Abstract
The stock market is an important part of the financial market, is closely related to economic development. Various analysis and forecasting problems of stock prices have always existed along with the establishment of financial markets. For this reason, this article uses the historical transaction data of the Shanghai A-share 50 as the research object to carry out forecasting and analysis of the closing price trend. Predict the stock price trend through ARIMA model and LSTM model. After empirical research, combined with error indicators and transaction performance to show the model's forecasting accuracy and forecasting effect, it is finally concluded that the deep neural network model based on the LSTM model has better forecasting accuracy. And by using a variety of deep learning methods, we can discover potential profit opportunities in the current market from historical transaction data in the financial market, and guide institutions and individual investors to make better investment behaviors.
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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ding, W., Jin, C., Yang, S. (2022). Comparison of Two Models Based on Deep Neural Network Prediction. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 102. Springer, Singapore. https://doi.org/10.1007/978-981-16-7466-2_20
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DOI: https://doi.org/10.1007/978-981-16-7466-2_20
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