Behavioral Finance

Behavioral Finance Economics 437

Spring Picnic Sunday April 291 PM to 3 PM • Live band • Burgers and Hot Dogs • Maybe a Chinese Buffet? • Everyone Invited • Address is just past Foxfield on the right • 2505 Hunt Country Lane • Need volunteers at 12 noon

Definition of absence of serial correlation Let pt-1, pt-2, pt-3, etc. be a series of past prices Now, think about, pt E[ pt | info, pt-1, pt-2, pt-3, etc.] = E[ pt | info] Then, no serial correlation

“Cross Section” vs “Time Series” • Cross Section • Pick a date (or a time period) • Collect data only for that date (or time period) • Explain variations in the data for that time period only • Time Series • Pick a stock • Collect data for that stock over many time periods • F-F and J-T are about explaining “cross section” difference in returns

W. Schwert (2002) Working Paper at Univ of Rochester ‘‘ …the size effect, the value effect, the weekend effect, and the dividend yield effect seem to have weakened or disappeared after the papers that highlighted them were published.’’

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

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

What is a “factor”model • CAPM is a “one factor” model • Ri = α + βi [RM] + ei • The “factor” is the market return, RM • Beta connects the factor to the individual stock return, Ri • Generally, a “multi-factor” model is: • Ri = α + βi1F1 + βi2F2 + βi3F3 + ei • This is a “three-factor” model • Factors might be GDP growth, change in treasury yields, change in inflation, etc.

“We find that the determinants of the cross-section of expected stock returns are stable in their identity and influence from period to period and from country to country. Out-of-sample predictions of expected return are strongly and consistently accurate. Two findings distinguish this paper from others in the contemporary literature: First, stocks with higher expected and realized rates of return are unambiguously lower in risk than stocks with lower returns. Second, the important determinants of expected stock returns are strikingly common to the major equity markets of the world. Overall, the results seem to reveal a major failure in the Efficient Markets Hypothesis.” From the Abstract of the Haugen, Baker Paper

Haugen-Baker, 1996 • Great summary of the literature • A Grand Synthesis • Use multi-factor model to create a “generalized” value portfolio • Incorporate J-T effects • 20 percent outperformance for H-B synthesis • Data used from five countries: France, Germany, Japan, UK, US

Hanna-Ready, 2005 • Dispute H-B results due to monthly turnover (40 percent) in HB rebalancing (causes high transaction costs) • Conclude that six month rebalancing of F-F portfolios is best • Most of H-B results come from J-T. J-T results fall if transaction costs considered • Cannot explain why F-F does so well

The End

Behavioral Finance