Browsing by Author "Ghachem,M."

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Citation - Scopus: 1
Identifying Information Types in the Estimation of Informed Trading: an Improved Algorithm
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Ersan,O.; Ersan, Oğuz; Ghachem,M.
The growing frequency of news arrivals, partly fueled by the proliferation of data sources, has made the assumptions of the classical probability of informed trading (PIN) model outdated. In particular, the model’s assumption of a single type of information event no longer reflects the complexity of modern financial markets, making the accurate detection of information types (layers) crucial for estimating the probability of informed trading. We propose a layer detection algorithm to accurately find the number of distinct information types within a dataset. It identifies the number of information layers by clustering order imbalances and examining their homogeneity using properly constructed confidence intervals for the Skellam distribution. We show that our algorithm manages to find the number of information layers with very high accuracy both when uninformed buyer and seller intensities are equal and when they differ from each other (i.e., between 86% and 95% accuracy rates). We work with more than 500,000 simulations of quarterly datasets with various characteristics and make a large set of robustness checks. © 2024 by the authors.
Citation - Scopus: 2
Pinstimation: an R Package for Estimating Probability of Informed Trading Models
(Technische Universitaet Wien, 2023) Ghachem,M.; Ersan, Oğuz; Ersan,O.
The purpose of this paper is to introduce the R package PINstimation. The package is designed for fast and accurate estimation of the probability of informed trading models through the implementation of well-established estimation methods. The models covered are the original PIN model (Easley and O’Hara 1992; Easley et al. 1996), the multilayer PIN model (Ersan 2016), the adjusted PIN model (Duarte and Young 2009), and the volume-synchronized PIN (Easley, De Prado, and O’Hara 2011; 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, we provide a brief theoretical review of the main methods implemented in the package. Further, we provide examples of use of the package on trade-level data for 58 Swedish stocks, and report straightforward, comparative and intriguing findings on informed trading. These examples aim to highlight the capabilities of the package in tackling relevant research questions and illustrate the wide usage possibilities of PINstimation for both academics and practitioners. © (2023), (Technische Universitaet Wien). All Rights Reserved.