Prediction of Enterprise Free Cash Flow Based on a Backpropagation Neural Network Model of the Improved Genetic Algorithm

被引:9
作者
Zhu, Lin [1 ]
Yan, Mingzhu [2 ]
Bai, Luyi [2 ]
机构
[1] Northeastern Univ, Supervising & Auditing Off, Qinhuangdao 066004, Hebei, Peoples R China
[2] Northeastern Univ, Sch Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
BP neural network; enterprise economy; free cash flow; improved genetic algorithm; prediction; CORPORATE GOVERNANCE; MARKET; PRICE; OIL;
D O I
10.3390/info13040172
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Enterprises with good long-term free cash flow data often have better prospects than enterprises with good net profit but unstable free cash flow for a long time, and free cash flow prediction is an important part of evaluating the enterprise value of an enterprise. By determining the fitness function, algorithm formula, population, and Backpropagation (BP) neural network design, a BP neural network model based on the improved genetic algorithm is proposed to predict the free cash flow of enterprises. Taking the free cash flow data of G Company from 1 January 2019 to 30 June 2019 as an example, after evaluating the most neurons and the best population, analyzing the relative errors and comparing the average relative errors of different prediction models, the results show that the model has better prediction accuracy. Cash flow forecasting can effectively improve decision making on productions and operations and the investment financing of enterprises, and has important practical significance for studying enterprise fund management.
引用
收藏
页数:12
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