Hackernoon logoSpeaking Quant to Crypto: The Six Sources of Alpha in Crypto Assets by@jrodthoughts

Speaking Quant to Crypto: The Six Sources of Alpha in Crypto Assets

Jesus Rodriguez Hacker Noon profile picture

@jrodthoughtsJesus Rodriguez

Chief Scientist, Managing Partner at Invector Labs. CTO at IntoTheBlock. Angel Investor, Writer, Boa

Earlier this year I came across one of those research papers that left you thinking for days. In “Who is on the other side?”, BlueMountain Capital Management analyst and renown author Michael J. Mauboussin provides a brilliantly simple thesis about the different sources of alpha in capital markets. Over the years, I must have read hundreds of papers about alpha generating strategies but only a handful capture a fundamental framework that can transcend a specific asset class and be explained without requiring exoteric mathematical or financial terms. Mauboussin’s paper describes four fundamental sources of alpha in a way that can be easily understood without any domain expertise. Recently, I’ve been thinking more about how some of the professor Mauboussin’s ideas apply to crypto assets and came up with a couple of variations that I would like to share today.

Its All About Inefficiencies

The idea of Alpha in financial markets is associated with the principle of generating excess returns that outperform that average market indices. This is partially done to the fact that most financial markets are reasonably efficient and there are indices that accurately reflect the performance of an asset class. Sort of the entire opposite of crypto 😉. Another way to think about Alpha is about exploiting inefficiencies in an often efficient market.

The idea of efficiency comes from physics and explains the relationship between the amount of energy needed to produce and outcome and the outcome itself. In financial markets, the term efficiency refers to the relationship between information and the price of an asset. The Rosetta stone of efficient financial markets is the famous, and often revisited, efficient market hypothesis(EMH) formulated by Nobel laureated economist Eugene Fama in his 1962 PH.D thesis. EMH states that stock prices reflect all known and relevant information and always trade at fair value. If stocks could not trade above or below fair value, investors would never be able to buy them at discounts or sell them at premiums. Therefore, “beating the market” on a risk-adjusted basis is impossible. Since its publication, EMH has become one of the most controversial theories in financial markets with legions of supporters and detractors.

If the markets are truly efficient, how can you have any sort of Alpha? Professor Mauboussin presents a very compelling argument that the reason has to do with cost. You see, the EMH theory makes sense in an environment in which the acquisition of information and the implementation of actions based on that information have a neglectable cost. But that’s rarely the case in financial markets. The cost of acquiring and implementing information introduces inefficiencies that can be exploited as a relevant source of Alpha.

Traditional Sources of Alpha

In his paper, Professor Mauboussin argues that there are four fundamental sources of Alpha:

· Behavioral: A behavioral inefficiency exists when an investor, or more likely a group of investors, behave in a way that causes price and value to diverge.

· Informational: An information inefficiency arises when some market participants have different information than others and can trade profitably on that asymmetry.

· Analytical: An analytical inefficiency arises when all participants have the same, or very similar, information and one investor can analyze it better than the others can.

· Technical: A technical inefficiency arises when some market participants have to buy or sell securities for reasons that are unrelated to fundamental value such as laws, regulations or internal policies that can produce a divergence between price and value.

The four sources of Alpha brilliantly summarize the dynamics of efficient capital markets but do they apply to the crypto space? After all, crypto markets are anything but efficient.

New Sources of Alpha for Crypto Assets

In the context of crypto assets, the four source of Alpha in Professor Mauboussin theory are certainly accentuated but they don’t paint a complete picture of inefficiencies in the space. In my opinion, there are two new sources of Alpha that are relevant in the current state of the crypto market: new product Alpha and Protocol Alpha.

The new product Alpha factor is related to the financial architecture of crypto markets and how impact the current launch of new products or assets can have in an inefficient market. In traditional capital markets, a new IPO or a new bond offering rarely disrupts the behavior of the overall market. There are isolated examples such as Facebook’s failed IPO which triggered months of bearish sentiment about tech stocks but those cases are extremely rare as the efficiencies built into the overall market tend to compensate for any anomalies over long periods of time. The opposite phenomenon happens in the crypto markets. While we transition from inefficient to “less inefficient” markets, the launch of new products like a new derivative or the entrance of an institutional investor can generate incredible Alpha returns.

The second factor is related to the fact that crypto asset run on decentralized blockchain networks whose behavior is governed by different protocols. The behavior of those protocols can generate relevant Alpha returns. For instance, the upcoming halving event in the Bitcoin network has been long anticipated as an event that can influence the price of the cryptocurrency and effectively move the market. While that information is readily available, there is a cost of implementing effective strategies to leverage it. Similar effects take place when a protocol mints or burns a specific portion of tokens impacting the circulating supply.

Crypto assets are very unique in so many factors and are likely to produce Alpha in many unique ways. The launch of new products and the dynamics of the underlying blockchain networks are two of the factors that complement the original thesis outlined by Professor Mauboussin but this theory is likely to change as the market evolves.

Jesus Rodriguez Hacker Noon profile picture

@jrodthoughtsJesus Rodriguez

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Chief Scientist, Managing Partner at Invector Labs. CTO at IntoTheBlock. Angel Investor, Writer, Boa


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