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