Abstract
Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company’s net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies.
Highlights
In recent years, driven by the rise of wireless sensor network[1][2], Internet of things[3], Radio Vehicular Networks [4], Cloud Systems[5] and other Cyber-Physical Systems[6], the development of radio frequency identification (RFID) industries has reached a new milestone
The huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method
Those show that using improved bacterial foraging optimization algorithm (IBFOA) to optimize Bayesian probability function (BPF) and clustering NG with BPF are good at predicting the profit of company and enhanced community mining method (ECMM) is expert in evaluating the business value
Summary
Driven by the rise of wireless sensor network[1][2], Internet of things[3], Radio Vehicular Networks [4], Cloud Systems[5] and other Cyber-Physical Systems[6], the development of radio frequency identification (RFID) industries has reached a new milestone It makes the breakthrough progress in all areas of production and life, especially in the financial payment, retail supply chain, clothing supply chain, logistics, and product traceability. Little attention has been directed to network graph (NG) method To address these problems, we define NG to describe the non-linear relationship between the company’s net profitability and its external and internal environment factors[13], and develop the enhanced community mining method (ECMM) based on Bayesian method (BM) for company profit predicting.
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