Stock Price Prediction based on CNN-LSTM Model in the PyTorch Environment

Weidong Xu ()
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Weidong Xu: Shanghai University of Electric Power

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 1272-1276 from Springer

Abstract: Abstract The stock market, as the main financing channel for listed companies and the most accessible wealth creation opportunity for investors, has always attracted attention from all walks of life. With the evolution of the technology, deep learning has started to play a very important role in forecasting stock price. Based on in-depth research on CNN and LSTM, this paper builds a CNN-LSTM stock price prediction model in PyTorch environment, and takes the data from the A-share market, choosing Shanghai Composite Index for a total of ten years from January 2012 to December 2021 as the experimental object, then verifying the feasibility of this joint model in the field of stock price forecasting, while comparing with the predicted values obtained using CNN and LSTM alone. The result confirms that the CNN-LSTM joint model performs well.

Keywords: Stock price prediction; PyTorch; CNN; LSTM (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_188

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DOI: 10.2991/978-94-6463-036-7_188

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