Institutional investors need trusted crypto market data

by Gordon James

As institutional investors and blockchain technology enthusiasts, we are excited to share that we are launching the first-of-its-kind crypto index fund that tracks a basket of the most liquid, biggest, and well-known digital currencies in the world.

A recent survey of institutional investors showed that 85% of them would like to have trusted market data for the most popular cryptocurrencies, and the vast majority of them want to use cryptocurrency indices to provide clear benchmarks for these assets.

Cryptocurrency market data is a powerful but privately-owned asset that is needed by institutional investors to make informed investment decisions. In order to ensure that data is trusted, there is a need to establish a system that is independent, secure and transparent.. Read more about what cryptocurrency are institutional investors buying and let us know what you think.

In this paper, I would like to discuss the importance of market data, decentralized financial econometrics (DeFi) and applied DeFi research on crypto (and digital) assets as implications of financial econometrics and applied research. I will also attempt to draw insights and conclusions from the seminal work of Eugene Fama, who was interested in measuring the statistical properties of stock prices and resolving the debate between technical analysis (which uses geometric patterns in price and volume graphs to predict future movements in stock prices) and fundamental analysis (which uses accounting and economic data to determine the fair value of a stock). Nobel laureate Fama operationalized the efficient market hypothesis – succinctly expressed in the epigram that in efficient markets prices fully reflect all available information.

So let’s focus on this information around cryptocurrencies and digital assets, data sources on cryptocurrencies and decentralized finance, market data analysis, and everything around the massively emerging DeFi industry that is needed to attract institutional investors to cryptocurrencies, DeFi, and the broader token markets in general.

In most markets, market data is defined as the price of an instrument (asset, security, commodity, etc.) and trade data. This data reflects market and asset class volatility, volume and transaction-specific data such as open, high, low, close, volume (OHLCV) and other ancillary data such as order book data (bid/ask spread, overall market depth, etc.) and pricing and valuation (benchmark data, traditional financial data such as initial exchange rates, etc.) and pricing and valuation (benchmark data, traditional financial data such as initial exchange rates, etc.). These market data play an important role in various studies in financial econometrics, applied finance and now DeFi, such as:

  • Risk management system and risk model
  • Quantitative trade
  • Pricing and evaluation
  • Creation and management of portfolios
  • Joint funding of cryptocurrencies

Using traditional methods to assess risk and identify different opportunities in the various emerging classes of crypto assets may be limited, but it is a start. New valuation models have been developed to meaningfully value these digital assets that now dominate truly global digital markets, but even these models require market data. Some of these models include, but are not limited to:

  • VWAP, or volume-weighted average price, is a method that generally determines the fair value of a digital asset by calculating a volume-weighted average price based on a pre-selected set of available post-trade market data.
  • TWAP, or time-weighted average price, which can be an oracle or smart contract that derives the prices of liquidity pool tokens based on a time interval to determine the collateral ratio.
  • The growth factor determines the collateral factor.
  • TVL, or Total Value Locked, is designed for liquidity pools and automated market makers (AMMs).
  • The total number of users reflects the impact of the network, as well as potential usage and growth.
  • The principal market method is applied to the underlying market, which is often defined as the market with the highest volume and activity for a digital asset. The fair value would be the price obtained for the digital asset in that market.
  • The trading volume on the CEX and DEX is the sum of the trading volumes on the central exchanges (CEX) and the decentralised exchanges (DEX).
  • The CVI, or Cryptocurrency Volatility Index, is created by calculating a decentralized volatility index based on cryptocurrency option prices and analyzing market expectations about future volatility.

Therefore, market data becomes a central part of all modeling and analysis tools to make sense of the markets and perform correlation analysis between different cryptocurrency sectors, such as Tier 1, Tier 2, Web 3.0 and DeFi. The main source of data on the cryptocurrency market is the ever-growing and disparate cryptocurrency exchanges. The data from these exchanges is not very reliable because we have seen cases of inflated volumes due to techniques such as money laundering and closed pools, which can distort the price by skewing demand and volume. Therefore, it may be difficult to model a hypothesis based on empirical data and then test that hypothesis to formulate an investment theory (inferences from empirical derivations). This leads to oracles designed to solve problems with trusted data flowing through the blockchain transaction system, or the middle layer between cryptocurrencies and traditional financial layers.

Related: Oracle wants to bring blockchain to the masses by offering crypto-currencies

Blockchain, the underlying technology that manages all cryptographic assets and networks, promotes its core principles of trade, trust and ownership based on transparency, reinforced by systems of trust (or consensus) – so why is market data so important? Isn’t part of the ethos of blockchain and the cryptocurrency industry to rely on data that belongs to the market and is readily available for analysis?

Answer: Yes! But! Things get more interesting when we cross the cryptocurrency markets with fiat money – transactions in US dollars, euros, yen and sterling are the rails of traditional finance facilitated by cryptocurrency exchanges.

Understanding the differences between Crypto-Macro and Global-Macro

As Peter Tchir, head of global macroeconomics at Academy Securities in New York, explains in an article by Simon Constable: The term global macro refers to broad trends that are so important that they can cause the economy or a large portion of the stock market to rise or fall. The officer added:

They can be distinguished from micro factors, which can affect the performance of an individual firm or a sub-sector of the market.

I would like to make a distinction between Global Macro and Crypto Macro. While global macro trends – such as inflation, money supply, and other macro events – influence global supply and demand curves, crypto-macro governs the relationship between different sectors (such as Web 3.0, Layer One, Layer Two, DeFi, and non-functional tokens), the tokens that represent these sectors, and the events that influence the corresponding movement of these asset classes.

