Overview
Below, we give a relatively detailed description of the original research articles related to the package by the authors. We report the abstract of each paper, a link to download it, as well as the functions that have been implemented by on the research in the paper. We also report a list of other research papers underlying the different functions implemented in the package.
Original Research papers
The package ‘PINstimation’ implements original research by the authors, which led to four research articles:
The first article (published in the R journal) provides a comprehensive overview of the package: it details the underlying theoretical background, provides a thorough description of the functions, before using them to tackle relevant research questions.
The second article presents a methodological approach to the computational problems of the estimation of the adjusted probability of informed trading by Duarte and Young (2009).
The third article presents a theoretical derivation, and an empirical investigation for the estimation of the PIN models using the expectation-conditional maximization (ECM) algorithm.
The fourth article develops an algorithm for detection of information layers in trade data, in line with the model of multilayer probability of informed trading of Ersan (2016).
PINstimation: An R Package for Estimating Probability of Informed Trading Models
Abstract
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 et al. 2011, 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.
Download
Data: The trade-level data for 58 Swedish stocks used in the paper is available for download here.
Article: The article is published in the R journal.
A methodological approach to the computational problems in the estimation of adjusted PIN model
Abstract
It is well documented that computational problems may lead to large biases in the estimation of prob- ability of informed trading (PIN) models. The complexity of the AdjPIN model, an extension of the con- ventional PIN model, exacerbates further these computational issues due to its larger parameter set. We introduce a dual approach to improve estimation reliability: a logarithmic factorization of the likelihood function, and a strategic algorithm for generating initial parameter sets. The logarithmic factorization addresses floating point exceptions and numerical instability, while the algorithm significantly reduces the likelihood of converging to local maxima. We show that our methodology outperforms existing best- practices and it enables accurate estimation of the adjPIN model. We, therefore, strongly suggest its use in future studies.
Functions
The paper derives a factorization of the likelihood function of the AdjPIN model, as well as proposes a strategic, and efficient algorithm to derive initial parameter sets that prime the maximum likelihood estimation of the model, either using the standard methods, or the expectation-conditional maximization algorithm. The function that implements the factorization of the likelihood function is fact_adjpin(); while the function that implements the algorithm of initial parameter sets is initials_adjpin().
Estimation of the Probability of Informed Trading Models Via an Expectation-Conditional Maximization Algorithm
Abstract
The estimation of the PIN model and its extensions has posed significant challenges due to various computational problems. To address these issues, we propose a novel estimation method called the Expectation-Conditional Maximization (ECM) algorithm, which can serve as an alternative to existing methods for estimating various PIN models. Our method provides optimal estimates for the original PIN model, as well as two of its extensions: the multilayer PIN model, and the adjusted PIN model, along with its restricted versions. Our results indicate that the estimation using the ECM algorithm is, by and large, faster, more accurate, and uses less memory than standard methods used in the literature, making it a robust alternative. Importantly, the ECM algorithm is not limited to the discussed models and can be easily adapted to estimate future extensions of the PIN model.
Functions
The paper details the theoretical adaptation of the ECM algorithm to estimate the MPIN model of Ersan (2016), and the adjusted PIN model of Duarte and Young (2009). The functions of the package that implements the estimation of the MPIN model is mpin_ecm(); while the one implementing the estimation of the AdjPIN model is adjpin().
Identifying information types in probability of informed trading (PIN) models: An improved algorithm
Abstract
The multilayer probability of informed trading (MPIN) model, developed by Ersan (2016), releases the assumption of single type of information events in the original PIN model of Easley et al. (1996). Identification of the number of layers in a dataset is applied through a layer detection algorithm suggested in Ersan (2016). The algorithm is based on clustering absolute order imbalances and examination of confidence intervals for the skellam distribution. When uninformed trading intensity is assumed to be identical in the buy and sell sides, the algorithm performs extremely well. When uninformed intensities are not equal, Ersan (2016) suggests the adjustment of the data using a correction term, proxied by the minimum levels of buys and sells in the data. We improve the algorithm of Ersan (2016) in two ways. We provide accurate estimates of uninformed trading intensities used for data adjustment, and slightly modify the algorithm of determining the information layers. The improved algorithm identifies the number of layers with substantially increased precision, between 86% and 95% accuracy for the simulated data with various settings.
Functions
The paper develops an algorithm to detect the number of layers in the trade dataset, inline with the MPIN model of Ersan (2016). The function implementing this algorithm is detectlayers_eg(), which constitutes an improvement of a previous algorithm developed by Ersan (2016), that is implemented in detectlayers_e().
Download
The article is available on SSRN.
Other research papers
- Cheng T, Lai H (2021). “Improvements in estimating the probability of informed trading models.” Quantitative Finance, 21(5), 771-796.
- Duarte J, Young L (2009). “Why is PIN priced?” Journal of Financial Economics, 91(2), 119–138. ISSN 0304405X.
- Easley D, De Prado MML, Ohara M (2011). “The microstructure of the "flash crash": flow toxicity, liquidity crashes, and the probability of informed trading.” The Journal of Portfolio Management, 37(2), 118–128.
- Easley D, Hvidkjaer S, Ohara M (2010). “Factoring information into returns.” Journal of Financial and Quantitative Analysis, 45(2), 293–309. ISSN 00221090.
- Easley D, Kiefer NM, Ohara M, Paperman JB (1996). “Liquidity, information, and infrequently traded stocks.” Journal of Finance, 51(4), 1405–1436. ISSN 00221082.
- Easley D, Lopez De Prado MM, OHara M (2012). “Flow toxicity and liquidity in a high-frequency world.” Review of Financial Studies, 25(5), 1457–1493. ISSN 08939454. Easley D, Ohara M (1992). “Time and the Process of Security Price Adjustment.” The Journal of Finance, 47(2), 577–605. ISSN 15406261.
- Ellis K, Michaely R, Ohara M (2000). “The Accuracy of Trade Classification Rules: Evidence from Nasdaq.” The Journal of Financial and Quantitative Analysis, 35(4), 529–551.
- Ersan O (2016). “Multilayer Probability of Informed Trading.” Available at SSRN 2874420.
- Ersan O, Alici A (2016). “An unbiased computation methodology for estimating the probability of informed trading (PIN).” Journal of International Financial Markets, Institutions and Money, 43, 74–94. ISSN 10424431.
- Ersan O, Ghachem M (2022a). “Identifying information types in probability of informed trading (PIN) models: An improved algorithm.” Available at SSRN 4117956.
- Ersan O, Ghachem M (2022b). “A methodological approach to the computational problems in the estimation of adjusted PIN model.” Available at SSRN 4117954.
- Gan Q, Wei WC, Johnstone D (2015). “A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering.” Quantitative Finance, 15(11), 1805–1821.
- Ghachem M, Ersan O (2022a). “Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm.” Available at SSRN 4117952.
- Ghachem M, Ersan O (2022b). “PINstimation: An R package for estimating models of probability of informed trading.” Available at SSRN 4117946.
- Lee CMC, Ready MJ (1991). “Inferring Trade Direction from Intraday Data.” The Journal of Finance, 46(2), 733–746. ISSN 00221082, 15406261.
- Lin H, KeW(2011). “A computing bias in estimating the probability of informed trading.” Journal of Financial Markets, 14(4), 625-640. ISSN 1386-4181.
- Yan Y, Zhang S (2012). “An improved estimation method and empirical properties of the probability of informed trading.” Journal of Banking and Finance, 36(2), 454–467. ISSN 03784266.