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ANOMALIES AND NEWS
JOEY ENGELBERG (UCSD)R. DAVID MCLEAN (GEORGETOWN)
JEFFREY PONTIFF (BOSTON COLLEGE)
3RD ANNUAL NEWS & FINANCE CONFERENCECOLUMBIA UNIVERSITY
MARCH 8, 2018
Academic research has uncovered many predictors of cross-sectional stock returns
E.g., long-term reversal, size, momentum, book-to-market, accruals, and post-earnings drift.
This “anomalies” research goes back to at least Blume and Husick (1973)
Yet 43 years later, academics still cannot agree on what causes this return predictability
Important Question: What explains cross-sectional return predictability?
Background and Motivation2
Theories of Stock Return Predictability3
Three popular explanations for cross-sectional predictability
Differences in discount rates, e.g., Fama (1991, 1998)
Mispricing, e.g., Barberis and Thaler (2003)
Data-mining, e.g., Fama (1998)
This Paper:
Uses 97 anomalies along with firm-specific news and earnings announcements to differentiate between the three explanations
The Discount Rate Story4
Cross-sectional return predictability is expected
The predictability may be surprising to academics, but it is not to other market participants
Ex-post return differences reflect ex-ante differences in discount rates
There are no surprises here
Ex-post returns were completely expected by rational investors ex-ante
E.g., Fama and French (1992, 1996)
Discount Rates and News5
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
-5 -4 -3 -2 -1 0 1 2 3 4 5
Anomaly Returns around an Earnings Announcement
Long
Short
Mispricing – Biased Expectations6
Investors have systematically biased expectations of cash flows and cash flow growth
Expectations are too high for some stocks, too low for others
The anomaly variables are correlated with such expectations
New information causes investors to update their beliefs, which corrects prices, and creates the return-predictability.
Goes to back to at least (Basu, 1977)
Mispricing and News7
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
-5 -4 -3 -2 -1 0 1 2 3 4 5
Anomaly Returns around an Earnings Announcement
Long
Short
Data Mining8
As Fama (1991) suggests, academics have likely tested thousands of variables
It’s not surprising to find that some predict returns in-sample
Realization of a “multiple testing bias” in empirical research dates at least back to Bonferroni (1935)
This is stressed more recently in the finance literature by Harvey, Lin, and Zhu (2015).
Mispricing vs. Data Mining9
Most anomalies focus on monthly returns
Stocks with high (low) monthly returns likely had good (bad) news during the month
A spurious anomaly would therefore likely perform better in-sample on earnings days and news days
Do anomaly strategies still have high returns on news and earnings days after controlling for this?
Our Findings10
Anomaly returns are higher by
7x on earnings announcement days
2x on corporate news days
Returns in Event Time (3-day
window)11
Financial Analysts12
We also examine financial analysts’ forecasts errors
For stocks in long portfolios, forecasts are too low
For stocks in the short portfolios, forecasts are too high
Interpretation – Difficult to Reconcile with Risk
13
Hard to tie stock-price reactions to firm-specific news to systematic risk
Anomalies do worse on days when macroeconomic news is announced
Anomalies do worse when market returns are higher, i.e., anomalies have a negative market beta
Risk cannot explain the analyst forecast error results
Interpretation – Not (just) Data Mining14
A spurious anomaly would likely perform better in-sample on earnings days and news days
However, controlling for contemporaneous monthly return, anomalies still perform better on news days
Out-of-sample anomalies perform better on news days and have the forecast error results
The relation between anomalies and news is stronger in small stocks
Interpretation – Consistent with Mispricing
15
The results are easy to explain with a simple behavioral theory of biased expectations
Expectations are too high for some stocks, too low for others
The anomaly variables are correlated with such expectations
New information causes investors to update their beliefs, which corrects prices, and creates the return-predictability.
