Chief Scientist, Managing Partner at Invector Labs. CTO at IntoTheBlock. Angel Investor, Writer, Boa
Let’s do a quick test, try to think about how many times you’ve heard expressions like this:
· Bitcoin is up because gold is down.
· Bitcoin is up because gold is up.
· Ether is down because Bitcoin is up.
· Bitcoin is down because the Yuan is up.
· XRP is down because….because of Ripple probably….
Do any of those explanations are based on robust quantitative analysis? Probably not. Do they matter? Who knows….
Understanding the causes of price fluctuations in cryptocurrencies is one of those elusive goals that have frustrated investors since the early days of the market. As a new and still highly irrational asset class, the crypto market is vulnerable to a lot of speculative theories related to the elements that influence price movements. In more mature markets such as equities, forex or commodities, the tendency has been gravitating towards finding quantifiable characteristics that influence specific trends. Financial markets refer to those characteristics as factors and they are the foundation of entire trends such as quantitative trading. As one of the data-richest asset classes in history, it is pretty obvious that factor investing is going to play an important role in the development of crypto-assets. There is only a small caveat: while old factors are still relevant, factor investing in crypto-asset definitely needs new factors.
The history of factor investing can be traced back to a few seminal papers. In 1976, Stephen A. Ross published a paper on arbitrage theory in which he explained that returns on different securities could be explained using a handful of factors. In 1985, economists Barr Rosenberg, Kenneth Reid, and Ronald Lanstein published a paper titled “Persuasive evidence of market inefficiency” in which they presented a series of strategies that exploit market inefficiencies in equities.
The most important academic milestone in factor investing was the publication of “The Cross-Section of Expected Stock Returns” by economics legend Eugene Fama and Kenneth R. French in 1992. That paper open the floodgates to numerous theories about factor investing including the controversial efficient market hypothesis. One of Fama’s most notable disciples, AQR Capital founder Clifford Asness worked on factor investing publishing one of the most influential papers in the space to this day.
Even though there have been variations of this idea, factor investing theory recognizes two main types of factor: macro and style. The former captures broad risks across asset classes while the latter aims to explain returns and risks within asset classes.
One of the most challenging characteristics in factor investing is the rapid growth in the number and diversity of factors. Fama’s initial research in factor investing was constrained to a handful of factors such as size, value or momentum but that didn’t last long. Today’s quantitative research recognizes over 400 factors that can be relevant price movements. In relatively efficient markets such as equities, finding new factors is nothing short of a nightmare.
The advent of a new asset class always brings a familiar phenomenon to factor investing that I like to call refactoring. Essentially, refactoring describes a dynamic in which the factors for a new asset class are a combination of versions of the factors of other asset classes plus new factors tailored for the new market.
The refactoring thesis tells us that crypto-assets are likely to produce several dozens or even hundreds of new factors as well as new versions of existing factors. The research in crypto-specific investable factors is still very nascent but an obvious conclusion is that those factors will be based on the characteristics that make crypto a unique asset class. When comparing crypto to previous asset classes, there are several unique characteristics or vectors that could serve as the foundation for a new generation of factors:
What are specific factors to crypto-assets that have no equivalent in other asset classes. Let’s try to be more pragmatic and land the previous theory in a practical example. Based on its immaturity and relatively small market cap, crypto-assets are still very vulnerable to the positions of large investors which are often referred to as whales. We could play with this idea that create a “whales factor” based on following variables such as the volume held by large investors as well as their buy or sell activity.
whales_factor == f(number of large investor, positions held, buy-sell activity)
The fake whales_factor is an example of a type of factor that is specific to crypto-assets and that is also really hard to recreate in other asset classes as the positions of investors is not globally available.
Factor investing is likely to become one of the most influential trends in the next phase of the crypto markets. As a new asset class with new fundamentals, crypto is likely to require new factors or new versions of existing factors adapted to the crypto space. The fact that the evolution of crypto markets is coinciding with an explosion in the number of statistical and machine learning technologies, could make crypto the prototypical asset class for factor investing in the future.
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