Time Varying Market Efficiency n Efficiency is dynamic

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Time Varying Market Efficiency n Efficiency is dynamic n We show this by looking

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

Estimating short-horizon price efficiency n We compute daily efficiency measures for individual stocks based

Estimating short-horizon price efficiency n We compute daily efficiency measures for individual stocks based on short-horizon return predictability n Chordia, Roll & Subrahmanyam (2005, 2008) n In particular, with RET being return, and OIB order imbalance, for each stock-day, we estimate efficiency as the R 2 from the following regression:

Time-Variation in Short. Horizon Efficiency (R 2)

Time-Variation in Short. Horizon Efficiency (R 2)

Funding Constraints and Market Efficiency n Profitability from growth-value, momentum, accounting profitability is time-varying

Funding Constraints and Market Efficiency n Profitability from growth-value, momentum, accounting profitability is time-varying n Varies with flows to mutual funds and hedge funds that most exploit these anomalies

Trends in Efficiency of the Cross-Section of Monthly Stock Returns

Trends in Efficiency of the Cross-Section of Monthly Stock Returns

Why is there cross-sectional return predictability? n Risk –Should be stable n Inefficiency –Should

Why is there cross-sectional return predictability? n Risk –Should be stable n Inefficiency –Should be unstable –arbitrageable

We investigate how cross-sectional predictability has changed in recent years n Separately for liquid

We investigate how cross-sectional predictability has changed in recent years n Separately for liquid and illiquid stocks n Separately for NYSE and Nasdaq

Why is the recent period special? n Volume has increased to astonishingly high levels

Why is the recent period special? n Volume has increased to astonishingly high levels n Spreads have decreased considerably n What has been the effect of dramatically increased trading (about fourfold) and substantially reduced spreads (by about 90%) on crosssectional return predictability?

Average turnover time [Chordia, Roll, Subrahmanyam (CRS) 2010]

Average turnover time [Chordia, Roll, Subrahmanyam (CRS) 2010]

Bid-ask spreads over time, for small (<$10 K) and large orders [CRS, 2010]

Bid-ask spreads over time, for small (<$10 K) and large orders [CRS, 2010]

We investigate how predictability has changed n Find that it has virtually disappeared for

We investigate how predictability has changed n Find that it has virtually disappeared for liquid stocks, but not for illiquid stocks n Liquid/Illiquid generally defined as stocks with below/above-median values of Amihud (2002) illiquidity measure n Findings Nasdaq hold across NYSE/AMEX and

Predictive variables n n n Momentum (RET 26, RET 712) Turnover Book/Market Illiquidity Information-based

Predictive variables n n n Momentum (RET 26, RET 712) Turnover Book/Market Illiquidity Information-based characteristics n n n Dispersion of analyst forecasts (DISP) SUE (earnings drift) Accounting Accruals (ACC)

NYSE/AMEX – Fama-Mac. Beth predictive return regressions

NYSE/AMEX – Fama-Mac. Beth predictive return regressions

Trend and turnover fits to Fama-Mac. Beth coefficients

Trend and turnover fits to Fama-Mac. Beth coefficients

Trend and turnover fits to Fama. Mac. Beth coefficients, contd.

Trend and turnover fits to Fama. Mac. Beth coefficients, contd.

Interpretation of trend coefficients n Since RET 26, RET 712, and SUE positively predict

Interpretation of trend coefficients n Since RET 26, RET 712, 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, NYSE/AMEX

Hedge Portfolio Returns- 5 Yr MA, NYSE/AMEX

Hedge Portfolio Returns-5 yr MA, Nasdaq

Hedge Portfolio Returns-5 yr MA, Nasdaq

Exponential decay model n n n Let x be the MA of Fama-Mac. Beth

Exponential decay model n n n Let x be the MA of Fama-Mac. Beth 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 significant

Estimates of decay model (positive b means decay)

Estimates of decay model (positive b means decay)

A portfolio approach that uses the entire cross-section n Based on Lehmann (1990) and

A portfolio approach that uses the entire cross-section n Based on Lehmann (1990) and Lewellen (2002) n One dollar long (short) in stocks whose characteristics are above (below) crosssectional mean:

Composite strategy n Rank stocks by characteristic and assign percentile ranks n Add percentile

Composite strategy n Rank stocks by characteristic and assign percentile ranks n Add percentile ranks to get composite characteristic n Use this rank as characteristic in portfolio weight computation

Portfolio strategies over time, individual components

Portfolio strategies over time, individual components

Composite portfolio strategy over time

Composite portfolio strategy over time

Composite portfolio strategy over time, by illiquidity

Composite portfolio strategy over time, by illiquidity

Monthly reversals, portfolio strategy

Monthly reversals, portfolio strategy

Portfolio strategy with and without 2008 and 2009

Portfolio strategy with and without 2008 and 2009

Potential critiques and defenses Data mining? But out-of-sample evidence has confirmed the phenomena in

Potential critiques and defenses Data mining? But out-of-sample evidence has confirmed the phenomena in other countries and time periods n Statistical power issue? But both subperiods have identical time-periods and many anomalies are statistically significant in the first subperiod n

Summary n Results are supportive of the notion that arbitrage due to lower trading

Summary n Results are supportive of the notion that arbitrage due to lower trading costs has improved market efficiency n Market phenomena based on market inefficiency are unstable n Perhaps new anomalies will arise even as old ones disappear

Remarks n The market seems to have become more efficient by conventional metrics n

Remarks n The market seems to have become more efficient by conventional metrics n But, unresolved issues: Is it an issue of academic research discovering anomalies or decreasing trading costs n Are there efficiency cycles (anomalies arbitraged, disappear, arbitrage stops, they appear again)? n

How should market efficiency be taught/presented? n It should be presented differently from a

How should market efficiency be taught/presented? n It should be presented differently from a static concept. I. e. , Efficiency is indeed time-varying n It also is non-stationary, and likely sensitive to time variation in liquidity n