Heteroscedasticity effects as component to future stock market predictions using RNNbased models
Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study prop...
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| Language: | en |
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Plos One
2024
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| Online Access: | http://eprints.uthm.edu.my/12363/1/J17815_074af7388a9327ccc9bec60f08812ab4.pdf http://eprints.uthm.edu.my/12363/ https://doi.org/10.1371/journal.pone.0297641 |
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| _version_ | 1833419774987599872 |
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| author | Sadon, Aida Nabilah Ismail, Shuhaida Khamis, Azme Tariq, Muhammad Usman |
| author_facet | Sadon, Aida Nabilah Ismail, Shuhaida Khamis, Azme Tariq, Muhammad Usman |
| author_sort | Sadon, Aida Nabilah |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH
model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the
results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices. |
| format | Article |
| id | my.uthm.eprints-12363 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | Plos One |
| record_format | eprints |
| spelling | my.uthm.eprints-123632025-04-24T01:13:44Z http://eprints.uthm.edu.my/12363/ Heteroscedasticity effects as component to future stock market predictions using RNNbased models Sadon, Aida Nabilah Ismail, Shuhaida Khamis, Azme Tariq, Muhammad Usman HG Finance Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices. Plos One 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12363/1/J17815_074af7388a9327ccc9bec60f08812ab4.pdf Sadon, Aida Nabilah and Ismail, Shuhaida and Khamis, Azme and Tariq, Muhammad Usman (2024) Heteroscedasticity effects as component to future stock market predictions using RNNbased models. RESEARCH ARTICLE. pp. 1-18. https://doi.org/10.1371/journal.pone.0297641 |
| spellingShingle | HG Finance Sadon, Aida Nabilah Ismail, Shuhaida Khamis, Azme Tariq, Muhammad Usman Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title | Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title_full | Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title_fullStr | Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title_full_unstemmed | Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title_short | Heteroscedasticity effects as component to future stock market predictions using RNNbased models |
| title_sort | heteroscedasticity effects as component to future stock market predictions using rnnbased models |
| topic | HG Finance |
| url | http://eprints.uthm.edu.my/12363/1/J17815_074af7388a9327ccc9bec60f08812ab4.pdf http://eprints.uthm.edu.my/12363/ https://doi.org/10.1371/journal.pone.0297641 |
| url_provider | http://eprints.uthm.edu.my/ |