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The Crypto Fit

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Blockchain
  • Sustainable Growth and Tech Trends


Publication | Update:
Mar 2020

Jeff Dorman, a CoinDesk columnist, is chief investment officer at Arca makes a point that bitcoin trading is exactly how stock and bond markets traded for the first 300 years.

In 1602, the Dutch East India Company issued the first paper shares. This exchangeable medium allowed shareholders to conveniently buy, sell and trade their stock with other shareholders and investors. For hundreds of years thereafter, investors and traders did their best to anticipate price moves, without any of the tools available today for valuing these securities. Back then, a stock trading at $ 100 was viewed more expensive than a stock trading at $ 10, independent of number of shares outstanding, underlying revenues, or business prospects.

It wasn’t until the 1920s, following the stock market crash and the Great Depression, that two Columbia University professors, Benjamin Graham and David Dodd, came up with a methodology for identifying and buying securities priced well below their true value. Their book, “Security Analysis,” was published in 1934, and Graham and Dodd’s principles provided a rational basis for investment decisions that are still applied today by the world’s top value investors. 

Warren Buffett chose to attend Columbia specifically to learn from Professor Graham (and received an A+ in his class). Almost 50 years later, Professor Frank Fabozzi introduced similar valuation techniques and concepts for investing in fixed income securities. And shortly thereafter, even newer valuation techniques (like Metcalfe’s Law) were introduced to help value computing networks, and these methods were utilized decades later to value pre-revenue internet giants like Facebook, Tencent and Netflix.

According to Gisli Eyland, who has written about the value investing philosophy, Graham and Dodd “described a fundamentally different approach to stock picking and investing in corporate securities by proposing that the investor should refrain from trying to anticipate price movements entirely. Instead, the investor should try to estimate the true Intrinsic Value of the underlying asset. Given time, the Intrinsic Value and market value would converge.” Today, investors and financial media throw around financial ratios like P/E, P/B, EV/EBITDA, P/S, Dividend Yield and many others as if they’ve been around forever, while smugly chastising digital assets for having no intrinsic value. This may be a good time to remind readers that digital assets are less than 10 years old.

Fundamental models emerging in crypto

When will the Graham and Dodd of crypto emerge? They’re likely already here, working tirelessly behind the scenes on valuation techniques that will be utilized by the Warren Buffets of crypto 50 years from now. Digital assets are still in their infancy, but new fundamental valuation techniques are being built, tested and discovered every day, from the original MV = PQ analysis, to discounted sum of utility models, to everything else in between. Many of the models in existence are unproven, with only a few years’ worth of data to support their methodologies, while other models have likely yet to be conceived. 

Each of these methods has advantages as well as shortcomings. Digital assets are unique, similar to corporate bonds, making different valuation techniques appropriate for specific token types. Just like a bond has different coupons, different maturities, different covenants and different features (callable, putable, convertible, warrants, etc.), most digital assets have unique features as well, making each analysis different than the last (there is a reason Fabozzi’s fixed income bible is over 1,800 pages long). 

In our view, the DCF analysis is best used for tokens issued by cash-producing companies such as exchange tokens like Binance Coin (BNB) or Unus Sed Leo (LEO). The NVT Ratio may be better when comparing across smart contract platforms such as Ethereum, EOS and NEO. A variation of Metcalfe’s law or total addressable market analysis can be used for tokens that are in the early pre-launch stage or are servicing a sector that is difficult to currently measure.

The smartest crypto analysts in Arca are developing new methodologies to value digital assets. Once these metrics become widely accepted, price floors and ceilings in crypto will be set based on agreed-upon, well-tested fundamental valuation – just like in the debt and equity markets. 

The crypto markets to date have been dominated by crypto-native investors. There is very little cross-asset ownership largely because the infrastructure is totally different. Crypto investing doesn’t fit with traditional investor mandates, nor does it fit within the work flows of traditional banks, prime brokerages, exchanges or algorithms. This is slowly changing with the entry of traditional financial powerhouses to the digital assets space like Fidelity, CME and NYSE, but this asset class is still largely foreign and unappealing to the majority of investors.

Digital assets. Investors might want to adopt a more open-minded, long-view approach to investing in this new asset class. Needs investors to come and go who aren't solely crypto investors. This asset rotation and opportunistic investing will help the market find equilibrium at both market tops and bottoms, helping to reduce the crazy highs and the depressing lows historically associated with this asset class.

What matters most is understanding how crypto assets can meet their goals and fit within their risk tolerances, as well as how it fits as a smaller piece of an overall balanced and diversified portfolio.

The ‘Traditional Hedge Fund Due Diligence’ Investors

They spend most of their time seeking out strategies that expose them to the potential upside while limiting downside risk:

  1. Don’t focus on how high it can go; focus on how low it can go. A good fund manager in any asset class tries to capture most of the upside, while ensuring that downside risk is mitigated. This is an especially important message for investors to hear in crypto, since most of what they see and hear strictly focuses on outlandish return potential.
  2. DO NOT short this market today. I’ve spent my entire career trying to isolate idiosyncratic risk and removing market risk through cap structure arbitrage trades and long-short trades, but this strategy does not yet work in crypto for a variety of reasons (asymmetric upside, low liquidity, high costs, etc). As such, the best hedge today is simply not to own a token that you don’t like. Currently, the best way to protect against the downside is by sizing positions correctly according to risk/return profiles, taking chips off the table when this equation is no longer favorable, and using derivatives to hedge tail risk.
  3. A top-down AND bottom-up approach. Active management matters in crypto, perhaps more than in any other asset class, because of the large swings and bifurcations between top performers and underperformers. Understanding the macro landscape (top down) while simultaneously searching for value (bottom-up approach to security selection) is how to take advantage of current market conditions. Few investors want to hear about best ideas because they are not ready to execute them on their own − but they do want to understand the process.

Conversely, the biggest pushback from this group is that the underlying asset class itself is still so new, and it’s hard to invest in something that has unknown tangible value. But it’s important to remember that many asset management strategies can work on top of any underlying asset class.

The ‘Savvy Crypto’ Investors

They are trying to figure out which managers they trust to generate high risk-adjusted returns in the crypto space.

  1. Developers are the new research analysts. Research has greatly evolved over the past decade, especially with the emergence of digital assets. The ability to read lines of code in GitHub, test pre-launch products and engage with various development communities is a must for any fund investing in this space.
  2. What is ‘fundamental research’ in crypto? Unlike traditional asset classes like equities and fixed income, there is no widely agreed upon Graham & Dodd Security Analysis in crypto. It’s right to question the fundamental value of these new technologies, but wrong to dismiss the lack of progress.

Looking ahead, this group is already excited about today’s crypto assets, but also focused on what is coming in the future. They want to align themselves with managers who are in a position to take advantage of today’s opportunities, while also being on the front-line when new opportunities arise.

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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

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The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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