Analysis of stock market volatility: Adjusted VPIN with high-frequency data

The volume-synchronized probability of informed trading (VPIN) is widely accepted as a proxy of volatility in the high-frequency market. We propose a novel VPIN model, called Adjusted VPIN, to improve the performance of VPIN so that it can directly analyze and better predict the information asymmetry of individual stocks. We extend the VPIN model by optimizing the classification algorithm with a neural network method and high-frequency data. Both trading volume and trends are considered to capture stock volatility. Empirical results on three different trading volume groups generate a 37.86% higher relevant result with logarithm stock yield than the VPIN model.

Automated summary

A novel VPIN model, named Adjusted VPIN, is proposed in the study to directly analyze and better predict the information asymmetry of individual stocks by optimizing the algorithm and using high-frequency data. The empirical results show a 37.86% higher relevance with logarithm stock yield compared to the traditional VPIN model.