Measuring liquidity

Question

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 1

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.

Answer 2

For my master thesis, I used the bid-ask spread as a liquidity measure. Intuitively, it is the price to the have the liquidity (or even the price of liquidity); the bigger the bid-ask spread, the lower the liquidity.

I know that Carlo Acerbi of MSCI is also looking into liquidity risk management and has a very interesting model for liquidity which is explained in this presentation. Maybe it can give you some ideas.

Answer 3

You can have a look at what the guys at Nanex.

Here is an example of what they look at. The chart is colour coded for market depth (the colder the colour the less depth)

Answer 4

You may also be interested in a series of papers by Easley, de Prado, and O'Hara (2011), Flow Toxicity and Volatility in a High Frequency World. This paper follows up on a measure of the effect of trades on prices developed by two of the authors in 1987. They show that the new measure, which takes volume and concurrent price movements into account, can predict rapid changes in liquidity such as the "flash crash". From the abstract (published in JPM Winter 2011):

The ‘flash crash’ of May 6th 2010 was the second largest point swing (1,010.14 points) and the biggest one-day point decline (998.5 points) in the history of the Dow Jones Industrial Average. For a few minutes, $1 trillion in market value vanished. In this paper, we argue that the ‘flash crash’ is the result of the new dynamics at play in the current market structure. We highlight the role played by order toxicity in affecting liquidity provision, and we show that a measure of this toxicity, the Volume-Synchronized Probability of Informed Trading (VPIN)*, captures the increasing toxicity of the order flow in the hours and days prior to collapse.