Jie Zhang, HKPU Forecasted Earnings per Share and the Cross Section of Expected Returns Ling Cen...
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Transcript of Jie Zhang, HKPU Forecasted Earnings per Share and the Cross Section of Expected Returns Ling Cen...
Jie Zhang, HKPU
Forecasted Earnings per Share and the Cross Section of
Expected Returns
Ling CenK.C. John Wei
Hong Kong University of Science and TechnologyJie Zhang
The Hong Kong Polytechnic University
Jie Zhang, HKPU 2
Outline
Major Findings Motivations Data and Sample Empirical Results Potential Explanations
Risk vs. Mispricing Conclusions and Contributions
Jie Zhang, HKPU 3
Major Findings
This paper finds a surprisingly strong positive relation between the levels of analysts’ forecasted earnings per share (FEPS) and future stock returns
The FEPS anomaly survives a number of well-known cross-sectional effects, such as the size, value and earnings-to-price effects, and price and earnings momentum
Jie Zhang, HKPU 4
Motivations
Cross-sectional behavior of stock returns Related to market beta or systematic risk
CAPM --- Sharpe (1964); Lintner (1965) ICAPM --- Merton (1973) CCAPM --- Lucas (1978) etc.
Asset-pricing anomalies --- FF (1992, 1996) Value strategies based on E/P, C/P, B/M etc. Long-term contrarian and medium-term momentum
Fama’s (1976) joint hypothesis problem
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Motivations(continued)
Why asset-pricing anomalies are interesting? Because they help us to understand more deeply about risk and return! To identify unknown risk factors
e.g. liquidity risk or volatility risk To understand market efficiency
e.g. market friction, limits of arbitrage
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Motivations(continued)
The role of FEPS in predicting future returns Prior empirical studies investigating the
information content of earnings focus mainly on earnings surprises
The return predictability based on either EPS or FEPS per se is ignored
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Data and Sample
The basic sample: all NYSE, AMEX and Nasdaq-listed common stocks in the intersection of (a) the CRSP stock file, (b) the merged Compustat annual industrial file, and (c) the I/B/E/S unadjusted summary historical file
Sample period: Jan. 1983 – Dec. 2004 Criteria for each month-stock:
Sufficient data on price, size, B/M, return (including past six months), and FEPS
Price higher than $5 Positive Book value
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Data and Sample(continued)
712,563 stock-month observations, or an average of 2,699 stocks per month
Summary statistics (Table I) FEPS is highly correlated with Price, FE/P, and BPS
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Table I: Summary Statistics
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Empirical Results
Trading strategies based on FEPS 10 FEPS-sorted decile portfolios (Table II)
Future stock returns increase across deciles as FEPS increases
The profits mainly come from the short side High FEPS firms are large in size, high price,
greater analyst coverage, higher FE/P, higher FROE => less risky
FEPS is not related to B/M or past returns
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Table II: Portfolio Characteristics for Equally Weighted Forecasted Earnings Per Share Deciles
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Empirical Results(continued)
Trading strategies based on FEPS Cumulative returns to the FEPS anomaly
(Figure 1) Accumulated at a diminishing speed Not reversal up to 36 months
Monthly returns for different holding periods (Figure 2A&B) The abnormal return spreads disappear after 6
months
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Figure 1: Cumulative Returns to a Hedge Strategy of Buying the Highest FEPS Stocks and Selling the lowest FEPS Stocks
0%
5%
10%
15%
20%
25%
30%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Holding Period (in Month)
Cu
mu
lati
ve H
edge
Por
tfol
io R
etu
rns
(in
%)
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Figure 2A: Raw Monthly Returns to a Hedge Strategy of Buying the Highest FEPS Stocks and Selling the Lowest FEPS Stocks for Different Holding Periods
-0.5
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12
Hodling Period (in Month)
Raw
Mon
thly
Hed
ge P
ortf
olio
Ret
urns
(in
%)
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Figure 2B: Risk-Adjusted Monthly Returns to a Hedge Strategy of Buying the Highest FEPS Stocks and Selling the Lowest FEPS Stocks for Different Holding Periods
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1 2 3 4 5 6 7 8 9 10 11 12
Hodling Period (in Month)
Ris
k-A
dju
sted
Mon
thly
Hed
ge P
ortf
olio
Ret
urn
s(i
n %
)
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Empirical Results(continued)
