ANOMALIES AND NEWS JOEY ENGELBERG (UCSD) R. DAVID MCLEAN - - PowerPoint PPT Presentation

ANOMALIES AND NEWS

JOEY ENGELBERG (UCSD)

• R. DAVID MCLEAN (GEORGETOWN)

JEFFREY PONTIFF (BOSTON COLLEGE) 3RD ANNUAL NEWS & FINANCE CONFERENCE COLUMBIA 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 Motivation

2

Theories of Stock Return Predictability

3

 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 Story

4

 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 News

5

• 0.015
• 0.01
• 0.005

0.005 0.01 0.015

• 5
• 4
• 3
• 2
• 1

1 2 3 4 5

Anomaly Returns around an Earnings Announcement

Long Short

Mispricing – Biased Expectations

6

 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 News

7

• 0.06
• 0.04
• 0.02

0.02 0.04 0.06

• 5
• 4
• 3
• 2
• 1

1 2 3 4 5

Anomaly Returns around an Earnings Announcement

Long Short

Data Mining

8

 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 Mining

9

 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 Findings

10

 Anomaly returns are higher by

 7x on earnings announcement days  2x on corporate news days

Returns in Event Time (3-day window)

11

Financial Analysts

12

 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 Mining

14

 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 Literature

16



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 Anomalies

17

 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 Anomalies

18

 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 Sample

19

 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 Sample

20

Aggregate Anomaly Variables

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 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 Variables

22

Variable Mean Std. Dev. Min Max Long 8.61 5.07 35 Short 9.21 5.93 45 Net

• 0.61

6.10

• 36

32

The Main Specification

23

24

Main Specification

Economic Magnitudes

25

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 Separately

26

Economic Magnitudes

27

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%

Robustness

28



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 Types

29

 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 Errors

30

 Biased expectations suggests biases in analysts’

earnings forecasts, risk does not

 Forecasts should be too low for stocks on the long side

• f the anomaly portfolios.

 Forecasts should be too high for stocks on the short

side of the predictor portfolios.

Analysts’ Forecast Error

31

Data Mining Tests

32

 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 Tests

33

Data Mining Tests – Analyst Forecast Errors

34

Conclusions

35

 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