Stock market investment decision-making method based on network analysis and multi-model fusion

Abstract

The invention discloses a stock market investment decision-making method based on network analysis and multi-model fusion. The method includes the steps that fundamental information is grabbed from a network and then network nodes and network connection are constructed; a complex social network model is constructed; an investment portfolio is selected by means of a network analysis method and then data involved in the investment portfolio are input into a multi-model fusion frame, wherein the multi-model fusion frame comprises a plurality of sub-models; all the sub-models conduct market trend prediction with different characteristics according to technical information of different characteristics grabbed in the network generate predicted values of the corresponding sub-models, the predicted values are weighted and summated to obtain a comprehensive market trend predicted value, and then corresponding investment strategies are generated according to the comprehensive market trend predicted value. According to the method, risk factors, which are ignored in general researches, of the investment portfolio are comprehensively considered from multiple angles, real-time requirements of the strategies are guaranteed through the methods such as dimension reduction data preselection of fundamentals and technical feature selection, and consequently the more reliable investment strategies are provided.

Description

technical field [0001] The invention belongs to the technical field of stock market investment strategy analysis and relates to a stock market investment decision-making method based on network analysis and multi-model fusion. Background technique [0002] The financial market is the core of a country's economic operation. Therefore, the issue of securities investment strategy has always been one of the core issues that are most concerned by the investment theory and investment practice circles of various countries. The securities investment strategy reflects our understanding of the laws of the financial market and investor psychology. , is a rule system and action plan to guide investment behavior specified according to investment objectives. Among them, technical analysis and fundamental analysis methods are two main investment analysis methods. Among them, technical analysis is mainly used in the time and space judgment of specific investment operations, and fundamental...

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Problems solved by technology

However, these forecasting methods are based on the law of historical data for predictive analysis, which belongs to the idea of ​​technical analysis, and its theory is refuted by the efficient market hypothesis, and the information it uses is limited

And the traditional research only considers the prediction accuracy rate, and considering the frequent fluctuation of the actual stock price, the accuracy rate does not directly correspond to the actual investment income, and even a high prediction accuracy rate may bring negative returns. In addition, such a portfolio May bring higher investment risk

[0006] At the same time, traditional stock price trend analysis and prediction methods do not take into account the scale and timeliness of data, so the amount of information used is very limited, and training is very time-consuming when forecasting under large amounts of data. Considering the actual investment decision-making Real-time requirements have been unable to adapt to the stock market analysis and prediction requirements in the network environment and massive data scale

Moreover, the existing stock forecasts mostly ignore the correlation between stocks, and think that the direct price changes of stocks are independent of each other. On this basis, the research and analysis of the law of stock price trend changes is carried out. This simplified assumption obviously violates our financial knowledge. The general understanding of the market, because the listed companies corresponding to each stock are related to each other and will be influenced and affected by each other

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