The improved business valuation model for RFID company based on the community mining method

Area of Science:

• Business Valuation
• Financial Modeling
• Network Analysis

Background:

• Growing investment and M&A activity in RFID companies necessitates advanced valuation models.
• Existing statistical and neural network models have limitations in capturing complex business relationships.
• Few studies explore network graph (NG) frameworks for business valuation.

Purpose of the Study:

• To propose a novel NG-based business valuation framework for RFID companies.
• To integrate community mining (CM) and real options approach (ROA) for improved net profit prediction and company value estimation.
• To enhance valuation model performance through network clustering and optimized prediction accuracy.

Main Methods:

• Development of an NG-based business valuation model.
• Integration of CM with ROA for financial forecasting.
• Application of evolutionary Bayesian methods for network clustering and node credibility assessment.
• Utilization of an improved bacterial foraging optimization algorithm (IBFOA) for optimizing Bayesian posterior probability.
• Employing a bi-objective method with a Pareto boundary approach within IBFOA for prediction accuracy assessment.

Main Results:

• The proposed model demonstrates lower forecasting error compared to 10 established forecasting models.
• The model's effectiveness was validated on 3 distinct time-interval valuation tasks using real-life RFID company data.
• The integration of NG, CM, and ROA provides a robust framework for business valuation.

Conclusions:

• The novel NG-based valuation model offers superior forecasting accuracy for RFID companies.
• The study highlights the potential of network graph analysis and community mining in financial valuation.
• The proposed framework provides a significant advancement over traditional business valuation methods.