LAUSR

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.