Misleading Metrics and Misconceptions About Crypto-Asset Analytics
The steady growth in the crypto-asset space has increased the need and popularity of market intelligence/analytics products. However, like any other new asset class, the methodologies and techniques to extract meaningful intelligence about crypto-assets are going to take some time to mature. Fortunately, the crypto market was born in the golden age of data science and machine learning so it has a shot at building the most sophisticated generation of market intelligence products ever seen for an asset class. Paradoxically, it seems that we prefer to remain lazy and come up with half-baked analytics that have the mathematical rigor of a fifth grade class.
The current wave of analytic products for crypto-assets are still very nascent and experimenting with all sorts of new ideas. However, there is a difference between experimentation and lack of rigor. Sadly, the crypto-market is constantly bombarded with outrageous claims from analytics providers that don’t require a PH.D in statistics to know they are flawed. Today, I would like to deep dive through some of the most common “flawed analytics” you might have read on research materials about crypto-assets.
Analytics are hard and its construction is hard to understand by most people. As a result, it is easy to misinterpret a semi-complex analytics by robust ones. If to that, we add the fact that the behavior of crypto-assets remains an enigma to most investors, we have the perfect storm to produce fantasy analytics and ridiculous explanations about the crypto-markets. While the are no silver bullets to determine whether a specific metric or analysis for crypto-assets is relevant, there are a few tips that might help dissect signal from noise in this area:
1) Crypto-Asset Specific: The most effective metrics to evaluate the behavior of crypto-assets are, surprise-surprise, those that factor in elements that are specific to crypto and that have no equivalent in other asset classes.
2) Regular Data Validation: Look for metrics/signals that offer regular data validations proofs about their impact of correlation with price(if any). You will be surprised what you find 😊.
3) Human Relatable: Crypto remains largely a retail investor market in which data is mostly free and available. Analytics and market metrics should mimic the demographic of the investor population and, in crypto, that means to have simple signals that normal people can understand. Complicated patterns in a market that we don’t quite understand yet is a recipe for disaster. Simple doesn’t only mean easy to understand but flexible to change and adapt to market changes.
Published at Sun, 08 Sep 2019 13:07:56 +0000
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By trendingtopics on 2019-03-11 00:00:07
