Asset Pricing Flashcards
Campbell and Shiller (1988) RFS
- Starting from Gordon 62 P = D/(r - g) rewritten as P/D = r - g,
- hypothesizes that log(D/P) should be a liner forecaster of discount rates & divided growth.
- Find some evidence in support of latter.
- “Show that aggregate stock market dividend yields or earning yields are positively correlated with subsequently observed returns over similar intervals.”
Chan and Chen (1991)
- Small, marginal firms likely fallen angels & highly leveraged. Dividends only cut at distress, thus if cut it is a sign firm marginal.
- H: small/large firms react differently to economic news b/c small firms (especially NYSE) tend to be marginal.
- R: log(size) loses explanatory power when div & lev added
- ⇒size proxies for “marginal firm” risk
Chen Roll Ross (1986) JB
- Only rewarded for rexposure to sys risk.
- Change(“d”) Rf ⇒d pricing ⇒ d discount rate(“DR”) ⇒ d returns
- d MU wealth⇒d pricing⇒ d E(Rm-Rf)⇒d DR⇒d R.
- H: Equity R = f(macro var, real asset returns).
- R: industrial production, d E(Rm-Rf), d term spread, d H-L bond spread significant.
Fama French 1992
Beta died in 1963. Size and B/M trump leverage and E/P in explaining the cross-section of returns. Prequel to HML and SMB and the FF3F asset pricing model.
Fama and French 1993
Birth of the 3-factor model. Rm-RF, SMB, HML, TERM, DEF explain variation in stock returns. TERM and DEF for bond returns. Thus, TERM and DEF link stock market to bond market. For a portfolio with no Rf, the three stock market factors explain returns.
Lakonishok Shleifer and Vishny 1994
Polar opposite to FF93/96 EHM still holds view. Assume investors irrational and that they bid up(down) price of Glamour (Value) stocks, those that have performed well (poorly) in the recent past. A contrarian investment strategy that buys and holds a portfolio of value stocks outperforms a portfolio of glamour stocks by 10% per year, while not being fundmentally riskier.
Haugen Baker 1996
Findings: 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.
Daniel Titman 1997
It is characteristics (B/M, size) not covariances (loadings on HML and SMB) that explain return dispersion. While they don’t seek an irrational explanation, they do oppose FF3F as capturing systematic risk. Moreover, since characteristics are idiosyncratic, result challenges market efficiency.
Campbell Vuolteenaho 2004
- Decomposes beta into bad (cash flow beta) and good (discount rate beta), with the former having a theoretically higher price of risk.
- Find that value stocks have a high bad beta and low good beta, with growth stocks having the converse.
- This explains why low total beta value stocks have higher returns than high total beta growth stocks.
Cochrane 2005
- in a way this is an update to the Roll (1977) critique; HML, SMB and the factors that followed may just be sample mean-variance efficient portfolios and, thus, explain asset returns merely as a consequence of the mathematics.
- In other words, they lack economic content without an intertermporal link to changes in the marginal value of wealth.
- Thus, the goal of finance should not just be to add to the growing list of factors, but to develop macro models that explain the expected returns of HML, SMB and their progeny, since these factors summarize the dispersion in asset returns.
Ghysels, Santa-Clara and Valkanov 2005
- Develop and implement a new estimator: the mixed data sampling (MIDAS) estimator.
- This regresses monthly aggregate return variance on the weighted sum of lagged squared daily returns, with the weights parameterized.
- find a positive and significant relationship between lagged squared daily returns and monthly return variance.
- find that positive return news has a persistent effect on variance while negative news has a more dramatic effect, but one that decays quickly.
Ang, Hodrick, Xing & Zhang 2006
- Gists: document volatility puzzle
- Stocks that load highly on innovations in aggregate volatility have low average returns in the cross section.
- Similarly, stocks with high idiosyncratic volatility have very low returns, but this is not explained by exposure to aggregate volatility, or by any of the popular factors.
Daniel Titman 2006
- We dispute this interpretation that size & B/M proxy for distress risk.
- stock’s future return is strongly negatively related to the “intangible” return, the component of its past return that is orthogonal to the firm’s past performance.
- Indeed, the book-to-market ratio forecasts returns because it is a good proxy for the intangible return.
Berk, Green, Naik 1999
- Theoretical model, centering around dynamics of asset turnover and, thus, dynamics of growth opportunities and assets in place.
- High firm performance relates to finding valuable investment opportunities. As they exploit these opportunities their sys risk changes.
- Model links to systematic risk and reproduces relationship between returns and B/M, market value, interest rates, s-t contrarian, l-t momentum.
Liew Vassalou 2000
Part of the FF3F is risk-based strand. Find that HML and SMB predict changes in GDP in several countries, though it does not for the U.S..
