Behavioral Finance Economics 437

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”

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

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

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.”

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

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)

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

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

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.”

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.”

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

The End