“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.”
The Adaptive Market theory, authored by MIT professor Andrew Lo, uses evolutionary concepts and ideas to explain financial markets. Cryptocurrencies markets are the perfect laboratory for these ideas as the markets are changing at an extremely fast pace. As we glance ahead at the future of crypto trading, we can look to the experiences of more traditional financial markets and see how they have evolved over the past several years to get some ideas of what to expect.
On Wall Street and in traditional financial markets, almost all trading is now done by machines and algorithms. Humans still give the machines requests or orders but actual execution is now overwhelmingly automated. This makes perfect sense as machines are much better at executing well defined tasks than humans, especially ones confined to the digital world. For tasks like exchange arbitrage or high frequency trading (HFT), only a machine can compete, as human reaction times are simply too slow and indeed there are already lots of bots in crypto markets pursuing similar strategies. In the future it is almost a certainty than any HFT or arbitrage opportunities will be done by machines and will displace human traders.
For less high frequency trading, say intra traders holding position more than a few minutes to a few hours, in traditional financial markets there are still some “screen traders” left but again here too machines have taken over. Advances in AI/Machine Learning and inconsistent intra-day volatility has given machines the edge here too. There used to be thousands of screen traders in London, New York, Chicago and many other cities but today many of them are either going ‘quant’, taking a longer-time horizon, or leaving the industry. While intraday trading in crypto markets is still very lucrative for traders who understand the markets, participants should be cautious about extrapolating current trends into the future. As market volatility declines (a likely outcome as crypto markets become more mainstream) and more data is generated for machine learning models to extrapolate with, machines will likely play a major role in intraday-day trading as well.
Fundamental investors and longer term traders are the least likely to be displaced by machines but that does not mean they will still not have to adapt to changing markets. As the size of crypto markets increases along with the amount of information generated, investors will be faced with the daunting task of analyzing huge amounts of data. In traditional financial markets, where there are thousands of stocks and standardized document disclosures required by authorities, (earnings reports, press releases etc) machines have increasingly taken a large role making investment decisions as well, with quant investing and factor analysis receiving lots of attention. An easy way to think about these strategies is that a computer processes various statistical information on thousands of stocks each day, like which stocks offer the best value, most growth, have had the biggest EPS surprise etc, and then ranks all stocks on these factors. The program then select the top stocks to buy and the bottom stocks to sell short. In crypto markets, its likely early times for strategies like these, as there simply isn’t enough historical or consistent comparable data across all cryptocurrencies. But as markets mature you can be sure people will try to implement similar strategies.
In any market where large financial gains are possible you should expect competition to emerge over time. In such cases, taking an evolutionary approach can help give you ideas about what to expect in the future. For traders and investors in crypto markets the message should be clear, machines are likely to play a larger role going forward. That is not necessarily anything to fear and not all traders will be face the same impact. Indeed traders and investors who can harness the power of machines and use them to generate insights or create strategies will likely flourish. For those interested stay tuned to articles where we discuss current advancements in Machine Learning and how it can be used by traders.