An intelligent stock prediction method based on a news text

A technology of intelligent prediction and text, applied in the field of data processing, can solve the problem of low accuracy of stock prediction, achieve the effect of solving low accuracy and improving accuracy

Inactive Publication Date: 2018-12-11
HOHAI UNIV
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Problems solved by technology

[0005] In order to solve the above problems, the present invention proposes an intelligent stock forecasting method combined with news tex

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  • An intelligent stock prediction method based on a news text
  • An intelligent stock prediction method based on a news text
  • An intelligent stock prediction method based on a news text

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[0036] The technical solutions of the present invention will be further elaborated below according to the accompanying drawings and in conjunction with the embodiments.

[0037]A stock intelligent prediction method combined with news text, including the following steps:

[0038] 1) as figure 1 As shown, preprocess the news text, filter Chinese word segmentation and stop words, convert the news text into a data format that is convenient for computer identification, and delete the news text without time tags;

[0039] Common text processing software Jieba or NLTK (natural language toolkit) can realize preprocessing such as filtering Chinese word segmentation and stop words;

[0040] 2) Determine the predicted duration Δt of the stock, and filter and select the news text according to the time label of the news text;

[0041] 21) Determine the stock forecast duration Δt;

[0042] 22) The data owned by the stock is in [t s , t e ] time period, from t s start, t 0 ∈[t s , t ...

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Abstract

The invention discloses a stock intelligent prediction method combined with a news text. Firstly, a news text is pretreated, Chinese word segmentation and stop words are filtered, and news text without time label is deleted; the prediction time length Delta t of the stock is determined, the news text is filtered and selected according to the time label of the news text; feature representation is performed on the selected news text, and a feature representation vector xt0 of the corresponding time is formed by the features of the news text and the stock data feature vector of the correspondingtime; a self-encoder deep learning network is constructed, the feature representation vector xt0 is input to the deep learning network from an encoder for compression and feature extraction, and a low-dimensional eigenvector new_xt0 is obtained; an ELM neural network model is constructed, the change degree of stock price is expressed quantitatively, and the target output value of ELM neural network model is determined; the parameters of ELM neural network model are optimized and a final prediction model is obtained. The invention solves the technical problem of low accuracy of stock prediction through the combination of a news event and historical market data.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an intelligent stock prediction method combined with news texts. Background technique [0002] The financial market, especially the stock market, is not only closely related to historical market conditions, but also extremely vulnerable to sudden financial news events. At present, the commonly used forecasting method is to model the stock market forecasting problem as a regression or classification problem in machine learning. Existing technologies include: building a forecasting system, simplifying the stock forecasting problem into a classification problem, and making judgments by analyzing some feature distributions of news texts that affect stock prices; using multi-core machine learning models to perform regression forecasting on stocks. However, most existing methods often rely on experts to select features. However, it is very difficult to discover an...

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Application Information

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IPC IPC(8): G06Q40/04G06F17/27G06K9/62G06N3/02
CPCG06N3/02G06Q40/04G06F40/216G06F40/289G06F18/214
Inventor 李晓东贡诚冯钧
Owner HOHAI UNIV
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