# How do I calculate approximate equity liquidity?

I am a developer rather than a quant. I need to decide whether a given equity passes some basic liquidity threshold.

It doesn't have to be precise, just good enough. I have a Bloomberg terminal data feed access (e.g. can get PX_LAST, VOLUME etc.).

Someone suggested using average volume multiplied by the price. Is this a good idea?

You can use refined methodologies but if you just need a rough estimation of liquidity, you can simply use an average of daily volume over N days. In practice, for equities, people tend to use N = 20 or 30.

Once you have the average daily volume (say 100,000 shares), you compare it to your holding (say 50,000 shares) to determine the the size of your position (in my example: 0.5 days of volume).

It is important to decide which volume to use (primary exchange vs. all exchange volume - on some securities the latter can be 2x or 3x the former).

You can then qualify the liquidity of the holding by making an assumption on the % of volume you think you can trade without impacting prices too much. 20-25% is typical although some argue that for larger positions 15% is already disruptive.

Let's say you think you can use 25% of the daily volume, in my previous example, you would conclude that it would take ca. 2 days to liquidate the position. Or put another way that you would be able to liquidate about 50% of the position in one day.

• Thank you. I have two further related questions, if you don't mind: 1. How useful is bid-ask spread for checking liquidity? 2. Is it possible to apply your suggestion to compare stocks' liquidity without having/planning a specific position? E.g. how do I compare liquidity of APPL to MSFT to GOOG without considering a specific trade? The reason I ask is that I am trying to decide whether a stock is "good" or "bad" to begin with.
– Den
Commented Aug 21, 2015 at 10:16

I would consider Amihud (2002) as a good first approximation with that level of data.

I think one of the main liquidity measures is the one from Pastor and Stambaugh (2003).

You can use it for both individual stocks or indexes.

Just run the following intra-month regression with daily data:

$$r^e_{i,d+1,t} = \theta_{i,t}+\phi_{i,t}r_{i,d,t}+\gamma_{i,t}sign(r^e_{i,d,t}) \times v_{i,d,t}+\epsilon_{i,d+1,t}$$.

Where $$r^e_{i,d+1,t}$$ is the excess stock return of stock $$i$$ at day $$d+1$$ of month $$t$$ and $$v_{i,d,t}$$ the dollar volume for stock $$i$$ on day $$d$$ in month $$t$$.

Check equation (1) of the paper for further details.