1. Behavioral Finance
Economics 437

2. General Theme: Predictability
Fama French: “Cross Section” Article
Book/Market and Size => Stock Returns
“Value Investing” or “Contrarian Investing”
DeBondt Thaler: “Over Reaction”
Buy past losers; Sell past winners
Value? Investing; Contrarian Investing
Jegadeesh Titman: “Price Momentum”
Possibly “Earnings Momentum”

3. Ball & Brown 1986 Market “underreacts” to earnings surprises
Article generally ignored until Jagdeesh-Titman
Time span suggests that Ball-Brown effect may be the same thing as Jagdeesh-Titman

4. Jegadeesh and Titman (1993)
Relative strength strategies, sometimes called “earnings momentum” strategies
Find past winners and and past losers (using 3 to 12 month holding periods) generate gains (winners gain; losers lose)
Construct W portfolio and L portfolio
W-L (using 6 month periods) earns more than12 % better than market portfolio
Longer term portfolios do best in next 12 months
Interpretation in “event time”
Doesn’t work in January

5. Chan, Jegadeesh, Lakonishok 1996
Is it earnings? Is it price?
They 7.7 percent six month gap between winner portfolios and loser portfolios using price momentum.
Conclusion (page 1709): “ In general, the price momentum effect tends to be stronger and longer-lived than the earnings momentum effect.”

6. Chordia-Shivakumar, 2006
Is it “pricing momentum” or “earnings momentum” that drives the “under-reaction” phenomenon?
Conclude the earnings momentum is the key factor.
Price momentum variables are a “noisy proxy” for earnings momentum

7. Hong, Lee & Swaminathan 2003
Earnings Momentum is the real driver of price momentum
Systematic relationship between earnings momentum and future GDP growth – hence a “risk factor”
This matters, because if there is a risk factor, then momentum might be consistent with EMH (which price momentum generally is not)

8. Later Research on Predictability
For good summaries of the literature, read Haugen-Baker and Lakonishok, Vishny and Shleifer introductions

9. Begin with Haugen-Baker 1996
Their introduction is an excellent summary of the pros and cons of the “predictability” literature
Their empirical works is supportive of
Fama-French
DeBondt-Thaler
Jegadeesh-Titman

10. Now the Critics: Conrad and Kaul, 1993 (data 1926-1988)
“We show that the returns to the typical long-term contrarian strategy…are upwardly biased because they are calculated by cumulating single-period (monthly) returns over long intervals”
Returns from ”buy and hold” strategies: (-1.7 percent)
Ball, Kothari, Shanken 1995
“We document problems in measuring raw and abnormal five year contrarian portfolio returns. Their 163 percent mean return is due largely to their lowest-price quartile position…..A contrarian portfolio formed at June-end earns negative abnormal returns, in contrast with the December-end portfolio.”

11. Welch and Goyal, 2008
“We find that, by and large, these models have predicted poorly both in-sample and out-of-sample for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.”

12. Lakonishov, Shleifer, Vishny, 1994
Questions:
Do value stocks really beat out growth stocks (the F-F issue revisited)? Growth based on earnings growth, etc.
Are value stocks actually riskier
Is there a reason that value stocks do better?
Answers:
Yes, by 10 – 11 percent annually
No, they outperform is all periods
Yes, future earnings of value stocks are better than predictions – opposite for growth stocks

13. The End