Liquidity Factor
A-share liquidity factors: turnover, trading value and capacity
Liquidity factors use turnover, trading value, price impact or related measures. Low liquidity may carry a premium but can be impossible to capture after costs.
Typical direction
The desired direction depends on the definition; state whether the study targets liquidity or illiquidity.
Data
Price, volume, free-float shares and trading-status data
Refresh
Daily measures aggregated monthly or quarterly
Research hypothesis
Write the hypothesis before reading the backtest
Liquidity characteristics may contain pricing information, but capacity and implementation assumptions determine whether a signal is usable.
A factor is a testable research hypothesis, not an investment recommendation or return promise.
Factor health card
Pre-backtest checks for this factor
Research purpose
Test liquidity characteristics while keeping capacity explicit.
Refresh and rebalance
Higher frequency requires stronger participation and impact constraints.
Data timing
Use contemporaneous free float, price and volume data.
Neutralisation
Control for size because liquidity and market cap often move together.
Overlapping exposures
Often overlaps with size and low-volatility exposures.
Check before use
Set minimum trading value, participation and suspension filters.
Definitions
Core measures
Turnover
Traded shares ÷ free-float sharesFree-float definition must be time-consistent.
Trading value
Close price × traded volumeUseful for minimum-capacity filters.
Amihud illiquidity
|daily return| ÷ daily trading valueSensitive to small-value observations.
Participation rate
Strategy traded value ÷ market trading valueAn implementation constraint, not a return factor.
Research protocol
Keep the same research conventions across factors
Data availability
Financial, dividend and share data become available on actual disclosure or implementation dates, not report-period end dates.
Universe and exclusions
Document index membership, listing age, ST, suspensions, delistings and missing-data rules.
Processing and neutralisation
Version winsorisation, standardisation, sector/size neutralisation and missing-value rules.
Tradability
Include price limits, suspensions, participation, fees, slippage and market impact.
Out-of-sample review
Report IC, grouped returns, exposures, turnover and rolling out-of-sample evidence together.
Build and validate
What to test
- 1Compare liquid and illiquid definitions separately.
- 2Show capacity and turnover beside returns.
- 3Review micro-cap and price-limit exposure.
Common pitfalls
- ×Interpreting an illiquid micro-cap premium as deployable alpha.
- ×Using current free float for historical dates.
- ×Ignoring market-impact assumptions.
A-share implementation
A-share checks that belong in the backtest
- Use the actual disclosure or implementation date; do not make a field available at the report-period end date.
- State the universe, listing-age, ST, suspension, delisting and missing-data rules before running the backtest.
- Model price limits, suspensions, fees, slippage and participation limits instead of assuming every close can be traded.
- Low trading value and price-limit events can prevent the assumed portfolio from being filled.
Research prompt
A reviewable starting prompt
“In an A-share universe with a minimum trading-value filter, test turnover and Amihud measures with size controls. Apply participation limits, price-limit constraints and costs, then report capacity and net returns.”
FAQ
Does low liquidity imply higher return?
It can be associated with a premium in some samples, but the same illiquidity can make the strategy impractical to execute.
Why control for size?
Small stocks are often less liquid. Without a control, the result may simply be a size exposure.