Information Risk and Momentum Anomalies

1. Information Risk and Momentum Anomalies Chuan-Yang Hwang Nanyang Technological University, Singapore and Xiaolin Qian University of Macao

2. Motivation • Hwang and Qian (2011) construct a measure of information risk/asymmetry (ECIN) based on the price discovery of large trades. • ECIN has stronger power in predicting stock returns than all the well-known variables. • Failure to take into account of the information risk can lead to false discovery of anomalies. • In this paper, we illustrate this point with momentum anomalies.

3. Momentum Literature • Price Momentum • Jegadeesh and Titman (1993) document the phenomenon in which past winenrs continue to be winners, and past losers continue to be losers for up to 12 months. • Post Earnings Announcement Drift (PEAD • High standardized-unexpected-earnings (SUE) firms earn higher risk-adjusted returns than low SUE firms for up to 12 months. Ball and Brown (1968), Bernard and Thomas (1990). Mendenhall (2004), Chordia and Shivakumar(2006), Zhang (2008).

4. Momentum Literature • Price Momentum • Jegadeesh and Titman (1993) document the phenomenon in which past continue to be winners, and past losers continue to be losers for up to 12 months. • Post Earnings Announcement Drift (PEAD) • High standardized-unexpected-earnings (SUE) firms earn higher risk-adjusted returns than low SUE firms for up to 12 months. Ball and Brown (1968), Bernard and Thomas (1990). Mendenhall (2004), Chordia and Shivakumar(2006), Zhang (2008).

5. Momentum Literature • Rational Asset Pricing Models • Fama (1998): One of the biggest challenges to rational asset pricing. • Sadka (2006) Liquidity factor explains 60%-80% of the momentum in NYSE stocks. • Behavioural Models • Daniel, Hirshleifer and Subrahmanyam (1998), Barberis, Shleifer and Vishny (1998), Hong and Stein (1999). • Zhang (2006) Momentum is stronger among firms with larger information uncertainty.

6. Information Risk Hypotheses of Momentum • Losers (bad news firms) are less attractive to informed traders because it is more difficult and riskier to do arbitrage on them. • Losers (bad new firms) have lower information risk than Winners (good new firms) do. • If information risk is priced, then winner have higher returns than losers--known as momentum.

7. Testable Implications (I) • H(1) Losers have lower information risk than Winners do. • H(2) After adjusting for the information risk differential between Winners and Losers, momentum anomalies should weaken or disappear altogether.

8. Testable Implications (II) • High idiosyncratic volatility (IVOL) makes arbitrage on both Winner and Losers riskier, but the impact will be larger on Losers. • H(3) The information risk decreases with IVOL for both Winners and Losers. • H(4) The information risk differential between Winners and Losers, and hence the strength of momentum anomalies increases with IVOL. • Irrespective of the level of IVOL, momentum should weaken or disappear after adjusting for information risk.

9. Agenda Ahead • Introduce ECIN • Construct ECINF from ECIN • Show that EICNF is a priced factor through two-stage-cross-sectional-regression (2SCSR) test • Test the information risk hypothesis of momentum • Extension

10. Motivating ECIN • Price discovery of trades: private information is revealed in the sequence of trade prices. • Glosten and Milgrom (1985) and Kyle (1985): private information revealed by the informed trading. • Informed traders prefer to trade in medium to large size • Easley and O'Hara (1987) ,Barclay and Warner (1993) ,Battalio and Mendenhall (2005) and Hvidkjaer (2006). • Stocks whose large trade have larger price discovery will have higher information risk. • ECIN is the price discovery of large trades.

