In this article, I intend to discuss the importance of market data, decentralized finance (DeFi) econometrics, and DeFi applied research on crypto (and digital) assets as a complement to financial econometrics and applied research. I will also try to draw on the perspective and lessons learned from Eugene Fama’s groundbreaking work, based on his interest in measuring the statistical properties of stock prices and resolving the debate between technical analysis (the use of geometric patterns in price and volume charts Predicting future prices (based on movements of a security) and fundamental analysis (the use of accounting and economic data to determine the fair value of a security). Nobel laureate Fama operationalized the hypothesis of the efficient market – summarized in a compact epigram that “prices fully reflect all available information” in efficient markets.
So let’s focus on this information about crypto and digital assets, crypto and decentralized financial data sources, market data analysis and everything surrounding the massively emerging DeFi industry that is essential for attracting institutional investors to crypto, DeFi and broader “tokens” is. Markets in general.
In most markets, market data is the price of an instrument (an asset, security, commodity, etc.) and trade-related data. This data reflects market and asset class volatility, volume and trade-specific data such as open, high, low, close, volume (OHLCV) and other value-added data such as order book data (bid-ask spread, aggregated market). Depth etc) and pricing and valuation (reference data, traditional financial data like first exchange rates etc) This market data is critical to various financial econometric, applied financial and now DeFi research such as:
- Risk management and risk model framework
- Quantitative trading
- Price and rating
- Portfolio construction and management
- All crypto financing
While applying a traditional methodology to assessing risk and identifying different opportunities spread across different and emerging crypto asset classes can be limiting, this is a start. New valuation models have emerged aimed at making sense of these digital assets that have risen to dominate the truly global digital marketplaces, 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 typically determines the fair value of a digital asset by calculating the volume-weighted average price from a preselected set of available post-trade data from each exchange.
- TWAP, or time-weighted average price, which can be an oracle or a smart contract that derives token prices from liquidity pools, using a time interval to determine the collateralization ratio.
- Growth rate determines the security factor.
- TVL, or Total Value Locked, is designed for liquidity pools and automated market makers (AMMs).
- Total number of users reflects the network effect and potential usage and growth.
- Main market methodology applies to the primary market, which is often defined as the market with the greatest volume and activity for a digital asset. Fair value would be the price you would get for a digital asset in that market.
- Trading volume of CEXs and DEXs are the sum of the trading volumes on central exchanges (CEXs) and decentralized exchanges (DEXs).
- CVI, or crypto volatility index, is created by calculating a decentralized volatility index from cryptocurrency option prices, along with analyzing market expectations for future volatility.
Therefore, market data is central to all modeling and analysis tools in order to understand markets and also to carry out correlation analyzes between different crypto sectors such as layer one, layer two, Web 3.0 and DeFi. The main source of this crypto market data comes from the ever growing and fragmented mix of crypto exchanges. The data from these exchanges cannot be generally trusted as we have seen cases of excessive volume through practices such as wash trading and closed pools which can skew the price by misrepresenting demand and volume. Therefore, it can be difficult to model a hypothesis based on empirical data and then test the hypothesis to formulate an investment theory (insights from empirical summaries). This leads to oracles aimed at solving the problems of trusted data getting into the blockchain transaction system or a mediation layer between the crypto and traditional finance layers.
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Blockchain, the underlying technology that governs all crypto assets and networks, touts its basic tenets of trade, trust, and ownership based on transparency that is augmented by systems of trust (or consensus). So why is market data such a big problem? Isn’t it part of the ethos of the blockchain and the crypto industry to rely on data that belongs to the market and is easily accessible for analysis?
The answer is yes! But! “It gets interesting when we cross the crypto markets with fiat-based liquidity – transactions denominated in US dollars, euros, yen and British pounds are the path to traditional financing, which is made easier by crypto exchanges.
Understanding crypto macros and differentiating global macros
As Peter Tchir, Head of Global Macro at New York Academy Securities, explains in an article by Simon Constable, “Global Macro is a term for underlying trends that are big enough to boost or boost the economy or large parts of the economy the securities markets could drop ”. Police officer added:
“They differ from microfactors that can affect the performance of an individual company or sub-sector of the market.”
I want to differentiate between Global Macro and Crypto Macro. While global macro trends – such as inflation, money supply and other macro events – influence global demand and supply curves, crypto macro regulates the correlation between the various sectors (such as Web 3.0, layer one, layer two, DeFi and non-fungible tokens). , Tokens that are representative of the sectors and events that affect the corresponding movement of these asset classes.
