Forecasting stock market return with nonlinearity: a genetic programming approach
The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved thro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020
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| Online Access: | https://eprints.nottingham.ac.uk/60489/ |
| _version_ | 1848799769718161408 |
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| author | Ding, Shusheng Cui, Tianxiang Xiong, Xihan Bai, Ruibin |
| author_facet | Ding, Shusheng Cui, Tianxiang Xiong, Xihan Bai, Ruibin |
| author_sort | Ding, Shusheng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading. |
| first_indexed | 2025-11-14T20:40:56Z |
| format | Article |
| id | nottingham-60489 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:40:56Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-604892020-04-28T02:09:35Z https://eprints.nottingham.ac.uk/60489/ Forecasting stock market return with nonlinearity: a genetic programming approach Ding, Shusheng Cui, Tianxiang Xiong, Xihan Bai, Ruibin The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading. Springer 2020-02-10 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/60489/1/Forecasting%20stock%20market%20return%20with%20nonlinearity%20a%20genetic%20programming%20approach.pdf Ding, Shusheng, Cui, Tianxiang, Xiong, Xihan and Bai, Ruibin (2020) Forecasting stock market return with nonlinearity: a genetic programming approach. Journal of Ambient Intelligence and Humanized Computing . ISSN 1868-5137 Return forecasting; Nonlinear models; Genetic programming http://dx.doi.org/10.1007/s12652-020-01762-0 doi:10.1007/s12652-020-01762-0 doi:10.1007/s12652-020-01762-0 |
| spellingShingle | Return forecasting; Nonlinear models; Genetic programming Ding, Shusheng Cui, Tianxiang Xiong, Xihan Bai, Ruibin Forecasting stock market return with nonlinearity: a genetic programming approach |
| title | Forecasting stock market return with nonlinearity: a genetic programming approach |
| title_full | Forecasting stock market return with nonlinearity: a genetic programming approach |
| title_fullStr | Forecasting stock market return with nonlinearity: a genetic programming approach |
| title_full_unstemmed | Forecasting stock market return with nonlinearity: a genetic programming approach |
| title_short | Forecasting stock market return with nonlinearity: a genetic programming approach |
| title_sort | forecasting stock market return with nonlinearity: a genetic programming approach |
| topic | Return forecasting; Nonlinear models; Genetic programming |
| url | https://eprints.nottingham.ac.uk/60489/ https://eprints.nottingham.ac.uk/60489/ https://eprints.nottingham.ac.uk/60489/ |