Chaotic Recurrent Neural Networks for Financial Forecast

Jeff Wang; Raymond Lee

doi:doi:10.11648/j.ajnna.20210701.12

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Chaotic Recurrent Neural Networks for Financial Forecast

Jeff Wang, Raymond Lee

Published in American Journal of Neural Networks and Applications (Volume 7, Issue 1)

Received: 2 February 2021 Accepted: 10 February 2021 Published: 23 February 2021

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Abstract

In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.

Published in	American Journal of Neural Networks and Applications (Volume 7, Issue 1)
DOI	10.11648/j.ajnna.20210701.12
Page(s)	7-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Deep Learning; Recurrent Neural Network, Chaotic Neural Network, Lee-Oscillator, FFBPN, RNN, LSTM, Financial Forecast

References

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APA Style

Jeff Wang, Raymond Lee. (2021). Chaotic Recurrent Neural Networks for Financial Forecast. American Journal of Neural Networks and Applications, 7(1), 7-14. https://doi.org/10.11648/j.ajnna.20210701.12

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ACS Style

Jeff Wang; Raymond Lee. Chaotic Recurrent Neural Networks for Financial Forecast. Am. J. Neural Netw. Appl. 2021, 7(1), 7-14. doi: 10.11648/j.ajnna.20210701.12

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AMA Style

Jeff Wang, Raymond Lee. Chaotic Recurrent Neural Networks for Financial Forecast. Am J Neural Netw Appl. 2021;7(1):7-14. doi: 10.11648/j.ajnna.20210701.12

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@article{10.11648/j.ajnna.20210701.12,
  author = {Jeff Wang and Raymond Lee},
  title = {Chaotic Recurrent Neural Networks for Financial Forecast},
  journal = {American Journal of Neural Networks and Applications},
  volume = {7},
  number = {1},
  pages = {7-14},
  doi = {10.11648/j.ajnna.20210701.12},
  url = {https://doi.org/10.11648/j.ajnna.20210701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210701.12},
  abstract = {In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.},
 year = {2021}
}

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TY  - JOUR
T1  - Chaotic Recurrent Neural Networks for Financial Forecast
AU  - Jeff Wang
AU  - Raymond Lee
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnna.20210701.12
DO  - 10.11648/j.ajnna.20210701.12
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 7
EP  - 14
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20210701.12
AB  - In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.
VL  - 7
IS  - 1
ER  -

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Author Information

Jeff Wang

Division of Computer Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
Raymond Lee

Division of Computer Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China

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Plain Text BibTeX RIS

APA Style

Jeff Wang, Raymond Lee. (2021). Chaotic Recurrent Neural Networks for Financial Forecast. American Journal of Neural Networks and Applications, 7(1), 7-14. https://doi.org/10.11648/j.ajnna.20210701.12

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ACS Style

Jeff Wang; Raymond Lee. Chaotic Recurrent Neural Networks for Financial Forecast. Am. J. Neural Netw. Appl. 2021, 7(1), 7-14. doi: 10.11648/j.ajnna.20210701.12

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AMA Style

Jeff Wang, Raymond Lee. Chaotic Recurrent Neural Networks for Financial Forecast. Am J Neural Netw Appl. 2021;7(1):7-14. doi: 10.11648/j.ajnna.20210701.12

Copy | Download

@article{10.11648/j.ajnna.20210701.12,
  author = {Jeff Wang and Raymond Lee},
  title = {Chaotic Recurrent Neural Networks for Financial Forecast},
  journal = {American Journal of Neural Networks and Applications},
  volume = {7},
  number = {1},
  pages = {7-14},
  doi = {10.11648/j.ajnna.20210701.12},
  url = {https://doi.org/10.11648/j.ajnna.20210701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210701.12},
  abstract = {In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Chaotic Recurrent Neural Networks for Financial Forecast
AU  - Jeff Wang
AU  - Raymond Lee
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnna.20210701.12
DO  - 10.11648/j.ajnna.20210701.12
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 7
EP  - 14
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20210701.12
AB  - In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.
VL  - 7
IS  - 1
ER  -

Copy | Download