Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913577097494528 |
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| author | Liu, Fang Guo, Shaobo Xing, Qianwen Sha, Xinye Chen, Ying Jin, Yuhui Zheng, Qi Yu, Chang |
| author_facet | Liu, Fang Guo, Shaobo Xing, Qianwen Sha, Xinye Chen, Ying Jin, Yuhui Zheng, Qi Yu, Chang |
| contents | Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, and hedge funds. Discovering the underlying patterns within the seemingly volatile yet intrinsically structured economic activities has become a central focus of research for many companies. Our study leverages widely-used modern financial forecasting algorithms, including LSTM, ANN, CNN, and BiLSTM. We begin by comparing the predictive performance of these well-known algorithms on our stock market data, utilizing metrics such as R2, MAE, MSE, RMSE for detailed evaluation. Based on the performance of these models, we then aim to combine their strengths while mitigating their weaknesses, striving to construct a powerful hybrid model that overcomes the performance limitations of individual models.Through rigorous experimentation and exploration, we ultimately developed an LSTM+ANN model that breaks through prior performance bottlenecks, achieving promising and exciting results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction Liu, Fang Guo, Shaobo Xing, Qianwen Sha, Xinye Chen, Ying Jin, Yuhui Zheng, Qi Yu, Chang Computational Engineering, Finance, and Science Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, and hedge funds. Discovering the underlying patterns within the seemingly volatile yet intrinsically structured economic activities has become a central focus of research for many companies. Our study leverages widely-used modern financial forecasting algorithms, including LSTM, ANN, CNN, and BiLSTM. We begin by comparing the predictive performance of these well-known algorithms on our stock market data, utilizing metrics such as R2, MAE, MSE, RMSE for detailed evaluation. Based on the performance of these models, we then aim to combine their strengths while mitigating their weaknesses, striving to construct a powerful hybrid model that overcomes the performance limitations of individual models.Through rigorous experimentation and exploration, we ultimately developed an LSTM+ANN model that breaks through prior performance bottlenecks, achieving promising and exciting results. |
| title | Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.20253 |