Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network
Stocks, sometimes known as equities, are fractional ownership shares in a firm, and the stock market is a venue where investors may purchase and sell these investible assets. Because it allows enterprises to quickly get funds from the public, a well-functioning stock market is critical to economic p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2022
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| Online Access: | http://eprints.utm.my/98782/1/NurArinaBazilahKamisan2022_ModellingStockMarketExchange.pdf |
| _version_ | 1796866688307691520 |
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| author | Firdaus, Mohamad Kamisan, Nur Arina Bazilah Aziz, Nur Arina Bazilah Chan, Weng Howe |
| author_facet | Firdaus, Mohamad Kamisan, Nur Arina Bazilah Aziz, Nur Arina Bazilah Chan, Weng Howe |
| author_sort | Firdaus, Mohamad |
| collection | ePrints |
| description | Stocks, sometimes known as equities, are fractional ownership shares in a firm, and the stock market is a venue where investors may purchase and sell these investible assets. Because it allows enterprises to quickly get funds from the public, a well-functioning stock market is critical to economic progress. The purpose of this study is to model Bursa Malaysia using autoregressive integrated moving average (ARIMA), multiple linear regression (MLR), and neural network (NN) model. To compare the modelling accuracy of these models for intraday trading, root mean square error (RMSE) and mean absolute percentage error (MAPE) as well as graphical plot will be used. From the results obtained from these three methods, the NN model provides the best trade signal. |
| first_indexed | 2024-03-05T21:15:55Z |
| format | Article |
| id | utm.eprints-98782 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:15:55Z |
| publishDate | 2022 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-987822023-02-02T08:45:06Z http://eprints.utm.my/98782/ Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network Firdaus, Mohamad Kamisan, Nur Arina Bazilah Aziz, Nur Arina Bazilah Chan, Weng Howe QA Mathematics Stocks, sometimes known as equities, are fractional ownership shares in a firm, and the stock market is a venue where investors may purchase and sell these investible assets. Because it allows enterprises to quickly get funds from the public, a well-functioning stock market is critical to economic progress. The purpose of this study is to model Bursa Malaysia using autoregressive integrated moving average (ARIMA), multiple linear regression (MLR), and neural network (NN) model. To compare the modelling accuracy of these models for intraday trading, root mean square error (RMSE) and mean absolute percentage error (MAPE) as well as graphical plot will be used. From the results obtained from these three methods, the NN model provides the best trade signal. Penerbit UTM Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/98782/1/NurArinaBazilahKamisan2022_ModellingStockMarketExchange.pdf Firdaus, Mohamad and Kamisan, Nur Arina Bazilah and Aziz, Nur Arina Bazilah and Chan, Weng Howe (2022) Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network. Jurnal Teknologi, 84 (5). pp. 137-144. ISSN 0127-9696 http://dx.doi.org/10.11113/jurnalteknologi.v84.18487 DOI: 10.11113/jurnalteknologi.v84.18487 |
| spellingShingle | QA Mathematics Firdaus, Mohamad Kamisan, Nur Arina Bazilah Aziz, Nur Arina Bazilah Chan, Weng Howe Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title | Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title_full | Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title_fullStr | Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title_full_unstemmed | Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title_short | Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network |
| title_sort | modelling stock market exchange by autoregressive integrated moving average multiple linear regression and neural network |
| topic | QA Mathematics |
| url | http://eprints.utm.my/98782/1/NurArinaBazilahKamisan2022_ModellingStockMarketExchange.pdf |
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