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LSTM Model Optimization on Stock Price Forecasting

Yifeng Wang · Yuying Liu · Meiqing Wang · Rong Liu

48 citations0/0 files storedInternational Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

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Stock Market Forecasting Methods Neural Networks and Applications Hydrological Forecasting Using AI

Abstract

In this paper, we mainly study the application of Long Short-Term Memory (LSTM) algorithms in the stock market. LSTM originates from the recurrent neural network (RNN) and has a significant effect on the time series problems. In this paper, the BP neural network model and the LSTM model are established respectively. Then we combine them with the stock data, a series of prediction results are obtained. Obviously, the prediction results of LSTM model are more accurate, and the prediction accuracy rate can reach 60%-65%. In the modeling process, in order to solve the 'saw-tooth phenomenon' of the gradient descent algorithm which is inevitable, we have improved the traditional gradient descent algorithm and specially designed the input data of the neural network. In addition, we defined a parameter combination library and use the skill of dropout to get the more ideal prediction results.

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1988Dividend yields and expected stock returns 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

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