Data Asset Pricing Approach Based on Hierarchical Analysis and Market Approach
Pricing data assets have been a challenging issue so far. With the advent of the big data era, data assets have become a critical element for enterprise core competitiveness and decision support. Due to the unique nature of data assets, traditional asset pricing methods are not entirely applicable to them. As a result, there is currently a lack of clear transaction rules and mature pricing methods for data assets. As a result, game theory, option pricing methods, and market comparison methods have emerged as new avenues for pricing data assets. This study proposes a pricing model that integrates the Analytic Hierarchy Process ( AHP ) and market-based approaches, building upon existing methods. Specifically, it starts by employing an expert scoring method to select suitable data evaluation indicators. The AHP is then applied to determine the weights of each evaluation indicator. Subsequently, transaction instances involving similar data assets are gathered from trading platforms. Market correction factors are calculated using a market pricing approach. The model utilizes both a weight-adjusted pricing model and a coefficient-adjusted model to assess the pricing of the data assets. The model takes into account the unique attributes of data assets and combines the strengths of various pricing methods, enabling a more accurate and scientific assessment of data assets.
data pricinganalytic hierarchy processmarket approachindicator weightsexpert scoring method
万方数据
维普
