Time Varying Market Efficiency Efficiency is dynamic Efficiency is dynamic We show this by looking...
-
Upload
teresa-mathews -
Category
Documents
-
view
220 -
download
0
Transcript of Time Varying Market Efficiency Efficiency is dynamic Efficiency is dynamic We show this by looking...
Time Varying Market Time Varying Market EfficiencyEfficiency
Efficiency is dynamicEfficiency is dynamic We show this by looking at two We show this by looking at two
efficiency metricsefficiency metrics Short (intraday) horizonShort (intraday) horizon Longer-term (cross-section of monthly Longer-term (cross-section of monthly
stock returns)stock returns) We then draw implications from We then draw implications from
results on efficiency dynamicsresults on efficiency dynamics
Estimating short-horizon price Estimating short-horizon price efficiencyefficiency
We compute daily efficiency measures for We compute daily efficiency measures for individual stocks based on short-horizon return individual stocks based on short-horizon return predictabilitypredictability Chordia, Roll & Subrahmanyam (2005, Chordia, Roll & Subrahmanyam (2005,
2008)2008) In particular, with RET being return, and OIB In particular, with RET being return, and OIB
order imbalance, for each stock-day, we order imbalance, for each stock-day, we estimate efficiency as the Restimate efficiency as the R22 from the following from the following regression:regression:
Time-Variation in Short-Time-Variation in Short-Horizon Efficiency (RHorizon Efficiency (R22))
Funding Constraints and Funding Constraints and Market EfficiencyMarket Efficiency
Profitability from growth-value, Profitability from growth-value, momentum, accounting momentum, accounting profitability is time-varyingprofitability is time-varying
Varies with flows to mutual funds Varies with flows to mutual funds and hedge funds that most exploit and hedge funds that most exploit these anomaliesthese anomalies
Trends in Efficiency of the Cross-Section of
Monthly Stock Returns
Why is there cross-Why is there cross-sectional return sectional return predictability?predictability?
RiskRisk–Should be stableShould be stable
InefficiencyInefficiency–Should be unstableShould be unstable–arbitrageablearbitrageable
We investigate how cross-sectional We investigate how cross-sectional predictability has changed in recent predictability has changed in recent yearsyears
Separately for liquid and illiquid Separately for liquid and illiquid stocksstocks
Separately for NYSE and NasdaqSeparately for NYSE and Nasdaq
Why is the recent period Why is the recent period special?special?
Volume has increased to Volume has increased to astonishingly high levelsastonishingly high levels
Spreads have decreased considerablySpreads have decreased considerably What has been the effect of What has been the effect of
dramatically increased trading (about dramatically increased trading (about fourfold) and substantially reduced fourfold) and substantially reduced spreads (by about 90%) on cross-spreads (by about 90%) on cross-sectional return predictability?sectional return predictability?
Average turnover over time [Chordia, Roll, Average turnover over time [Chordia, Roll, Subrahmanyam (CRS) 2010]Subrahmanyam (CRS) 2010]
Bid-ask spreads over time, for small Bid-ask spreads over time, for small (<$10K) and large orders [CRS, (<$10K) and large orders [CRS, 2010]2010]
We investigate how predictability We investigate how predictability has changedhas changed
Find that it has virtually disappeared Find that it has virtually disappeared for liquid stocks, but not for illiquid for liquid stocks, but not for illiquid stocksstocks Liquid/Illiquid generally defined as Liquid/Illiquid generally defined as
stocks with below/above-median values stocks with below/above-median values of Amihud (2002) illiquidity measureof Amihud (2002) illiquidity measure
Findings hold across NYSE/AMEX Findings hold across NYSE/AMEX and Nasdaqand Nasdaq
Predictive variablesPredictive variables
Momentum (RET26, RET712)Momentum (RET26, RET712) TurnoverTurnover Book/MarketBook/Market IlliquidityIlliquidity Information-based Information-based characteristicscharacteristics
Dispersion of analyst forecasts (DISP)Dispersion of analyst forecasts (DISP) SUE (earnings drift)SUE (earnings drift) Accounting Accruals (ACC)Accounting Accruals (ACC)
NYSE/AMEX – Fama-MacBeth NYSE/AMEX – Fama-MacBeth predictive return regressionspredictive return regressions
Trend and turnover fits to Trend and turnover fits to Fama-MacBeth coefficientsFama-MacBeth coefficients
Trend and turnover fits to Fama-Trend and turnover fits to Fama-MacBeth coefficients, contd.MacBeth coefficients, contd.
