From QuantCon 2017: Fundamental and quantitative stock selection research has long focused on creating accurate forecasts of company fundamentals such as earnings and revenues. In this talk we examine why fundamental forecasts are powerful and survey some classic methods for generating these forecasts. Next we explore some newer methodologies which can be effective in generating more accurate fundamental forecasts, including new uses of traditional data as well as novel crowdsourced and online behavior databases. Finally, we present new research examining the temporal variation in efficacy of these forecasts with an eye towards understanding the market conditions in which an accurate fundamental forecast can be more or less profitable.

1. Fundamental forecasts: methods and timing
QuantCon – Singapore, 29 September 2017
Vinesh Jha
CEO, ExtractAlpha

2. Agenda
➢Why predict fundamentals?
➢Cleaner than predicting returns!
➢ Classic methods
➢ Time series
➢ Cross section
➢ Newer methods
➢ Using traditional methods
➢ Crowdsourced
➢ New consumer and sentiment data sets
➢When do these forecasts work?
➢ Perfect foresight tests
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3. Why predict fundamentals?
➢ Predicting fundamentals is often “cleaner” because stock prices in the short
term can reflect over/underreaction, exogenous shocks, temporary sentiment
shifts
➢ Earnings volatility < asset volatility
➢ Over the long haul, stock prices should align with fundamentals, and in
particular earnings – same reason for the valuation anomaly (valuations
shouldn’t remain stretched for an extended period)
➢ So predicting earnings can be a good way to “back into” a stock price prediction
(and can be built with a shorter history)
➢We can also try to predict associated metrics
➢Revenues
➢Earnings & revenue surprises, or growth
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4. Classic methods
➢Time series effects
➢ An OK guess for this quarter’s earnings (or revenues) is the company’s earnings (revenues) 4
quarters ago
➢ Companies tend to beat or miss repeatedly, as some manage expectations more heavily, so
one can use prior surprises to forecast upcoming surprises
➢Cross sectional effects
➢ More companies beat than miss, often by a small amount, so higher forecasts are more
accurate, all things being equal
➢ More recent estimates tend to be more accurate
➢ Top analysts (leaders, historically more accurate) are consistently better at forecasting
earnings
Bernard andThomas (1990), Jha and Mozes (2001)
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5. Newer methods – using traditional data
➢ Companies reporting on Fridays are more likely to miss
➢ Companies which report later than expected are more likely to miss
➢ Beats and misses tend to cluster in an industry or set of peer stocks during a
quarter – perhaps because of customer/supplier linkages
➢ Looking at guidance, variance of estimates, etc.
Johnson and So, 2017; Zhu 2014
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6. Newer methods – using traditional data
➢ Regress surprise % against: lateness of report; earnings growth; loss firm
dummy variable; variance of estimates; reporting on a Friday; companies issuing
new guidance; did the company beat guidance; prior earnings and Sales
surprises; and amount of coverage
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Variable DF Parameter
Estimate
Standard Error t Value Pr > |t|
Intercept 1 0.02876 0.00346 8.32 <.0001
howlate 1 -0.00387 0.00119 -3.24 0.0012
pctgrowth 1 0.01690 0.00493 3.43 0.0006
lossfirm 1 -0.01497 0.00625 -2.40 0.0166
varest 1 -0.02993 0.00737 -4.06 <.0001
friday 1 -0.00352 0.00319 -1.10 0.2702
newguidance 1 -0.01754 0.00491 -3.57 0.0004
guidsurp1 1 0.03799 0.01591 2.39 0.0170
surp1 1 0.19972 0.01840 10.85 <.0001
surp2 1 0.08743 0.01476 5.92 <.0001
surp3 1 0.04036 0.01502 2.69 0.0073
surp4 1 0.05763 0.01541 3.74 0.0002
surp5 1 -0.00019138 0.01444 -0.01 0.9894
surp6 1 -0.01992 0.01430 -1.39 0.1637
surp7 1 0.02506 0.01406 1.78 0.0747
surp8 1 0.01867 0.01347 1.39 0.1655
surpS1 1 0.01395 0.03741 0.37 0.7094
surpS2 1 0.02527 0.03944 0.64 0.5218
surpS3 1 -0.05069 0.03881 -1.31 0.1916
surpS4 1 0.09449 0.03617 2.61 0.0090
numests 1 -0.00049928 0.00021676 -2.30 0.0213

