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BREXIT Election: Forecasting a Conservative Party Victory through the Pound using ARIMA and Facebook's Prophet

Published: 24 August 2020 Publication History

Abstract

On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to "Get BREXIT Done". The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4th December, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach focuses on modelling the GBP/EUR and GBP/USD Fx from the inception of BREXIT referendum talks from the 1st January, 2016 to the conclusion of the BREXIT election on the 12th December, 2019, focusing on forecasted increases in the pound from the 4th December, 2019. We construct two machine learning models in the form of an Auto Regressive Integrated Moving Average (ARIMA) financial time series and an additive regression financial time series using Facebook's Prophet to investigate the hypothesis that the polls prediction of a Conservative victory could be validated by forecasted increases in the pound. The efficiency of the forecasted models was then tested based on MAPE and MSE criteria. Our results found that the ARIMA and Prophet models were effective and proficient in forecasting the polls prediction on the 4th December, 2019 of a Conservative win by validation of forecasted increases in the pound. The ARIMA (4,1,0) model resulted in forecasts with the lowest MAPE and MAE.

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    WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics
    June 2020
    279 pages
    ISBN:9781450375429
    DOI:10.1145/3405962
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 24 August 2020

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    Author Tags

    1. ARIMA
    2. BREXIT Election
    3. Facebook Prophet
    4. Time series forecasting

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    Cited By

    View all
    • (2022)BIST Corporate Governance Index Price Prediction with a Facebook Prophet Analysis MethodMaliye Finans Yazıları10.33203/mfy.1081901(215-232)Online publication date: 1-Apr-2022
    • (2022)LSTM and ARIMA for Forecasting COVID-19 Positive and Mortality Cases in DKI Jakarta and West Java2022 Seventh International Conference on Informatics and Computing (ICIC)10.1109/ICIC56845.2022.10006959(1-6)Online publication date: 8-Dec-2022
    • (2021)A Time Series Combined Forecasting Model Based on Prophet-LGBM2021 2nd International Conference on Artificial Intelligence and Information Systems10.1145/3469213.3470280(1-6)Online publication date: 28-May-2021
    • (2021)Forecasting elections results via the voter model with stubborn nodesApplied Network Science10.1007/s41109-020-00342-76:1Online publication date: 7-Jan-2021
    • (2021)An NLP and LSTM Based Stock Prediction and Recommender System for KOSDAQ and KOSPIIntelligent Human Computer Interaction10.1007/978-3-030-68449-5_40(403-413)Online publication date: 6-Feb-2021

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