Introduction
The monthly United States nonfarm payroll (NFP) proclamation past times the U.S.A. Bureau of Labor Statistics (BLS) is 1 of the nearly closely watched economical indicators, for economists as well as investors alike. (When I was teaching a degree at a well-known proprietary trading firm, the traders of a abrupt ran out of the classroom to their desks on a Fri forenoon merely earlier 8:30am EST.) Naturally, at that spot were many efforts inwards the past times trying to predict this number, ranging from using other macroeconomic indicators such equally credit spreads to using Twitter sentiment equally predictive features. In this article, I volition study on enquiry conducted past times Radu Ciobanu as well as I using the unique as well as proprietary continuous survey information provided past times RIWI Corp. to predict this of import number.
RIWI is an option information provider that conducts online surveys as well as adventure measuring monitoring inwards all countries of the basis anonymously, without collecting whatever personally identifiable information or providing incentives to respondents. RIWI’s applied scientific discipline has collected as well as analyzed to a greater extent than than 1.5 billion responses globally. Critically, inwards their surveys, they tin laissez passer on a segment of the population that is unremarkably hidden: iii quarters of their respondents across the basis accept non answered a survey of whatever form inwards the preceding month. Their surveys strive to live equally illustration of the full general online population equally possible, without the green bias towards the loud social media voices. This is of import inwards predictive information for fiscal markets, where it is vital to divide dissonance from signal.
The fiscal marketplace reacts mainly to surprise, i.e. the departure betwixt the actual announced NFP publish as well as the Wall Street consensus. This surprise tin motility non only the United States fiscal markets, but international markets equally well. Case inwards point: I watched the German linguistic communication DAX index moved sharply higher final calendar week (December 6, 2019 ) due to the huge positive surprise (adding 266K jobs instead of the Wall Street consensus of 183K). Therefore the surprise is what nosotros desire to predict. We compared predicting the sign of this surprise using machine learning amongst the RIWI score equally the only characteristic vs. a publish of other benchmarks that produce non include the RIWI score, as well as constitute that the RIWI score generates higher predictive accuracy than all other benchmarks during cross validation test. We also predicted both the magnitude as well as sign of the NFP surprise. Including the RIWI score equally 1 of the features achieved the smallest averaged cross-validated hateful squared fault (MSE) than otherwise. Limited out-of-sample results dot the RIWI score continues to accept pregnant ability for both sign as well as magnitude predictions.
Data
The historical NFP monthly numbers were seasonally adjusted past times the BLS. These numbers were released on the commencement Fri of every month, at 8:30 am ET (except on sure as shooting national holidays when they are released 1 solar daytime earlier or delayed past times 1 week.) To compute the surprise, nosotros subtract the Wall Street consensus on the solar daytime earlier the proclamation from the actual NFP number.
The RIWI information were based on their online surveys of United States consumers, as well as consist of 2 datasets. The commencement 1 is dated Dec 2013 - Oct 2017 as well as the minute 1 is dated Sep 2018 - Sep 2019. The one-time dataset is based on the yes/no respond to the next survey question: ‘Are yous working for to a greater extent than than 35 hours per week?’. The latter dataset is based on several survey questions related to opinions regarding United States companies or products, along amongst respondents’ personal background, such equally their job condition (full-time/part-time/student/retired), marital status, etc. In guild to merge the 2 datasets, nosotros regard respondents who said they worked “full-time” or “part-time” equally equivalent to “working to a greater extent than than 35 hours per week”. If nosotros were to count only the “full-time” respondents, a pregnant structural intermission inwards the fourth dimension serial would live observed betwixt the 2 fourth dimension periods, equally seen inwards Figure 1 below.
Figure 1: Weighted monthly RIWI score, without seasonal adjustments, including only “Full-Time” respondents, for Dec 2013-Oct 2017 as well as Sep 2018-Sep 2019.
If nosotros include both “Full-time” as well as “Part-Time” respondents, nosotros obtain Figure 2 below, which clearly doesn’t accept that structural break.
Figure 2: Weighted monthly RIWI score, without seasonal adjustments, including “Full-time + part-time” respondents, for Dec 2013-Oct 2017 as well as Sep 2018-Sep 2019.
