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A stock market prediction method based on a convolutional neural network model

A technology of convolutional neural network and prediction method, which is applied in the field of stock market prediction based on convolutional neural network model, can solve the problems of consuming large labor costs and limiting the generalization performance of the method

Pending Publication Date: 2019-06-04
DALIAN UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

Most of the existing technologies are based on traditional machine learning methods to predict stock market trends, and to mine the relationship between financial news and the stock market by manually constructing features. Manual features consume a lot of labor costs and limit the generalization performance of the method to a certain extent.

Method used

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  • A stock market prediction method based on a convolutional neural network model
  • A stock market prediction method based on a convolutional neural network model
  • A stock market prediction method based on a convolutional neural network model

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Embodiment Construction

[0034] The present invention is described below in conjunction with accompanying drawing and specific embodiment and cooperates embodiment:

[0035] figure 1 It is a basic flowchart of a stock market prediction method based on a convolutional neural network model in the present invention. A stock market prediction method based on a convolutional neural network model, comprising the following steps:

[0036] S1. Data collection: use web crawlers to obtain financial news corpus of listed companies from financial websites (such as Reuters financial websites, etc.), and crawl historical data of Dow Jones Industrial Average (DJIA) through Yahoo Finance API interface;

[0037] Include the following steps:

[0038] S101, using crawler technology to obtain a list of stock codes (AAPL, GOOG, etc., this embodiment crawls a total of 2014 stock codes of listed companies) from financial websites;

[0039]S102. Use urllib2 in the python toolkit from the financial website to crawl listed ...

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Abstract

A stock market prediction method based on the convolutional neural network model comprises the four steps of S1, data collection, S2, data processing, S3, neural network model training and S4, stock market trend prediction. Financial news corpora of listed companies are collected and analyzed; according to the DJIA historical data, the financial news corpus is labeled; and the marked financial corpus information is used as an input value of the convolutional neural network model, so that a predicted value of news marking is output through processing of the convolutional neural network model, and the fluctuation condition and stock market trend of the DJIA can be predicted through the predicted value of the news marking. Through cross validation, the prediction accuracy can reach 65.5%.

Description

technical field [0001] The invention relates to the fields of financial forecasting, natural language processing and deep learning, in particular to a stock market forecasting method based on a convolutional neural network model. Background technique [0002] Internet media is recognized as the "fourth largest media" after newspapers, radio, and television. The timeliness of online news reports and the convenience of access have made it the main source of information for the public. Among them, Internet financial news not only has the advantages of timely reporting and convenient access, but also often involves the operating conditions of listed companies, financial reports, strategic decisions, stock price trends, etc., which can be analyzed by market researchers. Market trends and information to help investors grasp investment timing. However, Internet financial news is complex and mainly presented in the form of unstructured text, so how to dig out useful knowledge for ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/06G06F16/951G06N3/04G06N3/08
Inventor 林鸿飞徐博杨亮许侃李恒超
Owner DALIAN UNIV OF TECH
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