A Neural Network Model for Business Performance Management Based on Random Matrix Theory

Abstract

This paper uses random matrix theory to construct a neural network model for business performance management. The random sample covariance matrix of the random monitoring matrix is constructed, and the maximum eigenvalue and the minimum eigenvalue of the sample covariance matrix are solved. The ratio of eigenvalues is used to construct the eigenvalue detection index and determine the eigenvalue index detection threshold algorithm to judge the abnormal state of enterprise operation. The data of 66 listed Internet finance companies are selected, normalized, and correlation tested, and the index weights of each level are obtained using hierarchical analysis to derive the expected output of the BP neural network. Finally, the constructed BP neural network performance evaluation model is used for network training and simulation analysis, in which 192 data of 48 companies in the last four years are selected as training samples and 8 companies in the last four years are used as test samples to analyze the simulation output results. Using the original data instead of the main factor to join the BP neural network model, after two systematic optimizations, the final model with high accuracy, low mean square error, and low average error was formed. When applying the newly added data for testing, an accuracy of 95.98% was achieved for the ranking prediction, and the average deviation of 0.0021 points for the score prediction, which fully reflected the feasibility and adaptability of the model.

Improving target budgeting in a corporate performance management system

“…In addition, examples of analytical support for the planning process using artificial intelligence have been presented in international literature. Zhang [4] in his paper applied principal component analysis and artificial neural network analysis to build the RMT-BP model for predicting the performance indicators of companies. Various financial and non-financial indicators characterizing the performance of companies are used as input parameters, and the corresponding weights of indicators are determined by the hierarchy analysis method.…”

“…One reason for these issues is insufficient comprehension that translating corporate strategy into budget planning requires a distinct mathematical foundation for the company's performance management. The issue at hand has been pertinent in recent decades, as evidenced in studies by both national and international authors: Bourne, Maryška, Doucek, Zhang, Mari, Kitova, Bruskin, Odintsov, Nikishova [1,[3][4][5][6][7][8][9]. This paper presents an analytical approach to improving the standard budgeting process by designing and executing the inverse calculation algorithm.…”