Estimating Equity Liquidity from Trading Volume
Summary
The document presents practical ways to estimate whether an equity can accommodate a position when only basic market data is available. Its simplest method averages daily share volume across roughly 20 or 30 days, then compares the position size with that average to estimate how many days of trading the position represents. The example also applies a participation limit: assuming a trader can use only a portion of daily volume gives a more cautious liquidation-time estimate.
The notes stress that volume selection matters, since exchange-specific and consolidated totals can differ substantially. They also mention Amihud’s measure as a possible first approximation and the Pastor–Stambaugh approach, which estimates liquidity using a regression involving excess returns, lagged returns, signed returns, and dollar volume. No comparative results or implementation details are supplied for those measures. The volume-based estimate is therefore a screening tool, not a precise forecast: the assumed tradable share of volume is subjective, and actual price impact and liquidation time can vary with market conditions and security characteristics.
Key ideas
- Average daily share volume over a recent window can provide a basic equity liquidity screen.
- Compare the intended position with average volume to estimate its size in days of trading.
- Apply a participation assumption to estimate liquidation time while limiting expected market impact.
- Choose between primary-exchange and consolidated volume carefully because the totals can differ.
- Amihud and Pastor–Stambaugh measures offer alternatives when a more formal liquidity estimate is needed.
Tags
Full text
# How do I calculate approximate equity liquidity?
# 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?
## Answer by assylias (score 4, accepted)
https://quant.stackexchange.com/a/19410
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.
## Answer by user25064 (score 2)
https://quant.stackexchange.com/a/19408
I would consider Amihud (2002) as a good first approximation with that level of data.
## Answer by phdstudent (score 1)
https://quant.stackexchange.com/a/19409
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.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.