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Method based on cyclic neural network for predicting stock index price

A cyclic neural network, price prediction technology, applied in the field of stock index price prediction based on cyclic neural network, can solve the problems of explosion, gradient vanishing gradient, difficult to obtain time series dependencies, etc.

Inactive Publication Date: 2018-06-12
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, the traditional RNN structure is prone to the problem of gradient disappearance or gradient explosion, so it is difficult to obtain the long-term dependence of time series

Method used

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  • Method based on cyclic neural network for predicting stock index price
  • Method based on cyclic neural network for predicting stock index price
  • Method based on cyclic neural network for predicting stock index price

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Embodiment Construction

[0039] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0040]A stock index price prediction method based on a recurrent neural network, comprising the following steps:

[0041] 1) Clean and process the historical data of the stock index and its individual stocks;

[0042] 2), the data processed in step 1) is carried out as follows figure 1 Attention weighting adjustments shown on the left;

[0043] 3), after determining the value of the hyperparameter, training such as figure 1 For the network shown, get the corresponding parameter values, and then perform the operations described in the above steps on the closing price of the SSE 50 stock index to get the corresponding forecasting effect figure 2 .

[0044] The stock index price prediction method based on the cyclic neural network has basically the same function as the existing traditional cyclic neural network forecasting time series method. ...

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Abstract

Disclosed is a method based on a cyclic neural network of an attention mechanism for predicting a stock index price. The method comprises the following specific steps of 1) collecting and cleaning stock indexes and historical price data of a single stock, wherein the stock indexes include the historical price data; 2) using the attention mechanism to conduct weighting adjustment on model input values; 3) inputting the weighted model input values into the cyclic neural network for model training and prediction. By means of the method, the deep features of various influence factors of the stockindex price can be ingeniously extracted, and compared with a simple one-factor cyclic network or a traditional multi-factor cyclic network, the method can improve the accuracy of predicting the stockindex price.

Description

technical field [0001] The invention relates to a stock index price prediction method, in particular to a stock index price prediction method based on a cyclic neural network. Background technique [0002] With the gradual development of China's financial market, many financial products represented by stocks and containing economic interests have attracted more and more attention. At the same time, stock investors are paying more and more attention to the accurate prediction of stock prices when conducting investment activities, so as to minimize investment losses and increase their return on investment. Therefore, accurate modeling and analysis of stock prices has become one of the most important research contents when investors make investment decisions. Previous researchers mainly applied various statistical and probability theory methods to time series forecasting models, such as: ARMA (autoregressive moving average model), ARCH (autoregressive conditional heteroscedast...

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Application Information

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IPC IPC(8): G06Q40/04G06Q10/04G06N3/08
CPCG06Q40/04G06N3/084G06Q10/04
Inventor 刘震王惠敏薛腾腾王理同
Owner ZHEJIANG UNIV OF TECH
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