Method and device for predicting daily shareholder household number

Abstract

The invention aims to provide a method and a device for predicting the daily shareholder household number. The method comprises the following steps: firstly, determining a target prediction model for predicting the daily shareholder household number of a target listed company; then, acquiring a real-time rising and falling cause index and a real-time control variable of the target listed company on the current trading day in an actual application scene; and inputting the real-time ups and downs cause indexes and the real-time control variables into the target prediction model for prediction to obtain the corresponding real-time shareholder household number of the target listed company on the current trading day, thereby realizing timely and accurate prediction of the daily shareholder household number of the listed company. The timeliness of the timely and accurate daily shareholder household number is improved, and the practical value of the daily shareholder household number is also improved.

Description

technical field [0001] This application relates to the field of computer technology, in particular to a method and equipment for predicting the number of daily shareholders. Background technique [0002] In the existing technology, the real number of shareholders of listed companies is disclosed on a regular and quarterly basis or through irregular performance conferences of listed companies. The specific data on changes in the number of shareholders can only be obtained from annual reports, interim reports, and quarterly reports. The data in the annual report often lags behind by 3 to 4 months, and the interim report also lags behind by 1 to 2 months. The irregular performance release conference has no rules to follow, so the analysis based on this data often has a large lag and uncertainty. Difficult to provide predictable value. [0003] The change in the number of shareholders is mainly the result of shareholders' trading operations in the secondary market. Shareholders...

AI Technical Summary

Problems solved by technology

In the prior art, only the daily trading volume of stocks and the trend of futures prices are predicted, and there is no plan for predicting the daily number of shareholders of stocks. In the prior art of predicting financial data, , most of the technical solutions are forecasting research on trading volume and price trends, and for market data such as trading volume and price, the disclosure period is short, and there are almost real-time data. Compared with the data on the number of shareholders disclosed on a quarterly basis, It is because the disclosure cycle is long and it is impossible to keep abreast of changes in the number of shareholders of listed companies

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