Trading strategies based on FEPS FEPS strategies within five Size groups (Table IV) FEPS strategies within five Price groups (Table V) Overall, the abnormal returns to FEPS strategies are
robust after controlling for firm size, stock price (and analyst coverage)
The FEPS anomaly is greatest in stocks with small firm size, low price (and low analyst coverage)
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Table IV: Mean Portfolio Returns by Size and Forecasted Earnings Per Share
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Table V: Mean Portfolio Returns by Price and Forecasted Earnings Per Share
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Empirical Results(continued)
Trading strategies based on FEPS FEPS Strategies within 3×3 Size and Book-to-
Market Groups (Table VI) FEPS Strategies within 3×3 Size and
Momentum Groups (Table VII) The FEPS anomaly survives the book-to-
market effect and the price momentum The FEPS anomaly decreases with past
returns
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Table VI: Mean Portfolio Returns by Size, Book-to-Market, and Forecasted Earnings Per Share
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Table VII: Mean Portfolio Returns by Size, Momentum, and Forecasted Earnings Per Share
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Empirical Results(continued)
Regression tests Time-series regressions (Table III)
Risk-adjusted returns (Alpha) increase across FEPS decile portfolios as FEPS increases
Mixed risk profile The highest FEPS stocks behave like big, value stocks The lowest FEPS stocks behave like small, growth and loser
stocks
Fama-Macbeth cross-sectional regressions (Table IX) None of identified cross-sectional effects in returns captures
the FEPS effect Not driven by specific industries
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Table III: Time-Series Tests of Four-Factor Models for Equally Weighted Forecasted Earnings Per Share Deciles
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Table IX: Fama-MacBeth Regressions: Explaining the Cross-Section of Individual Stock Returns
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Empirical Results(continued)
Evidence on mispricing (Table VIII) Larger analyst forecast errors for low FEPS
stocks relative to high FEPS stocks Subsequent earnings surprises explain a
substantial proportion of the abnormal returns to FEPS strategies
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Table VIII: Forecast Errors and Earnings Surprises for Portfolios Classified by Size and Forecasted Earnings Per Share
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Empirical Results(continued)
Robustness checks Seasonality and subperiod analysis (Table X)
Similar January effect with momentum Countercyclical
Various measures of earnings Historical EPS; Time-weighted average of
forecasted EPS from the IBES detail file (similar results!)
total earnings (much weak!) Outliers? (No)
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Table X: Seasonality and Subperiod Analysis for Equally Weighted Forecasted Earnings Per Share Deciles
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Potential Explanations
Risk? Not easy to reconcile the FEPS anomaly with
an existing risk framework Firm characteristics Four-factor model Time-series pattern of the FEPS anomaly
However, strictly speaking, we cannot rule out the possibility that there is some unknown risk factor.
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Potential Explanations(continued)
Mispricing? The FEPS anomaly might capture systematic
errors-in-expectations of investors on EPS Ex ante forecast errors, i.e. (FEPS – Actual)/|Actual| Abnormal returns around future earnings
announcements Two key prerequisites
Psychological behavior of investors Limits of arbitrage
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Conclusions
Forecasted earnings per share (FEPS) has strong predictive power on future stock returns.
In particular, stocks with higher FEPS earn substantially higher future returns than stocks with lower FEPS, even after controlling for the market risk, the size, value, and earnings-to-price effects, and price and earnings momentum.
Time-series and cross-sectional patterns of the FEPS anomaly, as well as further evidence on forecast errors and abnormal returns around future earnings announcements supports the errors-in-expectations explanation that investors overvalue (undervalue) stocks when their expectations about EPS are low (high).
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Contributions of This Paper
This paper documents a novel asset-pricing anomaly that can be predicted by FEPS
This paper would open up a new field for scholars to study unknown risk factors and market efficiency