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Quantification method and system of stock news based on artificial intelligence
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A technology of artificial intelligence and quantitative method, applied in the field of artificial intelligence, to achieve the effect of high accuracy
Inactive Publication Date: 2018-04-10
宏谷信息科技(珠海)有限公司 +1
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[0006] The technical problem to be solved by the present invention is to provide an artificial intelligence-based stock news quantification method and system to solve the one-sided problem of the existing stock forecast news reference factors
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Embodiment 1
[0068] This embodiment provides a stock news quantification method based on artificial intelligence, such as figure 1 shown, including steps:
[0069] S11: Obtain the stock news sequence of the trading day within the preset time;
[0070] S12: Divide the stock news sequence into word sequences according to the preset length;
[0071] S13: Determine whether the stock news is the news of the day, and if so, use Word2Vec and GloVe to obtain the word vector feature of each term of the news;
[0072] S14: If the stock news is not the news of the trading day, use fastText to obtain the document vector feature of the news.
[0073] In recent years, text-based word vector identification methods and document identification methods are very popular. Through training a large number of predictions, a feature matrix is finally learned for each word or article, so that similar words are closer in the vector space. Text can be quantified effectively.
[0074] In applications such as st...
Embodiment 2
[0119] This embodiment provides a stock news quantification system based on artificial intelligence, such as figure 2 shown, including:
[0120] The obtaining module 21 is used to obtain the stock news sequence of the trading day within the preset time;
[0121] Divide module 22, be used for dividing stock news sequence into word sequence according to preset length;
[0122]Word vector module 23, for judging whether stock news is the news of the day, if so, then utilize Word2Vec and GloVe to obtain the word vector feature of each term of news;
[0123] The document vector module 24 is used to obtain the document vector feature of the news by using fastText if the stock news is not the news of the trading day.
[0124] In recent years, text-based word vector identification methods and document identification methods are very popular. Through training a large number of predictions, a feature matrix is finally learned for each word or article, so that similar words are close...
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Abstract
The invention discloses a quantification method and system of stock news based on artificial intelligence, which is used for solving the problem that existing stock forecasting news reference factorshave one-sidedness. The method comprises following steps: acquiring a stock news sequence on a trading day within a preset time; dividing the stock news sequence into word sequences according to a preset length; determining whether the stock news is the news on the day of trading; if the stock news is the news on the day of trading, obtaining word vector characteristics of each word of the news byusing Word2Vec and GloVe; otherwise, using fastText to obtain document vector characteristics of the news. The present invention extracts news features through three different vector representation learning methods, makes news more comprehensive as a reference factor, and has higher prediction accuracy.
Description
technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based stock news quantification method and system. Background technique [0002] Stock price forecasting refers to the use of historical price information and stock-related market information to predict the rise and fall of a stock or its price in the future. In recent years, deep learning methods have made many advances in the field of natural languageprocessing. Deep learning methods are also gradually applied to the field of stock forecasting. [0003] TH Nguyen et al. use the main body model to predict stock prices. In the literature [Topic modeling basedsentiment analysis on social media for stock market prediction], they propose a topic model that integrates emotion and topics, and apply the model to the subject analysis of stock-related news. After obtaining the topic distribution vector of each news, they added this topic vec...
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