It is crypto (economic) market data and various economic models that enable us to make sense of the emerging and messy crypto markets.
In this article, I intend to discuss the importance of market data, decentralized finance (DeFi) econometrics and applied DeFi research on crypto (and digital) assets as a corollary to financial econometrics and applied research. I will also attempt to draw upon the perspective and findings from Eugene Fama’s seminal papers 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 to forecast future price movements of a security) and fundamental analysis (the use of accounting and economic data to determine a security’s fair value). Nobel laureate Fama operationalized the efficient market hypothesis — summarized compactly in the epigram that “prices fully reflect all available information”in efficient markets.
So, let’s focus on this information around crypto and digital assets, on crypto and decentralized finance data sources, market data analysis, and everything that surrounds the massive emerging DeFi industry that is essential for attracting institutional investors to crypto, DeFi and broader “token” markets, in general.
In most markets, market data is defined as 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 finance data like first exchange rates, etc.) This market data is instrumental in various financial econometric, applied finance and, now, DeFi research such as:
- Risk management and risk model framework
- Quantitative trading
- Price and valuation
- Portfolio construction and management
- Overall crypto finance
Although applying a traditional methodology to evaluate risk and discern varying degrees of opportunity spread across diverse and emerging crypto-asset classes may be limiting, it is a start. New valuation models have emerged that aim to make sense of these digital assets that have ascended to dominate the truly global digital marketplaces, and even these models need market data. Some of these models include but are not limited to:
- VWAP, or volume-weighted average price, a methodology that typically determines the fair value of a digital asset by calculating the volume-weighted average price from a preselected group of constituent exchanges’ available post-trade data.
- TWAP, or time-weighted average price, which can be an oracle or smart contract that derives token prices from liquidity pools, using a time interval to determine the collateral ratio.
- Growth ratio determines the collateral factor.
- TVL, or total value locked, is for liquidity pools and automated market makers (AMMs).
- Total number of users reflects the network effect and potential usage and growth.
- Principal market methodology applies to the principal market, which is often defined as the market with the greatest volume and activity for a digital asset. The fair value would be the price received for a digital asset in that market.
- Trading volumes of CEXs and DEXs are the sum total of trading volumes on centralized exchanges (CEXs) and decentralized exchanges (DEXs).
- CVI, or crypto volatility index, is created by computing a decentralized volatility index from cryptocurrency option prices together with analyzing the market’s expectation of future volatility.
Therefore, market data becomes central to all the modeling and analysis tools for making sense of markets, and also for performing correlation analyses between various crypto sectors such as layer one, layer two, Web 3.0 and DeFi. The primary source of this crypto market data comes from the ever-growing and fragmented mix of crypto exchanges. The data from these exchanges cannot be widely trusted, as we have seen instances of inflated volumes through practices such as wash trading and closed pools that can distort the price by misrepresenting demand and volume. So, modeling a hypothesis based on empirical data and subsequently testing the hypothesis to formulate an investment theory (insights from empirical abstracts) can be tricky. This gives rise to oracles that aim to resolve the issues of trusted data coming into the blockchain transaction system or a mediation layer between the crypto and traditional finance layers.
Blockchain, the underlying technology that governs all crypto assets and networks, touts its fundamental tenets of trade, trust and ownership on the basis of transparency extended by trust systems (or consensus), so why is market data such a huge issue? Isn’t it part of the ethos of 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!” Things get interesting when we intersect the crypto markets with fiat-based liquidity — U.S. dollar-, euro-, yen- and British pound-denominated transactions are the rail to traditional finance that is being facilitated by crypto exchanges.
Understanding crypto macro and differentiating global macro
As Peter Tchir, head of global macro at New York-based Academy Securities, explains in an article written by Simon Constable: “Global macro is a term for underlying trends that are so large that they could lift or drop the economy or vast chunks of the securities markets.” Constable added:
“They differ from micro factors, which may affect the performance of a single company or subsector of the market.”
