FnGuide, a financial information company, said on the 4th that it published a research paper on a digital asset classification system co-developed with Bonanza Lab, a digital asset data company, in a domestic academic journal. The system classifies assets based on their intended use and role in the market, and is expected to be used for developing related indexes and valuation.
The paper was published in Venture Startup Research, a Korea Citation Index (KCI)–listed journal issued by the Korea Venture Startup Association. Researchers from both companies and Kang Hyeong-gu, a professor in the Department of Finance and Management at Hanyang University, took part in the study. Bonanza Lab is a data company that collects and processes price quotes and transaction data from domestic and overseas virtual asset exchanges and provides them to financial institutions and companies.
The subject of the study is the digital asset classification system K-DACS (Korean Digital Asset Classification Standard), which the two companies unveiled in Mar. They fleshed out a methodology that classifies existing digital assets by intended use and function performed in the market. They applied it to 13 major digital assets, including Bitcoin (BTC) and Ethereum (ETH), to analyze characteristics by asset.
The two companies believe this study will form the basis of "financial reasoning" that analyzes the value and risk of digital assets. Financial reasoning refers to the process of combining financial expertise with structured data to analyze and predict an asset's intrinsic value and risk factors.
The classification system is expected to be used for future digital asset index development, valuation model building, and risk management. They said that distinguishing characteristics by asset according to consistent criteria makes it easier to compare similar assets or identify risk factors in the market. The two companies also expect it to help alleviate information asymmetry in the digital asset market and strengthen investor protection.
An official from the two companies said, "The publication of this paper is meaningful in that it strengthens the academic foundation of the digital asset classification system," and added, "We will continue to advance our data-based analysis framework to help improve transparency and reliability in the digital asset market."
