Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model

Author:

Opeyemi Olalere Isaac1,Mendon Dewa1,Lenhle Dlamini1

Affiliation:

1. Durban University of Tech., Durban, 4000, Durban, KZN, South Africa

Abstract

Objective - This paper predicts a measurement indicator for the trade mispricing channel and its effectiveness in identifying IFFs. Methodology – A model gaussian multivariate anomaly detection algorithm, for classifying between legal and illegal transactions that are suspicious in terms of misreporting was developed. The method is a machine learning technique and uses data from South Africa, Botswana, the USA, and China over a period from 2000 to 2019, to learn whether there are any intriguing differences in the model performance based on these countries and the effect of other factors. Imports and Exports are used as features of the model while the net flow derived from these features is used as the third feature of the model. Imports and exports data are sourced from IMF’s Direction of Trade Statistics database. Annual tariffs data and corruption data come from the WDI database and Transparency International’s Corruption Perception Index, respectively. Data for ‘accounting and auditing standards’ comes from the world economic forum. Findings - The result showed that while the model may be effective in detecting IFFs due to mispricing, other factors may however contribute to irregularities of trading data that is flagged as IFFs. This in addition to accounting for total quantum, also provides details empowering governments with the information to stimulate and drive the desire to curb IFFs from its different sources and channels. Novelty - This study contributes to the debate on trade mispricing by proving a baseline measurement to help detect and track IFFs. Type of Paper: Empirical JEL Classification: F17, Q02 Keywords: Gaussian Multivariate Anomaly Detection; GMAD; Illicit Financial Flow; IFF., Trade Mispricing; Reference to this paper should be made as follows: Opeyemi, O.I; Mendon, D; Lenhle, D. (2022). Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model, J. Bus. Econ. Review, 7(1), 61–74. https://doi.org/10.35609/jber.2022.7.1(2)

Publisher

Global Academy of Training and Research (GATR) Enterprise

Subject

General Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3