7. Newer methods – crowdsourcing
➢ Crowdsourced earnings estimates from Estimize are more accurate than
traditional sell side estimates
➢ Broader potential pool of contributors, ideas
➢ Possibly more free of biases such as arise from investment banking conflicts
➢ A crowdsourced consensus is also more representative of the market’s
expectations than is the sell side
➢ Reactions to earnings “surprises” calculated vs Estimize are larger than the responses to traditional, Wall Street-
based surprises
➢We can apply some of the same ideas to crowdsourced estimates as we do to
Sell Side estimates
➢ Look at recency, consistent top analysts, etc.
Drogen and Jha (Estimize 2013) , Luo et al (Deustche Bank 2014), Luo et al (Wolfe Research 2017), Gillam et al (2017)
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8. Newer methods – crowdsourcing
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9. Newer methods – new data sets
➢ Measures of consumer demand, foot traffic, transactions, etc. have been used
to predict earnings, revenues, and surprises
➢ Can be closer to real time
➢ Are often panel data representing only a subset of, or proxy for, company revenues
➢ Breadth and applicability vary widely
➢ Common example in the press: satellite images for predicting revenues of big box retailers
➢ Consumer-driven metrics tend to be better for predicting revenues than
predicting earnings
➢ They don’t tell you anything about the company’s cost structure
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10. Consumer demand example
➢The Digital Revenue Signal: uses measures of online consumer attention as a
proxy for demand
➢ Consumers research a company’s brands and products before purchasing
➢ So search – and web traffic, and social media engagement – can be leading indicators of revenues
➢ Underlying data from alpha-DNA (digital marketing data experts)
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11. DRS digital bureau
➢We need to map brand and product names, websites, and social media
properties up to the company (and therefore security) level to make this work
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12. DRS – accurate surprise predictions
➢Trends in Search, Site, and Social tend to predict revenue surprises and revenue
growth
➢ Because Wall Street analysts do not adjust their expectations in real time to reflect consumer demand information
➢This is true quarter after quarter (well into our out of sample and live periods)
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30%
40%
50%
60%
70%
80%
1 2 3 4 5 6 7 8 9 10
% Revenue beat by DRS decile
2012-2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017

13. …resulting in good return forecasts
➢ Accurate revenue growth and revenue surprise forecasts tend to lead to
accurate return forecasts
➢ Example dollar neutral portfolio built using DRS
➢… but not always! (note 2016)
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0%
10%
20%
30%
40%
50%
60%
10/22/2012
4/22/2013
10/22/2013
4/22/2014
10/22/2014
4/22/2015
10/22/2015
4/22/2016
10/22/2016
4/22/2017

14. When do fundamental forecasts work?
➢ a la “When Do Earnings Revisions Work” (StarMine white paper, 2004)
➢We can gain some insight from a perfect foresight test:
➢ What if we knew fundamental data (earnings, surprises, etc) with a crystal ball one month in advance?
➢ We should be able to make money, right?
➢ Would we always make money?
➢ If perfect foresight of fundamentals doesn’t result in a strong return prediction,
we shouldn’t expect even a very accurate prediction of fundamentals to also
predict returns
➢There could also be variation in how well our predictor forecasts fundamentals
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15. Perfect foresight ICs
➢ After the GFC, even a perfect view on future revenues wouldn’t have made you
money!
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0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
IC of perfect foresight
next_eps_surpriseD next_sale_surpriseD next_eps_growthD next_sales_growthD

16. Perfect foresight ICs
➢ …so we can’t expect our forecasts of fundamentals to work at such times
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-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
IC of trailing surprise and growth
prior_eps_surpriseD prior_sale_surpriseD prior_eps_growthD prior_sales_growthD

