Stock fluctuation prediction method and device

Abstract

Embodiments of the invention provide a stock prediction method and apparatus. The method comprises the steps of obtaining multi-source historical data of a to-be-predicted target stock and multi-source historical data of a reference stock related to the target stock, wherein the multi-source historical data of the stock comprises stock market quantitative data, news title data and network evaluation data related to the stock in each unit time of a preset number of unit time before the prediction time point; extracting feature data from the multi-source historical data; correcting the feature data of the multi-source historical data of the target stock based on the feature data of the multi-source historical data of the reference stock to obtain corrected data of the target stock; and inputting the correction data into the prediction model to obtain a fluctuation prediction result of the target stock. Historical data, including stock market quantitative data, news title data and networkevaluation data, of multiple data sources are comprehensively considered, and the trend of stock fluctuation can be captured more accurately.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a stock fluctuation prediction method and device. Background technique [0002] The stock market is an important part of the financial market, and the volatility prediction of the stock market is highly dependent on data from various data sources. [0003] Existing stock volatility prediction methods rely on stock historical quantitative data, such as transaction price, turnover rate and other data. Only considering the historical quantitative data of stocks is too one-sided and cannot fully capture the trend of stock fluctuations. For example, market-related social networking posts may change investor perceptions, which in turn affect stock prices. However, the existing stock volatility forecasting methods do not take these factors into consideration. It can be seen that the existing stock volatility forecasting methods cannot accurately predict stock volatility. Conte...

AI Technical Summary

Problems solved by technology

Only considering the historical quantitative data of stocks is too one-sided and cannot fully capture the trend of stock fluctuations

For example, market-related social network posts may change investor perceptions, which in turn affect stock prices

However, the existing stock volatility forecasting methods do not take these factors into account. It can be seen that the existing stock volatility forecasting methods cannot accurately predict stock volatility.

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