According to Amihud (2002), liquidity reflects the impact of order flow on price. His illiquidity measure is defined as follows:
Here, illiq(t) is the Amihud's illiquidity measure for month t, Ri is the daily return at day i, Pi is the closing price at i, and Vi is the daily dollar trading volume at i. Since the illiquidity is the reciprocal of liquidity, the lower the illiquidity value, the higher the liquidity of the underlying security. First, let's look at an item-by-item division:
>>>x=np.array([1,2,3],dtype='float') >>>y=np.array([2,2,4],dtype='float') >>>np.divide(x,y) array([ 0.5 , 1. , 0.75]) >>>
In the following code, we estimate Amihud's illiquidity for IBM based on trading data in October 2013. The value is 1.21*10-11. It seems that this value is quite small. Actually, the absolute value is not important; the relative value matters. If we estimate the illiquidity for WMT over the same period, we would find a...