In this article, I intend to discuss the importance of market data, decentralized finance (DeFi) econometrics, and applied DeFi research to encrypted (and digital) assets as an inevitable result of financial econometrics and applied research. I will also try to draw on the opinions and findings of Eugene Fama’s seminal paper based on his interest in measuring the statistical properties of stock prices and resolving the dispute between technical analysis (using geometric patterns in price and volume charts to predict future prices). Changes in securities) and fundamental analysis (using accounting and economic data to determine the fair value of securities).Nobel Laureate Fama Operable The efficient market hypothesis- to sum up Concise aphorism “price fully reflects all available information” In an efficient market.
So let’s focus on information about encrypted and digital assets, encrypted and decentralized financial data source, Market data analysis, and everything surrounding the large-scale emerging DeFi industry, which is critical to attracting institutional investors into the crypto, DeFi, and broader “token” markets.
In most markets, market data is defined as the prices of instruments (assets, securities, commodities, etc.) and data related to trade. This data reflects the volatility, transaction volume, and specific transaction data of the market and asset class, such as opening price, highest price, lowest price, closing price, volume (OHLCV) and other value-added data, such as order book data (ask spread, Aggregate market depth, etc.) and pricing and valuation (reference data, traditional financial data such as the first exchange rate, etc.). These market data play an important role in various financial econometrics, applied finance, and current DeFi research, such as:
- Risk management and risk model framework
- Quantitative trading
- Price and valuation
- Portfolio construction and management
- Entire crypto finance
Although the application of traditional methods to assess risks and identify different degrees of opportunity distribution in different and emerging crypto asset classes may be restricted, this is the beginning. New valuation models have emerged, aiming to understand these digital assets that have occupied a truly dominant position in the global digital market, and even these models require market data. Some of these models include but are not limited to:
- VWAP, Or volume-weighted average price, a method that usually determines the fair value of digital assets by calculating the volume-weighted average price from a set of pre-selected component exchanges’ available post-trade data.
- TWAP, Or time-weighted average price, which can be an oracle or a smart contract, which derives the token price from the liquidity pool, and uses the time interval to determine the collateral ratio.
- growth rate Determine mortgage factors.
- TV line, Or lock the total value for use in the liquidity pool and automatic market maker (AMM).
- Total number of users Reflects network effects and potential usage and growth.
- Main market methods Applicable to major markets, which are usually defined as the market with the largest trading volume and activity volume of digital assets. The fair value will be the price received by the digital asset in the market.
- CEX and DEX trading volume It is the sum of the trading volume of centralized exchanges (CEX) and decentralized exchanges (DEX).
- CVIOr crypto volatility index, which is created by calculating the diversified volatility index from the price of cryptocurrency options and analyzing the market’s expectations for future volatility.
Therefore, market data becomes the core of all modeling and analysis tools used to understand the market, and is also used to perform correlation analysis between the first layer, second layer, Web 3.0, and DeFi. The main source of this crypto market data comes from a growing and decentralized portfolio of crypto exchanges.Data from these exchanges cannot be widely disseminated trustworthy, As we have seen examples of inflating trading volume through practices such as wash trading and closed pools, these practices may distort prices by distorting demand and trading volume. Therefore, modeling hypotheses based on empirical data and then testing that hypothesis to formulate investment theories (insights from experience summaries) can be tricky. This creates an oracle that aims to solve the problem of trusted data entering the blockchain transaction system or the intermediary layer between encryption and the traditional financial layer.
Blockchain is the basic technology for managing all encrypted assets and networks. It touts the basic principles of trade, trust and ownership based on the transparency of the trust system (or consensus) expansion. So why is market data such a big problem? Isn’t relying on marketable and easy-to-analyze data part of the spirit of the blockchain and crypto industry?
The answer is “Yes! But!” When we intersect the cryptocurrency market with fiat-based liquidity, things get interesting-transactions denominated in U.S. dollars, euros, yen, and pound sterling are promoted by cryptocurrency exchanges The track of traditional finance.
Understand encrypted macros and distinguish global macros
As Peter Tchir, Head of Global Macroeconomics at Academy Securities, based in New York, said, Explanation In an article written by Simon Constable: “Global macro is a term that indicates that the underlying trends are so great that they can raise or lower the economy or most of the stock market.” The officer added Say:
“They are different from micro factors, which may affect the performance of individual companies or sub-sectors of the market.”
I want to distinguish between global macros and encrypted macros.Although global macro trends-such as inflation, money supply, and other macro events-affect global demand and supply curves, cryptocurrency macro-manages various sectors (such as Web 3.0, first tier, second tier, DeFi, and Irreplaceable tokens), representing the tokens of the departments and events that affect the corresponding movement of these asset classes.