Interpretation of trend Interpretation of trend coefficientscoefficients
Since RET26, RET712, and SUE Since RET26, RET712, and SUE positively predict returns, but DISP positively predict returns, but DISP and ACC negatively predict and ACC negatively predict returns, the trend coefficients returns, the trend coefficients indicate that all of these effects indicate that all of these effects have become less material over have become less material over timetime
Hedge Portfolio Returns- 5 Yr MA, Hedge Portfolio Returns- 5 Yr MA, NYSE/AMEXNYSE/AMEX
Hedge Portfolio Returns-5yr MA, Hedge Portfolio Returns-5yr MA, NasdaqNasdaq
Exponential decay modelExponential decay model
Let x be the MA of Fama-MacBeth Let x be the MA of Fama-MacBeth coefficient, a be its initial value and t be coefficient, a be its initial value and t be timetime
x=a exp(-bt) orx=a exp(-bt) or Ln(x/a)=-b tLn(x/a)=-b t We can estimate the above model via We can estimate the above model via
OLS without interceptOLS without intercept A positive b implies decay. We find that A positive b implies decay. We find that
all b estimates are positive and most all b estimates are positive and most are highly significantare highly significant
Estimates of decay model Estimates of decay model (positive b means decay)(positive b means decay)
A portfolio approach that A portfolio approach that uses the entire cross-uses the entire cross-sectionsection
Based on Lehmann (1990) and Based on Lehmann (1990) and Lewellen (2002)Lewellen (2002)
One dollar long (short) in stocks One dollar long (short) in stocks whose characteristics are above whose characteristics are above (below) cross-sectional mean:(below) cross-sectional mean:
Composite strategyComposite strategy
Rank stocks by characteristic and Rank stocks by characteristic and assign percentile ranksassign percentile ranks
Add percentile ranks to get Add percentile ranks to get composite characteristiccomposite characteristic
Use this rank as characteristic in Use this rank as characteristic in portfolio weight computationportfolio weight computation
Portfolio strategies over Portfolio strategies over time, individual time, individual componentscomponents
Composite portfolio Composite portfolio strategy over timestrategy over time
Composite portfolio Composite portfolio strategy over time, by strategy over time, by illiquidityilliquidity
Monthly reversals, Monthly reversals, portfolio strategyportfolio strategy
Portfolio strategy with and Portfolio strategy with and without 2008 and 2009without 2008 and 2009
Potential critiques and Potential critiques and defensesdefenses
Data mining? But out-of-sample Data mining? But out-of-sample evidence has confirmed the phenomena evidence has confirmed the phenomena in other countries and time periodsin other countries and time periods
Statistical power issue? But both Statistical power issue? But both subperiods have identical time-periods subperiods have identical time-periods and many anomalies are statistically and many anomalies are statistically significant in the first subperiodsignificant in the first subperiod
SummarySummary
Results are supportive of the Results are supportive of the notion that arbitrage due to lower notion that arbitrage due to lower trading costs has improved market trading costs has improved market efficiencyefficiency
Market phenomena based on Market phenomena based on market inefficiency are unstablemarket inefficiency are unstable
Perhaps new anomalies will arise Perhaps new anomalies will arise even as old ones disappeareven as old ones disappear
RemarksRemarks
The market seems to have become The market seems to have become more efficient by conventional metricsmore efficient by conventional metrics
But, unresolved issues:But, unresolved issues: Is it an issue of academic research Is it an issue of academic research
discovering anomalies or decreasing discovering anomalies or decreasing trading coststrading costs
Are there efficiency cycles (anomalies Are there efficiency cycles (anomalies arbitraged, disappear, arbitrage stops, arbitraged, disappear, arbitrage stops, they appear again)?they appear again)?
How should market How should market efficiency be efficiency be taught/presented?taught/presented?
It should be presented differently It should be presented differently from a static concept. I.e., from a static concept. I.e., Efficiency is indeed time-varying Efficiency is indeed time-varying It also is non-stationary, and likely It also is non-stationary, and likely
sensitive to time variation in liquiditysensitive to time variation in liquidity