Does the original 1963-1997 study identifying (Amihud) illiquidity as a stock pricing factor hold in recent data? In their December 2016 paper entitled “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects: A Replication”, Lawrence Harris and Andrea Amato replicate the original research and extend it to 1998-2015 data. As in the prior study, they: (1) each month measure Amihud illiquidity as the annual average ratio of a stock’s daily absolute return to its daily dollar volume; (2) use monthly regressions to relate stock illiquidity to next-month stock returns and other stock/firm characteristics; (3) quantify how next-month and next-year excess equally weighted stock market return varies with average expected (explained by autoregression) and unexpected (not explained by autoregression) stock illiquidity; and (4) compare the explanatory power of Amihud illiquidity to that of other simple illiquidity measures based on the same absolute returns and dollar volumes. Calculations exclude stocks with extreme (top and bottom 1%) illiquidities as unreliable. Using daily return and trading volume data and contemporaneous monthly characteristics for a broad sample of U.S. stocks during 1963 through 2015, they find that:
- In 1998-2015 data, average illiquidity is only a tenth that found in older data, likely a consequence of an increasingly liquid U.S. stock market.
- While consistently positive and statistically significant, regression coefficients indicating the strength of relationship between illiquidity and expected monthly return decline between 1963-1980 and 1981-1997, and between 1981-1997 and 1998-2015. In other words, illiquidity becomes less important as an individual stock pricing factor over time.
- Original and replicating research finds that expected (unexpected) average stock illiquidity relates positively (negatively) to future stock market excess return. During 1998-2015, however, the predictive power of expected average stock market illiquidity disappears, while the negative relationship with unexpected average stock market illiquidity persists (but is statistically less reliable).
- Other simple measures of illiquidity based on relationships between daily absolute return and daily trading volume perform very similarly to Amihud illiquidity.
In summary, evidence from recent data indicates that stock illiquidity based on relationships between absolute daily return and trading volume has substantially lost power to predict individual stock returns and stock market return. Average illiquidity unexplained by regression models may still usefully relate negatively to future stock market return.
Cautions regarding findings include:
- Analyses are gross, not net. Accounting for trading frictions and, if applicable, shorting costs implicit in any exploitative strategy would weaken findings. Also:
- The most illiquid stocks are likely the most costly to trade due to elevated bid-ask spreads, such that net findings would differ from gross findings.
- Illiquid stocks, by definition, cannot support large trades without material impact on price.
- Shorting of illiquid stocks may not be feasible.
- The tests in the paper are statistical. The authors consider no trading strategies to assess exploitability.
See also “Measuring the Stock Illiquidity Premium”, “The Illiquidity Premium Worldwide” and “Equity Market Liquidity as an Asset Allocation Signal”.
