Jesus Rodriguez is the CEO of IntoTheBlock, a market intelligence platform for crypto assets. He has held leadership roles at major technology companies and hedge funds. He is an active investor, speaker, author and guest lecturer at Columbia University in New York.
The terms “crypto” and “quant” seem to go perfectly together. Bitcoin and crypto assets were born during one of the most exciting times in capital markets coinciding with the golden era of quantitative finance. The technological acceleration caused by movements such as cloud computing and big data together with the renaissance of machine learning have collided to cause the perfect storm in favor of the quant revolution. Billions of dollars are shifting hands every year from discretionary funds into quant vehicles, and Wall Street cannot hire mathematicians and machine learning experts fast enough.
Being a completely digital asset class, crypto seems like the perfect target for quant models. And yet, quant strategies remain constrained to relatively simple techniques such as statistical arbitrage (a pair trade strategy that looks to exploit market inefficiencies in a pair of securities) and we still haven’t seen the emergence of large dominant quant desks in the market. Despite the attractive characteristics of crypto assets for quant strategies, crypto poses unique challenges for quant models and the reality is that most quant strategies in crypto fail. In this article, I would like to explore some of the fundamental but not obvious reasons that can cause the failure of most quant strategies in the crypto space.
See also: Jesus Rodriguez – Crypto Needn’t Fear GPT-3. It Should Embrace It
By claiming that most quant strategies in crypto fail, I am referring mostly to machine learning strategies. Statistical arbitrage has proven to be an effective mechanism to develop algorithmic strategies, but we should expect those opportunities to disappear as the market increases in size and efficiency. In traditional capital markets, we have seen an explosion in the implementation of machine learning-based quant models and the body of research in the space is growing exponentially.
However, most of the quant strategies proven effective in traditional capital markets are likely to not work as well when applied to crypto assets. Based on some of our recent experience at IntoTheBlock working on predictive models and quant strategies, I’ve listed some of the factors that I believe can cause the failure of quant models for crypto assets.
1. Small datasets
Many of the machine learning-based quant strategies you find in research papers are trained in decades of data from capital markets. The trading history of most crypto assets can be counted in months, and, even for vehicles like Bitcoin and Ethereum, the datasets remain relatively small. Many machine learning models will have a hard time generalizing any knowledge from such small datasets. Let’s say that you are trying to…
Read more:Jesus Rodriguez: 10 Reasons Why Quant Strategies for Crypto Fail – CoinDesk
