Method for predicting correlation risk between stocks and terminal equipment
A correlation and stock technology, applied in the information field, can solve problems such as high difficulty, strong noise, and large prediction error
Inactive Publication Date: 2021-09-10
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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[0004] The purpose of the present invention is to solve the problems of large prediction error, high difficulty and strong noise in the prior art, and propose a method and terminal equipment for predicting the correlation risk between stocks
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[0029] In order to make the technical problems, technical solutions and beneficial effects to be solved by the embodiments of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0030] It should be noted that when an element is referred to as being “fixed” or “disposed on” another element, it may be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, the connection can be used for both fixing and circuit communication.
[0031] It is to be understood that the terms "length", "width", "top", "bottom", "front", "rear", "left", ...
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The invention discloses a method for predicting correlation risks between stocks and terminal equipment. The method comprises the steps of: performing data collection: selecting stock market information and basic data in a period of time; performing data processing: calculating by using a multi-factor model to obtain a public risk factor revenue matrix so as to achieve the purpose of dimension reduction; calculating a covariance matrix; decomposing the covariance matrix into a lower triangular matrix and a transposed product of the lower triangular matrix by adopting cholesky decomposition; performing a contrast experiment; and using the coding-prediction convolution long-short term memory network model corresponding to the minimum loss function as an optimal model to realize stock correlation prediction in the next day. According to the method, the space-time sequence convolution long-short term memory network is applied to stock correlation prediction, so that the problems of excessive hypothesis limitation, excessive to-be-estimated parameters and incapability of fitting nonlinearity and tail risks of a traditional financial model are solved, and the prediction accuracy is improved; and real information and noise in a covariance matrix are identified by adopting a random matrix theory, so that the problem of false correlation is solved.
Description
technical field [0001] The present invention relates to the field of information technology, in particular to a method for predicting correlation risks between stocks, terminal equipment and a computer-readable storage medium. Background technique [0002] The correlation between stocks has important reference value for investors in asset pricing and evaluation, portfolio risk management, and formulation of trading and hedging strategies. According to Markowitz's portfolio theory, the risk of a portfolio depends on the characteristics of all the stocks that make up the portfolio and the degree of correlation between stocks. Statistics from Bloomberg show that the 2020 COVID-19 pandemic caused the average correlation coefficient of S&P 500 constituents to rise to 0.82 from February 19 to March 31, a year-on-year increase of 67%. The increase in the degree of correlation between stocks makes the allocation of the whole market meaningless, the investment portfolio and hedging ...
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IPC IPC(8): G06Q40/04G06N3/04G06N3/08
CPCG06Q40/04G06N3/08G06N3/044
Inventor 孙佳琪江勇林健武
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV