Probability-of-Informed-Trading

Implementation of PIN ( Probability of Informed trading) on A-Share daily public data

based on Yan Y, Zhang S. An improved estimation method and empirical properties of the probability of informed trading[J]. Journal of Banking & Finance, 2012, 36(2): 454-467.

By construction, the probability of informed trading (PIN) measures the proportion of trades that are likely to be motivated by private information. PIN is usually estimated with intraday data.

Due to the limitation of the data availability, we here use daily public available data to calculate A-share daily level probabiliry of informed trading.

Data is downloaded from Wind. Download of the data is implemented in WindDataGet_dailyBS.py.

The only available buy/sell data available in Wind is the buy/sell amount of different investor types. As such, investor types and their critiria are as following:

A_1	A_2	A_3	A_4
Trading Quantity exceed 1000000 CNY	Trading Quantity exceed 500000 CNY	Trading Quantity exceed 150000 CNY	Trading Quantity exceed 40000 CNY

PIN measures the fraction of trades in a day taht arise from informed traders, is defined as

$PIN = \frac {\alpha \mu}{\alpha \mu +\epsilon_B+ \epsilon_S}$

where $\alpha$ denotes the probability taht an information event occurs. The information would be bad at the probability of $\delta$ and be good at the probability of $1-\delta$

informed traders who know the new information submit orders at the daily arrival rate $\mu$. uniformed traders submit buy orders at rate $\epsilon_B$, sell orders at rate $\epsilon_S$

Two PIN parameter estimation methods are included in the code, the first is the Easley, Hvidkjaer, and O’Hara (EHO, 2010) method as the following:

The other is using the following joint likelihood function in MLE to overcome floating-point exception, from Lin and Ke (LK, 2011)

Reference:

Easley, D., Hvidkjaer, S., O’Hara, M., 2010. Factoring information into returns. Journal of Financial and Quantitative Analysis 45, 293–309.

Yan Y, Zhang S. An improved estimation method and empirical properties of the probability of informed trading[J]. Journal of Banking & Finance, 2012, 36(2): 454-467.

Lin, H.W., Ke, W.C., 2011. A computing bias in estimating the probability of informed trading. Journal of Financial Markets 14, 625–640.