Financial market predictions with deep learning
Forecasting the financial market has proven to be a challenging task due to high volatility. However, with the growing involvement of computational methods in econometrics, models built with deep learning neural networks have been more accurate in capturing the dynamics of financial market data comp...
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| Format: | Article |
| Language: | en |
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Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22247/1/Paper7%20-.pdf http://journalarticle.ukm.my/22247/ http://www.ukm.my/jqma |
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| _version_ | 1834434401058947072 |
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| author | Yap, Zhong Jing Dharini Pathmanathan, |
| author_facet | Yap, Zhong Jing Dharini Pathmanathan, |
| author_sort | Yap, Zhong Jing |
| building | Tun Sri Lanang Library |
| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| description | Forecasting the financial market has proven to be a challenging task due to high volatility. However, with the growing involvement of computational methods in econometrics, models built with deep learning neural networks have been more accurate in capturing the dynamics of financial market data compared to the commonly used time series models such as the ARIMA and GARCH models. In this study, four deep learning models were applied to eight separate investments, namely stocks (AAPL, TSLA, ROKU, BAC), currency exchange rates (GBP/USD and USD/SEK) and exchange-traded funds (SQQQ and SPXS) to compare their forecasting abilities. The four deep learning models consists of three recurrent neural networks (RNN) which are the vanilla recurrent network (VRNN), long short-term memory (LSTM) and gated recurrent units (GRU), along with the convolutional neural networks (CNN). The models were tuned to be time efficient and evaluated with RMSE and MAPE. Results show that GRU was the overall best model, with exceptions to the LSTM performing better with the exchange traded funds. |
| format | Article |
| id | my-ukm.journal.22247 |
| institution | Universiti Kebangsaan Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | eprints |
| spelling | my-ukm.journal.222472023-09-19T06:42:37Z http://journalarticle.ukm.my/22247/ Financial market predictions with deep learning Yap, Zhong Jing Dharini Pathmanathan, Forecasting the financial market has proven to be a challenging task due to high volatility. However, with the growing involvement of computational methods in econometrics, models built with deep learning neural networks have been more accurate in capturing the dynamics of financial market data compared to the commonly used time series models such as the ARIMA and GARCH models. In this study, four deep learning models were applied to eight separate investments, namely stocks (AAPL, TSLA, ROKU, BAC), currency exchange rates (GBP/USD and USD/SEK) and exchange-traded funds (SQQQ and SPXS) to compare their forecasting abilities. The four deep learning models consists of three recurrent neural networks (RNN) which are the vanilla recurrent network (VRNN), long short-term memory (LSTM) and gated recurrent units (GRU), along with the convolutional neural networks (CNN). The models were tuned to be time efficient and evaluated with RMSE and MAPE. Results show that GRU was the overall best model, with exceptions to the LSTM performing better with the exchange traded funds. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22247/1/Paper7%20-.pdf Yap, Zhong Jing and Dharini Pathmanathan, (2023) Financial market predictions with deep learning. Journal of Quality Measurement and Analysis, 19 (2). pp. 81-97. ISSN 1823-5670 http://www.ukm.my/jqma |
| spellingShingle | Yap, Zhong Jing Dharini Pathmanathan, Financial market predictions with deep learning |
| title | Financial market predictions with deep learning |
| title_full | Financial market predictions with deep learning |
| title_fullStr | Financial market predictions with deep learning |
| title_full_unstemmed | Financial market predictions with deep learning |
| title_short | Financial market predictions with deep learning |
| title_sort | financial market predictions with deep learning |
| url | http://journalarticle.ukm.my/22247/1/Paper7%20-.pdf http://journalarticle.ukm.my/22247/ http://www.ukm.my/jqma |
| url_provider | http://journalarticle.ukm.my/ |