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/ Investing

Behavioral vs. Mechanical: Ideas to Evaluate Cryptocurrencies

Behavioral vs. Mechanical: Ideas to Evaluate Cryptocurrencies

It's easy to fall into the trap of seeing the crypto space within a mechanical framework. After all, Bitcoin and blockchain is just an amalgam of code, maintained on a decentralized network, and supported through a literal army of machines permeating across the globe. The architecture and design of crypto and blockchain matter a lot. However, it is easy to get lost in the engineering of this nascent technology and lose out on the bigger picture of actual utility and function. No matter if this peer to peer cash system is seen as a currency by some or as a store of value by others, one thing is evident: people buy, sell, and use Bitcoin for different reasons.

As the cryptocurrency ecosystem continues to mature, more and more players in the traditional finance space have latched on. With Wall Street execs observing and commenting and even bankers, researchers, analysts, and economists making their presence known, imparting their thoughts and speculation, nearly everyone has an opinion on digital currencies. They have their own perspectives, and those perspectives are much older, built upon decades of our time’s best economic minds, like Friedman, Hayek and Keynes. After all, money has existed far longer than the digital currency boom of 2009 and will continue to exist long after. That means, like good cryptography, these ideas have withstood the test of time, and as a result, we should carry the weight of their expertise with us when re-envisioning our economic ecosystem

In crypto circles, the current breakdown of cryptocurrencies is structured mechanically. In the case of stablecoins, they fall  into three neat categories which will help us understand their functions better:  

1. Fiat-collateralized (Centralized)
2. Crypto-collateralized (Decentralized)
3. Non-collateralized (Algorithmic)

However, these 3 mechanical classifications only tell you part of the narrative, and from an investment standpoint, a very small and potentially unremarkable part. For example, knowing that TrueUSD or Tether simply collateralizes fiat (US Dollars) and puts dollars on the blockchain doesn’t tell you very much as an investor. The mechanical component really leaves something to be desired when you want to know how a market might receive a coin, let alone behave once it’s released into that market.

A look at Tether ($USDT) showcases this. Tether recently had a period of heavily negative news. The news was inescapable and it focused on the fact that Tether was not being fully backed by US Dollars (USD). Mechanically, Tether stated that every 1 USDT would be backed by 1 USD. But when it was revealed that this 1:1 backing was not true, what happened? The price of Tether stayed the same. Why? Let’s look at what users care about when using Tether:

They could buy at $1
They could sell at $1
They had confidence they could do this whenever they wanted

In short, what makes Tether useful for investors is not whether it’s actually, mechanically backed 1:1 with the dollar. What matters is liquidity. This is behavioral and it’s how the coin behaves that caused it to retain it’s usefulness and therefore value.

Let’s key in on that word: Behave. It just so happens that a very valuable framework stems from how things behave. Instead of using a mechanical filter, what would happen if we used a behavioral classification. How would it be received if we put coins into behavioral buckets instead of mechanical ones? Let’s apply a few ideas and see how new buckets might provide us with additional, important information.

Inside vs. Outside Money

To start, let’s look at the fundamental differences between inside vs. outside money. Understanding whether a coin is an inside or an outside money can tell us about how the coin functions and behaves. For example, fiat-collateralized digital assets are inside money. You have an inside money anytime an asset is backed by a form of private credit (an “IOU”). Simply stated, inside money is collateralized. TrueUSD, for example, is an inside money. For every 1 TrueUSD, there is a corresponding liability of 1 actual US Dollar, assuming the system is working correctly.

Bitcoin on the other hand is an outside money. Ampleforth’s Amples (AMPL) are also an outside money. Outside money is not collateralized. Outside money is also a net asset for the private sector. There are different performance characteristics between outside money and inside money, with some being advantageous at times and harmful in other instances. That's why it's critical to understand if a digital asset is an inside money or an outside money, much like its crucial to know what a layer 1 protocols consensus mechanism is - PoW or PoS for example.

Rules vs. Discretion

Another view which can shed light on the behavioral side of things surrounds rules vs. discretion, with advantages and disadvantages to each approach. To outline some potential disadvantages, discretionary policies have the vulnerability to have subjective applications, which can introduce errors in terms of subjective timing, scope and participation. Discretion in the traditional finance world is often regulated to give protection to consumers. An example of this could be transparency about how an interest rate can change and by how much. In the decentralized world with no strong authorities, there's no guaranteed regulation except for rules-based code. Contrastly, rules-based policies can have the potential disadvantage of being too rigid, not allowing for unique action in times of full blown crisis.

