Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Abstract

The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

[Retracted] Analyzing the Relationship among Social Capital, Dynamic Capability, and Farmers′ Cooperative Performance Using Lightweight Deep Learning Model: A Case Study of Liaoning Province

“…However, with the continuous deepening of research, many scholars have applied it to evaluation problems. Luo and Ren (2021) established an enterprise performance evaluation model based on neural network (NN) and optimized the model using factor analysis and a genetic algorithm (GA). Te main factors afecting enterprise performance were analyzed.…”

“…Ten, it quantitatively analyzed the performance of the acquirer in the merger and acquisition (M&A) commitment period under the income compensation mechanism. Te corresponding assumptions and evaluation indicators were established [5]. Liang and Li [6] proposed a spatial network-distributed data mining algorithm based on backpropagation NN (BPNN) to evaluate personnel performance efectively.…”

Assets Valuation, Mergers and Acquisitions of Business Organisations in Nigeria

“…The "takeover of one company's ownership and managerial control by another" is referred to as an acquisition (Apreku-Djan, Ameyaw, Ayittah, Ahiale, & Owusu, 2022). An asset, such as a "plant, a division, or perhaps an entire company," is purchased by the purchasing organization (Luo & Ren, 2021). A corporation is bought by another party in a transaction known as an acquisition (Qian & Zhu, 2018).…”

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

“…Quantitative data can be obtained directly, while qualitative data are scored by experts to quantify qualitative data. Experts' scoring of quantitative data is based on the actual performance of related companies and the risk classification standards of company A. e expert scores are [0-10], "extremely poor" is [0-2), "poor" is [2][3][4], "moderate" is [4][5][6], and "good" is [6][7][8], "excellent" is [8][9][10]. According to the original data of company A and expert scores, the risk assessment samples are shown in Table 2.…”

“…From the existing research results, it can be seen that most high-tech enterprises do not pay enough attention to the comprehensive performance of science and technology [9], which leads to unreasonable evaluation indicators, single evaluation standards, and unscienti c evaluation methods, which brings great in uence to the development and management of comprehensive performance. ere are big problems [10], such as reduced overall performance satisfaction, or even resignation. In the long run, the management of the enterprise will fall into chaos, and the goals of the enterprise will be difficult to achieve.…”