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					Investor sentiment and the
cross-section of stock returns


  Malcolm Baker – HBS
  Jeffrey Wurgler – NYU Stern
Introduction
 Classical finance theory
   ―Investor sentiment‖ doesn’t affect prices, because
     the demands of any sentimental investors are
     neutralized by arbs

 Challenges to classical theory
   Clear violations of market efficiency (momentum,
    post-earnings announcement drift, index inclusion
    effects, negative stub values, etc.)

 This paper
   Theory and evidence that investor sentiment is real,
     measurable time-series phenomenon that and that it
     has pervasive cross-sectional effects
Theory
 What is ―investor sentiment‖? Does it affect different
  stocks in different ways?

 Observation
   Mispricings are invariably caused by two factors
   1. An uninformed (e.g. ―sentimental‖) demand shock
   2. A binding constraint on arbitrage

 Implication
   For a wave of sentiment to have cross-sectional
    effects—not just cause equal mispricings across all
    stocks—factor 1, 2, or both, must vary across stocks
Cross-sectional variation in
sentiment
   One potential definition of sentiment: the marginal
    investor’s propensity to speculate

   Then sentiment is the relative demand for intrinsically
    speculative stocks, and thus causes cross-sectional effects
    even when arbitrage is equally difficult across stocks.

   What is an “intrinsically speculative” stock? A stock
    with a highly subjective/uncertain valuation

   Prediction: stocks whose valuations are most subjective
    – canonical young, unprofitable, extreme-growth potential
    stock, or a distressed stock – will be especially sensitive to
    fluctuations in propensity to speculate
Cross-sectional variation in
arbitrage
   Another potential definition of sentiment: marginal
    investor’s (over-) optimism or (over-)pessimism about
    stocks in general.

   By this definition, indiscriminate waves of sentiment will still
    affect the cross-section to the extent that arbitrage forces
    are weaker in certain subsets of stocks.

   Arbitrage limits that vary across stocks: fundamental risks,
    transaction costs/liquidity, short-selling costs, predatory
    trading risks, noise-trader risks, etc.

   Prediction: time-varying optimism or pessimism has
    biggest effects on stocks that are hardest to arbitrage
Main hypothesis
 Observation: Roughly speaking, the
  same stocks that are the hardest to
  arbitrage are also the most
  speculative /hardest to value

 Robust prediction: Young, small,
  unprofitable, extreme-growth and
  distressed stocks are most
  sensitive to fluctuations in
  investor sentiment
Anecdotal history of investor
sentiment, 1961-2002

   ―high sentiment‖ period  demand for speculative stocks

   ―low sentiment‖ period  demand for safety, ―quality‖
    stocks

       1960-61 ―tronics‖ small, growth stocks bubble

       1967-69 small, growth stocks bubble

       early 1970’s ―nifty fifty‖ bubble

       late 1970’s through mid-1980’s small, sometimes industry-
        concentrated bubbles, e.g. biotech, oil

       late 1990’s Internet bubble
Empirical approach
 Mispricing is hard to identify directly. Our
  approach is to look for systematic patterns
  of correction of mispricings.

 E.g., if returns on young and unprofitable
  firms are low when beginning-of-period
  sentiment is estimated to be high – may
  represent the correction of a bubble in
  growth stocks. Ex post evidence of ex ante
  mispricing.
Measuring investor sentiment
 We consider six proxies – the average
  discount on closed-end equity funds, NYSE
  share turnover, the number of and average
  first-day returns on IPOs, the equity share
  in new issues, and the dividend premium

 To smooth out noise, we also form a
  composite index based on their first
  principal component:

 Sentiment proxies are annual, 1962
  through 2001
Sentiment Index
3.0                                                            3.0

2.0                                                            2.0

1.0                                                            1.0

0.0                                                            0.0

-1.0                                                           -1.0

-2.0                                                           -2.0

-3.0                                                           -3.0
       1962   1967   1972   1977   1982   1987   1992   1997
Conditional predictability:
Size portfolios
2.5
2.0
1.5
1.0
0.5
0.0                        1
-0.5
-1.0
-1.5
-2.0
       1   2   3   4   5       6   7   8   9   10
Conditional predictability:
Volatility portfolios
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
       1   2   3   4   5   6   7   8   9   10
Conditional predictability:
Sales growth portfolios
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
       1   2   3   4   5   6   7   8   9   10
Et cetera
 Patterns are not due to time-varying betas
  or plausible patterns of compensation for
  systematic risk

 (The EMH explanation would require that
  older, profitable, dividend-paying, and less-
  volatile firms are (when sentiment is high)
  actually require higher returns than
  younger, unprofitable, nonpaying, highly-
  volatile firms. Very counterintuitive.)
Conclusion
 ―Investor sentiment‖ is a real, measurable
  phenomenon. It has large effects on the
  cross-section of stocks.
 Several novel findings emerge –
  characteristics that have no unconditional
  predictive power have much power once
  one conditions on sentiment!
 Approach embraces, rather than ignores,
  evidence of bubbles and crashes

				
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posted:5/21/2010
language:English
pages:15