RIWI provides a weight for each respondent inwards guild to transform the information as well as so that it tin reverberate the demographics of the full general United States population, hence the describing word “Weighted” inwards the figure captions. Note that the survey is conducted such that each respondent tin become dorsum as well as modify their answers but they volition non present upwards equally to a greater extent than than 1 sample inwards the information set. In guild to extract a summary score inwards advance of each month’s NFP announcement, nosotros compute a monthly average of the production of the respondents’ weights as well as the indicator (0 or 1) of whether the private respondent is working total or part-time. The monthly average is computed over the same calendar month that the NFP publish measures. We telephone phone this the “RIWI score”. As the NFP information were seasonally adjusted, nosotros take away to produce the same to the monthly differences of the RIWI score. We employ the same adjustment that the BLS uses: X12-ARIMA. But for comparing purposes, nosotros did non apply seasonal adjustment to Figures 1 as well as 2.
Classification models
Our classification models were used to predict whether the sign of the NFP surprisewas positive or negative (there were no null surprises inwards the data.) The models were trained on the information on Dec 2013 – Oct 2017 (“train set”), where cross validation testing also took place. Out-of-sample testing was done on the information Sep 2018-Oct 2019 (“test set”). As mentioned above, the exam set’s RIWI survey questions were somewhat dissimilar from the prepare laid upwards questions. So exam laid upwards lawsuit is a articulation exam of whether the classification model plant out-of-sample as well as whether the slight departure inwards the RIWI information degrades predictive accuracy significantly.
To render benchmark comparisons against RIWI score, nosotros also studied several other criterion features, or as well as so of which were constitute useful for NFP predictions:
· Previous 1-month NFP surprise
· Previous 12-month NFP surprise
· Bloomberg Barclays United States Corporate High Yield Average Option Adjusted Spread Index (a.k.a. credit spreads)
· Index of Consumer Sentiment (University of Michigan)
The Bloomberg Barclays United States Corporate High Yield Average Option Adjusted Spread Index denotes the departure (spread) betwixt a computed Option Adjusted Spread index of all high yield corporate bonds as well as a spot United States Treasury curve. An Option Adjusted Spread index is computed using element bonds’ pick adjusted spreads, weighted past times marketplace capitalization. In what follows, nosotros volition refer to the Bloomberg Barclays United States Corporate High Yield Average Option Adjusted Spread Index equally the “credit spreads” feature.
Since machine learning tin only live performed on stationary features, nosotros volition use the monthly differences inwards the RIWI score as well as other features.
The benchmarks models nosotros tested are:
- Logistic regression* on Previous surprise.
- Trend-following model predicts adjacent sign(surprise)=sign(previous surprise).
- Contrarian model predicts adjacent sign(surprise)=-sign(previous surprise).
- Logistic regression on credit spreads.
- Logistic regression on Index of Consumer Sentiment.
Here are the results, compared to applying Random Forest to the RIWI score alone:
ML model | Features | CV accuracy (in-sample) | Out-of-sample accuracy |
Contrarian model | Prev 1-month surprise | 0.46 | 0.66 |
LogReg (Ridge) | Credit spreads | 0.52 | 0.51 |
LogReg (Ridge) | Prev 1-month surprise | 0.53 | 0.50 |
LogReg (Ridge) | Consumer sentiment index | 0.53 | 0.50 |
Random Forest | All features | 0.53 | 0.58 |
Trend next model | Prev 1-month surprise | 0.54 | 0.33 |
Random Forest | RIWI score alone | 0.63 +/- 0.03 | 0.58 +/- 0.04 |
Table 1: Classification benchmarks as well as other features
Based on the predictive accuracy on the cross validation data, the best machine learning model is 1 that uses the RIWI score equally the only feature. This model applied the random woods classifier to the RIWI score to predict sign(NFP surprise). It obtained an average cross-validated (CV) accuracy of 63% +/- 0.03 (using 10-fold cross-validation on Dec 2013 – Oct 2017 data) as well as a 58.3% +/- 0.04 out-of-sample accuracy. As the out-of-sample information consists only of 12 information points, nosotros view that equally a exam of whether the random woods classifier overfitted on preparation data, as well as whether the slightly dissimilar RIWI information affected predictions, but non equally a fair comparing of the diverse models. Since the predictive accuracy did non deteriorate significantly on the out-of-sample data, nosotros conclude that no overfitting was likely, as well as the novel RIWI information did non differ significantly from that which nosotros trained on. We accept also applied random woods to all the features including the RIWI score, as well as constitute lower CV (53%) as well as out-of-sample (58%) accuracies than using the RIWI score alone.
