Coinbase Institutional reposted this
🆕 Monthly Outlook: What Machine Learning Models Tell Us 📘 Has crypto market performance in 1H24 been driven more by macro factors or fundamentals? Linear regression and random forest models are two commonly used machine learning algorithms that help provide valuable insights into the factors influencing cryptocurrency price movements. The difference is that where linear regression models provide a relatively straightforward interpretation of how certain factors directly impact cryptocurrency values, random forest models capture the intricate, non-linear relationships and interactions between various input factors. Ultimately, our goal is to determine whether the relationships that may have driven crypto performance in the first half of 2024 could be relevant in the second half of this year. Our model results suggest that although the macro environment continues to be relevant for crypto performance, fundamentals like network activity and user adoption are likely to remain dominant forces for the market in 2H24. This is consistent with our view that the recent price action in June had as much to do with a lack of directional macro momentum as it did with technical factors, like token supply overhangs. Given an absence of internal crypto narratives in the short term, we expect the price action to remain choppy through 3Q24 but believe the start of Fed rate cuts in September will support more interest in long duration assets in 4Q24. However, there are some things that we weren’t able to fully capture in our models, such as shifts in the US regulatory environment. For example, we think the House of Representatives’ decision to approve the Financial Innovation and Technology for the 21st Century Act (FIT21) may have contributed to a change in market sentiment earlier this year. With the US elections likely to be an important driver in 2H24, this could be a notable omission. https://lnkd.in/edQBHcN3
CTO at Triton Liquid Fund | Former CTO & Founder Loopbio GmbH | Former Neuroscientist | Forever Engineer
3moHi David, appreciate you sharing this so openly, particularly being so open with your feature variables. Our internal models, of similar structure but of different goals, do not end up weighing the market features as highly as shown here. Still, good food for thought. One thing that jumped out at me was that BTC ETF flows only show highly in the BTC models. Did you explicitly test BTC ETF flows in non-BTC asset models and find they were not predictive?