{"title":"估算:一个估算知情交易模型概率的R包","authors":"Montasser Ghachem, Oguz Ersan","doi":"10.32614/rj-2023-044","DOIUrl":null,"url":null,"abstract":"The purpose of this paper is to introduce the R package [PINstimation](https://CRAN.R-project.org/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 [@easley1992time; @easley1996liquidity], the multilayer PIN model [@ersan2016multilayer], the adjusted PIN model [@duarte2009why], and the volume- synchronized PIN [@Easley2011microstructure; @Easley2012Flow]. 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.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"108 1-2","pages":"0"},"PeriodicalIF":2.3000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PINstimation: An R Package for Estimating Probability of Informed Trading Models\",\"authors\":\"Montasser Ghachem, Oguz Ersan\",\"doi\":\"10.32614/rj-2023-044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this paper is to introduce the R package [PINstimation](https://CRAN.R-project.org/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 [@easley1992time; @easley1996liquidity], the multilayer PIN model [@ersan2016multilayer], the adjusted PIN model [@duarte2009why], and the volume- synchronized PIN [@Easley2011microstructure; @Easley2012Flow]. 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.\",\"PeriodicalId\":51285,\"journal\":{\"name\":\"R Journal\",\"volume\":\"108 1-2\",\"pages\":\"0\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2023-044\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2023-044","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
PINstimation: An R Package for Estimating Probability of Informed Trading Models
The purpose of this paper is to introduce the R package [PINstimation](https://CRAN.R-project.org/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 [@easley1992time; @easley1996liquidity], the multilayer PIN model [@ersan2016multilayer], the adjusted PIN model [@duarte2009why], and the volume- synchronized PIN [@Easley2011microstructure; @Easley2012Flow]. 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.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.