Abstract
In the field of quantitative finance, how to accurately predict stock prices has become an important issue in the current research. The emergence of LSTM network algorithm solves the complex serialized data learning problem of stock price prediction. However, the current results show that if the LSTM algorithm is used alone, there are still problems such as imbalance and inaccurate local extremes. The explanation of GA (genetic algorithm) is not certainly conclusive in the current financial world, but it has a prominent effect in solving the problem of tuning. In the construction of a new stock price prediction model, the closing price can be predicted by LST neural network algorithm, then the GA genetic algorithm can be used to ensure the accuracy of the model prediction, and the signal of stock price rise and fall can be obtained by the identification mechanism. Based on this, this paper makes a comprehensive analysis of the principles and applications of the existing LSTM model, and highlights the application of LSTM-GA in the field of stock price prediction.
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Jia, H. (2022). Based on the LSTM-GA Stock Price Ups and Downs Forecast Model. In: Sugumaran, V., Sreedevi, A.G., Xu, Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-031-05237-8_15
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DOI: https://doi.org/10.1007/978-3-031-05237-8_15
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