Measuring liquidity

While liquidity is one of the key figure of financial markets, It seems to be very difficult to measure. Volume is sometime used as a proxy but can sometimes be completly irrelevant.

Could you point to relevant research on what data to use and how to compute the measure?

Answer

Volume merely indicates how much buy-side interest exists in a stock. For liquidity, the sell-side interest is more relevant, which implies the quote characteristics (the limit-order book).

In addition to the bid-ask spread, I look at the top-of-book quote size. Here's an example from BATS:

sym | bid    ask    bidsize asksize
----| -----------------------------
AAPL| 325.12 325.21  100     100    
MSFT|  24.70  24.71 3900    5900   

I can only buy \$32,521 worth of Apple without impacting price, as opposed to \$145,789 of Microsoft. So the slippage is smaller.

There are more sophisticated measurements for order book entries. I could look at the full book ("level II data") to see the depth of the order chain. I could look across multiple exchanges, which is what a smart order router must do anyway. I could even look at related asset classes if the investor's goal is merely to gain exposure to general risk.

To be really swanky, I could investigate dark pools, though that's harder since the quotes aren't displayed. For this, a quant would need historical data regarding how much has been executed in the past. That's one reason why the big banks have a competitive advantage in dark-pool aggregator algorithms: they have enough client flow to record execution patterns.