17. Perfect foresight ICs
➢ …even if they continue to accurately predict fundamentals
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-
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
Autocorrelations
EPS Surprise Sales Surprise EPS Growth Sales Growth

18. Explaining the forecast’s IC
➢ Regress the forecast’s time series IC against
➢ The autocorrelation in the factor by month
➢ The perfect foresight version’s IC
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19. Perfect foresight is weak in market “bounces”
➢ S&P down trailing 12 months, up trailing 1 month
. 19
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Perfect foresight ICs and market bounces
Bounce? next_eps_surpriseD next_sale_surpriseD
next_eps_growthD next_sales_growthD
EPS surprise Sales surprise EPS growth Sales growth
Overall 0.113 0.081 0.091 0.050
Bounce 0.091 0.056 0.029 0.008
Non bounce 0.116 0.084 0.099 0.055

20. An attempt at refining the timing…
➢ Regress time series of perfect foresight IC against:
PFICt = b1 * AFICt-1 Actionable factor’s prior month IC
+ b2 * SP12m1 t-1 S&P return month -12 through month -2
+ b3 * SP1t-1 S&P return prior month
+ b4 *Tbillt-1 3-monthT Bill rate
+ b5 * deltaTbillt-1 Prior month change inT Bill rate
+ b6 *VIXt-1 VIX level
+ b7 * deltaVIXt-1 Prior month change inVIX
+ Intercept
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21. …isn’t too convincing, but suggests “Risk On”
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EPS Surprise Sales surprise
Variable Parameter
Estimate
t Value Pr > |t| Parameter
Estimate
t Value Pr > |t| Perfect foresight works worst when…
Intercept 0.08 4.35 <.0001 0.06 4.25 <.0001
AFIC 0.01 0.10 0.92 0.07 0.72 0.47 Actionable factor did poorly,
SP12m1 0.03 0.93 0.36 0.05 1.57 0.12 Market has been down previous 12 months
SP1 0.01 0.09 0.93 (0.05) (0.45) 0.65 …but up the last 1 month,
Tbill 0.01 3.49 0.00 0.00 1.96 0.05 Interest rates are low
deltaTbill 0.04 1.20 0.23 0.02 0.98 0.33 …and falling.
VIX 0.00 1.03 0.31 0.00 0.58 0.56
deltaVIX (0.00) (1.13) 0.26 0.00 0.29 0.77
EPS Growth Sales Growth
Variable Parameter
Estimate
t Value Pr > |t| Parameter
Estimate
t Value Pr > |t|
Intercept 0.09 4.01 <.0001 0.04 2.17 0.03
AFIC 0.15 1.73 0.09 0.10 1.20 0.23
SP12m1 0.04 0.90 0.37 0.06 1.41 0.16
SP1 (0.18) (1.12) 0.26 (0.07) (0.51) 0.61
Tbill 0.01 1.85 0.07 0.00 1.04 0.30
deltaTbill 0.01 0.39 0.70 0.02 0.58 0.56
VIX (0.00) (0.79) 0.43 (0.00) (0.25) 0.80
deltaVIX 0.00 0.85 0.40 0.00 0.57 0.57

22. Factor timing is hard! What can be done?
➢ Identify unfavorable conditions
➢ Risk On / “Low quality rallies”
➢ Refine the idea using factor returns, macro variables, etc.
➢ Mitigate correlation of your predictor to trailing fundamentals like revenue growth
➢ So it will not be as affected when fundamentals are out of favor
➢ This might weaken it during more “normal” times however
. 22
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
IC of Digital Revenue Signal before and after growth bucketing
IC IC growthbucketed MA(IC) MA(IC growthbucketed)

23. Summary
➢ Predicting fundamentals can lead to robust models
➢ One can use traditional predictors
➢ …or some newer methods with traditional data
➢ …or alternative data.
➢These predictions need to be consistently good, but even when they are, the
market may not care
➢ Figuring out when the market cares or doesn’t is the hard part!
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24. www.extractalpha.com
vinesh@extractalpha.com
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Thank you!