foreign countries
Identify articles related to the topic ”Economy” using machine learningmethods
Construct two field-specific lexicon related (a) to employment and (b)to global sentiment
Derive article tonality using a dictionary-based approach
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Methodology
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Data
1,297,292 articles, of which 212,721 have been classified as ”economy”by journalists from Le Monde (195,051 ”international”, 158,969”society”, 122,718 ”politics, 95,426 ”europe”, 85,649 ”sports”, 63,579”planet”...). Classification starts in 2005.
Naive Bayes classifier on a training dataset of 20,000 economic newsand 20,000 non-economic news
Named-entity recognition to remove articles not related to France
Final dataset of 202,674 articles related to the French economy
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Data
We first create a field-specific lexicon with positive (negative) bigramsand trigrams related to employment / company’s outlook :
Positive words list (Total 53 ngrams) : ”job creation”, ”hiring plan”,”higher profit”, ”increase activity”, ”staff increase”...Negative words list (Total 121 ngrams) : ”job destruction”, ”redundancyplan”, ”lower profit”, ”industrial restructuring”, ”judicial liquidation”...
Then, we create a lexicon with global positive (negative) words :
Positive words list (Total 485 ngrams) : ”improve”, ”favorable”,”surplus”, ”cooperation”, ”expand”, ”success”...Negative words list (Total 1,507 ngrams) : ”instability”, ”trouble”,”uncertainty”, ”weaken”, ”depress”, ”erode”...
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Data
For each article, we count the number of words in each lexicon, andwe define :
SENTIMENTi =# of Positive word − # of Negative word
Total word(1)
INDICATORi =
INDICATORi = 1 if SENTIMENTi > 0INDICATORi = 0 if SENTIMENTi = 0INDICATORi = −1 if SENTIMENTi < 0
(2)
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Data
Figure: Employment Media Indicator (EMI)
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Data
Figure: Global Media Indicator (GMI)
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Nowcasting French Non-Farm Payrolls Employment
The baseline model is a simple auto-regressive model. Different mediaindicators (X) are then added, and compared to the baseline model. Theemployment equation takes the form :
∆NFPt = α + β1∆NFPt−1 + β2∆NFPt−2 + ΦXt + εt (3)
We also consider an augmented baseline model by adding availableinformation from business climate surveys (monthly data, published at theend of the month for current month)
∆NFPt = α + β1∆NFPt−1 + β2∆NFPt−2 + β3∆Surveyt + ΦXt + εt (4)
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Nowcasting French Non-Farm Payrolls Employment
Table: Employment Regression Results [Month 2]
Baseline EMI GMI Survey Survey+GMI
[1] [2] [3] [4] [5]
α 0.0002 0.0012 0.0058 0.0002 0.0037
(0.000) (0.001) (0.001) (0.000) (0.001)
∆NFPt−1 0.5989*** 0.5336*** 0.4164*** 0.3832*** 0.3182***
(0.083) (0.102) (0.090 ) (0.071) (0.078 )
∆NFPt−2 0.2295*** 0.1988** 0.0509 0.4760*** 0.3038***
(0.087) (0.086) (0.100) (0.076) (0.085)
EMIt 1.7614*
(0.446)
GMIt 0.3927*** 0.2499***
(0.068) (0.073)
∆Surveyt 0.0383*** 0.0292***
(0.004) (0.005)
Adjusted R2 0.622 0.631 0.714 0.740 0.770
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Nowcasting French Non-Farm Payrolls Employment
Table: Root Mean Square Forecast Error [2000-2016]
Baseline EMI GMI Survey Survey+GMI
[1] [2] [3] [4] [5]
RMSFE [1 month] 0.2732 0.2737 0.2623 0.2162 0.2259
RMSFE [2 months] 0.2415 0.2471 0.2062 0.1876 0.1787
RMSFE [3 months] 0.2415 0.2392 0.2064 0.1751 0.1699
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Conclusion
Sentiment derived from articles published in traditional media seemsto help nowcasting French non-farm payrolls employment
When two months of data are available, both business climate andglobal media tonality are significant at the 1% level [Full Sample]
RMSFE is lower when a media indicator is added to anauto-regressive model augmented with business climate survey [Fittedon 1990-1999 and rolling forecast on 2000-2016]
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Directions for future research
Use more advanced Natural Language Processing techniques toimprove the detection and weighting of media sentiment
Extend time period (data from LeMonde are available since 1944).Could be especially useful for periods where other indicators are notavailable (for example, business climate is available since 1980)
Add more media to avoid bias related to the analysis of a uniquecontent provider
Analyze if media content helps nowcasting/forecasting othermacro-economics of financial variables (stock prices, market volatility,investment, GDP...)
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Questions
Thanks for your attention.
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