The analyst forecast error results fit this framework too
Our Place in the Literature16
We build on previous studies showing anomalies predict returns on earnings announcement days
E.g., Chopra Lakonishok and Ritter (1992), La Porta et al. (1994), and Sloan (1996)
Edelen, Kadlec, and Ince (2015) – anomalies and institutions
Our paper:
Investigates 6 million news days that are not earnings announcements
Uses 97 anomalies – compare across anomaly types
Relates a large sample of anomalies to analyst forecast errors
Develops new data-mining tests
The Anomalies17
Choosing the Anomalies
The list is from McLean and Pontiff (2016)
The anomaly has to be documented in an academic study
Primarily top 3 finance journals
Can be constructed with COMPUSTAT, CRSP, and IBES data
Cross-sectional predictors only
The Anomalies18
97 in Anomalies in Total
Oldest: Blume and Husic (1973)
Stocks sorted each month into long and short quintiles
16 of the 97 variables are binary
Can be replicated with CRSP, COMPUSTAT and I/B/E/S
Average pairwise correlation of anomaly returns is low (.05)
The Sample19
Earnings announcements from COMPUSTAT
Corporate news from the Dow Jones Archive
Used in Tetlock (2010)
Sample period is 1979-2013
40,220,437 firm-day observations in total
The Sample20
Aggregate Anomaly Variables21
We construct 3 aggregate anomaly variables
The variables are the sum of the number of stock i’s anomaly portfolio memberships in month t
Long, Short, and Net
Net = Long - Short
Aggregate Anomaly Variables22
Variable Mean Std.
Dev.
Min Max
Long 8.61 5.07 0 35
Short 9.21 5.93 0 45
Net -0.61 6.10 -36 32
The Main Specification23
24
Main Specification
Economic Magnitudes25
Net = 10 Daily Basis Points
Annualized Buy and
Hold Return
No Earnings Day 2.59 6.7%
Earnings Day 22.39 75.7%
Long and Short Separately26
Economic Magnitudes27
Long = 10 Daily Basis Points
Annualized Buy and
Hold Return
No Earnings Day 3.69 9.7%
Earnings Day 25.61 90.5%
Short = 10 Daily Basis Points
Annualized Buy and
Hold Return
No Earnings Day -1.93 -5%
Earnings Day -21.55 -72%
Robustness28
Are the results related to a day of the week effect (Birru, 2016)?
Controlling for day-of-week does not alter our findings
Macroeconomic news (Savor and Wilson, 2016)?
Perhaps firm-specific news reflects systematic risk?
No, anomalies do worse on macro announcement days
Endogeneity of news?
Stock return volatility causes news?
We control for daily volatility and nothing changes
Anomaly Types29
The effects are robust across anomaly types
1. Event – Corporate events, changes in performance, downgrades
2. Fundamental – constructed only with accounting data
3. Market – Constructed only with market data and no accounting data
4. Valuation – Ratios of market values to fundamentals
Analyst Forecast Errors30
Biased expectations suggests biases in analysts’ earnings forecasts, risk does not
Forecasts should be too low for stocks on the long side of the anomaly portfolios.
Forecasts should be too high for stocks on the short side of the predictor portfolios.
Analysts’ Forecast Error31
Data Mining Tests32
A spurious anomaly would likely perform better in-sample on earnings days and news days
Stocks with high (low) monthly returns likely had good (bad) news during the month
Do anomaly strategies still have high returns on news and earnings days after controlling for this?
Data Mining Tests33
Data Mining Tests – Analyst Forecast
Errors34
Conclusions35
Evidence of cross-sectional return-predictability goes back at least 43 years to Blume and Husick (1973) – still disagreement over why
In this paper we provide evidence that the cross-section of stock returns is best explained by a cross-section of biased expectations.
Anomaly returns 9x on info days
Anomaly signal predicts analyst forecast errors
Difficult to explain the results with risk
Harder to rule out data mining, but it does not seem to explain the full effects