11. Estimating ECIN • Large and small trade prices are co-integrated since they are the prices of the same stock. • Co-integrated series can then be represented by a specific form of the Vector Error Correction Model (VECM) • the price discovery of trades can then be estimated as the error-correction coefficient from this VECM Harris et al. (1995), Hasbrouck (1995) , Eun and Sabherwal (2003) , Werner and Kleidon (1996) use VECM to study the price discovery in different markets

12. PL and PS are non-stationary I(1) but they are cointegrated. is stationary process with zero mean. Estimating ECIN (II) PL PL αLZt-1 αLZt-1 Zt-1 Zt-1 αSZt-1 αSZt-1 PS PS t-1 t Zt-1>0 Zt=0 Large Price Discovery More Liquid t-1 t Zt-1>0 Zt=0 Small Price Discovery Less Liquid

13. Data Samples • NYSE and Amex • January 1983-December 2006 for ECIN estimation • January 1984-December 2007 for asset pricing test • VECM is estimated for each firm in each calendar year

14. Large and Small Trade Classification • (1) For each stock i and in each month m, we sort individual trades by their dollar-based trade size. We then identify the 60th percentile of the dollar-based trade size for stock i in month m. • (2) The average of the monthly 60th percentiles over every five-year period beginning in 1983 are used as the criterion for the dollar-based trade size for stock i for that particular period. Say it is $20,000. • (3) We divide the dollar-based criterion from Step (2) by the closing price in month t-1 of stock i to obtain the number of round lot shares. This number of round lot shares is the share-based trade size criterion for stock i in month t. For example, if the price of stock i in 198506 is $10, then the share-based trade size cutoff is 20,000/10=2000 shares in 198507.

15. Prices Series of Large and Small Trades • MINISPAN (Harris, McInish, Shoesmith, and Wood (1995)) • Retrieve the matched pairs at fixed intervals of 20 minutes. S L S L S S S L S S L • Open and close trades are deleted

16. Yearly distribution of ECIN

17. Asset Pricing Tests

18. Different from liquidity effect • The strong liquidity effect dominates the positive ECIN in January. • High ECIN firms are more liquid, so liquidity effect weaken the ECIN effect, but the positive ECIN effect dominates the negative liquidity effect. • The liquidity effect is strongest in January due to tax loss selling

19. Creating the ECINF Factor ECIN

20. Creating the ECINF Factor Market Cap

21. Creating the ECINF Factor Stock Returns ECIN portfolio=(HECIN-LECIN)-(SSIZE-LSIZE)

22. Is ECINF a Priced Risk Factor ?

23. Is ECINF a Priced Risk Factor ?

24. Is ECINF a Priced Risk Factor ?

25. ECINF and Carhart’s UMD (I)

26. ECINF and Carhart’s UMD (II)

27. Momentum and Information Risk • H(1) Losers have lower information risk than Winners do. • H(2) After adjusting for the information risk differential between Winners and Losers, the momentum anomalies would weaken or disappear altogether.

28. Momentum

29. Momentum (The 2nd year)

30. Momentum and Idiosyncratic Volatility • H(3) The information risk decreases with idiosyncratic volatility for both good news and bad news firms. • H(4) The information risk differential between good news and bad news firms, and hence the momentum anomalies increase with idiosyncratic volatility.

31. Momentum and Idiosyncratic Volatility

32. Momentum and Idiosyncratic Volatility

33. Post Earning Announcement Drift

34. Momentum and Idiosyncratic Volatility

35. Momentum and Idiosyncratic Volatility

36. Post Earning Announcement Drift

37. PEAD and Idiosyncratic Volatility

38. PEAD and Idiosyncratic Volatility

39. Extension: Other Anomalies Explained by ECINF • SEO underperformance /Equity issuing puzzle /Asset growth anomaly • Lower information risk after issuing equity, SEO and asset growth • Idiosyncratic Volatility (IVOL) Puzzle/Dispersion Effect • High IVOL firms and firms with high disperison in A • Accrual Anomaly • High Accrual firms have lower information risk.

40. Conclusions • Construct an information risk factor ECINF and show it is is a priced factor. • ECINF fully explains both Price momentum and PEAD, because losers (bad news firms) have lower information risk. • ECINF also explains why momentum anomalies are stronger when information uncertainty (volatility) is higher. • ECINF can also explain a wide range of other anomalies