Abstract
In recent years, how to carry out effective high technology investment risk prediction in the financial market with constraints has attracted the attention of scholars. In this paper, partial linear regression model is used to describe and analyze the stock data with inconsistent length in different markets. And high technology investment risk prediction is carried out on the basis of improved yield and covariance matrix. The results of the numerical example show that the high technology investment risk prediction using partial linear regression analysis under inequality constraints has better performance than the investment model using the ordinary least square regression when the length of stock data is inconsistent.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
