PINstimation: An R Package for Estimating Models of Probability of Informed Trading
47 Pages Posted: 7 Jun 2022 Last revised: 9 Jul 2022
Date Written: May 23, 2022
The purpose of this paper is to introduce the R package PINstimation. The package is designed for estimating, in a precise and fast way, the probability of informed trading models through the implementation of the main estimation methods suggested in the literature so far. The models covered are the original PIN model of Easley and OHara (1992), and Easley et al. (1996); the multilayer PIN model of Ersan (2016); the adjusted PIN model of Duarte and Young (2009); and the volume-synchronized PIN of Easley, De Prado, and O’Hara (2011), and Easley, López De Prado, and O’Hara (2012). These core functionalities of the package are supplemented with utilities for data simulation, aggregation and classification tools. In addition to a detailed overview of the package functions, their arguments, and their outputs, we have included a theoretical review of the methods behind these functions while presenting the major challenges and related solutions. Finally, we conduct two applications with trade-level data for 58 Swedish stocks, and report straightforward, comparative and intriguing findings on informed trading. These applications aim to highlight the capabilities of the package in tackling relevant research questions and provide instances of the wide possibilities of use of PINstimation for both academicians and practitioners.
Keywords: Probability of Informed Trading, PIN, Multilayer PIN, Adjusted PIN, Volume-Synchronized PIN, Trade Classification Algorithm, Expectation-Conditional Maximization Algorithm, EM Algorithm, Information Asymmetry, Market Microstructure
JEL Classification: C13, C38, G14, G17
Suggested Citation: Suggested Citation