When limited to interchangeability between asset classes and exchange mechanisms (such as loans, collateral, and exchanges), crypto (and digital) asset classes define a whole new field of asset creation, trading, and asset movement. This creates a macro environment based on the principles and theories of cryptoeconomics. When we try to link these two main macroeconomic environments to inject or transfer liquidity from one economic system to another, due to the conflict of value systems, we basically complicate our metrics and market data化.
Let me use an example to illustrate the complexity of the importance of market data and other factors in formulating investment theories based on insights from experience summaries.
Although the first layer provides important utility for many ecosystems that appear on the first layer network, not all first layer networks are equal and do not provide the same identification value and characteristics. Bitcoin (Bitcoin), for example, has a first-mover advantage, and to some extent is the face of the cryptocurrency ecosystem. It started as a public utility, but has evolved into a store of value and an asset class as an inflation hedge trying to replace gold.
Ether (Ethereum), on the other hand, the concept of programmability (the ability to apply conditions and rules) is put forward to value movement, thereby creating a rich ecosystem such as DeFi and NFT. Therefore, ETH becomes a utility token that powers these ecosystems to promote co-creation. The increase in transaction activity drives the demand for ether because it is needed for transaction processing.
Bitcoin, as a store of value and an inflation hedge, is completely different from the growing and emerging businesses on the first layer of the network. Therefore, it is important to understand what gives these tokens value. The utility of a token as a network toll makes it valuable, or its ability to store and transfer (large) value in a short period of time gives it an advantage over existing value mobile or payment systems.
In either case, utility, transaction volume, circulation supply and related transaction indicators provide insights into the valuation of tokens. If we want to analyze and study the impact of deeper macroeconomics on valuation (such as interest rates, money supply, inflation, etc.), as well as crypto macros that directly or indirectly affect the correlation of other crypto assets and cryptocurrencies at the first layer Factors, the resulting theory will include the growth of basic technology, the role of local asset classes, and maturity premiums. This will indicate technical risks and market adoption, network effects and liquidity premiums, and indicate widespread acceptance in various crypto-driven ecosystems. For example, the strategic matching investment view for the construction of the crypto portfolio includes consideration of the macroeconomic cycle, crypto liquidity (the ability to convert crypto assets), and the macro impact of crypto, and treat these as the medium-term low-risk framework of our risk model.
The availability of trusted encrypted market data not only enables real-time and on-site transaction decisions, but also enables various risk and optimization analyses required for portfolio construction and analysis. This analysis requires additional traditional market data, because we have begun a dialogue with traditional finance-related market cycles and liquidity, which can also try to link the crypto macro sector with the global macro sector. From a modeling perspective, this can quickly become complicated, simply due to the difference between the diversity and speed of market data between the two value systems.
As important as the efficiency of the crypto market is for good financial decisions, it is misunderstood and distorted by poor or inadequate information. It is the crypto (economic) market data and various economic models that enable us to understand the emerging and chaotic crypto market. The principle of the efficient market hypothesis-which means that in an efficient market, prices always reflect available information-also applies to the encrypted market.
Therefore, market data becomes the core of all modeling and analysis tools, used to understand the market and perform correlation analysis between various encryption industries, such as the first layer, the second layer, Web 3.0, and DeFi. The main source of this crypto market data comes from a growing and decentralized portfolio of crypto exchanges. Encryption and digital asset classes define new areas of asset creation, trading, and asset movement, especially when limited to interchangeability between asset classes and exchange mechanisms (such as loans, collateral, and exchanges). This creates a macro environment based on the principles and theories of cryptoeconomics.
When we try to link these two major macroeconomic environments to inject or transfer liquidity from one economic system to another, we basically complicated Due to conflicts in the value system, our measurement indicators and market data. This analysis requires additional traditional market data, as we have started a conversation with traditional finance-related market cycles and liquidity, and tried to link the crypto macro sector with the global macro sector. From a modeling perspective, this can quickly become complicated, simply due to the difference between the diversity and speed of market data between the two value systems.
This article does not contain investment advice or recommendations. Every investment and trading action involves risks, and readers should research on their own when making a decision.
The views, thoughts and opinions expressed here are only those of the author, and do not necessarily reflect or represent the views and opinions of Cointelegraph.
Niding Gower He is the founder and director of the IBM Digital Asset Lab, where he designs industry standards and use cases, and is committed to making enterprise blockchain a reality. He previously served as the CTO of IBM World Wire and IBM Mobile Payments and Enterprise Mobile Solutions, and founded IBM Blockchain Labs, where he led the work of establishing blockchain practices for enterprises. Gaur is also an IBM Distinguished Engineer and IBM Master Inventor, with a rich patent portfolio. In addition, he also serves as the research and portfolio manager of Portal Asset Management, a multi-manager fund specializing in digital assets and DeFi investment strategies.