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A stock recommendation method and system based on a bidirectional long-short-term memory model

A technology of long and short-term memory and recommendation method, which is applied in the fields of instruments, finance, data processing applications, etc. It can solve the problems of not considering the time factor, reduce the accuracy of stock prediction and recommendation, and achieve the effect of high accuracy

Pending Publication Date: 2019-04-16
广州市大智网络科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the existing stock recommendation methods based on deep learning do not consider the impact of time factors on stock fluctuations, which reduces the accuracy of stock forecasting and recommendation.

Method used

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  • A stock recommendation method and system based on a bidirectional long-short-term memory model
  • A stock recommendation method and system based on a bidirectional long-short-term memory model
  • A stock recommendation method and system based on a bidirectional long-short-term memory model

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Embodiment Construction

[0058] refer to figure 1 , the embodiment of the present invention provides a stock recommendation method based on a two-way long-short-term memory model, comprising the following steps:

[0059] Get the stock data to be predicted;

[0060] Input the stock data to be predicted into the stock prediction model based on the two-way long-short-term memory model, and obtain the stock prediction result;

[0061] Stock recommendations are made based on stock prediction results.

[0062] Specifically, the stock data to be predicted can be a stock index (such as the S&P500 index), and the stock prediction result can also be a stock index (such as the S&P500 index), so that the corresponding high-yield stocks can be recommended for investors according to the level of the stock index .

[0063]The two-way long-short-term memory model is a variant of the cyclic neural network, which introduces a gate mechanism and a memory unit, and can solve the problem of gradient disappearance and g...

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Abstract

The invention discloses a stock recommendation method and system based on a bidirectional long-short time memory model. The method comprises the steps of obtaining stock data to be predicted; Inputting the stock data to be predicted into a stock prediction model based on a bidirectional long-short time memory model to obtain a stock prediction result; And performing stock recommendation accordingto the stock prediction result. According to the invention, stock prediction is carried out through a bidirectional long-short time memory model which is a neural network with time performance; According to the stock recommendation method based on deep learning, the context relation in the forward time direction and the backward time direction of the time sequence is fully utilized, the influenceof time factors on stock fluctuation is considered, and compared with a traditional stock recommendation method based on deep learning, a stock prediction and recommendation result with higher precision can be provided. The method can be widely applied to the field of financial data mining.

Description

technical field [0001] The invention relates to the field of financial data mining, in particular to a stock recommendation method and system based on a two-way long-short-term memory model. Background technique [0002] Forecasting stock market volatility is important to investors, investment groups and managers, asset valuation, and risk management. Due to the professionalism of stock forecasting and the lack of stock market information, stock forecasting and recommendation have become the focus of attention. Faced with an increasingly complex data environment, stock forecasting models based on classical statistical models can no longer meet people's requirements for forecasting accuracy to a certain extent. Deep learning has been gradually used in stock recommendation because of its strong learning ability and anti-interference ability. However, most of the existing stock recommendation methods based on deep learning do not consider the impact of time factors on stock f...

Claims

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Application Information

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IPC IPC(8): G06Q40/04
CPCG06Q40/04
Inventor 曾安潘丹聂文俊
Owner 广州市大智网络科技有限公司
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