1. Anomalies and NEWS Joey engelberg (UCSD) R
Anomalies and NEWS Joey engelberg (UCSD) R. David McLean (Georgetown) Jeffrey Pontiff (BOSTON College) 11th Annual Hedge Fund Conference December 8, 2016

2. Background and Motivation
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
See the 2013 Nobel Prize
Important Question: What explains cross-sectional return predictability?

3. Theories of Stock Return Predictability
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

4. The Discount Rate Story
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)

5. Discount Rates and News

6. Mispricing – Biased Expectations
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)

7. Mispricing and News

8. Data Mining
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).

9. Mispricing vs. Data Mining
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?

10. Our Findings Anomaly returns are higher by
7x on earnings announcement days
2x on corporate news days

11. Returns in Event Time (3-day window)

12. Financial Analysts
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

13. Interpretation – Difficult to Reconcile with Risk
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

14. Interpretation – Not (just) Data Mining
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

15. Interpretation – Consistent with Mispricing
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

16. Our Place in the Literature
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

17. The Anomalies 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

18. The Anomalies 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)

19. The Sample Earnings announcements from COMPUSTAT
Corporate news from the Dow Jones Archive
Used in Tetlock (2010)
Sample period is
40,220,437 firm-day observations in total

20. The Sample

21. Aggregate Anomaly Variables
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

22. Aggregate Anomaly Variables
Mean
Std. Dev.
Min
Max
Long
8.61
5.07 35
Short
9.21
5.93 45
Net
-0.61
6.10
-36 32

23. The Main Specification

24. Main Specification

25. Annualized Buy and Hold Return
Economic Magnitudes
Net = 10
Daily Basis Points
Annualized Buy and Hold Return
No Earnings Day
2.59
6.7%
Earnings Day
22.39
75.7%

26. Long and Short Separately

27. Annualized Buy and Hold Return Annualized Buy and Hold Return
Economic Magnitudes
Long = 10
Daily Basis Points
Annualized Buy and Hold Return
No Earnings Day
3.69
9.7%
Earnings Day
2.56
90.5%
Short = 10
Daily Basis Points
Annualized Buy and Hold Return
No Earnings Day
-1.93
-5%
Earnings Day
-19.62
-72%

28. Robustness
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

29. Anomaly Types The effects are robust across anomaly types
Event – Corporate events, changes in performance, downgrades
Fundamental – constructed only with accounting data
Market – Constructed only with market data and no accounting data
Valuation – Ratios of market values to fundamentals

30. Robust Across Anomaly Types

31. Analyst Forecast Errors
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.

32. Analysts’ Forecast Error

33. Data Mining Tests
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?

34. Data Mining Tests

35. Data Mining Tests – Analyst Forecast Errors

36. Conclusions
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