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Non-trading days model for Tesco stock market forecasting

Stock market prices are only available during the weekdays but not for the weekends and holidays. Due to the issue that leads to a gap in the stock market, the dependency between two consecutive trading days will be probably underestimated and the dependency between two trading days separated by a w...

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Main Authors: Mohd. Zainudin, Che Normelissa, Nor, Maria Elena, Kamisan, Nur Arina Bazilah
Format: Conference or Workshop Item
Published: 2023
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/107821/
http://dx.doi.org/10.1063/5.0111062
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spelling my.utm.1078212024-10-05T01:54:26Z http://eprints.utm.my/107821/ Non-trading days model for Tesco stock market forecasting Mohd. Zainudin, Che Normelissa Nor, Maria Elena Kamisan, Nur Arina Bazilah QA Mathematics Stock market prices are only available during the weekdays but not for the weekends and holidays. Due to the issue that leads to a gap in the stock market, the dependency between two consecutive trading days will be probably underestimated and the dependency between two trading days separated by a weekend or holiday will be overestimated. Thus, this issue might affect the forecast accuracy. In this study, the issue had been addressed by applying Autoregressive Moving Average Non-Trading days (ARMA-NT) model on the daily return of stock market price of Tesco forecasting. From the results, ARMA-NT model outperformed the ARMA model in which the data were not divided into trading and non-trading days. This is because the ARMA-NT model has small values of error measurements which are 0.9604 for MSE, 0.7580 for MAE, and 2.8268 for MAPE. Hence, it can be concluded that the daily stock return forecasting can be improved by splitting the data into trading and non-trading days. 2023-02-08 Conference or Workshop Item PeerReviewed Mohd. Zainudin, Che Normelissa and Nor, Maria Elena and Kamisan, Nur Arina Bazilah (2023) Non-trading days model for Tesco stock market forecasting. In: 5th ISM International Statistical Conference 2021: Statistics in the Spotlight: Navigating the New Norm, ISM 2021, 17 August 2021 - 19 August 2021, Virtual, Johor Bahru, Johor, Malaysia. http://dx.doi.org/10.1063/5.0111062
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Mohd. Zainudin, Che Normelissa
Nor, Maria Elena
Kamisan, Nur Arina Bazilah
Non-trading days model for Tesco stock market forecasting
description Stock market prices are only available during the weekdays but not for the weekends and holidays. Due to the issue that leads to a gap in the stock market, the dependency between two consecutive trading days will be probably underestimated and the dependency between two trading days separated by a weekend or holiday will be overestimated. Thus, this issue might affect the forecast accuracy. In this study, the issue had been addressed by applying Autoregressive Moving Average Non-Trading days (ARMA-NT) model on the daily return of stock market price of Tesco forecasting. From the results, ARMA-NT model outperformed the ARMA model in which the data were not divided into trading and non-trading days. This is because the ARMA-NT model has small values of error measurements which are 0.9604 for MSE, 0.7580 for MAE, and 2.8268 for MAPE. Hence, it can be concluded that the daily stock return forecasting can be improved by splitting the data into trading and non-trading days.
format Conference or Workshop Item
author Mohd. Zainudin, Che Normelissa
Nor, Maria Elena
Kamisan, Nur Arina Bazilah
author_facet Mohd. Zainudin, Che Normelissa
Nor, Maria Elena
Kamisan, Nur Arina Bazilah
author_sort Mohd. Zainudin, Che Normelissa
title Non-trading days model for Tesco stock market forecasting
title_short Non-trading days model for Tesco stock market forecasting
title_full Non-trading days model for Tesco stock market forecasting
title_fullStr Non-trading days model for Tesco stock market forecasting
title_full_unstemmed Non-trading days model for Tesco stock market forecasting
title_sort non-trading days model for tesco stock market forecasting
publishDate 2023
url http://eprints.utm.my/107821/
http://dx.doi.org/10.1063/5.0111062
_version_ 1814043532328960000
score 13.211869

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