I would like to distinguish between global macro and crypto macro. While global-macro trends — such as inflation, money supply and other macro events — impact global demand and supply curves, crypto macro governs the correlation between the various sectors (such as Web 3.0, layer one, layer two, DeFi and nonfungible tokens), tokens that are representative of those sectors and events that impact the corresponding movement of these asset classes.
Crypto (and digital) asset classes define a whole new realm of asset creation, transaction and asset movement when confined to fungibility between asset classes and exchange mechanisms, such as loans, collateral and exchanges. This creates a macro environment underpinned by crypto-economic principles and theories. When we attempt to link these two major macroeconomic environments for either injecting or transferring liquidity from one economic system to another, we essentially complicate our measurement 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 insights from empirical abstracts.
While layer one provides an important utility for many ecosystems that emerge on layer-one networks, not all layer-one networks are created equal and do not provide the same discerning value and characteristics. Bitcoin (BTC), for instance, had the first-move advantage and is sort of the face of the cryptocurrency ecosystem. It started as a utility but has morphed into a store of value and an asset class as an inflation hedge attempting to displace gold.
Ether (ETH), on the other hand, came up with the notion of programmability (the ability to apply conditions and rules) to value movement, thereby creating rich ecosystems such as DeFi and NFTs. So, ETH becomes the utility token that powers these ecosystems facilitating co-creation. The rise in transaction activity pushed the demand for Ether, as it is needed for transaction processing.
Bitcoin as a store of value and an inflation hedge is quite different from an ever-growing and emerging business on a layer-one network. It is hence vital to understand what gives these tokens value. It is the utility of a token as a toll on the network that makes it valuable, or its ability to store and transfer (large) value in near time giving it an advantage over existing value movement or payment systems.
In either case, the utility, transaction volume, circulating supply and related transaction metrics provide insights into the token valuation. If we were to analyze and look into the deeper macroeconomic impact on valuation (such as interest rates, money supply, inflation and so forth) and also crypto macro factors involving correlation of other crypto assets and cryptocurrencies that directly or indirectly impact layer one, the resulting theory would include the growth of foundational technology, the roles of native asset classes and maturity premiums. It would be indicative of technology risk and market adoption, network effect and liquidity premium that demonstrate wide acceptance across various crypto-driven ecosystems. An investment view on strategic fit to, say, a crypto portfolio construction includes considerations around macroeconomic cycles, crypto liquidity (the ability to convert crypto assets) and crypto macro impact, and views these as a mid-term low risk on our risk-model framework.
Availability of trusted crypto market data enables not only real-time and on-spot trading decisions but also various risk and optimization analyses needed 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 can also attempt to correlate the crypto macro sectors with global macro sectors. This can get quickly complicated from a modeling perspective, simply due to the disparity between the diversity and velocity of market data between two value systems.
As fundamental as crypto market efficiency is to good financial decision-making, it is poorly understood and distorted by poor or inadequate information. It is crypto (economic) market data and various economic models that enable us to make sense of emerging and messy crypto markets. The principles of the efficient market hypothesis — which implies that in efficient markets, price is always reflective of available information — also apply to crypto markets.
Market data, therefore, becomes central to all the modeling and analysis tools for making sense of markets and also for performing correlation analyses between various crypto sectors, such as layer one, layer two, Web 3.0 and DeFi. The primary 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 realm of asset creation, transaction and asset movement, especially when confined to fungibility between asset classes and exchange mechanisms, such as loans, collateral and exchanges. This creates a macro environment underpinned by crypto economic principles and theories.
When we attempt to link these two major macroeconomic environments for either injecting or transferring liquidity from one economic system to another, we essentially complicate our measurements 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 attempt to correlate the crypto macro sectors with global macro sectors. This can get complicated quickly from a modeling perspective, simply due to the disparity between the diversity and velocity of market data between two value systems.