The important take-away in either event is to understand what type of digital asset you are dealing with, which will provide you with an indication of how the asset might perform in a given scenario. As an investor, you may achieve a  more predictable performance out of a rules-based digital asset than a discretion-based digital asset.  

Maker DAO is in the news as a result of their discretionary policy. According to a Coindesk report borrowers are unhappy with exponentially increasing stability fees (basically, interest rates) in extremely short time frames. These stability fees are set via a vote, and the people casting votes are arbitrarily doing so based on their own discretion. They have passed rates that are 40X higher than they were mere months ago in order to prevent a negative outcome for their token. However, the outcome is increasingly negative despite their best efforts. The uncertainty surrounding how the chosen few will vote only adds to the problem.

This information is  - necessary - for an investor to know.

A discretion-based system may fail to act when action is needed, and may act when inaction is best. A rules-based system will act exactly when the rules dictate, irrespective of whether or not action is prudent. In a discretionary system, additional, sometimes unpredictable variables are added in the form of subjectivity, which are eliminated in a rules-based system.

It becomes increasingly important to know how digital assets behave. Be sure to understand if a digital asset is rules-based or discretionary if you want a full picture of that digital asset.

Tying It All Together: A More Complete Picture

Having a complete awareness and knowledge of asset classification and behavior provides a more useful, fuller picture of a digital asset.

Framing a digital asset under the lens of behavior and mechanics helps guide one to ask better questions to determine if the digital asset is the right one to invest in. For example, with the right behavioral lense, you can better understand the impact of how coins enter or leave the system. You have an idea of what to ask- are new coins entering through minting/selling? Buy-back and burn? Collateralized Debt Position (CDP)? If it’s through CDP we can apply another lens to determine another question we need answered- If it is CDP how is demand for CDP and stablecoin balanced?

Additionally, taking into account behavioral characteristics helps you to question other important ideas, such as what assumptions about the underlying economy does the protocol of a digital asset rely on? For example, if a project relies on bonds, there must be a long-term underlying growth for that bond market to make sense. Other questions will arise as well, such as what information is propagated into supply, if any? If that’s not the case, is supply scheduled deterministically? If it is scheduled deterministically, what might that mean for the future of the digital asset? Mechanically, that answer is simple - you know for example that Bitcoin will only have 21 million coins ever. Behaviorally, how will a fixed supply affect the price and performance of that digital asset? Clearly, the behavioral characteristics of a digital asset are important to consider.

Hopefully this has provided you with a few new ideas and additional lenses with which to view digital assets. Knowledge and information are powerful, so I hope this additional framework helps you in the future while evaluating digital assets.

/ Central Bankers

Famous Central Bankers: Alan Greenspan

Famous Central Bankers: Alan Greenspan

Alan Greenspan is one of the most notable and well known economists of the last century, due to his position as Chairman of the Federal Reserve during the dotcom bubble as well as his pre Great recession forecasts. Besides his role as the Chairman of the Fed, Greenspan has held many other roles, both in an advisory capacity to many large banks and funds, as well as owning his own consulting firm, Greenspan Associates, LLC.

After his appointment to the Fed in 1987, Greenspan had to navigate multiple recessions and crises, notably the stock market crash of October 1987, the Asian financial crisis of 1997, the dotcom bubble burst of 2000, and the terrorist attacks of September 11th, 2001. The policies Greenspan implemented helped lead the US to what was at the time the longest expansionary period in history - this was during the 1990s. After acting quickly and ensuring liquidity in the markets during the crash of 1987, Greenspan’s policies of anti-inflation and price control allowed the US economy to expand for a decade, before the dotcom bubble burst. As is the theme with all central bankers, their policies fall under discretionary monetary policy as opposed to rules based policy.

Once Greenspan left his position in 2006, he began his own consulting firm, and most notably forecast the recession of 2008 in 2007. After his forecast, he was hired in an advisory capacity by Deutsche Bank and hedge fund Paulson & Co. The recession, caused largely in part by the housing market and the collapse of the subprime mortgage market, has been in part blamed on Greenspan due to his economic policies implemented during the early and mid 2000s. Despite Greenspan’s work to help the US economy expand so dramatically, he has a controversial legacy due to some of the errors made during his tenure, particularly in the early 2000s. Greenspan cut interest rates to almost unprecedented levels in the early 2000s, which combined with his endorsement of adjustable rate mortgages, make him a polarizing figure to those who lost money in the recession.

