私募股权投资基金定向增资新三板投资策略研究——基于BP和Hopfield神经网络模型的实证分析

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Computational Intelligence and Applications Pub Date : 2023-10-26 DOI:10.1142/s1469026823420014

Liu Yajuan, Xu Wenbin

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引用次数: 0

摘要

私募股权投资基金定向增发新三板已成为私募股权投资的新策略。然而，目前采用的Logit回归和单因素方差分析模型不适合分析非线性投资活动，投资评价效果不佳。本文以2017年实施定向增发的新三板公司为研究样本。本文还对基于BP和Hopfield神经网络模型的国内私募股权投资基金现状进行了实证分析，并对两种模型的结果进行了比较。结果表明，BP神经网络模型的准确率可达90%以上。因此，BP神经网络可以作为私募股权投资基金在新三板投资策略的最优模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research on the Investment Strategy of Private Equity Investment Fund Targeted Increase in NEEQ — An Empirical Analysis Based on BP and Hopfield Neural Network Model

Private equity investment funds targeted increase in NEEQ has become a new strategy for PE investment. However, the currently adopted Logit regression and one-factor ANOVA models are not suitable for analyzing nonlinear investment activities, and the investment appraisal does not work well. In this paper, all NEEQ companies that implemented private placement in 2017 are used as the study sample. This paper also empirically analyzes the current situation of domestic private equity investment funds based on BP and Hopfield neural network models, then the results of the two models are compared. It is concluded that the accuracy of the BP neural network model can be more than 90%. So, the BP neural network can be used as the optimal model of private equity investment funds investment strategy in NEEQ.

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来源期刊

International Journal of Computational Intelligence and Applications COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.90

自引率

0.00%

发文量

期刊介绍： The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.