Abstract
Intellectual property pledge financing is effective in alleviating the financing problems of scientific and technological enterprises to a certain extent. However, compared with traditional loans, intellectual property pledge financing is at greater risk with an eye to the particularity of intellectual property pledges. Therefore, it is very important to evaluate the risk of intellectual property pledge financing. This article first outlines the information entropy method, functionalizing indicator data and weight data, and applies the combination of the weight function and indicator function in dynamic evaluation function, to get the final evaluation result. Second, taking four high-tech listed companies on the growth enterprises market (GEM) as example, an evaluation indicator system has been constructed with 16 relevant financial indicators from 2015 to 2019. Then, based on the dynamic function method, we can construct the evaluation function to describe the change trend of the financial indicators of enterprises with a comparison of the advantages and disadvantages of the financial indicators of enterprises. Finally, this article has made a comprehensively comparative analysis on the dynamic evaluation, and the financial risk of intellectual property pledge financing of high-tech enterprises has been done with the combination of the dynamic evaluation curve with the optimal time weight determined by the maximum entropy method.
Highlights
Erefore, it is necessary to comprehensively evaluate the financial situation of pledge financing enterprises
It is more likely to minimize the cost of default risk when the problem is found early; the sooner, the better. e method commonly used in science and technology evaluation consists of the AHP analysis method by Schenkerman [15], fuzzy comprehensive evaluation method by Chen and Hwang [16], DEA evaluation method by Ding et al [17], and VIKOR method by Liu et al [18]
It is of great significance to enrich information mining technology in the era of big data. e implementation of the traditional dynamic comprehensive evaluation method is mainly based on the cross-sectional data and time series data, which turns out to be discrete data in the process of evaluation, including original data, weight, and final evaluation
Summary
Intellectual property pledge financing is risky with great uncertainty. If the default happens to enterprises, debt assets are difficult to realize with creating a chain reaction, which requires financial institutions to evaluate the financial status of enterprises’ pledge financing. Due to errors or noise in the original data, the relationship between the actual observed data and the underlying function is as follows: yh x th + εh, h 1, 2, . K, estimation of x(t) can be obtained by the linear combination of the basis functions as follows: K x th ckφk th. When a linear combination of basis functions is used to smooth the data, the coefficients ck, k 1, 2, . To ensure the smoothness of the fitting, a penalty term is introduced in the object function: PENSSE min x th − x th2 + λ D2x(t)dt h 1. Λ means a measure between the degree that the function of x(t) fits the data and the volatility of the function itself, which is called a smoothing parameter. It is easy to estimate λ using the generalized cross-validation (GCV) method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