Related: How NFT, DeFi and Web 3.0 are connected

Cryptographic (and digital) asset classes define a whole new domain of asset creation, transactions and movements when limited to interchangeability between asset classes and exchange mechanisms such as credit, collateral and swaps. This creates a macroeconomic environment supported by crypto-economic principles and theories. When we try to link these two major macroeconomic environments in order to inject or transfer liquidity from one economic system to another, we complicate our measurements and market data considerably because of the collision of the value systems.

Let me demonstrate this complexity with an example of the importance of market data and other factors in formulating an investment theory based on empirical derivations.

While Level 1 provides significant value to many Level 1 network ecosystems, not all Level 1 networks are the same and do not provide the same value and features. Bitcoin (BTC), for example, was the first to catch on and is somewhat the face of the crypto-currency ecosystem. It began as a useful object, but it evolved into a store of value and an asset class to protect against inflation, in an attempt to supplant gold.

The Ether (ETH), on the other hand, invented the concept of programmability (the ability to apply conditions and rules) to the movement of value, creating rich ecosystems such as DeFi and NFT. In this way, ETH becomes a useful token that nurtures these ecosystems and encourages co-creation. The increase in transaction activity has driven up the demand for Ether as it is needed to settle transactions.

Bitcoin, as a store of value and inflation hedge, is very different from the ever-growing and evolving activity of peer-to-peer networks. It is therefore very important to understand what makes these chips valuable. It is the utility of the token as payment for network use that gives it value, or the ability to store and pass (more) value in proximity that gives it an advantage over existing value or payment systems.

In each case, utility, trading volume, tradable supply and related trading parameters provide insight into the valuation of tokens. If we were to analyze and take into account the deeper macro impact on valuation (e.g., interest rates, money supply, inflation, etc.), as well as crypto macro factors that include correlations of other crypto assets and cryptocurrencies that directly or indirectly affect the first level, the resulting theory would include fundamental technology growth, the role of proprietary asset classes, and term premiums. This would be indicative of the technology risk and market acceptance, network effect and liquidity premium that have been found to be widely accepted in different cryptocurrency ecosystems. The strategically appropriate investment view for building a portfolio of cryptocurrencies, for example, includes considerations of macroeconomic cycles, cryptoliquidity (the ability to convert crypto assets) and exposure to cryptocurrencies, and treats these as low risk over the medium term in our risk model.

With reliable data on the crypto-currency market, you can not only make real-time trading decisions on the spot, but also perform the various risk and optimization analyses necessary to build and analyze a portfolio. The analysis requires additional data from traditional markets, as we begin to interact with the cycles and liquidity of traditional financial markets, which may also attempt to correlate crypto macro sectors with global macro sectors. This can quickly become complicated from a modeling perspective simply because of the inconsistency in the variety and velocity of market data in the two value systems.

Perspectives

As important as the effectiveness of the cryptocurrency market is for making sound financial decisions, it is misunderstood and distorted by poor or inadequate information. It is the (economic) data of the crypto-currency market and the different business models that help us understand the evolving and confusing crypto-currency markets. The principles of the efficient market hypothesis – which states that in efficient markets, price always reflects available information – also apply to cryptocurrency markets.

Therefore, market data becomes a central part of all modeling and analysis tools to make sense of the markets and perform correlation analysis between different cryptocurrency sectors, such as Tier 1, Tier 2, Web 3.0 and DeFi. The main source of data on the cryptocurrency market is the ever-growing and disparate cryptocurrency exchanges. Cryptocurrencies and digital asset classes define a whole new domain of asset creation, transactions and asset movement, especially with respect to interchangeability between asset classes and exchange mechanisms such as credit, collateral and swaps. This creates a macro environment that is underpinned by the principles and theories of crypto-economics.

When we try to link these two major macroeconomic environments in order to inject or transfer liquidity from one economic system to another, we complicate our measurements and market data considerably because of the collision of the value systems. The analysis requires additional data on traditional markets, as we begin to look at cycles and liquidity in traditional financial markets, and we attempt to correlate the cryptocurrency macro sector with global macro sectors. This can quickly become complicated from a modelling point of view, simply because the variety and speed of market data in the two value systems do not match.

This article contains no investment advice or recommendations. Any investment or business transaction involves risk, and readers should do their own research before making a decision.

The views, thoughts and opinions expressed herein are those of the author and do not necessarily reflect or represent those 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 make blockchain a reality for businesses. Previously, he was CTO of IBM World Wire and IBM Mobile Payments and Enterprise Mobile Solutions and founded IBM Blockchain Labs, where he led the creation of an enterprise blockchain practice. Mr. Gaur is also an IBM Distinguished Engineer and IBM Master Inventor, with an extensive patent portfolio. He also works as a researcher and portfolio manager for Portal Asset Management, a multi-manager fund specializing in digital assets and DeFi investment strategies.The average crypto investor often lacks the knowledge to participate in the industry’s latest trends. This is where Etherracing.io can help. We provide daily and weekly Ethereum price charts for the top tokens, market data and information about the company behind the token.. Read more about institutional crypto trading and let us know what you think.

Related Tags:

bitcoin institutional investors listinstitutional investing cryptoregulatory clarity cryptoinstitutional crypto tradinginstitutional crypto exchangeswhat is regulatory clarity,People also search for,Privacy settings,How Search works,what cryptocurrency are institutional investors buying,bitcoin institutional investors list,institutional investing crypto,regulatory clarity crypto,institutional crypto trading,institutional crypto exchanges,what is regulatory clarity,institutional crypto trading platform

Related Posts