Connected: How NFTs, DeFi and Web 3.0 are intertwined
Crypto (and digital) asset classes define a whole new area of wealth creation, transaction, and wealth movement when limited to fungibility between asset classes and exchange mechanisms such as loans, collateral, and exchanges. This creates a macro environment that is underpinned by crypto-economic principles and theories. When we try to tie these two major macroeconomic environments together for the infusion or transfer of liquidity from one economic system to another, we are essentially complicating our metrics and market data due to a collision of value systems.
Let me demonstrate the complexity with an example of the importance of market data and other factors in formulating an investment theory based on evidence from empirical abstracts.
While layer one provides important benefits for many ecosystems that emerge in layer one networks, not all layer one networks are created equal and do not offer the same critical value and characteristics. Bitcoin (BTC), for example, had the first-move advantage and is, so to speak, the face of the cryptocurrency ecosystem. It started out as a utility but has grown into a store of value and an inflation hedge asset class trying to displace gold.
Ether (ETH), on the other hand, developed the idea of programmability (the ability to apply conditions and rules) to assess movement, creating rich ecosystems like DeFi and NFTs. ETH is thus becoming a utility token that drives these ecosystems and facilitates co-creation. The surge in transaction activity has driven the demand for ether as it is needed for transaction processing.
Bitcoin as a store of value and protection against inflation differs significantly from an ever-growing and emerging business in a layer-one network. Hence, it is important to understand what gives these tokens value. It is the use of a token as a toll for the network that makes it valuable, or its ability to store and transfer (large) values in a short period of time, which gives it an advantage over existing value movement or payment systems.
In both cases, the benefit, the transaction volume, the circulating supply and the associated transaction metrics provide insights into the token valuation. If we analyze and examine the deeper macroeconomic impact on valuation (such as interest rates, money supply, inflation, etc.) and also crypto macro factors that include the correlation of other crypto assets and cryptocurrencies that directly or indirectly affect Level 1, The The resulting theory would include the growth of base technology, the role of the native asset classes, and term premiums. This would be an indication of the technology risk and market acceptance, the network effect and the liquidity premium, which show widespread acceptance in various crypto-driven ecosystems. An investment view on strategic suitability for a crypto portfolio construction, for example, includes considerations of macroeconomic cycles, crypto liquidity (the ability to convert crypto assets) and crypto macro effects and regards these as low medium-term risk in our risk model framework.
The availability of trustworthy crypto market data enables not only real-time and on-site trading decisions to be made, but also various risk and optimization analyzes required for portfolio construction and analysis. The analysis requires additional traditional market data as we begin to converse with traditional finance related market cycles and liquidity which may also try to correlate the crypto macro sectors with global macro sectors. This can get complicated quickly from a modeling perspective, simply because of the inequality in the variety and speed of market data between two value systems.
As fundamental as the efficiency of the crypto market is to good financial decision making, it is poorly understood and distorted by poor or insufficient information. It is crypto (economic) market data and various economic models that allow us to understand emerging and messy crypto markets. The principles of the efficient market hypothesis – which implies that the price in efficient markets always reflects the information available – also apply to crypto markets.
Market data is therefore central to all modeling and analysis tools in order to understand markets and also to carry out correlation analyzes between different crypto sectors such as layer one, layer two, Web 3.0 and DeFi. The main source of this crypto market data comes from the ever growing and fragmented mix of crypto exchanges. Crypto and digital asset classes define a whole new area of wealth creation, transaction, and wealth movement, especially when limited to fungibility between asset classes and exchange mechanisms such as loans, collateral, and exchanges. This creates a macro environment based on crypto-economic principles and theories.
When we try to tie these two major macroeconomic environments together for the infusion or transfer of liquidity from one economic system to another, we are essentially complicating our metrics and market data due to a collision of value systems. The analysis requires additional traditional market data as we begin to converse with traditional finance related market cycles and liquidity and also try to correlate the crypto macro sectors with global macro sectors. This can get complicated quickly from a modeling perspective, simply because of the inequality in the variety and speed of market data between two value systems.
This article does not provide investment advice or recommendations. Every step of investing and trading involves risk, and readers should do their own research when making a decision.
The views, thoughts, and opinions expressed herein are solely those of the author and do not necessarily reflect the views and opinions of Cointelegraph.
Nitin Gaur is the founder and director of IBM Digital Asset Labs, where he develops industry standards and use cases and works to bring blockchain to business. Previously, he was the chief technology officer of IBM World Wire and IBM Mobile Payments and Enterprise Mobile Solutions, and founded IBM Blockchain Labs, where he led efforts to establish blockchain practice for the company. Gaur is also an excellent IBM engineer and master IBM inventor with a rich patent portfolio. He also works as a research and portfolio manager for Portal Asset Management, a multi-manager fund specializing in digital assets and DeFi investment strategies.