Forecasted Earnings per Share and the Cross Section of Expected Returns

1. Forecasted Earnings per Share and the Cross Section of Expected Returns Ling Cen K.C. John Wei Hong Kong University of Science and Technology Jie 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 Jie Zhang, HKPU

5. 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 Jie Zhang, HKPU

6. 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 Jie Zhang, HKPU

7. 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 Jie Zhang, HKPU

8. 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 Jie Zhang, HKPU

9. Table I: Summary Statistics Jie Zhang, HKPU

10. 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 Jie Zhang, HKPU

11. Table II: Portfolio Characteristics for Equally Weighted Forecasted Earnings Per Share Deciles Jie Zhang, HKPU

12. 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 Jie Zhang, HKPU

13. Figure 1: Cumulative Returns to a Hedge Strategy of Buying the Highest FEPS Stocks and Selling the lowest FEPS Stocks Jie Zhang, HKPU

14. 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 Jie Zhang, HKPU

15. 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 Jie Zhang, HKPU

16. 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) Jie Zhang, HKPU

17. Table IV: Mean Portfolio Returns by Size and Forecasted Earnings Per Share Jie Zhang, HKPU

18. Table V: Mean Portfolio Returns by Price and Forecasted Earnings Per Share Jie Zhang, HKPU

19. 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 Jie Zhang, HKPU

20. Table VI: Mean Portfolio Returns by Size, Book-to-Market, and Forecasted Earnings Per Share Jie Zhang, HKPU

21. Table VII: Mean Portfolio Returns by Size, Momentum, and Forecasted Earnings Per Share Jie Zhang, HKPU

22. 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 Jie Zhang, HKPU

23. Table III: Time-Series Tests of Four-Factor Models for Equally Weighted Forecasted Earnings Per Share Deciles Jie Zhang, HKPU

24. Table IX: Fama-MacBeth Regressions: Explaining the Cross-Section of Individual Stock Returns Jie Zhang, HKPU

25. 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 Jie Zhang, HKPU

26. Table VIII: Forecast Errors and Earnings Surprises for Portfolios Classified by Size and Forecasted Earnings Per Share Jie Zhang, HKPU

27. 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) Jie Zhang, HKPU

28. Table X: Seasonality and Subperiod Analysis for Equally Weighted Forecasted Earnings Per Share Deciles Jie Zhang, HKPU

29. 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. Jie Zhang, HKPU

30. 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 Jie Zhang, HKPU

31. 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). Jie Zhang, HKPU

32. 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 Jie Zhang, HKPU