Regression models
Our regression models were used to predict the actual NFP surprise (sign + magnitude). The prepare vs. exam information were the same equally for the classification models, as well as features laid upwards were also the same.
To render benchmark comparisons against the RIWI score, nosotros studied the next models:
- ARMA (2,1) model* that uses past times NFP surprises.
- Trend-following model predicts adjacent surprise=(previous surprise).
- Contrarian model predicts adjacent surprise=-(previous surprise).
Here are the results, compared to applying Random Forest to the RIWI score alone:
ML method | Features | CV MSE (in-sample) | Out-of-sample MSE |
Trend next model | Prev 1-month surprise | 6788.60 | 19575.16 |
Contrarian model | Prev 1-month surprise | 5941.78 | 9652.16 |
ARMA(2,1) | Prev 1-month surprise | 3317.47 | 7192.9 |
Linear regression (Ridge) | Prev 1mth surprise +prev 12mth surprise | 3310.66 | 7302.94 |
Random Forest | RIWI score | 3280.13 | 7208.01 |
Random Forest | Credit spreads | 3257.51 | 7227.63 |
Random Forest | Consumer sentiment index | 3251.48 | 7231.74 |
Random Forest | All features | 3251.18 | 7268.75 |
Random Forest | RIWI score + prev 1mth surprise + prev 12mth surprise | 3249.35 +/- 70 | 7269.20 +/- 134 |
Table 2: Regression benchmarks
Based on the hateful squared fault (MSE) of predicted surprises on the cross validation data, the best machine learning model is 1 that includes the RIWI score equally a feature. It applied the random woods classifier to the RIWI score, previous 1-month as well as 12-month surprises inwards guild to predict actual NFP surprise. It obtained an average cross-validated MSE of 3249.35 +/- seventy and a 7269.2+/- 134 out-of-sample accuracy. It marginally outperformed all benchmarks inwards cross-validation. As amongst all other benchmarks, including the Contrarian model which requires no training, out-of-sample MSE increased significantly over the CV MSE. But again, equally the out-of-sample information consists only of 12 information points, nosotros don’t view it equally a fair comparing of the diverse models. We also applied random woods to all the features including the RIWI score, as well as constitute somewhat higher CV MSE (and hence a worse model) than using the RIWI score alone, but the departure is within fault bounds.
Conclusion as well as Future Work
Using the technique of cross validation on RIWI information from December 2013 - Oct 2017, nosotros constitute that the RIWI score (after weighting, seasonal adjustment, as well as differentiation), has outperformed all other benchmarks inwards predictive accuracy for the sign of the NFP surprises. We also constitute that the similarly transformed RIWI score, if supplemented amongst other indicators, has performed equally good or amend than all other benchmarks. While such absolute authorisation needs to live confirmed inwards an extended out-of-sample test, nosotros believe at that spot is dandy potential for using the RIWI score for predicting the all-important Nonfarm Payroll number.
But beyond predicting NFP surprises, RIWI’s information accept the potential to live a to a greater extent than accurate justice of the actual U.S. job situation, as well as thus economical growth, than the NFP number. The “gig economy” is employing to a greater extent than workers whose information produce non easily expose their agency into the official BLS count. (Here is an article on why BLS’ endeavor to count these workers has been a failure. This Bank of Canada report also concluded that official numbers were undercounting gig workers.) Undocumented workers are non counted inwards the NFP but they produce contribute to the economy. Even illegal activities could accept contributed to a greater extent than than 1% to the U.S. GDP, according to this Wall Street Journal report. In contrast, RIWI’s survey methodology was cited inwards this paper past times Harvard researchers amidst others equally the preferred method of collecting information on hard-to-reach populations. One tin imagine an ambitious researcher using RIWI information to straight predict gross domestic product growth as well as achieving amend results than using the traditional economical indicators such equally NFP.
Acknowledgement
We give cheers Jason Cho, Head of Data Operations at RIWI, for providing us the Company’s proprietary information for our evaluation purposes.
*Note a PDF version of this article tin live downloaded from www.epchan.com.
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