Investor Psychology by bO8D5fks


									Time Varying Market
  Efficiency is dynamic
  We show this by looking at two
   efficiency metrics
      Short (intraday) horizon
      Longer-term (cross-section of monthly
       stock returns)
    We then draw implications from results
     on efficiency dynamics
Estimating short-horizon price efficiency

   We compute daily efficiency measures for individual
    stocks based on short-horizon return predictability
      Chordia, Roll & Subrahmanyam (2005, 2008)

   In particular, with RET being return, and OIB order
    imbalance, for each stock-day, we estimate efficiency
    as the R2 from the following regression:
Time-Variation in Short-
Horizon Efficiency (R2)
Funding Constraints and
Market Efficiency
  Profitability from growth-value,
   momentum, accounting profitability is
  Varies with flows to mutual funds and
   hedge funds that most exploit these
Trends in Efficiency of
 the Cross-Section of
Monthly Stock Returns
Why is there cross-sectional
return predictability?
      Risk

        –Should be stable
      Inefficiency

        –Should be unstable
We investigate how cross-sectional
predictability has changed in recent years

   Separately for liquid and illiquid stocks
   Separately for NYSE and Nasdaq
Why is the recent period
  Volume has increased to astonishingly
   high levels
  Spreads have decreased considerably
  What has been the effect of
   dramatically increased trading (about
   fourfold) and substantially reduced
   spreads (by about 90%) on cross-
   sectional return predictability?
Average turnover over time [Chordia, Roll,
Subrahmanyam (CRS) 2010]
Bid-ask spreads over time, for small
(<$10K) and large orders [CRS, 2010]
We investigate how predictability has

     Find that it has virtually disappeared for
      liquid stocks, but not for illiquid stocks
         Liquid/Illiquid generally defined as stocks
          with below/above-median values of
          Amihud (2002) illiquidity measure
     Findings hold across NYSE/AMEX and
Predictive variables
    Momentum (RET26, RET712)
    Turnover
    Book/Market
    Illiquidity
    Information-based characteristics
        Dispersion of analyst forecasts (DISP)
        SUE (earnings drift)
        Accounting Accruals (ACC)
NYSE/AMEX – Fama-MacBeth predictive
return regressions
Trend and turnover fits to
Fama-MacBeth coefficients
Trend and turnover fits to Fama-
MacBeth coefficients, contd.
Interpretation of trend
    Since RET26, RET712, and SUE
     positively predict returns, but DISP and
     ACC negatively predict returns, the
     trend coefficients indicate that all of
     these effects have become less material
     over time
Hedge Portfolio Returns- 5 Yr MA,
Hedge Portfolio Returns-5yr MA, Nasdaq
Exponential decay model
    Let x be the MA of Fama-MacBeth coefficient,
     a be its initial value and t be time
    x=a exp(-bt) or
    Ln(x/a)=-b t
    We can estimate the above model via OLS
     without intercept
    A positive b implies decay. We find that all b
     estimates are positive and most are highly
Estimates of decay model
(positive b means decay)
A portfolio approach that uses
the entire cross-section
  Based on Lehmann (1990) and Lewellen
  One dollar long (short) in stocks whose
   characteristics are above (below) cross-
   sectional mean:
Composite strategy
  Rank stocks by characteristic and assign
   percentile ranks
  Add percentile ranks to get composite
  Use this rank as characteristic in
   portfolio weight computation
Portfolio strategies over time,
individual components
Composite portfolio strategy
over time
Composite portfolio strategy
over time, by illiquidity
Monthly reversals, portfolio
Portfolio strategy with and
without 2008 and 2009
Potential critiques and
  Data mining? But out-of-sample evidence
   has confirmed the phenomena in other
   countries and time periods
  Statistical power issue? But both subperiods
   have identical time-periods and many
   anomalies are statistically significant in the
   first subperiod
  Results are supportive of the notion
   that arbitrage due to lower trading
   costs has improved market efficiency
  Market phenomena based on market
   inefficiency are unstable
  Perhaps new anomalies will arise even
   as old ones disappear
 The market seems to have become
  more efficient by conventional metrics
 But, unresolved issues:
     Is it an issue of academic research
      discovering anomalies or decreasing
      trading costs
     Are there efficiency cycles (anomalies
      arbitraged, disappear, arbitrage stops, they
      appear again)?
How should market efficiency
be taught/presented?
    It should be presented differently from
     a static concept. I.e.,
      Efficiency is indeed time-varying
      It also is non-stationary, and likely
       sensitive to time variation in liquidity

To top