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Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model

Author

Listed:
  • Olalere Isaac Opeyemi

    (Durban University of Tech., Durban, 4000, Durban, KZN, South Africa Author-2-Name: Dewa Mendon Author-2-Workplace-Name: Durban University of Tech., Durban, 4000, Durban, KZN, South Africa Author-3-Name: Dlamini Lenhle Author-3-Workplace-Name: Durban University of Tech., Durban, 4000, Durban, KZN, South Africa Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

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"

Suggested Citation

  • Olalere Isaac Opeyemi, 2022. "Predicting Trade Mispricing: A Gaussian Multivariate Anomaly Detection Model ," GATR Journals jber221, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:jber221
    DOI: https://doi.org/10.35609/jber.2022.7.1(2)
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    References listed on IDEAS

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    More about this item

    Keywords

    Gaussian Multivariate Anomaly Detection; GMAD; Illicit Financial Flow; IFF.; Trade Mispricing;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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