/ Central Bankers

Famous Central Bankers: Ben Bernanke

Famous Central Bankers: Ben Bernanke

Ben Bernanke is an American economist, who’s work on monetary policy and macroeconomics earned him positions as a professor of Economics at Princeton, Stanford, and MIT, as well as led him to become Chairman of the Federal Reserve from 2006 to 2014. He was tasked with navigating the housing crisis and recession of 2008, a task which many experts believe he performed well.

The 2008 banking crisis led to a lack of confidence in the banking system as a whole, but Beranke’s aggressive and largely experimental approach to the crisis helped restore confidence to the financial system, as seen by the longest expansionary market in US history following the crisis. One of Bernanke’s most notable moves was to slash the interest rates to almost 0%, allowing banks to lend to each other and customers at lower rates, stimulating the economy.

Besides his policy moves, Bernanke made a highly controversial decision to bail out a few of the huge financial institutions that were about to go bankrupt during the crisis, fearing their failure would only serve to worsen the crisis. For example, the Fed incentivized companies like JP Morgan to take control of Bear Stearns or Merrill Lynch. Bernanke wrote about how close the global economy came to collapsing in his 2015 book, which would have occurred if not for the Fed’s moves. However, contrarians believe Bernanke should have foreseen the crisis and is in part responsible for the events. Overall, Bernanke was the most influential person in the US during the crisis due to his control over Fed policies, which is a place that naturally brings critics.

Bernake's control over Fed policy highlights the room for interpretation created with a discretion based monetary policy. With the housing crisis, people and policy makers showed that it is very possible for those in charge to miss markers that point towards the downfall of a market and it is very clear that they can fail to act in time to prevent or reduce the impact of downswings. With Ampleforth, the protocol acts on market forces in a rules based format, removing the likelihood of human emotion, biases, and political alignment in times of crisis. Rules based systems are not without their challenges. However, if a rules based system like Bitcoin or Ampleforth were used as a globally prior to 2008 the great recession may have played out much differently.

/ John B. Taylor

Famous Central Bankers: John B. Taylor

Famous Central Bankers: John B. Taylor

John B. Taylor is one of the greatest economic minds of the 20th and 21st centuries. His work in macroeconomics and monetary policy helped create one of the most important Macroeconomic tools today, The Taylor Rule, which is used as an interest rate forecasting tool. He served as an  economic advisor to President’s Ford, Carter, George H. W. Bush, and George W. Bush. Currently, Taylor is a professor of economics at Stanford University and has won numerous awards in the field of economics, including the 2016 Adam Smith Award and 2015 Truman Medal for economic policy.

The Taylor Rule, as coined following his 1993 research paper, proposed that manipulation of short-term interest rates by the central bank could serve as a counter and pro cyclical force on the economy. He proposed to raise rates when growth and inflation was excessive and cut rates if they fell below targets. His simple interest rate equation is now largely used as an effective formula for interest rate levels.

Besides his work on monetary policy and what the central bank should attempt to manipulate, Taylor has also done extensive research on the financial crisis of 2008. In particular, Taylor focuses on the actions of the Federal reserve, noting how interest rates were kept too low for too long, allowing for an excessive amount of output growth and inflation to occur. He proposed that the economic crisis was a direct result of government inactions, actions, and interventions, rather than a failure by the private economy.

Following the crisis, Taylor has stressed the importance of a stable monetary policy, rather than the quantitative pro and counter cyclical measures that the Fed had been using. Balance in the economy is of the utmost importance to Taylor, and his work reflects that.

Ampleforth’s protocols and automatic expansions as well as contractions serve to create the stability in the Ampleforth network that John B. Taylor believes should be present in the US economy. Fiat counter cyclical pressures are driven by those in charge of the Fed and can be influenced by any number of factors. The countercyclical pressures present in Ampleforth are market drive, rules-based, and non-dilutive for anyone holding the token, creating a static, stable policy. There is not an easing due to external pressures, rather, the policy to absorb shocks in the Ampleforth Network is constant, just as Taylor wants of US economic policy.

To understand more about creating a stable system that is less prone to biases and human driven overcorrection and error, read our chapter on Rules vs. Discretion.