Interview with Lithium co-founder David Lighton: How to accurately value illiquid assets?

Decentralized finance and centralized finance are still in two disjoint worlds. If these two ecosystems can be connected together, there will be huge business opportunities and room for profit. Lithium Finance is exploring technical bridges in this direction, seeking valuation consensus in a decentralized world for opaque assets in traditional finance.

Recently we invited David Lighton, co-founder of Lithium Finance, to explain in detail their unique technical direction and how to connect two very different financial fields. 

Q: Can you introduce yourself to everyone?

David Lighton: I have been in the blockchain industry for five years. Previously, I developed the SendFriend cryptocurrency payment project, which is still in operation. It is the first compliant cryptocurrency payment system deployed on the Ripple blockchain. Before that, I worked as a special assistant in the Haiti region of the World Bank, responsible for the development of the local financial ecology. It was also because of this experience that I entered the blockchain industry.

Q: How does Lithium substitute real-life scenes into DeFi?

David Lighton: The problem with decentralized DeFi is the lack of connection with traditional finance. Many people think that DeFi and traditional financial ecology are not overlapped. Although DeFi is very popular now, it is still too niche.

The next step is to connect DeFi with traditional finance. The core concept of Lithium is to bring real application scenarios into DeFi. Our strategic partner Linear is currently the largest synthetic asset exchange. The platform supports the trading of synthetic stocks of unlisted companies and other products that are not visible in the open market.

We can bring new investment opportunities to this field and provide valuations for illiquid private assets. In traditional finance, participants can obtain this information through market makers and research institutions, but the information is not necessarily accurate, and no one knows where the price of the stock IPO is. We will price illiquid assets based on game theory and algorithmic models. The core problem of DeFi is the lack of connection with traditional capital markets. Lithium will build this bridge.

Q: How can users obtain accurate information about the valuation of private companies by using Lithium?

David Lighton: There are two types of users in the Lithium platform ecology. Wisdom Seeker is the questioner. If you want to know what the Ripple IPO price is, the questioner needs to provide platform tokens as a bonus. This question will be sent to the Wisdom Node answerer and they will submit them separately Their estimated price is sent to the platform system. After the system analyzes the information, it will give the questioning party a final answer.

The platform is composed of both the questioning party and the answering party. The function of the platform is to summarize information. We will ask the questioning party to give rewards for answering, the answering party that provides a good answer will be rewarded, and the answering party that provides a bad answer will be punished.

We used to make market value forecasts for Coinbase IPO. At that time, many analysts’ forecasts were in the range of 1,800, 2,000, and 220 billion U.S. dollars. The valuation given by the Lithium system at the time was US$80 billion. We thought the system was underestimated at the time, but in the end it was confirmed that the system’s valuation was more accurate, which verified the value and accuracy of the platform.

Interview with Lithium co-founder David Lighton: How to accurately value illiquid assets?

Q: How is the mathematical model of the Lithium system selected among the many answers provided by the answering party ?

David Lighton: The probability model we use comes from the innovation of computer scientists at Peking University. This is a mechanism called DMI, which is an application of game theory. In a nutshell, if there are 100 people in a group, and each person submits answers to 100 questions, in principle, we can get the consensus of this group through the model.

When the system finds that the quality of the answers given by someone every time is good, the platform will increase his weight. If the answer given by another person is bad every time, his weight will be lowered, thus forming a system mechanism with self-learning ability. At the same time, supporting incentive mechanism allows excellent answerers to get more reputation points and communication. Certificate rewards.

Q: Where is Lithium’s target market? In what areas is there a demand for this kind of valuation forecasting method?

David Lighton: We hope that Lithium will participate in the pre-IPO price prediction of stocks and also provide valuations for assets in the private market. Suppose that a private equity fund wants to acquire a medium-sized company, but there are not many prospective buyers in the market. In this case, the company’s valuation will be very opaque. This is the market opportunity for the Lithium platform. 

The current valuation models taught by schools are actually based on fundamental analysis like DCF, but market valuations often do not follow this logic. Generally, there is not much competition in private equity acquisitions, and the acquirer is very likely to buy high or buy low. Lithium will play a role when the market price cannot be obtained through open auction.

There is also an area where the unit price is low, such as the antique market such as heirlooms and gold watches. These areas are suitable for the public to participate in assisting in the valuation of items. Now that the technology is very mature, there is no reason not to explore in this direction. The information in the private market is very asymmetrical. When you approach an antique shop, you are very likely to find undiscovered goods, but it is also very likely to spend wrong money to buy expensive.

We are very optimistic about these three markets.


Posted by:CoinYuppie,Reprinted with attribution to:
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