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Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network

Author

Listed:
  • Guangji Tong

    (Northeast Forestry University)

  • Zhiwei Yin

    (Northeast Forestry University)

Abstract

This study explores the prediction of financial time series and adaptive trading based on deep learning neural networks so as to strengthen the international cooperation in agricultural finance. Firstly, related concepts of international cooperation in agricultural finance are introduced, and the long short-term memory (LSTM) is applied in the prediction of financial time. Then, an adaptive trading system is established based on the deep learning and reinforcement learning. Finally, adoption result of the financial prediction model based on LSTM neural network is compared with that of the adaptive trading system proposed through empirical researches. When the Hong Kong Hang Seng Index is predicted by the prediction model based on LSTM neural network, it is found that the mean absolute percentage error in recent years has reached 0.9%, which is superior to other prediction models in predicting the financial time series. The proposed adaptive trading system of financial asset based on deep learning is more reliable and stable than the traditional adaptive trading system. This study provides a theoretical basis for applying the deep learning methods in adaptive trading system, and has important reference value for the free trade zone to promote the international cooperation in agricultural finance.

Suggested Citation

  • Guangji Tong & Zhiwei Yin, 2022. "Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1557-1576, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10136-3
    DOI: 10.1007/s10614-021-10136-3
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    References listed on IDEAS

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